diff --git a/README.md b/README.md
index 1b2f11c33e..cb0a8411ba 100644
--- a/README.md
+++ b/README.md
@@ -262,7 +262,7 @@ class CustomModel:
 
 model = CustomModel()
 tasks = mteb.get_tasks(tasks=["Banking77Classification"])
-evaluation = MTEB(tasks=tasks)
+evaluation = mteb.MTEB(tasks=tasks)
 evaluation.run(model)
 ```
 
diff --git a/docs/tasks.md b/docs/tasks.md
index f4f0b0b7cf..b376d55807 100644
--- a/docs/tasks.md
+++ b/docs/tasks.md
@@ -12,10 +12,10 @@ The following tables give you an overview of the tasks in MTEB.
 | [AILAStatutes](https://zenodo.org/records/4063986) | ['eng'] | Retrieval | p2p | [Legal, Written] | None | None |
 | [AJGT](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66/) (Alomari et al., 2017) | ['ara'] | Classification | s2s | [Social, Written] | None | None |
 | [ARCChallenge](https://allenai.org/data/arc) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
-| [AROCocoOrder](https://proceedings.neurips.cc/paper_files/paper/2023/hash/63461de0b4cb760fc498e85b18a7fe81-Abstract-Datasets_and_Benchmarks.html) (Hsieh et al., 2024) | ['eng'] | ImageTextPairClassification | i2t | [Encyclopaedic] | None | None |
-| [AROFlickrOrder](https://proceedings.neurips.cc/paper_files/paper/2023/hash/63461de0b4cb760fc498e85b18a7fe81-Abstract-Datasets_and_Benchmarks.html) (Hsieh et al., 2024) | ['eng'] | ImageTextPairClassification | i2t | [Encyclopaedic] | None | None |
-| [AROVisualAttribution](https://openreview.net/forum?id=KRLUvxh8uaX) (Yuksekgonul et al., 2023) | ['eng'] | ImageTextPairClassification | i2t | [Encyclopaedic] | None | None |
-| [AROVisualRelation](https://openreview.net/forum?id=KRLUvxh8uaX) (Yuksekgonul et al., 2023) | ['eng'] | ImageTextPairClassification | i2t | [Encyclopaedic] | None | None |
+| [AROCocoOrder](https://openreview.net/forum?id=KRLUvxh8uaX) (Yuksekgonul et al., 2023) | ['eng'] | Compositionality | i2t | [Encyclopaedic] | None | None |
+| [AROFlickrOrder](https://openreview.net/forum?id=KRLUvxh8uaX) (Yuksekgonul et al., 2023) | ['eng'] | Compositionality | i2t | [Encyclopaedic] | None | None |
+| [AROVisualAttribution](https://openreview.net/forum?id=KRLUvxh8uaX) (Yuksekgonul et al., 2023) | ['eng'] | Compositionality | i2t | [Encyclopaedic] | None | None |
+| [AROVisualRelation](https://openreview.net/forum?id=KRLUvxh8uaX) (Yuksekgonul et al., 2023) | ['eng'] | Compositionality | i2t | [Encyclopaedic] | None | None |
 | [ATEC](https://aclanthology.org/2021.emnlp-main.357) | ['cmn'] | STS | s2s |  | None | None |
 | [AfriSentiClassification](https://arxiv.org/abs/2302.08956) | ['amh', 'arq', 'ary', 'hau', 'ibo', 'kin', 'pcm', 'por', 'swa', 'tso', 'twi', 'yor'] | Classification | s2s | [Social, Written] | None | None |
 | [AfriSentiLangClassification](https://huggingface.co/datasets/HausaNLP/afrisenti-lid-data/) | ['amh', 'arq', 'ary', 'hau', 'ibo', 'kin', 'pcm', 'por', 'swa', 'tso', 'twi', 'yor'] | Classification | s2s | [Social, Written] | None | None |
@@ -44,9 +44,9 @@ The following tables give you an overview of the tasks in MTEB.
 | [Assin2STS](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) (Real et al., 2020) | ['por'] | STS | s2s | [Written] | None | None |
 | [AutoRAGRetrieval](https://arxiv.org/abs/2410.20878) (Dongkyu Kim, 2024) | ['kor'] | Retrieval | s2p | [Financial, Government, Legal, Medical, Social] | {'test': 834} | {'test': {'number_of_characters': 894.22, 'num_samples': 834, 'num_queries': 114, 'num_documents': 720, 'average_document_length': 1.15, 'average_query_length': 0.61, 'average_relevant_docs_per_query': 1.0}} |
 | [BIOSSES](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html) (Soğancıoğlu et al., 2017) | ['eng'] | STS | s2s | [Medical] | None | None |
-| [BLINKIT2IMultiChoice](https://arxiv.org/abs/2404.12390) (Fu et al., 2024) | ['eng'] | Any2AnyMultiChoice | it2i | [Encyclopaedic] | None | None |
+| [BLINKIT2IMultiChoice](https://arxiv.org/abs/2404.12390) (Fu et al., 2024) | ['eng'] | VisionCentric | it2i | [Encyclopaedic] | {'test': 1206} | {'test': {'number_of_characters': 21204, 'num_samples': 1206, 'num_queries': 402, 'num_documents': 804, 'min_document_length': 0, 'average_document_length': 0, 'max_document_length': 0, 'unique_documents': 0, 'min_document_image_width': 83, 'average_document_image_width': 788.44, 'max_document_image_width': 5087, 'min_document_image_height': 127, 'average_document_image_height': 813.95, 'max_document_image_height': 3230, 'num_document_images': 804, 'min_query_length': 51, 'average_query_length': 52.75, 'max_query_length': 57, 'unique_queries': 3, 'num_query_images': 402, 'min_query_image_width': 166, 'average_query_image_width': 815.13, 'max_query_image_width': 2733, 'min_query_image_height': 254, 'average_query_image_height': 875.38, 'max_query_image_height': 5687, 'min_relevant_docs_per_query': 2, 'average_relevant_docs_per_query': 2.0, 'max_relevant_docs_per_query': 2, 'unique_relevant_docs': 804}} |
 | [BLINKIT2IRetrieval](https://arxiv.org/abs/2404.12390) (Fu et al., 2024) | ['eng'] | Any2AnyRetrieval | it2i | [Encyclopaedic] | None | None |
-| [BLINKIT2TMultiChoice](https://arxiv.org/abs/2404.12390) (Fu et al., 2024) | ['eng'] | Any2AnyMultiChoice | it2t | [Encyclopaedic] | None | None |
+| [BLINKIT2TMultiChoice](https://arxiv.org/abs/2404.12390) (Fu et al., 2024) | ['eng'] | VisionCentric | it2t | [Encyclopaedic] | None | None |
 | [BLINKIT2TRetrieval](https://arxiv.org/abs/2404.12390) (Fu et al., 2024) | ['eng'] | Any2AnyRetrieval | it2t | [Encyclopaedic] | None | None |
 | [BQ](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s |  | None | None |
 | [BSARDRetrieval](https://huggingface.co/datasets/maastrichtlawtech/bsard) (Louis et al., 2022) | ['fra'] | Retrieval | s2p | [Legal, Spoken] | None | None |
@@ -175,10 +175,10 @@ The following tables give you an overview of the tasks in MTEB.
 | [CUADWarrantyDurationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
 | [CUB200I2IRetrieval](https://www.florian-schroff.de/publications/CUB-200.pdf) (Welinder et al., 2010) | ['eng'] | Any2AnyRetrieval | i2i | [Encyclopaedic] | None | None |
 | [CUREv1](https://huggingface.co/datasets/clinia/CUREv1) | ['eng', 'fra', 'spa'] | Retrieval | s2p | [Academic, Medical, Written] | None | None |
-| [CVBenchCount](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | Any2TextMutipleChoice | it2t | [Academic] | {'test': 788} | {'test': {'num_samples': 788, 'min_image_width': 200, 'average_image_width': 757.68, 'max_image_width': 2200, 'min_image_height': 181, 'average_image_height': 631.31, 'max_image_height': 2200, 'min_num_choices': 4, 'average_num_choices': 4.55, 'max_num_choices': 6, 'min_question_length': 30, 'average_question_length': 34.35, 'max_question_length': 45, 'answers': {'2': {'count': 169}, '4': {'count': 63}, '3': {'count': 167}, '1': {'count': 184}, '0': {'count': 182}, '5': {'count': 23}}}} |
-| [CVBenchDepth](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | Any2TextMutipleChoice | it2t | [Academic] | {'test': 600} | {'test': {'num_samples': 600, 'min_image_width': 561, 'average_image_width': 1090.96, 'max_image_width': 1600, 'min_image_height': 427, 'average_image_height': 715.99, 'max_image_height': 900, 'min_num_choices': 2, 'average_num_choices': 2.0, 'max_num_choices': 2, 'min_question_length': 130, 'average_question_length': 136.04, 'max_question_length': 147, 'answers': {'0': {'count': 300}, '1': {'count': 300}}}} |
-| [CVBenchDistance](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | Any2TextMutipleChoice | it2t | [Academic] | {'test': 600} | {'test': {'num_samples': 600, 'min_image_width': 561, 'average_image_width': 1099.29, 'max_image_width': 1600, 'min_image_height': 427, 'average_image_height': 721.0, 'max_image_height': 900, 'min_num_choices': 2, 'average_num_choices': 2.0, 'max_num_choices': 2, 'min_question_length': 204, 'average_question_length': 212.4, 'max_question_length': 223, 'answers': {'0': {'count': 303}, '1': {'count': 297}}}} |
-| [CVBenchRelation](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | Any2TextMutipleChoice | it2t | [Academic] | {'test': 650} | {'test': {'num_samples': 650, 'min_image_width': 189, 'average_image_width': 546.32, 'max_image_width': 2200, 'min_image_height': 190, 'average_image_height': 448.45, 'max_image_height': 2200, 'min_num_choices': 2, 'average_num_choices': 2.0, 'max_num_choices': 2, 'min_question_length': 132, 'average_question_length': 181.46, 'max_question_length': 224, 'answers': {'0': {'count': 327}, '1': {'count': 323}}}} |
+| [CVBenchCount](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | VisionCentric | it2t | [Academic] | None | None |
+| [CVBenchDepth](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | VisionCentric | it2t | [Academic] | None | None |
+| [CVBenchDistance](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | VisionCentric | it2t | [Academic] | None | None |
+| [CVBenchRelation](https://arxiv.org/pdf/2406.16860) (Tong et al., 2024) | ['eng'] | VisionCentric | it2t | [Academic] | None | None |
 | [Caltech101](https://ieeexplore.ieee.org/document/1384978) (Li Fei-Fei, 2004) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | {'test': 6084} | {'test': {'num_samples': 6084, 'unique_num_labels': 102, 'min_image_width': 80, 'average_image_width': 311.72, 'max_image_width': 3481, 'min_image_height': 101, 'average_image_height': 241.84, 'max_image_height': 3999, 'labels': {'4': {'count': 437}, '37': {'count': 405}, '38': {'count': 405}, '57': {'count': 170}, '66': {'count': 768}, '0': {'count': 25}, '1': {'count': 770}, '2': {'count': 12}, '3': {'count': 12}, '5': {'count': 17}, '6': {'count': 24}, '7': {'count': 16}, '8': {'count': 3}, '9': {'count': 98}, '10': {'count': 68}, '11': {'count': 13}, '12': {'count': 55}, '13': {'count': 61}, '14': {'count': 20}, '15': {'count': 13}, '16': {'count': 93}, '17': {'count': 17}, '18': {'count': 29}, '19': {'count': 32}, '20': {'count': 77}, '22': {'count': 39}, '23': {'count': 43}, '24': {'count': 40}, '25': {'count': 20}, '26': {'count': 21}, '27': {'count': 27}, '28': {'count': 37}, '29': {'count': 22}, '30': {'count': 35}, '31': {'count': 38}, '32': {'count': 45}, '33': {'count': 34}, '34': {'count': 23}, '35': {'count': 34}, '36': {'count': 55}, '39': {'count': 37}, '40': {'count': 37}, '41': {'count': 15}, '42': {'count': 4}, '43': {'count': 4}, '44': {'count': 21}, '45': {'count': 69}, '46': {'count': 70}, '47': {'count': 12}, '48': {'count': 24}, '49': {'count': 58}, '50': {'count': 50}, '51': {'count': 1}, '52': {'count': 34}, '53': {'count': 56}, '54': {'count': 84}, '55': {'count': 31}, '56': {'count': 51}, '58': {'count': 48}, '59': {'count': 11}, '60': {'count': 36}, '61': {'count': 13}, '62': {'count': 10}, '63': {'count': 57}, '64': {'count': 2}, '65': {'count': 46}, '67': {'count': 25}, '68': {'count': 5}, '69': {'count': 9}, '70': {'count': 17}, '71': {'count': 8}, '72': {'count': 15}, '73': {'count': 23}, '74': {'count': 4}, '75': {'count': 27}, '76': {'count': 52}, '77': {'count': 29}, '78': {'count': 19}, '79': {'count': 10}, '80': {'count': 33}, '81': {'count': 9}, '82': {'count': 54}, '83': {'count': 27}, '84': {'count': 5}, '85': {'count': 34}, '86': {'count': 15}, '87': {'count': 56}, '88': {'count': 29}, '89': {'count': 34}, '90': {'count': 5}, '91': {'count': 55}, '92': {'count': 19}, '93': {'count': 56}, '94': {'count': 45}, '95': {'count': 209}, '96': {'count': 7}, '97': {'count': 29}, '98': {'count': 4}, '99': {'count': 26}, '100': {'count': 9}, '101': {'count': 30}, '21': {'count': 17}}}} |
 | [Caltech101ZeroShot](https://ieeexplore.ieee.org/document/1384978) (Li Fei-Fei, 2004) | ['eng'] | ZeroShotClassification | i2t | [Encyclopaedic] | {'test': 1986} | {'test': {'num_samples': 1986, 'unique_num_labels': 63, 'min_image_width': 105, 'average_image_width': 277.19, 'max_image_width': 300, 'min_image_height': 114, 'average_image_height': 255.33, 'max_image_height': 300, 'min_label_text_length': 17, 'average_label_text_length': 21.88, 'max_label_text_length': 31, 'labels': {'36': {'count': 55}, '39': {'count': 37}, '40': {'count': 37}, '41': {'count': 15}, '42': {'count': 4}, '43': {'count': 4}, '44': {'count': 21}, '45': {'count': 69}, '46': {'count': 70}, '47': {'count': 12}, '48': {'count': 24}, '49': {'count': 58}, '50': {'count': 50}, '51': {'count': 1}, '52': {'count': 34}, '53': {'count': 56}, '54': {'count': 84}, '55': {'count': 31}, '56': {'count': 51}, '58': {'count': 48}, '59': {'count': 11}, '60': {'count': 36}, '61': {'count': 13}, '62': {'count': 10}, '63': {'count': 57}, '64': {'count': 2}, '65': {'count': 46}, '67': {'count': 25}, '68': {'count': 5}, '69': {'count': 9}, '70': {'count': 17}, '71': {'count': 8}, '72': {'count': 15}, '73': {'count': 23}, '74': {'count': 4}, '75': {'count': 27}, '76': {'count': 52}, '77': {'count': 29}, '78': {'count': 19}, '79': {'count': 10}, '80': {'count': 33}, '81': {'count': 9}, '82': {'count': 54}, '83': {'count': 27}, '84': {'count': 5}, '85': {'count': 34}, '86': {'count': 15}, '87': {'count': 56}, '88': {'count': 29}, '89': {'count': 34}, '90': {'count': 5}, '91': {'count': 55}, '92': {'count': 19}, '93': {'count': 56}, '94': {'count': 45}, '95': {'count': 209}, '96': {'count': 7}, '97': {'count': 29}, '98': {'count': 4}, '99': {'count': 26}, '100': {'count': 9}, '101': {'count': 30}, '21': {'count': 17}}}} |
 | [CanadaTaxCourtOutcomesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
@@ -282,7 +282,7 @@ The following tables give you an overview of the tasks in MTEB.
 | [Farsick](https://github.com/ZahraGhasemi-AI/FarSick) | ['fas'] | STS | s2s |  | None | None |
 | [Fashion200kI2TRetrieval](https://openaccess.thecvf.com/content_iccv_2017/html/Han_Automatic_Spatially-Aware_Fashion_ICCV_2017_paper.html) (Han et al., 2017) | ['eng'] | Any2AnyRetrieval | i2t | [Encyclopaedic] | None | None |
 | [Fashion200kT2IRetrieval](https://openaccess.thecvf.com/content_iccv_2017/html/Han_Automatic_Spatially-Aware_Fashion_ICCV_2017_paper.html) (Han et al., 2017) | ['eng'] | Any2AnyRetrieval | t2i | [Encyclopaedic] | None | None |
-| [FashionIQIT2IRetrieval](https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Fashion_IQ_A_New_Dataset_Towards_Retrieving_Images_by_Natural_CVPR_2021_paper.html) (Wu et al., 2021) | ['eng'] | Any2AnyRetrieval | it2i | [Encyclopaedic] | None | None |
+| [FashionIQIT2IRetrieval](https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Fashion_IQ_A_New_Dataset_Towards_Retrieving_Images_by_Natural_CVPR_2021_paper.html) (Wu et al., 2021) | ['eng'] | Any2AnyRetrieval | it2i | [Encyclopaedic] | {'test': 80384} | {'test': {'number_of_characters': 361250, 'num_samples': 80384, 'num_queries': 6003, 'num_documents': 74381, 'min_document_length': 0, 'average_document_length': 0, 'max_document_length': 0, 'unique_documents': 0, 'num_document_images': 74381, 'min_query_length': 18, 'average_query_length': 60.18, 'max_query_length': 138, 'unique_queries': 5973, 'num_query_images': 6003, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 4, 'unique_relevant_docs': 6003}} |
 | [FeedbackQARetrieval](https://arxiv.org/abs/2204.03025) | ['eng'] | Retrieval | s2p | [Government, Medical, Web, Written] | None | None |
 | [FiQA-PL](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['pol'] | Retrieval | s2p | [Financial, Written] | None | None |
 | [FiQA2018](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['eng'] | Retrieval | s2p | [Financial, Written] | None | None |
@@ -344,7 +344,7 @@ The following tables give you an overview of the tasks in MTEB.
 | [IN22ConvBitextMining](https://huggingface.co/datasets/ai4bharat/IN22-Conv) (Jay Gala, 2023) | ['asm', 'ben', 'brx', 'doi', 'eng', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Fiction, Social, Spoken, Spoken] | {'test': 760518} | {'test': {'num_samples': 760518, 'number_of_characters': 82637104, 'unique_pairs': 759283, 'min_sentence1_length': 3, 'average_sentence1_length': 54.33, 'max_sentence1_length': 239, 'unique_sentence1': 34430, 'min_sentence2_length': 3, 'average_sentence2_length': 54.33, 'max_sentence2_length': 239, 'unique_sentence2': 34430, 'hf_subset_descriptive_stats': {'asm_Beng-ben_Beng': {'num_samples': 1503, 'number_of_characters': 155988, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 50.03, 'max_sentence2_length': 178, 'unique_sentence2': 1497}, 'asm_Beng-brx_Deva': {'num_samples': 1503, 'number_of_characters': 162044, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 54.06, 'max_sentence2_length': 210, 'unique_sentence2': 1498}, 'asm_Beng-doi_Deva': {'num_samples': 1503, 'number_of_characters': 167032, 'unique_pairs': 1500, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 6, 'average_sentence2_length': 57.38, 'max_sentence2_length': 209, 'unique_sentence2': 1499}, 'asm_Beng-eng_Latn': {'num_samples': 1503, 'number_of_characters': 160716, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.18, 'max_sentence2_length': 201, 'unique_sentence2': 1497}, 'asm_Beng-gom_Deva': {'num_samples': 1503, 'number_of_characters': 156282, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 50.23, 'max_sentence2_length': 203, 'unique_sentence2': 1500}, 'asm_Beng-guj_Gujr': {'num_samples': 1503, 'number_of_characters': 158269, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 51.55, 'max_sentence2_length': 205, 'unique_sentence2': 1500}, 'asm_Beng-hin_Deva': {'num_samples': 1503, 'number_of_characters': 159964, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 52.68, 'max_sentence2_length': 192, 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'unique_sentence2': 1499}, 'asm_Beng-mal_Mlym': {'num_samples': 1503, 'number_of_characters': 172838, 'unique_pairs': 1498, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 61.24, 'max_sentence2_length': 219, 'unique_sentence2': 1495}, 'asm_Beng-mar_Deva': {'num_samples': 1503, 'number_of_characters': 162747, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 54.53, 'max_sentence2_length': 221, 'unique_sentence2': 1501}, 'asm_Beng-mni_Mtei': {'num_samples': 1503, 'number_of_characters': 157316, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 50.91, 'max_sentence2_length': 239, 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'unique_sentence2': 1500}, 'urd_Arab-sat_Olck': {'num_samples': 1503, 'number_of_characters': 169100, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 7, 'average_sentence2_length': 58.94, 'max_sentence2_length': 225, 'unique_sentence2': 1500}, 'urd_Arab-snd_Deva': {'num_samples': 1503, 'number_of_characters': 162344, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 4, 'average_sentence2_length': 54.45, 'max_sentence2_length': 195, 'unique_sentence2': 1490}, 'urd_Arab-tam_Taml': {'num_samples': 1503, 'number_of_characters': 174587, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 3, 'average_sentence2_length': 62.59, 'max_sentence2_length': 224, 'unique_sentence2': 1492}, 'urd_Arab-tel_Telu': {'num_samples': 1503, 'number_of_characters': 157411, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 6, 'average_sentence2_length': 51.16, 'max_sentence2_length': 182, 'unique_sentence2': 1495}}}} |
 | [IN22GenBitextMining](https://huggingface.co/datasets/ai4bharat/IN22-Gen) (Jay Gala, 2023) | ['asm', 'ben', 'brx', 'doi', 'eng', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Government, Legal, News, Non-fiction, Religious, Web, Written] | {'test': 518144} | {'test': {'num_samples': 518144, 'number_of_characters': 162367876, 'unique_pairs': 518101, 'min_sentence1_length': 9, 'average_sentence1_length': 156.68, 'max_sentence1_length': 692, 'unique_sentence1': 23550, 'min_sentence2_length': 9, 'average_sentence2_length': 156.68, 'max_sentence2_length': 692, 'unique_sentence2': 23550, 'hf_subset_descriptive_stats': {'asm_Beng-ben_Beng': {'num_samples': 1024, 'number_of_characters': 310622, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 146.64, 'max_sentence2_length': 538, 'unique_sentence2': 1024}, 'asm_Beng-brx_Deva': {'num_samples': 1024, 'number_of_characters': 323609, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 159.33, 'max_sentence2_length': 631, 'unique_sentence2': 1024}, 'asm_Beng-doi_Deva': {'num_samples': 1024, 'number_of_characters': 319020, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 154.84, 'max_sentence2_length': 500, 'unique_sentence2': 1024}, 'asm_Beng-eng_Latn': {'num_samples': 1024, 'number_of_characters': 320098, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 155.9, 'max_sentence2_length': 532, 'unique_sentence2': 1024}, 'asm_Beng-gom_Deva': {'num_samples': 1024, 'number_of_characters': 312594, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 148.57, 'max_sentence2_length': 537, 'unique_sentence2': 1024}, 'asm_Beng-guj_Gujr': {'num_samples': 1024, 'number_of_characters': 309440, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 145.49, 'max_sentence2_length': 488, 'unique_sentence2': 1024}, 'asm_Beng-hin_Deva': {'num_samples': 1024, 'number_of_characters': 320106, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 21, 'average_sentence2_length': 155.91, 'max_sentence2_length': 531, 'unique_sentence2': 1024}, 'asm_Beng-kan_Knda': {'num_samples': 1024, 'number_of_characters': 332064, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 167.58, 'max_sentence2_length': 668, 'unique_sentence2': 1024}, 'asm_Beng-kas_Arab': {'num_samples': 1024, 'number_of_characters': 322764, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 158.5, 'max_sentence2_length': 520, 'unique_sentence2': 1024}, 'asm_Beng-mai_Deva': {'num_samples': 1024, 'number_of_characters': 308682, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 144.75, 'max_sentence2_length': 562, 'unique_sentence2': 1024}, 'asm_Beng-mal_Mlym': {'num_samples': 1024, 'number_of_characters': 343636, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 178.88, 'max_sentence2_length': 692, 'unique_sentence2': 1024}, 'asm_Beng-mar_Deva': {'num_samples': 1024, 'number_of_characters': 321784, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 157.54, 'max_sentence2_length': 555, 'unique_sentence2': 1024}, 'asm_Beng-mni_Mtei': {'num_samples': 1024, 'number_of_characters': 313134, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 149.1, 'max_sentence2_length': 597, 'unique_sentence2': 1024}, 'asm_Beng-npi_Deva': {'num_samples': 1024, 'number_of_characters': 313419, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 149.38, 'max_sentence2_length': 525, 'unique_sentence2': 1024}, 'asm_Beng-ory_Orya': {'num_samples': 1024, 'number_of_characters': 334226, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 169.69, 'max_sentence2_length': 578, 'unique_sentence2': 1024}, 'asm_Beng-pan_Guru': {'num_samples': 1024, 'number_of_characters': 306863, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 19, 'average_sentence2_length': 142.97, 'max_sentence2_length': 476, 'unique_sentence2': 1024}, 'asm_Beng-san_Deva': {'num_samples': 1024, 'number_of_characters': 318079, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 153.93, 'max_sentence2_length': 601, 'unique_sentence2': 1024}, 'asm_Beng-sat_Olck': {'num_samples': 1024, 'number_of_characters': 326732, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 11, 'average_sentence2_length': 162.38, 'max_sentence2_length': 536, 'unique_sentence2': 1024}, 'asm_Beng-snd_Deva': {'num_samples': 1024, 'number_of_characters': 320421, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 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'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 151.05, 'max_sentence2_length': 574, 'unique_sentence2': 1024}, 'ben_Beng-asm_Beng': {'num_samples': 1024, 'number_of_characters': 310622, 'unique_pairs': 1024, 'min_sentence1_length': 9, 'average_sentence1_length': 146.64, 'max_sentence1_length': 538, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 156.7, 'max_sentence2_length': 582, 'unique_sentence2': 1024}, 'ben_Beng-brx_Deva': {'num_samples': 1024, 'number_of_characters': 313313, 'unique_pairs': 1024, 'min_sentence1_length': 9, 'average_sentence1_length': 146.64, 'max_sentence1_length': 538, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 159.33, 'max_sentence2_length': 631, 'unique_sentence2': 1024}, 'ben_Beng-doi_Deva': {'num_samples': 1024, 'number_of_characters': 308724, 'unique_pairs': 1024, 'min_sentence1_length': 9, 'average_sentence1_length': 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'max_sentence2_length': 597, 'unique_sentence2': 1024}, 'tel_Telu-npi_Deva': {'num_samples': 1024, 'number_of_characters': 311546, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 149.38, 'max_sentence2_length': 525, 'unique_sentence2': 1024}, 'tel_Telu-ory_Orya': {'num_samples': 1024, 'number_of_characters': 332353, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 169.69, 'max_sentence2_length': 578, 'unique_sentence2': 1024}, 'tel_Telu-pan_Guru': {'num_samples': 1024, 'number_of_characters': 304990, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 19, 'average_sentence2_length': 142.97, 'max_sentence2_length': 476, 'unique_sentence2': 1024}, 'tel_Telu-san_Deva': {'num_samples': 1024, 'number_of_characters': 316206, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 153.93, 'max_sentence2_length': 601, 'unique_sentence2': 1024}, 'tel_Telu-sat_Olck': {'num_samples': 1024, 'number_of_characters': 324859, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 11, 'average_sentence2_length': 162.38, 'max_sentence2_length': 536, 'unique_sentence2': 1024}, 'tel_Telu-snd_Deva': {'num_samples': 1024, 'number_of_characters': 318548, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 18, 'average_sentence2_length': 156.21, 'max_sentence2_length': 545, 'unique_sentence2': 1024}, 'tel_Telu-tam_Taml': {'num_samples': 1024, 'number_of_characters': 346473, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 32, 'average_sentence2_length': 183.48, 'max_sentence2_length': 614, 'unique_sentence2': 1023}, 'tel_Telu-urd_Arab': {'num_samples': 1024, 'number_of_characters': 313261, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 151.05, 'max_sentence2_length': 574, 'unique_sentence2': 1024}, 'urd_Arab-asm_Beng': {'num_samples': 1024, 'number_of_characters': 315134, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 156.7, 'max_sentence2_length': 582, 'unique_sentence2': 1024}, 'urd_Arab-ben_Beng': {'num_samples': 1024, 'number_of_characters': 304838, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 146.64, 'max_sentence2_length': 538, 'unique_sentence2': 1024}, 'urd_Arab-brx_Deva': {'num_samples': 1024, 'number_of_characters': 317825, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 159.33, 'max_sentence2_length': 631, 'unique_sentence2': 1024}, 'urd_Arab-doi_Deva': {'num_samples': 1024, 'number_of_characters': 313236, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 154.84, 'max_sentence2_length': 500, 'unique_sentence2': 1024}, 'urd_Arab-eng_Latn': {'num_samples': 1024, 'number_of_characters': 314314, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 155.9, 'max_sentence2_length': 532, 'unique_sentence2': 1024}, 'urd_Arab-gom_Deva': {'num_samples': 1024, 'number_of_characters': 306810, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 148.57, 'max_sentence2_length': 537, 'unique_sentence2': 1024}, 'urd_Arab-guj_Gujr': {'num_samples': 1024, 'number_of_characters': 303656, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 145.49, 'max_sentence2_length': 488, 'unique_sentence2': 1024}, 'urd_Arab-hin_Deva': {'num_samples': 1024, 'number_of_characters': 314322, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 21, 'average_sentence2_length': 155.91, 'max_sentence2_length': 531, 'unique_sentence2': 1024}, 'urd_Arab-kan_Knda': {'num_samples': 1024, 'number_of_characters': 326280, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 167.58, 'max_sentence2_length': 668, 'unique_sentence2': 1024}, 'urd_Arab-kas_Arab': {'num_samples': 1024, 'number_of_characters': 316980, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 158.5, 'max_sentence2_length': 520, 'unique_sentence2': 1024}, 'urd_Arab-mai_Deva': {'num_samples': 1024, 'number_of_characters': 302898, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 144.75, 'max_sentence2_length': 562, 'unique_sentence2': 1024}, 'urd_Arab-mal_Mlym': {'num_samples': 1024, 'number_of_characters': 337852, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 178.88, 'max_sentence2_length': 692, 'unique_sentence2': 1024}, 'urd_Arab-mar_Deva': {'num_samples': 1024, 'number_of_characters': 316000, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 157.54, 'max_sentence2_length': 555, 'unique_sentence2': 1024}, 'urd_Arab-mni_Mtei': {'num_samples': 1024, 'number_of_characters': 307350, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 149.1, 'max_sentence2_length': 597, 'unique_sentence2': 1024}, 'urd_Arab-npi_Deva': {'num_samples': 1024, 'number_of_characters': 307635, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 149.38, 'max_sentence2_length': 525, 'unique_sentence2': 1024}, 'urd_Arab-ory_Orya': {'num_samples': 1024, 'number_of_characters': 328442, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 169.69, 'max_sentence2_length': 578, 'unique_sentence2': 1024}, 'urd_Arab-pan_Guru': {'num_samples': 1024, 'number_of_characters': 301079, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 19, 'average_sentence2_length': 142.97, 'max_sentence2_length': 476, 'unique_sentence2': 1024}, 'urd_Arab-san_Deva': {'num_samples': 1024, 'number_of_characters': 312295, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 153.93, 'max_sentence2_length': 601, 'unique_sentence2': 1024}, 'urd_Arab-sat_Olck': {'num_samples': 1024, 'number_of_characters': 320948, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 11, 'average_sentence2_length': 162.38, 'max_sentence2_length': 536, 'unique_sentence2': 1024}, 'urd_Arab-snd_Deva': {'num_samples': 1024, 'number_of_characters': 314637, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 18, 'average_sentence2_length': 156.21, 'max_sentence2_length': 545, 'unique_sentence2': 1024}, 'urd_Arab-tam_Taml': {'num_samples': 1024, 'number_of_characters': 342562, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 32, 'average_sentence2_length': 183.48, 'max_sentence2_length': 614, 'unique_sentence2': 1023}, 'urd_Arab-tel_Telu': {'num_samples': 1024, 'number_of_characters': 313261, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 154.87, 'max_sentence2_length': 658, 'unique_sentence2': 1024}}}} |
 | [IWSLT2017BitextMining](https://aclanthology.org/2017.iwslt-1.1/) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'jpn', 'kor', 'nld', 'ron'] | BitextMining | s2s | [Fiction, Non-fiction, Written] | {'validation': 21938} | {'validation': {'num_samples': 21938, 'number_of_characters': 4256244, 'unique_pairs': 21840, 'min_sentence1_length': 2, 'average_sentence1_length': 97.01, 'max_sentence1_length': 521, 'unique_sentence1': 11563, 'min_sentence2_length': 2, 'average_sentence2_length': 97.01, 'max_sentence2_length': 521, 'unique_sentence2': 11563, 'hf_subset_descriptive_stats': {'ar-en': {'num_samples': 888, 'number_of_characters': 172499, 'unique_pairs': 887, 'min_sentence1_length': 4, 'average_sentence1_length': 85.49, 'max_sentence1_length': 369, 'unique_sentence1': 887, 'min_sentence2_length': 10, 'average_sentence2_length': 108.77, 'max_sentence2_length': 462, 'unique_sentence2': 881}, 'de-en': {'num_samples': 888, 'number_of_characters': 202336, 'unique_pairs': 883, 'min_sentence1_length': 6, 'average_sentence1_length': 119.03, 'max_sentence1_length': 521, 'unique_sentence1': 881, 'min_sentence2_length': 10, 'average_sentence2_length': 108.83, 'max_sentence2_length': 462, 'unique_sentence2': 881}, 'en-ar': {'num_samples': 888, 'number_of_characters': 172499, 'unique_pairs': 887, 'min_sentence1_length': 10, 'average_sentence1_length': 108.77, 'max_sentence1_length': 462, 'unique_sentence1': 881, 'min_sentence2_length': 4, 'average_sentence2_length': 85.49, 'max_sentence2_length': 369, 'unique_sentence2': 887}, 'en-de': {'num_samples': 888, 'number_of_characters': 202336, 'unique_pairs': 883, 'min_sentence1_length': 10, 'average_sentence1_length': 108.83, 'max_sentence1_length': 462, 'unique_sentence1': 881, 'min_sentence2_length': 6, 'average_sentence2_length': 119.03, 'max_sentence2_length': 521, 'unique_sentence2': 881}, 'en-fr': {'num_samples': 890, 'number_of_characters': 197619, 'unique_pairs': 883, 'min_sentence1_length': 10, 'average_sentence1_length': 108.41, 'max_sentence1_length': 462, 'unique_sentence1': 883, 'min_sentence2_length': 6, 'average_sentence2_length': 113.63, 'max_sentence2_length': 493, 'unique_sentence2': 881}, 'en-it': {'num_samples': 929, 'number_of_characters': 191803, 'unique_pairs': 924, 'min_sentence1_length': 10, 'average_sentence1_length': 103.0, 'max_sentence1_length': 433, 'unique_sentence1': 922, 'min_sentence2_length': 7, 'average_sentence2_length': 103.46, 'max_sentence2_length': 444, 'unique_sentence2': 918}, 'en-ja': {'num_samples': 871, 'number_of_characters': 132742, 'unique_pairs': 867, 'min_sentence1_length': 10, 'average_sentence1_length': 109.81, 'max_sentence1_length': 462, 'unique_sentence1': 864, 'min_sentence2_length': 5, 'average_sentence2_length': 42.59, 'max_sentence2_length': 225, 'unique_sentence2': 866}, 'en-ko': {'num_samples': 879, 'number_of_characters': 142659, 'unique_pairs': 874, 'min_sentence1_length': 10, 'average_sentence1_length': 107.74, 'max_sentence1_length': 462, 'unique_sentence1': 872, 'min_sentence2_length': 3, 'average_sentence2_length': 54.56, 'max_sentence2_length': 250, 'unique_sentence2': 872}, 'en-nl': {'num_samples': 1003, 'number_of_characters': 189637, 'unique_pairs': 1000, 'min_sentence1_length': 10, 'average_sentence1_length': 95.27, 'max_sentence1_length': 433, 'unique_sentence1': 996, 'min_sentence2_length': 4, 'average_sentence2_length': 93.8, 'max_sentence2_length': 477, 'unique_sentence2': 1000}, 'en-ro': {'num_samples': 914, 'number_of_characters': 194128, 'unique_pairs': 910, 'min_sentence1_length': 10, 'average_sentence1_length': 104.72, 'max_sentence1_length': 433, 'unique_sentence1': 907, 'min_sentence2_length': 9, 'average_sentence2_length': 107.67, 'max_sentence2_length': 448, 'unique_sentence2': 910}, 'en-zh': {'num_samples': 879, 'number_of_characters': 131126, 'unique_pairs': 877, 'min_sentence1_length': 10, 'average_sentence1_length': 109.37, 'max_sentence1_length': 462, 'unique_sentence1': 872, 'min_sentence2_length': 2, 'average_sentence2_length': 39.81, 'max_sentence2_length': 230, 'unique_sentence2': 867}, 'fr-en': {'num_samples': 890, 'number_of_characters': 197619, 'unique_pairs': 883, 'min_sentence1_length': 6, 'average_sentence1_length': 113.63, 'max_sentence1_length': 493, 'unique_sentence1': 881, 'min_sentence2_length': 10, 'average_sentence2_length': 108.41, 'max_sentence2_length': 462, 'unique_sentence2': 883}, 'it-en': {'num_samples': 929, 'number_of_characters': 191803, 'unique_pairs': 924, 'min_sentence1_length': 7, 'average_sentence1_length': 103.46, 'max_sentence1_length': 444, 'unique_sentence1': 918, 'min_sentence2_length': 10, 'average_sentence2_length': 103.0, 'max_sentence2_length': 433, 'unique_sentence2': 922}, 'it-nl': {'num_samples': 1001, 'number_of_characters': 188858, 'unique_pairs': 998, 'min_sentence1_length': 7, 'average_sentence1_length': 94.64, 'max_sentence1_length': 459, 'unique_sentence1': 994, 'min_sentence2_length': 7, 'average_sentence2_length': 94.03, 'max_sentence2_length': 505, 'unique_sentence2': 998}, 'it-ro': {'num_samples': 914, 'number_of_characters': 193339, 'unique_pairs': 911, 'min_sentence1_length': 7, 'average_sentence1_length': 103.91, 'max_sentence1_length': 435, 'unique_sentence1': 907, 'min_sentence2_length': 9, 'average_sentence2_length': 107.62, 'max_sentence2_length': 448, 'unique_sentence2': 910}, 'ja-en': {'num_samples': 871, 'number_of_characters': 132742, 'unique_pairs': 867, 'min_sentence1_length': 5, 'average_sentence1_length': 42.59, 'max_sentence1_length': 225, 'unique_sentence1': 866, 'min_sentence2_length': 10, 'average_sentence2_length': 109.81, 'max_sentence2_length': 462, 'unique_sentence2': 864}, 'ko-en': {'num_samples': 879, 'number_of_characters': 142659, 'unique_pairs': 874, 'min_sentence1_length': 3, 'average_sentence1_length': 54.56, 'max_sentence1_length': 250, 'unique_sentence1': 872, 'min_sentence2_length': 10, 'average_sentence2_length': 107.74, 'max_sentence2_length': 462, 'unique_sentence2': 872}, 'nl-en': {'num_samples': 1003, 'number_of_characters': 189637, 'unique_pairs': 1000, 'min_sentence1_length': 4, 'average_sentence1_length': 93.8, 'max_sentence1_length': 477, 'unique_sentence1': 1000, 'min_sentence2_length': 10, 'average_sentence2_length': 95.27, 'max_sentence2_length': 433, 'unique_sentence2': 996}, 'nl-it': {'num_samples': 1001, 'number_of_characters': 188858, 'unique_pairs': 998, 'min_sentence1_length': 7, 'average_sentence1_length': 94.03, 'max_sentence1_length': 505, 'unique_sentence1': 998, 'min_sentence2_length': 7, 'average_sentence2_length': 94.64, 'max_sentence2_length': 459, 'unique_sentence2': 994}, 'nl-ro': {'num_samples': 913, 'number_of_characters': 191376, 'unique_pairs': 911, 'min_sentence1_length': 7, 'average_sentence1_length': 102.02, 'max_sentence1_length': 478, 'unique_sentence1': 909, 'min_sentence2_length': 9, 'average_sentence2_length': 107.59, 'max_sentence2_length': 515, 'unique_sentence2': 909}, 'ro-en': {'num_samples': 914, 'number_of_characters': 194128, 'unique_pairs': 910, 'min_sentence1_length': 9, 'average_sentence1_length': 107.67, 'max_sentence1_length': 448, 'unique_sentence1': 910, 'min_sentence2_length': 10, 'average_sentence2_length': 104.72, 'max_sentence2_length': 433, 'unique_sentence2': 907}, 'ro-it': {'num_samples': 914, 'number_of_characters': 193339, 'unique_pairs': 911, 'min_sentence1_length': 9, 'average_sentence1_length': 107.62, 'max_sentence1_length': 448, 'unique_sentence1': 910, 'min_sentence2_length': 7, 'average_sentence2_length': 103.91, 'max_sentence2_length': 435, 'unique_sentence2': 907}, 'ro-nl': {'num_samples': 913, 'number_of_characters': 191376, 'unique_pairs': 911, 'min_sentence1_length': 9, 'average_sentence1_length': 107.59, 'max_sentence1_length': 515, 'unique_sentence1': 909, 'min_sentence2_length': 7, 'average_sentence2_length': 102.02, 'max_sentence2_length': 478, 'unique_sentence2': 909}, 'zh-en': {'num_samples': 879, 'number_of_characters': 131126, 'unique_pairs': 877, 'min_sentence1_length': 2, 'average_sentence1_length': 39.81, 'max_sentence1_length': 230, 'unique_sentence1': 867, 'min_sentence2_length': 10, 'average_sentence2_length': 109.37, 'max_sentence2_length': 462, 'unique_sentence2': 872}}}} |
-| [ImageCoDeT2IMultiChoice](https://aclanthology.org/2022.acl-long.241.pdf) (Krojer et al., 2022) | ['eng'] | Any2AnyMultiChoice | it2i | [Web, Written] | None | None |
+| [ImageCoDeT2IMultiChoice](https://aclanthology.org/2022.acl-long.241.pdf) (Krojer et al., 2022) | ['eng'] | Compositionality | it2i | [Web, Written] | None | None |
 | [ImageCoDeT2IRetrieval](https://aclanthology.org/2022.acl-long.241.pdf) (Krojer et al., 2022) | ['eng'] | Any2AnyRetrieval | t2i | [Web, Written] | None | None |
 | [ImageNet10Clustering](https://www.kaggle.com/datasets/liusha249/imagenet10) (Deng et al., 2009) | ['eng'] | ImageClustering | i2t | [Web] | {'test': 13000} | {'test': {'num_samples': 13000, 'unique_num_labels': 10, 'min_image_width': 224, 'average_image_width': 224.0, 'max_image_width': 224, 'min_image_height': 224, 'average_image_height': 224.0, 'max_image_height': 224, 'labels': {'0': {'count': 1300}, '1': {'count': 1300}, '2': {'count': 1300}, '3': {'count': 1300}, '4': {'count': 1300}, '5': {'count': 1300}, '6': {'count': 1300}, '7': {'count': 1300}, '8': {'count': 1300}, '10': {'count': 1300}}}} |
 | [ImageNetDog15Clustering](http://vision.stanford.edu/aditya86/ImageNetDogs/main.html) (Deng et al., 2009) | ['eng'] | ImageClustering | i2i | [Web] | {'test': 1076} | {'test': {'num_samples': 1076, 'unique_num_labels': 15, 'min_image_width': 224, 'average_image_width': 224.0, 'max_image_width': 224, 'min_image_height': 224, 'average_image_height': 224.0, 'max_image_height': 224, 'labels': {'0': {'count': 152}, '1': {'count': 88}, '2': {'count': 75}, '3': {'count': 96}, '4': {'count': 57}, '5': {'count': 50}, '6': {'count': 52}, '7': {'count': 50}, '8': {'count': 50}, '9': {'count': 50}, '10': {'count': 53}, '11': {'count': 57}, '12': {'count': 50}, '13': {'count': 100}, '14': {'count': 96}}}} |
@@ -545,8 +545,8 @@ The following tables give you an overview of the tasks in MTEB.
 | [OpusparcusPC](https://gem-benchmark.com/data_cards/opusparcus) (Mathias Creutz, 2018) | ['deu', 'eng', 'fin', 'fra', 'rus', 'swe'] | PairClassification | s2s | [Spoken, Spoken] | None | None |
 | [OralArgumentQuestionPurposeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
 | [OverrulingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
-| [OxfordFlowersClassification](https://huggingface.co/datasets/nelorth/oxford-flowers/viewer/default/train) | ['eng'] | ImageClassification | i2i | [Reviews] | {'test': 1020} | {'test': {'num_samples': 1020, 'unique_num_labels': 102, 'min_image_width': 500, 'average_image_width': 618.07, 'max_image_width': 873, 'min_image_height': 500, 'average_image_height': 538.26, 'max_image_height': 928, 'labels': {'0': {'count': 9}, '1': {'count': 9}, '2': {'count': 10}, '3': {'count': 9}, '4': {'count': 11}, '5': {'count': 11}, '6': {'count': 10}, '7': {'count': 10}, '8': {'count': 11}, '9': {'count': 10}, '10': {'count': 10}, '11': {'count': 9}, '12': {'count': 10}, '13': {'count': 10}, '14': {'count': 10}, '15': {'count': 9}, '16': {'count': 11}, '17': {'count': 11}, '18': {'count': 10}, '19': {'count': 9}, '20': {'count': 9}, '21': {'count': 10}, '22': {'count': 11}, '23': {'count': 11}, '24': {'count': 10}, '25': {'count': 11}, '26': {'count': 10}, '27': {'count': 9}, '28': {'count': 11}, '29': {'count': 10}, '30': {'count': 10}, '31': {'count': 9}, '32': {'count': 10}, '33': {'count': 10}, '34': {'count': 10}, '35': {'count': 11}, '36': {'count': 9}, '37': {'count': 10}, '38': {'count': 10}, '39': {'count': 11}, '40': {'count': 10}, '41': {'count': 10}, '42': {'count': 11}, '43': {'count': 10}, '44': {'count': 10}, '45': {'count': 10}, '46': {'count': 10}, '47': {'count': 9}, '48': {'count': 10}, '49': {'count': 11}, '50': {'count': 10}, '51': {'count': 10}, '52': {'count': 10}, '53': {'count': 10}, '54': {'count': 10}, '55': {'count': 10}, '56': {'count': 10}, '57': {'count': 11}, '58': {'count': 10}, '59': {'count': 10}, '60': {'count': 10}, '61': {'count': 10}, '62': {'count': 9}, '63': {'count': 10}, '64': {'count': 10}, '65': {'count': 9}, '66': {'count': 11}, '67': {'count': 10}, '68': {'count': 11}, '69': {'count': 9}, '70': {'count': 9}, '71': {'count': 10}, '72': {'count': 10}, '73': {'count': 10}, '74': {'count': 10}, '75': {'count': 10}, '76': {'count': 10}, '77': {'count': 10}, '78': {'count': 9}, '79': {'count': 10}, '80': {'count': 10}, '81': {'count': 11}, '82': {'count': 10}, '83': {'count': 9}, '84': {'count': 11}, '85': {'count': 10}, '86': {'count': 10}, '87': {'count': 10}, '88': {'count': 10}, '89': {'count': 10}, '90': {'count': 10}, '91': {'count': 10}, '92': {'count': 10}, '93': {'count': 9}, '94': {'count': 9}, '95': {'count': 10}, '96': {'count': 10}, '97': {'count': 10}, '98': {'count': 10}, '99': {'count': 10}, '100': {'count': 10}, '101': {'count': 11}}}} |
-| [OxfordPets](https://arxiv.org/abs/1306.5151) (Subhransu Maji, 2013) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | {'test': 3669} | {'test': {'num_samples': 3669, 'unique_num_labels': 37, 'min_image_width': 137, 'average_image_width': 443.46, 'max_image_width': 1646, 'min_image_height': 103, 'average_image_height': 399.38, 'max_image_height': 2160, 'labels': {'0': {'count': 98}, '1': {'count': 100}, '2': {'count': 100}, '3': {'count': 100}, '4': {'count': 100}, '5': {'count': 100}, '6': {'count': 100}, '7': {'count': 88}, '8': {'count': 99}, '9': {'count': 100}, '10': {'count': 100}, '11': {'count': 97}, '12': {'count': 100}, '13': {'count': 100}, '14': {'count': 100}, '15': {'count': 100}, '16': {'count': 100}, '17': {'count': 100}, '18': {'count': 99}, '19': {'count': 100}, '20': {'count': 100}, '21': {'count': 100}, '22': {'count': 100}, '23': {'count': 100}, '24': {'count': 100}, '25': {'count': 100}, '26': {'count': 100}, '27': {'count': 100}, '28': {'count': 100}, '29': {'count': 100}, '30': {'count': 99}, '31': {'count': 100}, '32': {'count': 100}, '33': {'count': 100}, '34': {'count': 89}, '35': {'count': 100}, '36': {'count': 100}}}} |
+| [OxfordFlowersClassification](https://huggingface.co/datasets/nelorth/oxford-flowers/viewer/default/train) (Nilsback et al., 2008) | ['eng'] | ImageClassification | i2i | [Reviews] | {'test': 1020} | {'test': {'num_samples': 1020, 'unique_num_labels': 102, 'min_image_width': 500, 'average_image_width': 618.07, 'max_image_width': 873, 'min_image_height': 500, 'average_image_height': 538.26, 'max_image_height': 928, 'labels': {'0': {'count': 9}, '1': {'count': 9}, '2': {'count': 10}, '3': {'count': 9}, '4': {'count': 11}, '5': {'count': 11}, '6': {'count': 10}, '7': {'count': 10}, '8': {'count': 11}, '9': {'count': 10}, '10': {'count': 10}, '11': {'count': 9}, '12': {'count': 10}, '13': {'count': 10}, '14': {'count': 10}, '15': {'count': 9}, '16': {'count': 11}, '17': {'count': 11}, '18': {'count': 10}, '19': {'count': 9}, '20': {'count': 9}, '21': {'count': 10}, '22': {'count': 11}, '23': {'count': 11}, '24': {'count': 10}, '25': {'count': 11}, '26': {'count': 10}, '27': {'count': 9}, '28': {'count': 11}, '29': {'count': 10}, '30': {'count': 10}, '31': {'count': 9}, '32': {'count': 10}, '33': {'count': 10}, '34': {'count': 10}, '35': {'count': 11}, '36': {'count': 9}, '37': {'count': 10}, '38': {'count': 10}, '39': {'count': 11}, '40': {'count': 10}, '41': {'count': 10}, '42': {'count': 11}, '43': {'count': 10}, '44': {'count': 10}, '45': {'count': 10}, '46': {'count': 10}, '47': {'count': 9}, '48': {'count': 10}, '49': {'count': 11}, '50': {'count': 10}, '51': {'count': 10}, '52': {'count': 10}, '53': {'count': 10}, '54': {'count': 10}, '55': {'count': 10}, '56': {'count': 10}, '57': {'count': 11}, '58': {'count': 10}, '59': {'count': 10}, '60': {'count': 10}, '61': {'count': 10}, '62': {'count': 9}, '63': {'count': 10}, '64': {'count': 10}, '65': {'count': 9}, '66': {'count': 11}, '67': {'count': 10}, '68': {'count': 11}, '69': {'count': 9}, '70': {'count': 9}, '71': {'count': 10}, '72': {'count': 10}, '73': {'count': 10}, '74': {'count': 10}, '75': {'count': 10}, '76': {'count': 10}, '77': {'count': 10}, '78': {'count': 9}, '79': {'count': 10}, '80': {'count': 10}, '81': {'count': 11}, '82': {'count': 10}, '83': {'count': 9}, '84': {'count': 11}, '85': {'count': 10}, '86': {'count': 10}, '87': {'count': 10}, '88': {'count': 10}, '89': {'count': 10}, '90': {'count': 10}, '91': {'count': 10}, '92': {'count': 10}, '93': {'count': 9}, '94': {'count': 9}, '95': {'count': 10}, '96': {'count': 10}, '97': {'count': 10}, '98': {'count': 10}, '99': {'count': 10}, '100': {'count': 10}, '101': {'count': 11}}}} |
+| [OxfordPets](https://ieeexplore.ieee.org/abstract/document/6248092) (Parkhi et al., 2012) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | {'test': 3669} | {'test': {'num_samples': 3669, 'unique_num_labels': 37, 'min_image_width': 137, 'average_image_width': 443.46, 'max_image_width': 1646, 'min_image_height': 103, 'average_image_height': 399.38, 'max_image_height': 2160, 'labels': {'0': {'count': 98}, '1': {'count': 100}, '2': {'count': 100}, '3': {'count': 100}, '4': {'count': 100}, '5': {'count': 100}, '6': {'count': 100}, '7': {'count': 88}, '8': {'count': 99}, '9': {'count': 100}, '10': {'count': 100}, '11': {'count': 97}, '12': {'count': 100}, '13': {'count': 100}, '14': {'count': 100}, '15': {'count': 100}, '16': {'count': 100}, '17': {'count': 100}, '18': {'count': 99}, '19': {'count': 100}, '20': {'count': 100}, '21': {'count': 100}, '22': {'count': 100}, '23': {'count': 100}, '24': {'count': 100}, '25': {'count': 100}, '26': {'count': 100}, '27': {'count': 100}, '28': {'count': 100}, '29': {'count': 100}, '30': {'count': 99}, '31': {'count': 100}, '32': {'count': 100}, '33': {'count': 100}, '34': {'count': 89}, '35': {'count': 100}, '36': {'count': 100}}}} |
 | [OxfordPetsZeroShot](https://arxiv.org/abs/1306.5151) (Subhransu Maji, 2013) | ['eng'] | ZeroShotClassification | i2t | [Encyclopaedic] | {'test': 3669} | {'test': {'num_samples': 3669, 'unique_num_labels': 37, 'min_image_width': 137, 'average_image_width': 443.46, 'max_image_width': 1646, 'min_image_height': 103, 'average_image_height': 399.38, 'max_image_height': 2160, 'min_label_text_length': 32, 'average_label_text_length': 40.68, 'max_label_text_length': 55, 'labels': {'0': {'count': 98}, '1': {'count': 100}, '2': {'count': 100}, '3': {'count': 100}, '4': {'count': 100}, '5': {'count': 100}, '6': {'count': 100}, '7': {'count': 88}, '8': {'count': 99}, '9': {'count': 100}, '10': {'count': 100}, '11': {'count': 97}, '12': {'count': 100}, '13': {'count': 100}, '14': {'count': 100}, '15': {'count': 100}, '16': {'count': 100}, '17': {'count': 100}, '18': {'count': 99}, '19': {'count': 100}, '20': {'count': 100}, '21': {'count': 100}, '22': {'count': 100}, '23': {'count': 100}, '24': {'count': 100}, '25': {'count': 100}, '26': {'count': 100}, '27': {'count': 100}, '28': {'count': 100}, '29': {'count': 100}, '30': {'count': 99}, '31': {'count': 100}, '32': {'count': 100}, '33': {'count': 100}, '34': {'count': 89}, '35': {'count': 100}, '36': {'count': 100}}}} |
 | [PAC](https://arxiv.org/pdf/2211.13112.pdf) (Łukasz Augustyniak, 2022) | ['pol'] | Classification | p2p | [Legal, Written] | None | None |
 | [PAWSX](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s |  | None | None |
@@ -592,18 +592,18 @@ The following tables give you an overview of the tasks in MTEB.
 | [RARbMath](https://arxiv.org/abs/2404.06347) (Xiao et al., 2024) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
 | [RESISC45](https://ieeexplore.ieee.org/abstract/document/7891544) (Cheng et al., 2017) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | {'test': 6300} | {'test': {'num_samples': 6300, 'unique_num_labels': 45, 'min_image_width': 256, 'average_image_width': 256.0, 'max_image_width': 256, 'min_image_height': 256, 'average_image_height': 256.0, 'max_image_height': 256, 'labels': {'31': {'count': 135}, '11': {'count': 144}, '28': {'count': 135}, '43': {'count': 154}, '41': {'count': 144}, '33': {'count': 134}, '19': {'count': 130}, '16': {'count': 127}, '22': {'count': 130}, '34': {'count': 143}, '24': {'count': 164}, '0': {'count': 169}, '13': {'count': 146}, '25': {'count': 115}, '6': {'count': 132}, '36': {'count': 135}, '39': {'count': 142}, '18': {'count': 140}, '23': {'count': 147}, '37': {'count': 159}, '15': {'count': 122}, '29': {'count': 140}, '9': {'count': 159}, '27': {'count': 140}, '21': {'count': 131}, '3': {'count': 134}, '1': {'count': 162}, '32': {'count': 153}, '26': {'count': 150}, '35': {'count': 151}, '44': {'count': 118}, '30': {'count': 154}, '20': {'count': 139}, '4': {'count': 130}, '42': {'count': 127}, '40': {'count': 137}, '5': {'count': 140}, '17': {'count': 142}, '2': {'count': 123}, '38': {'count': 130}, '10': {'count': 140}, '12': {'count': 146}, '8': {'count': 146}, '7': {'count': 143}, '14': {'count': 118}}}} |
 | [RESISC45ZeroShot](https://ieeexplore.ieee.org/abstract/document/7891544) (Cheng et al., 2017) | ['eng'] | ZeroShotClassification | i2t | [Encyclopaedic] | {'test': 6300} | {'test': {'num_samples': 6300, 'unique_num_labels': 45, 'min_image_width': 256, 'average_image_width': 256.0, 'max_image_width': 256, 'min_image_height': 256, 'average_image_height': 256.0, 'max_image_height': 256, 'min_label_text_length': 26, 'average_label_text_length': 32.16, 'max_label_text_length': 43, 'labels': {'31': {'count': 135}, '11': {'count': 144}, '28': {'count': 135}, '43': {'count': 154}, '41': {'count': 144}, '33': {'count': 134}, '19': {'count': 130}, '16': {'count': 127}, '22': {'count': 130}, '34': {'count': 143}, '24': {'count': 164}, '0': {'count': 169}, '13': {'count': 146}, '25': {'count': 115}, '6': {'count': 132}, '36': {'count': 135}, '39': {'count': 142}, '18': {'count': 140}, '23': {'count': 147}, '37': {'count': 159}, '15': {'count': 122}, '29': {'count': 140}, '9': {'count': 159}, '27': {'count': 140}, '21': {'count': 131}, '3': {'count': 134}, '1': {'count': 162}, '32': {'count': 153}, '26': {'count': 150}, '35': {'count': 151}, '44': {'count': 118}, '30': {'count': 154}, '20': {'count': 139}, '4': {'count': 130}, '42': {'count': 127}, '40': {'count': 137}, '5': {'count': 140}, '17': {'count': 142}, '2': {'count': 123}, '38': {'count': 130}, '10': {'count': 140}, '12': {'count': 146}, '8': {'count': 146}, '7': {'count': 143}, '14': {'count': 118}}}} |
-| [ROxfordEasyI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyMultiChoice | i2i | [Web] | None | None |
+| [ROxfordEasyI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [ROxfordEasyI2IRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
-| [ROxfordHardI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyMultiChoice | i2i | [Web] | None | None |
+| [ROxfordHardI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [ROxfordHardI2IRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
-| [ROxfordMediumI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyMultiChoice | i2i | [Web] | None | None |
+| [ROxfordMediumI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [ROxfordMediumI2IRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Oxford_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [RP2kI2IRetrieval](https://arxiv.org/abs/2006.12634) (Peng et al., 2020) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
-| [RParisEasyI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyMultiChoice | i2i | [Web] | None | None |
+| [RParisEasyI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [RParisEasyI2IRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
-| [RParisHardI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyMultiChoice | i2i | [Web] | None | None |
+| [RParisHardI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [RParisHardI2IRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
-| [RParisMediumI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyMultiChoice | i2i | [Web] | None | None |
+| [RParisMediumI2IMultiChoice](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [RParisMediumI2IRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/html/Radenovic_Revisiting_Paris_and_CVPR_2018_paper.html) (Radenovi{'c, 2018) | ['eng'] | Any2AnyRetrieval | i2i | [Web] | None | None |
 | [RTE3](https://aclanthology.org/W07-1401/) | ['deu', 'eng', 'fra', 'ita'] | PairClassification | s2s | [Encyclopaedic, News, Web, Written] | None | None |
 | [RUParaPhraserSTS](https://aclanthology.org/2020.ngt-1.6) (Pivovarova et al., 2017) | ['rus'] | STS | s2s | [News, Written] | None | None |
@@ -665,22 +665,22 @@ The following tables give you an overview of the tasks in MTEB.
 | [STL10](https://cs.stanford.edu/~acoates/stl10/) (Coates et al., 2011) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | {'test': 8000} | {'test': {'num_samples': 8000, 'unique_num_labels': 10, 'min_image_width': 96, 'average_image_width': 96.0, 'max_image_width': 96, 'min_image_height': 96, 'average_image_height': 96.0, 'max_image_height': 96, 'labels': {'0': {'count': 800}, '1': {'count': 800}, '2': {'count': 800}, '3': {'count': 800}, '4': {'count': 800}, '5': {'count': 800}, '6': {'count': 800}, '7': {'count': 800}, '8': {'count': 800}, '9': {'count': 800}}}} |
 | [STL10ZeroShot](https://cs.stanford.edu/~acoates/stl10/) (Coates et al., 2011) | ['eng'] | ZeroShotClassification | i2t | [Encyclopaedic] | {'test': 8000} | {'test': {'num_samples': 8000, 'unique_num_labels': 10, 'min_image_width': 96, 'average_image_width': 96.0, 'max_image_width': 96, 'min_image_height': 96, 'average_image_height': 96.0, 'max_image_height': 96, 'min_label_text_length': 17, 'average_label_text_length': 18.5, 'max_label_text_length': 22, 'labels': {'0': {'count': 800}, '1': {'count': 800}, '2': {'count': 800}, '3': {'count': 800}, '4': {'count': 800}, '5': {'count': 800}, '6': {'count': 800}, '7': {'count': 800}, '8': {'count': 800}, '9': {'count': 800}}}} |
 | [STS12](https://www.aclweb.org/anthology/S12-1051.pdf) (Agirre et al., 2012) | ['eng'] | STS | s2s | [Encyclopaedic, News, Written] | {'test': 3108} | {'test': {'num_samples': 3108, 'number_of_characters': 402118, 'min_sentence1_length': 3, 'average_sentence1_len': 63.79, 'max_sentence1_length': 220, 'unique_sentence1': 2236, 'min_sentence2_length': 7, 'average_sentence2_len': 65.59, 'max_sentence2_length': 204, 'unique_sentence2': 2797, 'min_score': 0.0, 'avg_score': 3.51, 'max_score': 5.0}} |
-| [STS12VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS | i2i | [Encyclopaedic, News, Written] | {'test': 3108} | {'test': {'num_samples': 3108, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 3.51, 'max_score': 5.0}} |
+| [STS12VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [Encyclopaedic, News, Written] | None | None |
 | [STS13](https://www.aclweb.org/anthology/S13-1004/) (Eneko Agirre, 2013) | ['eng'] | STS | s2s | [News, Non-fiction, Web, Written] | None | None |
-| [STS13VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS | i2i | [News, Non-fiction, Web, Written] | {'test': 1500} | {'test': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.34, 'max_score': 5.0}} |
+| [STS13VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [News, Non-fiction, Web, Written] | None | None |
 | [STS14](https://www.aclweb.org/anthology/S14-1002) | ['eng'] | STS | s2s | [Blog, Spoken, Web] | None | None |
-| [STS14VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS | i2i | [Blog, Spoken, Web] | {'test': 3750} | {'test': {'num_samples': 3750, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.81, 'max_score': 5.0}} |
+| [STS14VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [Blog, Spoken, Web] | None | None |
 | [STS15](https://www.aclweb.org/anthology/S15-2010) | ['eng'] | STS | s2s | [Blog, News, Spoken, Web, Written] | None | None |
-| [STS15VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS | i2i | [Blog, News, Spoken, Web, Written] | {'test': 3000} | {'test': {'num_samples': 3000, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.41, 'max_score': 5.0}} |
+| [STS15VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [Blog, News, Spoken, Web, Written] | None | None |
 | [STS16](https://www.aclweb.org/anthology/S16-1001) | ['eng'] | STS | s2s | [Blog, Spoken, Web] | None | None |
-| [STS16VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS | i2i | [Blog, Spoken, Web] | {'test': 1186} | {'test': {'num_samples': 1186, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.41, 'max_score': 5.0}} |
+| [STS16VisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [Blog, Spoken, Web] | None | None |
 | [STS17](https://alt.qcri.org/semeval2017/task1/) | ['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur'] | STS | s2s | [News, Web, Written] | {'test': 5346} | {'test': {'num_samples': 5346, 'number_of_characters': 400264, 'min_sentence1_length': 6, 'average_sentence1_len': 38.15, 'max_sentence1_length': 976, 'unique_sentence1': 4900, 'min_sentence2_length': 6, 'average_sentence2_len': 36.73, 'max_sentence2_length': 1007, 'unique_sentence2': 4470, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0, 'hf_subset_descriptive_stats': {'ko-ko': {'num_samples': 2846, 'number_of_characters': 183387, 'min_sentence1_length': 6, 'average_sentence1_len': 31.99, 'max_sentence1_length': 976, 'unique_sentence1': 2650, 'min_sentence2_length': 6, 'average_sentence2_len': 32.44, 'max_sentence2_length': 1007, 'unique_sentence2': 2720, 'min_score': 0.0, 'avg_score': 2.47, 'max_score': 5.0}, 'ar-ar': {'num_samples': 250, 'number_of_characters': 16247, 'min_sentence1_length': 11, 'average_sentence1_len': 32.21, 'max_sentence1_length': 99, 'unique_sentence1': 250, 'min_sentence2_length': 9, 'average_sentence2_len': 32.78, 'max_sentence2_length': 83, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.22, 'max_score': 5.0}, 'en-ar': {'num_samples': 250, 'number_of_characters': 18764, 'min_sentence1_length': 13, 'average_sentence1_len': 42.36, 'max_sentence1_length': 105, 'unique_sentence1': 250, 'min_sentence2_length': 10, 'average_sentence2_len': 32.7, 'max_sentence2_length': 104, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.14, 'max_score': 5.0}, 'en-de': {'num_samples': 250, 'number_of_characters': 22177, 'min_sentence1_length': 12, 'average_sentence1_len': 43.95, 'max_sentence1_length': 94, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 44.76, 'max_sentence2_length': 104, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'en-en': {'num_samples': 250, 'number_of_characters': 21669, 'min_sentence1_length': 12, 'average_sentence1_len': 43.95, 'max_sentence1_length': 94, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'en-tr': {'num_samples': 250, 'number_of_characters': 20879, 'min_sentence1_length': 15, 'average_sentence1_len': 41.92, 'max_sentence1_length': 101, 'unique_sentence1': 250, 'min_sentence2_length': 10, 'average_sentence2_len': 41.6, 'max_sentence2_length': 107, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.13, 'max_score': 5.0}, 'es-en': {'num_samples': 250, 'number_of_characters': 23216, 'min_sentence1_length': 12, 'average_sentence1_len': 50.84, 'max_sentence1_length': 160, 'unique_sentence1': 250, 'min_sentence2_length': 14, 'average_sentence2_len': 42.02, 'max_sentence2_length': 117, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.15, 'max_score': 5.0}, 'es-es': {'num_samples': 250, 'number_of_characters': 25265, 'min_sentence1_length': 18, 'average_sentence1_len': 49.84, 'max_sentence1_length': 136, 'unique_sentence1': 250, 'min_sentence2_length': 13, 'average_sentence2_len': 51.22, 'max_sentence2_length': 129, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.23, 'max_score': 5.0}, 'fr-en': {'num_samples': 250, 'number_of_characters': 23087, 'min_sentence1_length': 19, 'average_sentence1_len': 49.62, 'max_sentence1_length': 115, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'it-en': {'num_samples': 250, 'number_of_characters': 23188, 'min_sentence1_length': 15, 'average_sentence1_len': 50.03, 'max_sentence1_length': 113, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'nl-en': {'num_samples': 250, 'number_of_characters': 22385, 'min_sentence1_length': 14, 'average_sentence1_len': 46.82, 'max_sentence1_length': 123, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}}}} |
-| [STS17MultilingualVisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur'] | VisualSTS | i2i | [News, Social, Spoken, Web, Written] | {'test': 5346} | {'test': {'num_samples': 5346, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0, 'hf_subset_descriptive_stats': {'ko-ko': {'num_samples': 2846, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.47, 'max_score': 5.0}, 'ar-ar': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.22, 'max_score': 5.0}, 'en-ar': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.14, 'max_score': 5.0}, 'en-de': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'en-en': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'en-tr': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.13, 'max_score': 5.0}, 'es-en': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.15, 'max_score': 5.0}, 'es-es': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.23, 'max_score': 5.0}, 'fr-en': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'it-en': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'nl-en': {'num_samples': 250, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}}}} |
+| [STS17MultilingualVisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur'] | VisualSTS(multi) | i2i | [News, Social, Spoken, Web, Written] | None | None |
 | [STS22.v2](https://competitions.codalab.org/competitions/33835) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'pol', 'rus', 'spa', 'tur'] | STS | p2p | [News, Written] | None | None |
 | [STSB](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s |  | None | None |
 | [STSBenchmark](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['eng'] | STS | s2s | [Blog, News, Written] | None | None |
 | [STSBenchmarkMultilingualSTS](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['cmn', 'deu', 'eng', 'fra', 'ita', 'nld', 'pol', 'por', 'rus', 'spa'] | STS | s2s | [News, Social, Spoken, Web, Written] | None | None |
-| [STSBenchmarkMultilingualVisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['cmn', 'deu', 'eng', 'fra', 'ita', 'nld', 'pol', 'por', 'rus', 'spa'] | VisualSTS | i2i | [News, Social, Spoken, Web, Written] | {'dev': 15000, 'test': 13790} | {'dev': {'num_samples': 15000, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0, 'hf_subset_descriptive_stats': {'en': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'de': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'es': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'fr': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'it': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'nl': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'pl': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'pt': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'ru': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}, 'zh': {'num_samples': 1500, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0}}}, 'test': {'num_samples': 13790, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0, 'hf_subset_descriptive_stats': {'en': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'de': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'es': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'fr': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'it': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'nl': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'pl': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'pt': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'ru': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}, 'zh': {'num_samples': 1379, 'min_image1_width': 448, 'average_image1_width': 448.0, 'max_image1_width': 448, 'min_image1_height': 448, 'average_image1_height': 448.0, 'max_image1_height': 448, 'min_image2_width': 448, 'average_image2_width': 448.0, 'max_image2_width': 448, 'min_image2_height': 448, 'average_image2_height': 448.0, 'max_image2_height': 448, 'min_score': 0.0, 'avg_score': 2.61, 'max_score': 5.0}}}} |
+| [STSBenchmarkMultilingualVisualSTS](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['cmn', 'deu', 'eng', 'fra', 'ita', 'nld', 'pol', 'por', 'rus', 'spa'] | VisualSTS(multi) | i2i | [News, Social, Spoken, Web, Written] | None | None |
 | [STSES](https://huggingface.co/datasets/PlanTL-GOB-ES/sts-es) (Agirre et al., 2015) | ['spa'] | STS | s2s | [Written] | None | None |
 | [SUN397](https://ieeexplore.ieee.org/abstract/document/5539970) (Xiao et al., 2010) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | {'test': 21750} | {'test': {'num_samples': 21750, 'unique_num_labels': 397, 'min_image_width': 125, 'average_image_width': 354.22, 'max_image_width': 696, 'min_image_height': 94, 'average_image_height': 291.17, 'max_image_height': 595, 'labels': {'227': {'count': 439}, '213': {'count': 335}, '53': {'count': 23}, '350': {'count': 40}, '73': {'count': 38}, '316': {'count': 63}, '177': {'count': 80}, '25': {'count': 39}, '275': {'count': 31}, '328': {'count': 33}, '263': {'count': 47}, '239': {'count': 26}, '41': {'count': 213}, '319': {'count': 51}, '91': {'count': 16}, '95': {'count': 183}, '396': {'count': 20}, '259': {'count': 36}, '107': {'count': 167}, '381': {'count': 164}, '174': {'count': 167}, '246': {'count': 44}, '67': {'count': 31}, '374': {'count': 28}, '354': {'count': 22}, '72': {'count': 100}, '97': {'count': 32}, '256': {'count': 57}, '247': {'count': 57}, '159': {'count': 49}, '270': {'count': 135}, '133': {'count': 215}, '197': {'count': 40}, '12': {'count': 38}, '2': {'count': 226}, '115': {'count': 75}, '200': {'count': 93}, '47': {'count': 103}, '9': {'count': 37}, '22': {'count': 76}, '255': {'count': 34}, '267': {'count': 22}, '244': {'count': 93}, '85': {'count': 115}, '342': {'count': 87}, '55': {'count': 50}, '7': {'count': 41}, '337': {'count': 99}, '38': {'count': 28}, '269': {'count': 69}, '106': {'count': 15}, '298': {'count': 27}, '361': {'count': 53}, '8': {'count': 108}, '166': {'count': 47}, '280': {'count': 51}, '35': {'count': 61}, '147': {'count': 82}, '214': {'count': 26}, '284': {'count': 28}, '286': {'count': 66}, '113': {'count': 67}, '83': {'count': 38}, '82': {'count': 236}, '365': {'count': 17}, '242': {'count': 116}, '186': {'count': 38}, '87': {'count': 111}, '274': {'count': 48}, '27': {'count': 95}, '283': {'count': 22}, '4': {'count': 76}, '334': {'count': 139}, '364': {'count': 21}, '48': {'count': 408}, '311': {'count': 41}, '101': {'count': 64}, '131': {'count': 55}, '172': {'count': 31}, '355': {'count': 28}, '308': {'count': 56}, '5': {'count': 47}, '318': {'count': 155}, '86': {'count': 87}, '46': {'count': 230}, '111': {'count': 69}, '88': {'count': 54}, '23': {'count': 47}, '70': {'count': 61}, '217': {'count': 34}, '11': {'count': 76}, '193': {'count': 207}, '0': {'count': 99}, '303': {'count': 23}, '324': {'count': 47}, '377': {'count': 19}, '345': {'count': 39}, '154': {'count': 49}, '393': {'count': 68}, '152': {'count': 58}, '317': {'count': 27}, '384': {'count': 46}, '257': {'count': 38}, '294': {'count': 47}, '145': {'count': 23}, '289': {'count': 33}, '375': {'count': 19}, '57': {'count': 42}, '15': {'count': 62}, '109': {'count': 24}, '139': {'count': 24}, '66': {'count': 26}, '340': {'count': 32}, '150': {'count': 41}, '118': {'count': 105}, '333': {'count': 27}, '126': {'count': 55}, '366': {'count': 116}, '358': {'count': 151}, '251': {'count': 37}, '309': {'count': 35}, '54': {'count': 20}, '327': {'count': 38}, '3': {'count': 60}, '21': {'count': 56}, '17': {'count': 62}, '146': {'count': 84}, '94': {'count': 42}, '243': {'count': 48}, '335': {'count': 85}, '245': {'count': 141}, '279': {'count': 187}, '360': {'count': 25}, '192': {'count': 105}, '49': {'count': 31}, '230': {'count': 81}, '357': {'count': 22}, '64': {'count': 72}, '112': {'count': 26}, '338': {'count': 70}, '216': {'count': 99}, '234': {'count': 183}, '300': {'count': 153}, '188': {'count': 48}, '254': {'count': 41}, '184': {'count': 183}, '373': {'count': 47}, '221': {'count': 86}, '84': {'count': 49}, '81': {'count': 119}, '161': {'count': 97}, '352': {'count': 21}, '105': {'count': 43}, '39': {'count': 59}, '383': {'count': 40}, '341': {'count': 56}, '63': {'count': 158}, '125': {'count': 29}, '302': {'count': 83}, '262': {'count': 40}, '392': {'count': 51}, '326': {'count': 173}, '228': {'count': 93}, '339': {'count': 25}, '80': {'count': 73}, '30': {'count': 42}, '264': {'count': 112}, '56': {'count': 94}, '321': {'count': 16}, '395': {'count': 52}, '68': {'count': 45}, '211': {'count': 45}, '44': {'count': 26}, '299': {'count': 21}, '220': {'count': 35}, '61': {'count': 20}, '138': {'count': 55}, '108': {'count': 111}, '10': {'count': 35}, '386': {'count': 28}, '297': {'count': 49}, '210': {'count': 36}, '175': {'count': 77}, '260': {'count': 68}, '391': {'count': 69}, '102': {'count': 77}, '26': {'count': 44}, '232': {'count': 54}, '6': {'count': 158}, '124': {'count': 43}, '14': {'count': 23}, '201': {'count': 39}, '168': {'count': 18}, '202': {'count': 26}, '140': {'count': 31}, '261': {'count': 60}, '104': {'count': 27}, '356': {'count': 22}, '34': {'count': 147}, '225': {'count': 111}, '60': {'count': 84}, '156': {'count': 35}, '237': {'count': 45}, '268': {'count': 87}, '310': {'count': 31}, '249': {'count': 73}, '281': {'count': 46}, '75': {'count': 89}, '77': {'count': 53}, '132': {'count': 45}, '235': {'count': 42}, '336': {'count': 84}, '123': {'count': 27}, '349': {'count': 90}, '180': {'count': 49}, '378': {'count': 17}, '332': {'count': 30}, '185': {'count': 29}, '389': {'count': 60}, '382': {'count': 77}, '198': {'count': 54}, '74': {'count': 48}, '231': {'count': 85}, '76': {'count': 54}, '151': {'count': 64}, '182': {'count': 17}, '209': {'count': 39}, '344': {'count': 37}, '204': {'count': 67}, '329': {'count': 23}, '380': {'count': 91}, '388': {'count': 32}, '116': {'count': 29}, '24': {'count': 103}, '199': {'count': 33}, '369': {'count': 14}, '359': {'count': 77}, '325': {'count': 39}, '323': {'count': 34}, '162': {'count': 35}, '33': {'count': 46}, '129': {'count': 21}, '287': {'count': 30}, '155': {'count': 24}, '170': {'count': 157}, '296': {'count': 40}, '110': {'count': 102}, '304': {'count': 21}, '164': {'count': 37}, '278': {'count': 23}, '71': {'count': 18}, '194': {'count': 24}, '136': {'count': 117}, '103': {'count': 134}, '330': {'count': 26}, '347': {'count': 26}, '206': {'count': 50}, '178': {'count': 43}, '362': {'count': 26}, '119': {'count': 111}, '208': {'count': 33}, '165': {'count': 44}, '90': {'count': 36}, '167': {'count': 40}, '187': {'count': 26}, '99': {'count': 50}, '390': {'count': 64}, '205': {'count': 16}, '65': {'count': 30}, '293': {'count': 23}, '223': {'count': 19}, '96': {'count': 31}, '305': {'count': 44}, '100': {'count': 57}, '385': {'count': 18}, '78': {'count': 42}, '59': {'count': 20}, '37': {'count': 59}, '219': {'count': 76}, '212': {'count': 28}, '1': {'count': 26}, '122': {'count': 35}, '92': {'count': 62}, '43': {'count': 39}, '196': {'count': 56}, '19': {'count': 25}, '128': {'count': 35}, '376': {'count': 77}, '313': {'count': 30}, '114': {'count': 54}, '121': {'count': 31}, '169': {'count': 62}, '331': {'count': 55}, '238': {'count': 16}, '179': {'count': 31}, '127': {'count': 31}, '370': {'count': 98}, '149': {'count': 47}, '346': {'count': 41}, '250': {'count': 22}, '276': {'count': 25}, '163': {'count': 43}, '18': {'count': 33}, '282': {'count': 23}, '215': {'count': 33}, '258': {'count': 60}, '240': {'count': 29}, '233': {'count': 14}, '93': {'count': 27}, '69': {'count': 23}, '266': {'count': 26}, '387': {'count': 55}, '141': {'count': 18}, '191': {'count': 26}, '183': {'count': 42}, '271': {'count': 22}, '120': {'count': 32}, '98': {'count': 53}, '29': {'count': 34}, '28': {'count': 21}, '144': {'count': 26}, '351': {'count': 50}, '368': {'count': 20}, '314': {'count': 27}, '45': {'count': 17}, '218': {'count': 50}, '348': {'count': 25}, '157': {'count': 35}, '117': {'count': 24}, '367': {'count': 24}, '13': {'count': 31}, '363': {'count': 22}, '79': {'count': 28}, '312': {'count': 27}, '372': {'count': 29}, '189': {'count': 21}, '50': {'count': 22}, '160': {'count': 35}, '16': {'count': 39}, '222': {'count': 21}, '58': {'count': 37}, '153': {'count': 64}, '62': {'count': 21}, '290': {'count': 25}, '292': {'count': 24}, '285': {'count': 25}, '343': {'count': 32}, '301': {'count': 19}, '190': {'count': 46}, '195': {'count': 24}, '135': {'count': 30}, '315': {'count': 25}, '203': {'count': 29}, '307': {'count': 18}, '142': {'count': 25}, '173': {'count': 28}, '236': {'count': 41}, '171': {'count': 23}, '371': {'count': 17}, '130': {'count': 15}, '277': {'count': 39}, '248': {'count': 22}, '181': {'count': 35}, '40': {'count': 20}, '322': {'count': 15}, '273': {'count': 23}, '148': {'count': 23}, '295': {'count': 25}, '32': {'count': 21}, '320': {'count': 25}, '137': {'count': 32}, '253': {'count': 36}, '31': {'count': 19}, '306': {'count': 27}, '51': {'count': 19}, '52': {'count': 29}, '176': {'count': 31}, '241': {'count': 23}, '265': {'count': 32}, '394': {'count': 26}, '158': {'count': 26}, '226': {'count': 28}, '288': {'count': 21}, '353': {'count': 19}, '291': {'count': 21}, '224': {'count': 26}, '36': {'count': 38}, '20': {'count': 22}, '252': {'count': 18}, '134': {'count': 24}, '143': {'count': 21}, '207': {'count': 28}, '89': {'count': 16}, '272': {'count': 23}, '379': {'count': 24}, '229': {'count': 20}, '42': {'count': 23}}}} |
 | [SUN397ZeroShot](https://ieeexplore.ieee.org/abstract/document/5539970) (Xiao et al., 2010) | ['eng'] | ZeroShotClassification | i2t | [Encyclopaedic] | {'test': 21750} | {'test': {'num_samples': 21750, 'unique_num_labels': 397, 'min_image_width': 125, 'average_image_width': 354.22, 'max_image_width': 696, 'min_image_height': 94, 'average_image_height': 291.17, 'max_image_height': 595, 'min_label_text_length': 17, 'average_label_text_length': 25.9, 'max_label_text_length': 41, 'labels': {'227': {'count': 439}, '213': {'count': 335}, '53': {'count': 23}, '350': {'count': 40}, '73': {'count': 38}, '316': {'count': 63}, '177': {'count': 80}, '25': {'count': 39}, '275': {'count': 31}, '328': {'count': 33}, '263': {'count': 47}, '239': {'count': 26}, '41': {'count': 213}, '319': {'count': 51}, '91': {'count': 16}, '95': {'count': 183}, '396': {'count': 20}, '259': {'count': 36}, '107': {'count': 167}, '381': {'count': 164}, '174': {'count': 167}, '246': {'count': 44}, '67': {'count': 31}, '374': {'count': 28}, '354': {'count': 22}, '72': {'count': 100}, '97': {'count': 32}, '256': {'count': 57}, '247': {'count': 57}, '159': {'count': 49}, '270': {'count': 135}, '133': {'count': 215}, '197': {'count': 40}, '12': {'count': 38}, '2': {'count': 226}, '115': {'count': 75}, '200': {'count': 93}, '47': {'count': 103}, '9': {'count': 37}, '22': {'count': 76}, '255': {'count': 34}, '267': {'count': 22}, '244': {'count': 93}, '85': {'count': 115}, '342': {'count': 87}, '55': {'count': 50}, '7': {'count': 41}, '337': {'count': 99}, '38': {'count': 28}, '269': {'count': 69}, '106': {'count': 15}, '298': {'count': 27}, '361': {'count': 53}, '8': {'count': 108}, '166': {'count': 47}, '280': {'count': 51}, '35': {'count': 61}, '147': {'count': 82}, '214': {'count': 26}, '284': {'count': 28}, '286': {'count': 66}, '113': {'count': 67}, '83': {'count': 38}, '82': {'count': 236}, '365': {'count': 17}, '242': {'count': 116}, '186': {'count': 38}, '87': {'count': 111}, '274': {'count': 48}, '27': {'count': 95}, '283': {'count': 22}, '4': {'count': 76}, '334': {'count': 139}, '364': {'count': 21}, '48': {'count': 408}, '311': {'count': 41}, '101': {'count': 64}, '131': {'count': 55}, '172': {'count': 31}, '355': {'count': 28}, '308': {'count': 56}, '5': {'count': 47}, '318': {'count': 155}, '86': {'count': 87}, '46': {'count': 230}, '111': {'count': 69}, '88': {'count': 54}, '23': {'count': 47}, '70': {'count': 61}, '217': {'count': 34}, '11': {'count': 76}, '193': {'count': 207}, '0': {'count': 99}, '303': {'count': 23}, '324': {'count': 47}, '377': {'count': 19}, '345': {'count': 39}, '154': {'count': 49}, '393': {'count': 68}, '152': {'count': 58}, '317': {'count': 27}, '384': {'count': 46}, '257': {'count': 38}, '294': {'count': 47}, '145': {'count': 23}, '289': {'count': 33}, '375': {'count': 19}, '57': {'count': 42}, '15': {'count': 62}, '109': {'count': 24}, '139': {'count': 24}, '66': {'count': 26}, '340': {'count': 32}, '150': {'count': 41}, '118': {'count': 105}, '333': {'count': 27}, '126': {'count': 55}, '366': {'count': 116}, '358': {'count': 151}, '251': {'count': 37}, '309': {'count': 35}, '54': {'count': 20}, '327': {'count': 38}, '3': {'count': 60}, '21': {'count': 56}, '17': {'count': 62}, '146': {'count': 84}, '94': {'count': 42}, '243': {'count': 48}, '335': {'count': 85}, '245': {'count': 141}, '279': {'count': 187}, '360': {'count': 25}, '192': {'count': 105}, '49': {'count': 31}, '230': {'count': 81}, '357': {'count': 22}, '64': {'count': 72}, '112': {'count': 26}, '338': {'count': 70}, '216': {'count': 99}, '234': {'count': 183}, '300': {'count': 153}, '188': {'count': 48}, '254': {'count': 41}, '184': {'count': 183}, '373': {'count': 47}, '221': {'count': 86}, '84': {'count': 49}, '81': {'count': 119}, '161': {'count': 97}, '352': {'count': 21}, '105': {'count': 43}, '39': {'count': 59}, '383': {'count': 40}, '341': {'count': 56}, '63': {'count': 158}, '125': {'count': 29}, '302': {'count': 83}, '262': {'count': 40}, '392': {'count': 51}, '326': {'count': 173}, '228': {'count': 93}, '339': {'count': 25}, '80': {'count': 73}, '30': {'count': 42}, '264': {'count': 112}, '56': {'count': 94}, '321': {'count': 16}, '395': {'count': 52}, '68': {'count': 45}, '211': {'count': 45}, '44': {'count': 26}, '299': {'count': 21}, '220': {'count': 35}, '61': {'count': 20}, '138': {'count': 55}, '108': {'count': 111}, '10': {'count': 35}, '386': {'count': 28}, '297': {'count': 49}, '210': {'count': 36}, '175': {'count': 77}, '260': {'count': 68}, '391': {'count': 69}, '102': {'count': 77}, '26': {'count': 44}, '232': {'count': 54}, '6': {'count': 158}, '124': {'count': 43}, '14': {'count': 23}, '201': {'count': 39}, '168': {'count': 18}, '202': {'count': 26}, '140': {'count': 31}, '261': {'count': 60}, '104': {'count': 27}, '356': {'count': 22}, '34': {'count': 147}, '225': {'count': 111}, '60': {'count': 84}, '156': {'count': 35}, '237': {'count': 45}, '268': {'count': 87}, '310': {'count': 31}, '249': {'count': 73}, '281': {'count': 46}, '75': {'count': 89}, '77': {'count': 53}, '132': {'count': 45}, '235': {'count': 42}, '336': {'count': 84}, '123': {'count': 27}, '349': {'count': 90}, '180': {'count': 49}, '378': {'count': 17}, '332': {'count': 30}, '185': {'count': 29}, '389': {'count': 60}, '382': {'count': 77}, '198': {'count': 54}, '74': {'count': 48}, '231': {'count': 85}, '76': {'count': 54}, '151': {'count': 64}, '182': {'count': 17}, '209': {'count': 39}, '344': {'count': 37}, '204': {'count': 67}, '329': {'count': 23}, '380': {'count': 91}, '388': {'count': 32}, '116': {'count': 29}, '24': {'count': 103}, '199': {'count': 33}, '369': {'count': 14}, '359': {'count': 77}, '325': {'count': 39}, '323': {'count': 34}, '162': {'count': 35}, '33': {'count': 46}, '129': {'count': 21}, '287': {'count': 30}, '155': {'count': 24}, '170': {'count': 157}, '296': {'count': 40}, '110': {'count': 102}, '304': {'count': 21}, '164': {'count': 37}, '278': {'count': 23}, '71': {'count': 18}, '194': {'count': 24}, '136': {'count': 117}, '103': {'count': 134}, '330': {'count': 26}, '347': {'count': 26}, '206': {'count': 50}, '178': {'count': 43}, '362': {'count': 26}, '119': {'count': 111}, '208': {'count': 33}, '165': {'count': 44}, '90': {'count': 36}, '167': {'count': 40}, '187': {'count': 26}, '99': {'count': 50}, '390': {'count': 64}, '205': {'count': 16}, '65': {'count': 30}, '293': {'count': 23}, '223': {'count': 19}, '96': {'count': 31}, '305': {'count': 44}, '100': {'count': 57}, '385': {'count': 18}, '78': {'count': 42}, '59': {'count': 20}, '37': {'count': 59}, '219': {'count': 76}, '212': {'count': 28}, '1': {'count': 26}, '122': {'count': 35}, '92': {'count': 62}, '43': {'count': 39}, '196': {'count': 56}, '19': {'count': 25}, '128': {'count': 35}, '376': {'count': 77}, '313': {'count': 30}, '114': {'count': 54}, '121': {'count': 31}, '169': {'count': 62}, '331': {'count': 55}, '238': {'count': 16}, '179': {'count': 31}, '127': {'count': 31}, '370': {'count': 98}, '149': {'count': 47}, '346': {'count': 41}, '250': {'count': 22}, '276': {'count': 25}, '163': {'count': 43}, '18': {'count': 33}, '282': {'count': 23}, '215': {'count': 33}, '258': {'count': 60}, '240': {'count': 29}, '233': {'count': 14}, '93': {'count': 27}, '69': {'count': 23}, '266': {'count': 26}, '387': {'count': 55}, '141': {'count': 18}, '191': {'count': 26}, '183': {'count': 42}, '271': {'count': 22}, '120': {'count': 32}, '98': {'count': 53}, '29': {'count': 34}, '28': {'count': 21}, '144': {'count': 26}, '351': {'count': 50}, '368': {'count': 20}, '314': {'count': 27}, '45': {'count': 17}, '218': {'count': 50}, '348': {'count': 25}, '157': {'count': 35}, '117': {'count': 24}, '367': {'count': 24}, '13': {'count': 31}, '363': {'count': 22}, '79': {'count': 28}, '312': {'count': 27}, '372': {'count': 29}, '189': {'count': 21}, '50': {'count': 22}, '160': {'count': 35}, '16': {'count': 39}, '222': {'count': 21}, '58': {'count': 37}, '153': {'count': 64}, '62': {'count': 21}, '290': {'count': 25}, '292': {'count': 24}, '285': {'count': 25}, '343': {'count': 32}, '301': {'count': 19}, '190': {'count': 46}, '195': {'count': 24}, '135': {'count': 30}, '315': {'count': 25}, '203': {'count': 29}, '307': {'count': 18}, '142': {'count': 25}, '173': {'count': 28}, '236': {'count': 41}, '171': {'count': 23}, '371': {'count': 17}, '130': {'count': 15}, '277': {'count': 39}, '248': {'count': 22}, '181': {'count': 35}, '40': {'count': 20}, '322': {'count': 15}, '273': {'count': 23}, '148': {'count': 23}, '295': {'count': 25}, '32': {'count': 21}, '320': {'count': 25}, '137': {'count': 32}, '253': {'count': 36}, '31': {'count': 19}, '306': {'count': 27}, '51': {'count': 19}, '52': {'count': 29}, '176': {'count': 31}, '241': {'count': 23}, '265': {'count': 32}, '394': {'count': 26}, '158': {'count': 26}, '226': {'count': 28}, '288': {'count': 21}, '353': {'count': 19}, '291': {'count': 21}, '224': {'count': 26}, '36': {'count': 38}, '20': {'count': 22}, '252': {'count': 18}, '134': {'count': 24}, '143': {'count': 21}, '207': {'count': 28}, '89': {'count': 16}, '272': {'count': 23}, '379': {'count': 24}, '229': {'count': 20}, '42': {'count': 23}}}} |
@@ -722,7 +722,7 @@ The following tables give you an overview of the tasks in MTEB.
 | [StanfordCarsI2IRetrieval](https://pure.mpg.de/rest/items/item_2029263/component/file_2029262/content) (Jonathan Krause, 2013) | ['eng'] | Any2AnyRetrieval | i2i | [Encyclopaedic] | None | None |
 | [StanfordCarsZeroShot](https://pure.mpg.de/rest/items/item_2029263/component/file_2029262/content) (Jonathan Krause, 2013) | ['eng'] | ZeroShotClassification | i2t | [Scene] | {'test': 8041} | {'test': {'num_samples': 8041, 'unique_num_labels': 196, 'min_image_width': 78, 'average_image_width': 701.18, 'max_image_width': 7800, 'min_image_height': 41, 'average_image_height': 483.75, 'max_image_height': 5400, 'min_label_text_length': 29, 'average_label_text_length': 40.83, 'max_label_text_length': 68, 'labels': {'180': {'count': 38}, '102': {'count': 39}, '144': {'count': 44}, '186': {'count': 43}, '184': {'count': 38}, '77': {'count': 37}, '117': {'count': 41}, '164': {'count': 44}, '31': {'count': 41}, '59': {'count': 36}, '48': {'count': 37}, '107': {'count': 44}, '115': {'count': 37}, '134': {'count': 42}, '82': {'count': 40}, '50': {'count': 43}, '153': {'count': 42}, '32': {'count': 42}, '21': {'count': 42}, '150': {'count': 43}, '3': {'count': 42}, '80': {'count': 45}, '106': {'count': 44}, '190': {'count': 46}, '169': {'count': 44}, '194': {'count': 43}, '90': {'count': 38}, '4': {'count': 40}, '163': {'count': 43}, '147': {'count': 45}, '187': {'count': 43}, '43': {'count': 44}, '6': {'count': 39}, '30': {'count': 44}, '73': {'count': 43}, '29': {'count': 41}, '165': {'count': 41}, '179': {'count': 42}, '105': {'count': 41}, '2': {'count': 43}, '64': {'count': 45}, '34': {'count': 41}, '74': {'count': 44}, '84': {'count': 43}, '24': {'count': 39}, '167': {'count': 42}, '136': {'count': 43}, '133': {'count': 33}, '155': {'count': 39}, '119': {'count': 42}, '129': {'count': 41}, '127': {'count': 39}, '35': {'count': 41}, '170': {'count': 46}, '36': {'count': 38}, '63': {'count': 29}, '182': {'count': 42}, '42': {'count': 46}, '17': {'count': 42}, '75': {'count': 43}, '0': {'count': 44}, '62': {'count': 44}, '173': {'count': 41}, '16': {'count': 40}, '104': {'count': 43}, '49': {'count': 42}, '122': {'count': 44}, '81': {'count': 45}, '191': {'count': 42}, '92': {'count': 39}, '145': {'count': 43}, '95': {'count': 41}, '54': {'count': 39}, '114': {'count': 45}, '112': {'count': 42}, '151': {'count': 35}, '91': {'count': 40}, '188': {'count': 40}, '20': {'count': 42}, '33': {'count': 44}, '86': {'count': 44}, '128': {'count': 38}, '142': {'count': 40}, '19': {'count': 46}, '177': {'count': 41}, '11': {'count': 36}, '45': {'count': 43}, '60': {'count': 43}, '8': {'count': 41}, '56': {'count': 37}, '28': {'count': 42}, '120': {'count': 44}, '5': {'count': 44}, '85': {'count': 42}, '68': {'count': 38}, '22': {'count': 39}, '108': {'count': 44}, '89': {'count': 41}, '132': {'count': 42}, '125': {'count': 42}, '137': {'count': 39}, '158': {'count': 36}, '58': {'count': 44}, '123': {'count': 39}, '52': {'count': 44}, '27': {'count': 41}, '13': {'count': 42}, '70': {'count': 35}, '25': {'count': 34}, '185': {'count': 38}, '171': {'count': 44}, '9': {'count': 33}, '40': {'count': 35}, '178': {'count': 45}, '44': {'count': 32}, '97': {'count': 46}, '87': {'count': 39}, '159': {'count': 44}, '146': {'count': 44}, '51': {'count': 41}, '121': {'count': 40}, '1': {'count': 32}, '160': {'count': 48}, '78': {'count': 48}, '109': {'count': 43}, '103': {'count': 42}, '174': {'count': 30}, '181': {'count': 46}, '23': {'count': 45}, '111': {'count': 45}, '166': {'count': 47}, '172': {'count': 43}, '66': {'count': 38}, '192': {'count': 41}, '148': {'count': 42}, '72': {'count': 44}, '141': {'count': 32}, '71': {'count': 45}, '7': {'count': 45}, '152': {'count': 44}, '183': {'count': 40}, '98': {'count': 27}, '94': {'count': 45}, '126': {'count': 41}, '100': {'count': 42}, '131': {'count': 43}, '116': {'count': 42}, '39': {'count': 39}, '149': {'count': 36}, '101': {'count': 39}, '139': {'count': 42}, '69': {'count': 42}, '12': {'count': 41}, '14': {'count': 43}, '96': {'count': 42}, '41': {'count': 34}, '189': {'count': 43}, '10': {'count': 38}, '140': {'count': 34}, '26': {'count': 35}, '57': {'count': 44}, '88': {'count': 44}, '67': {'count': 40}, '93': {'count': 43}, '193': {'count': 45}, '161': {'count': 45}, '118': {'count': 68}, '110': {'count': 42}, '154': {'count': 42}, '138': {'count': 42}, '143': {'count': 46}, '61': {'count': 37}, '176': {'count': 44}, '113': {'count': 45}, '18': {'count': 40}, '53': {'count': 40}, '47': {'count': 42}, '157': {'count': 29}, '168': {'count': 38}, '124': {'count': 43}, '79': {'count': 43}, '130': {'count': 42}, '46': {'count': 35}, '55': {'count': 46}, '195': {'count': 40}, '38': {'count': 36}, '37': {'count': 40}, '99': {'count': 33}, '83': {'count': 42}, '162': {'count': 36}, '135': {'count': 24}, '175': {'count': 38}, '156': {'count': 36}, '15': {'count': 43}, '65': {'count': 41}, '76': {'count': 40}}}} |
 | [StatcanDialogueDatasetRetrieval](https://mcgill-nlp.github.io/statcan-dialogue-dataset/) | ['eng', 'fra'] | Retrieval | s2p | [Government, Web, Written] | None | None |
-| [SugarCrepe](https://proceedings.neurips.cc/paper_files/paper/2023/hash/63461de0b4cb760fc498e85b18a7fe81-Abstract-Datasets_and_Benchmarks.html) (Hsieh et al., 2024) | ['eng'] | ImageTextPairClassification | i2t | [Encyclopaedic] | None | None |
+| [SugarCrepe](https://proceedings.neurips.cc/paper_files/paper/2023/hash/63461de0b4cb760fc498e85b18a7fe81-Abstract-Datasets_and_Benchmarks.html) (Hsieh et al., 2024) | ['eng'] | Compositionality | i2t | [Encyclopaedic] | {'test': 7511} | {'test': {'num_samples': 7511, 'num_images': 7511, 'num_texts': 15022, 'num_unique_texts': 11844, 'min_text_length': 24, 'average_text_length': 56.49, 'max_text_length': 210}} |
 | [SummEvalFrSummarization.v2](https://github.com/Yale-LILY/SummEval) (Fabbri et al., 2020) | ['fra'] | Summarization | p2p | [News, Written] | None | None |
 | [SummEvalSummarization.v2](https://github.com/Yale-LILY/SummEval) (Fabbri et al., 2020) | ['eng'] | Summarization | p2p | [News, Written] | None | None |
 | [SwahiliNewsClassification](https://huggingface.co/datasets/Mollel/SwahiliNewsClassification) | ['swa'] | Classification | s2s | [News, Written] | None | None |
@@ -757,7 +757,7 @@ The following tables give you an overview of the tasks in MTEB.
 | [SynPerChatbotToneUserClassification](https://mcinext.com/) | ['fas'] | Classification | p2p | [Spoken] | None | None |
 | [SynPerChatbotTopicsRetrieval](https://huggingface.co/datasets/MCINext/synthetic-persian-chatbot-topics-retrieval) | ['fas'] | Retrieval | s2p | [Spoken] | None | None |
 | [SynPerQAPC](https://mcinext.com/) | ['fas'] | PairClassification | s2p | [Blog, News, Religious, Web] | None | None |
-| [SynPerQARetrieval](https://huggingface.co/datasets/MCINext/synthetic-persian-qa-retrieval/settings) | ['fas'] | Retrieval | s2p | [Web] | None | None |
+| [SynPerQARetrieval](https://huggingface.co/datasets/MCINext/synthetic-persian-qa-retrieval/) | ['fas'] | Retrieval | s2p | [Web] | None | None |
 | [SynPerSTS](https://mcinext.com/) | ['fas'] | STS | s2s | [Blog, News, Religious, Web] | None | None |
 | [SynPerTextKeywordsPC](https://mcinext.com/) | ['fas'] | PairClassification | s2p | [Blog, News, Religious, Web] | None | None |
 | [SyntecReranking](https://huggingface.co/datasets/lyon-nlp/mteb-fr-reranking-syntec-s2p) (Mathieu Ciancone, 2024) | ['fra'] | Reranking | s2p | [Legal, Written] | None | None |
@@ -822,27 +822,31 @@ The following tables give you an overview of the tasks in MTEB.
 | [UrduRomanSentimentClassification](https://archive.ics.uci.edu/dataset/458/roman+urdu+data+set) (Sharf,Zareen, 2018) | ['urd'] | Classification | s2s | [Social, Written] | None | None |
 | [VGHierarchicalClusteringP2P](https://huggingface.co/datasets/navjordj/VG_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [News, Non-fiction, Written] | None | None |
 | [VGHierarchicalClusteringS2S](https://huggingface.co/datasets/navjordj/VG_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [News, Non-fiction, Written] | None | None |
-| [VOC2007](http://host.robots.ox.ac.uk/pascal/VOC/) | ['eng'] | ImageMultilabelClassification | i2i | [Encyclopaedic] | {'test': 4952} | {'test': {'num_samples': 4952, 'min_image_width': 148, 'average_image_width': 471.25, 'max_image_width': 500, 'min_image_height': 139, 'average_image_height': 381.54, 'max_image_height': 500, 'min_labels_per_sample': 1, 'average_label_per_sample': 1.42, 'max_labels_per_sample': 5, 'unique_num_labels': 20, 'labels': {'14': {'count': 2007}, '11': {'count': 418}, '18': {'count': 259}, '17': {'count': 223}, '8': {'count': 417}, '6': {'count': 721}, '10': {'count': 190}, '15': {'count': 224}, '12': {'count': 274}, '7': {'count': 322}, '9': {'count': 127}, '5': {'count': 174}, '1': {'count': 239}, '13': {'count': 222}, '2': {'count': 282}, '19': {'count': 229}, '16': {'count': 97}, '0': {'count': 204}, '3': {'count': 172}, '4': {'count': 212}}}} |
+| [VOC2007](http://host.robots.ox.ac.uk/pascal/VOC/) | ['eng'] | ImageClassification | i2i | [Encyclopaedic] | None | None |
 | [VQA2IT2TRetrieval](https://openaccess.thecvf.com/content_cvpr_2017/html/Goyal_Making_the_v_CVPR_2017_paper.html) (Goyal et al., 2017) | ['eng'] | Any2AnyRetrieval | it2t | [Web] | None | None |
 | [VideoRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p |  | None | None |
-| [VidoreArxivQARetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreDocVQARetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreInfoVQARetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreShiftProjectRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreSyntheticDocQAAIRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreSyntheticDocQAEnergyRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreSyntheticDocQAGovernmentReportsRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreSyntheticDocQAHealthcareIndustryRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreTabfquadRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
-| [VidoreTatdqaRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | Any2AnyRetrieval | t2i | [Academic] | None | None |
+| [VidoreArxivQARetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreDocVQARetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreInfoVQARetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreShiftProjectRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreSyntheticDocQAAIRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreSyntheticDocQAEnergyRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreSyntheticDocQAGovernmentReportsRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreSyntheticDocQAHealthcareIndustryRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreTabfquadRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
+| [VidoreTatdqaRetrieval](https://arxiv.org/pdf/2407.01449) (Faysse et al., 2024) | ['eng'] | DocumentUnderstanding | t2i | [Academic] | None | None |
 | [VieMedEVBitextMining](https://aclanthology.org/2015.iwslt-evaluation.11/) (Nhu Vo, 2024) | ['eng', 'vie'] | BitextMining | s2s | [Medical, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'number_of_characters': 575910, 'unique_pairs': 2048, 'min_sentence1_length': 11, 'average_sentence1_length': 139.23, 'max_sentence1_length': 1291, 'unique_sentence1': 2048, 'min_sentence2_length': 11, 'average_sentence2_length': 141.98, 'max_sentence2_length': 1217, 'unique_sentence2': 2047}} |
 | [VieQuADRetrieval](https://aclanthology.org/2020.coling-main.233.pdf) | ['vie'] | Retrieval | s2p | [Encyclopaedic, Non-fiction, Written] | None | None |
 | [VieStudentFeedbackClassification](https://ieeexplore.ieee.org/document/8573337) (Nguyen et al., 2018) | ['vie'] | Classification | s2s | [Reviews, Written] | None | None |
 | [VisualNewsI2TRetrieval](https://aclanthology.org/2021.emnlp-main.542/) (Liu et al., 2021) | ['eng'] | Any2AnyRetrieval | i2t | [Encyclopaedic] | None | None |
 | [VisualNewsT2IRetrieval](https://aclanthology.org/2021.emnlp-main.542/) (Liu et al., 2021) | ['eng'] | Any2AnyRetrieval | t2i | [Encyclopaedic] | None | None |
+| [VisualSTS-b-Eng](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [News, Social, Spoken, Web, Written] | None | None |
+| [VisualSTS-b-Multilingual](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['cmn', 'deu', 'fra', 'ita', 'nld', 'pol', 'por', 'rus', 'spa'] | VisualSTS(multi) | i2i | [News, Social, Spoken, Web, Written] | None | None |
+| [VisualSTS17Eng](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['eng'] | VisualSTS(eng) | i2i | [News, Social, Spoken, Web, Written] | None | None |
+| [VisualSTS17Multilingual](https://arxiv.org/abs/2402.08183/) (Xiao et al., 2024) | ['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur'] | VisualSTS(multi) | i2i | [News, Social, Spoken, Web, Written] | None | None |
 | [VizWizIT2TRetrieval](https://openaccess.thecvf.com/content_cvpr_2018/papers/Gurari_VizWiz_Grand_Challenge_CVPR_2018_paper.pdf) (Gurari et al., 2018) | ['eng'] | Any2AnyRetrieval | it2t | [Web] | None | None |
 | [VoyageMMarcoReranking](https://arxiv.org/abs/2312.16144) (Benjamin Clavié, 2023) | ['jpn'] | Reranking | s2s | [Academic, Non-fiction, Written] | None | None |
-| [WITT2IRetrieval](https://proceedings.mlr.press/v162/bugliarello22a/bugliarello22a.pdf) (Bugliarello et al., 2022) | ['ara', 'bul', 'dan', 'ell', 'eng', 'est', 'ind', 'jpn', 'kor', 'tur', 'vie'] | Any2AnyRetrieval | t2i | [Encyclopaedic, Written] | None | None |
+| [WITT2IRetrieval](https://proceedings.mlr.press/v162/bugliarello22a/bugliarello22a.pdf) (Bugliarello et al., 2022) | ['ara', 'bul', 'dan', 'ell', 'eng', 'est', 'ind', 'jpn', 'kor', 'tur', 'vie'] | Any2AnyMultilingualRetrieval | t2i | [Encyclopaedic, Written] | None | None |
 | [WRIMEClassification](https://aclanthology.org/2021.naacl-main.169/) | ['jpn'] | Classification | s2s | [Social, Written] | None | None |
 | [Waimai](https://aclanthology.org/2023.nodalida-1.20/) (Xiao et al., 2023) | ['cmn'] | Classification | s2s |  | None | None |
 | [WebLINXCandidatesReranking](https://mcgill-nlp.github.io/weblinx) (Xing Han Lù, 2024) | ['eng'] | Reranking | p2p | [Academic, Web, Written] | None | None |
@@ -870,10 +874,10 @@ The following tables give you an overview of the tasks in MTEB.
 | [WikipediaSpecialtiesInChemistryClustering](https://arxiv.org/abs/2412.00532) (Kasmaee et al., 2024) | ['eng'] | Clustering | s2p | [Chemistry] | None | None |
 | [WikipediaTheoreticalAppliedClassification](https://arxiv.org/abs/2412.00532) (Kasmaee et al., 2024) | ['eng'] | Classification | s2s | [Chemistry] | None | None |
 | [WinoGrande](https://winogrande.allenai.org/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
-| [Winoground](https://openaccess.thecvf.com/content/CVPR2022/html/Thrush_Winoground_Probing_Vision_and_Language_Models_for_Visio-Linguistic_Compositionality_CVPR_2022_paper) (Tristan Thrush, 2022) | ['eng'] | ImageTextPairClassification | i2t | [Social] | None | None |
+| [Winoground](https://openaccess.thecvf.com/content/CVPR2022/html/Thrush_Winoground_Probing_Vision_and_Language_Models_for_Visio-Linguistic_Compositionality_CVPR_2022_paper) (Tristan Thrush, 2022) | ['eng'] | Compositionality | i2t | [Social] | {'test': 400} | {'test': {'num_samples': 400, 'num_images': 800, 'num_texts': 800, 'num_unique_texts': 800, 'min_text_length': 8, 'average_text_length': 45.47, 'max_text_length': 151}} |
 | [WisesightSentimentClassification](https://github.com/PyThaiNLP/wisesight-sentiment) | ['tha'] | Classification | s2s | [News, Social, Written] | None | None |
-| [XFlickr30kCoT2IRetrieval](https://proceedings.mlr.press/v162/bugliarello22a/bugliarello22a.pdf) (Bugliarello et al., 2022) | ['deu', 'eng', 'ind', 'jpn', 'rus', 'spa', 'tur', 'zho'] | Any2AnyRetrieval | t2i | [Encyclopaedic, Written] | None | None |
-| [XM3600T2IRetrieval](https://aclanthology.org/2022.emnlp-main.45/) (Thapliyal et al., 2022) | ['ara', 'ben', 'ces', 'dan', 'deu', 'ell', 'eng', 'fas', 'fil', 'fin', 'fra', 'heb', 'hin', 'hrv', 'hun', 'ind', 'ita', 'jpn', 'kor', 'mri', 'nld', 'nor', 'pol', 'por', 'quz', 'ron', 'rus', 'spa', 'swa', 'swe', 'tel', 'tha', 'tur', 'ukr', 'vie', 'zho'] | Any2AnyRetrieval | t2i | [Encyclopaedic, Written] | None | None |
+| [XFlickr30kCoT2IRetrieval](https://proceedings.mlr.press/v162/bugliarello22a/bugliarello22a.pdf) (Bugliarello et al., 2022) | ['deu', 'eng', 'ind', 'jpn', 'rus', 'spa', 'tur', 'zho'] | Any2AnyMultilingualRetrieval | t2i | [Encyclopaedic, Written] | None | None |
+| [XM3600T2IRetrieval](https://aclanthology.org/2022.emnlp-main.45/) (Thapliyal et al., 2022) | ['ara', 'ben', 'ces', 'dan', 'deu', 'ell', 'eng', 'fas', 'fil', 'fin', 'fra', 'heb', 'hin', 'hrv', 'hun', 'ind', 'ita', 'jpn', 'kor', 'mri', 'nld', 'nor', 'pol', 'por', 'quz', 'ron', 'rus', 'spa', 'swa', 'swe', 'tel', 'tha', 'tur', 'ukr', 'vie', 'zho'] | Any2AnyMultilingualRetrieval | t2i | [Encyclopaedic, Written] | None | None |
 | XMarket (Bonab et al., 2021) | ['deu', 'eng', 'spa'] | Retrieval | s2p |  | None | None |
 | [XNLI](https://aclanthology.org/D18-1269/) (Conneau et al., 2018) | ['ara', 'bul', 'deu', 'ell', 'eng', 'fra', 'hin', 'rus', 'spa', 'swa', 'tha', 'tur', 'vie', 'zho'] | PairClassification | s2s | [Fiction, Government, Non-fiction, Written] | {'test': 19110, 'validation': 19110} | {'test': {'num_samples': 19110, 'number_of_characters': 2907145, 'min_sentence1_length': 3, 'avg_sentence1_length': 103.24, 'max_sentence1_length': 401, 'unique_sentence1': 15328, 'min_sentence2_length': 2, 'avg_sentence2_length': 48.89, 'max_sentence2_length': 187, 'unique_sentence2': 19104, 'unique_labels': 2, 'labels': {'0': {'count': 9562}, '1': {'count': 9548}}, 'hf_subset_descriptive_stats': {'ar': {'num_samples': 1365, 'number_of_characters': 179591, 'min_sentence1_length': 11, 'avg_sentence1_length': 89.57, 'max_sentence1_length': 242, 'unique_sentence1': 1095, 'min_sentence2_length': 8, 'avg_sentence2_length': 41.99, 'max_sentence2_length': 115, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'bg': {'num_samples': 1365, 'number_of_characters': 220646, 'min_sentence1_length': 14, 'avg_sentence1_length': 110.02, 'max_sentence1_length': 303, 'unique_sentence1': 1095, 'min_sentence2_length': 8, 'avg_sentence2_length': 51.63, 'max_sentence2_length': 150, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'de': {'num_samples': 1365, 'number_of_characters': 241224, 'min_sentence1_length': 3, 'avg_sentence1_length': 119.93, 'max_sentence1_length': 301, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 56.79, 'max_sentence2_length': 187, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'el': {'num_samples': 1365, 'number_of_characters': 240222, 'min_sentence1_length': 13, 'avg_sentence1_length': 119.05, 'max_sentence1_length': 344, 'unique_sentence1': 1095, 'min_sentence2_length': 13, 'avg_sentence2_length': 56.93, 'max_sentence2_length': 172, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'en': {'num_samples': 1365, 'number_of_characters': 212223, 'min_sentence1_length': 19, 'avg_sentence1_length': 105.67, 'max_sentence1_length': 268, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 49.8, 'max_sentence2_length': 137, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'es': {'num_samples': 1365, 'number_of_characters': 232207, 'min_sentence1_length': 11, 'avg_sentence1_length': 115.43, 'max_sentence1_length': 385, 'unique_sentence1': 1094, 'min_sentence2_length': 8, 'avg_sentence2_length': 54.68, 'max_sentence2_length': 163, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'fr': {'num_samples': 1365, 'number_of_characters': 245259, 'min_sentence1_length': 9, 'avg_sentence1_length': 121.1, 'max_sentence1_length': 327, 'unique_sentence1': 1095, 'min_sentence2_length': 10, 'avg_sentence2_length': 58.58, 'max_sentence2_length': 169, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'hi': {'num_samples': 1365, 'number_of_characters': 211312, 'min_sentence1_length': 16, 'avg_sentence1_length': 104.63, 'max_sentence1_length': 401, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 50.17, 'max_sentence2_length': 162, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'ru': {'num_samples': 1365, 'number_of_characters': 222797, 'min_sentence1_length': 11, 'avg_sentence1_length': 110.77, 'max_sentence1_length': 306, 'unique_sentence1': 1095, 'min_sentence2_length': 8, 'avg_sentence2_length': 52.45, 'max_sentence2_length': 167, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'sw': {'num_samples': 1365, 'number_of_characters': 210103, 'min_sentence1_length': 10, 'avg_sentence1_length': 104.44, 'max_sentence1_length': 266, 'unique_sentence1': 1094, 'min_sentence2_length': 2, 'avg_sentence2_length': 49.48, 'max_sentence2_length': 146, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'th': {'num_samples': 1365, 'number_of_characters': 192788, 'min_sentence1_length': 12, 'avg_sentence1_length': 96.69, 'max_sentence1_length': 262, 'unique_sentence1': 1095, 'min_sentence2_length': 6, 'avg_sentence2_length': 44.54, 'max_sentence2_length': 129, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'tr': {'num_samples': 1365, 'number_of_characters': 208658, 'min_sentence1_length': 15, 'avg_sentence1_length': 103.68, 'max_sentence1_length': 255, 'unique_sentence1': 1095, 'min_sentence2_length': 6, 'avg_sentence2_length': 49.19, 'max_sentence2_length': 140, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'vi': {'num_samples': 1365, 'number_of_characters': 223549, 'min_sentence1_length': 14, 'avg_sentence1_length': 111.31, 'max_sentence1_length': 265, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 52.46, 'max_sentence2_length': 143, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'zh': {'num_samples': 1365, 'number_of_characters': 66566, 'min_sentence1_length': 4, 'avg_sentence1_length': 33.04, 'max_sentence1_length': 112, 'unique_sentence1': 1095, 'min_sentence2_length': 3, 'avg_sentence2_length': 15.73, 'max_sentence2_length': 59, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}}}, 'validation': {'num_samples': 19110, 'number_of_characters': 2909058, 'min_sentence1_length': 5, 'avg_sentence1_length': 103.21, 'max_sentence1_length': 323, 'unique_sentence1': 11171, 'min_sentence2_length': 3, 'avg_sentence2_length': 49.02, 'max_sentence2_length': 172, 'unique_sentence2': 19101, 'unique_labels': 2, 'labels': {'0': {'count': 9562}, '1': {'count': 9548}}, 'hf_subset_descriptive_stats': {'ar': {'num_samples': 1365, 'number_of_characters': 177355, 'min_sentence1_length': 13, 'avg_sentence1_length': 88.32, 'max_sentence1_length': 214, 'unique_sentence1': 798, 'min_sentence2_length': 6, 'avg_sentence2_length': 41.61, 'max_sentence2_length': 137, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'bg': {'num_samples': 1365, 'number_of_characters': 219988, 'min_sentence1_length': 16, 'avg_sentence1_length': 109.2, 'max_sentence1_length': 316, 'unique_sentence1': 798, 'min_sentence2_length': 10, 'avg_sentence2_length': 51.97, 'max_sentence2_length': 151, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'de': {'num_samples': 1365, 'number_of_characters': 241852, 'min_sentence1_length': 20, 'avg_sentence1_length': 119.81, 'max_sentence1_length': 298, 'unique_sentence1': 798, 'min_sentence2_length': 12, 'avg_sentence2_length': 57.37, 'max_sentence2_length': 162, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'el': {'num_samples': 1365, 'number_of_characters': 241275, 'min_sentence1_length': 16, 'avg_sentence1_length': 119.88, 'max_sentence1_length': 302, 'unique_sentence1': 798, 'min_sentence2_length': 6, 'avg_sentence2_length': 56.88, 'max_sentence2_length': 171, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'en': {'num_samples': 1365, 'number_of_characters': 212384, 'min_sentence1_length': 20, 'avg_sentence1_length': 105.72, 'max_sentence1_length': 271, 'unique_sentence1': 798, 'min_sentence2_length': 8, 'avg_sentence2_length': 49.88, 'max_sentence2_length': 139, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'es': {'num_samples': 1365, 'number_of_characters': 232451, 'min_sentence1_length': 14, 'avg_sentence1_length': 115.17, 'max_sentence1_length': 265, 'unique_sentence1': 798, 'min_sentence2_length': 7, 'avg_sentence2_length': 55.12, 'max_sentence2_length': 148, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'fr': {'num_samples': 1365, 'number_of_characters': 246857, 'min_sentence1_length': 19, 'avg_sentence1_length': 121.76, 'max_sentence1_length': 323, 'unique_sentence1': 798, 'min_sentence2_length': 11, 'avg_sentence2_length': 59.09, 'max_sentence2_length': 172, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'hi': {'num_samples': 1365, 'number_of_characters': 212269, 'min_sentence1_length': 18, 'avg_sentence1_length': 105.06, 'max_sentence1_length': 277, 'unique_sentence1': 798, 'min_sentence2_length': 7, 'avg_sentence2_length': 50.44, 'max_sentence2_length': 152, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'ru': {'num_samples': 1365, 'number_of_characters': 221152, 'min_sentence1_length': 15, 'avg_sentence1_length': 109.75, 'max_sentence1_length': 310, 'unique_sentence1': 798, 'min_sentence2_length': 8, 'avg_sentence2_length': 52.27, 'max_sentence2_length': 140, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'sw': {'num_samples': 1365, 'number_of_characters': 210482, 'min_sentence1_length': 13, 'avg_sentence1_length': 104.32, 'max_sentence1_length': 264, 'unique_sentence1': 798, 'min_sentence2_length': 8, 'avg_sentence2_length': 49.88, 'max_sentence2_length': 153, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'th': {'num_samples': 1365, 'number_of_characters': 192640, 'min_sentence1_length': 7, 'avg_sentence1_length': 97.28, 'max_sentence1_length': 255, 'unique_sentence1': 798, 'min_sentence2_length': 3, 'avg_sentence2_length': 43.84, 'max_sentence2_length': 140, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'tr': {'num_samples': 1365, 'number_of_characters': 208305, 'min_sentence1_length': 15, 'avg_sentence1_length': 102.97, 'max_sentence1_length': 269, 'unique_sentence1': 798, 'min_sentence2_length': 10, 'avg_sentence2_length': 49.64, 'max_sentence2_length': 139, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'vi': {'num_samples': 1365, 'number_of_characters': 224811, 'min_sentence1_length': 18, 'avg_sentence1_length': 112.26, 'max_sentence1_length': 323, 'unique_sentence1': 798, 'min_sentence2_length': 9, 'avg_sentence2_length': 52.43, 'max_sentence2_length': 159, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'zh': {'num_samples': 1365, 'number_of_characters': 67237, 'min_sentence1_length': 5, 'avg_sentence1_length': 33.41, 'max_sentence1_length': 135, 'unique_sentence1': 798, 'min_sentence2_length': 3, 'avg_sentence2_length': 15.85, 'max_sentence2_length': 66, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}}}} |
 | [XNLIV2](https://arxiv.org/pdf/2301.06527) (Upadhyay et al., 2023) | ['asm', 'ben', 'bho', 'ell', 'guj', 'kan', 'mar', 'ory', 'pan', 'rus', 'san', 'tam', 'tur'] | PairClassification | s2s | [Fiction, Government, Non-fiction, Written] | None | None |
@@ -898,1061 +902,1061 @@ The following tables give you an overview of the tasks in MTEB.
 <details>
 
 <!-- TASK LANG TABLE START -->
-| ISO Code | Language | Family | Any2AnyMultiChoice | Any2AnyRetrieval | Any2TextMutipleChoice | BitextMining | Classification | Clustering | ImageClassification | ImageClustering | ImageMultilabelClassification | ImageTextPairClassification | InstructionRetrieval | MultilabelClassification | PairClassification | Reranking | Retrieval | STS | Speed | Summarization | VisualSTS | ZeroShotClassification | Sum |
-|---|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|---|
-| aai | Arifama-Miniafia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aak | Ankave | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aau | Abau | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aaz | Amarasi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| abs | Ambonese Malay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| abt | Ambulas | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| abx | Inabaknon | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aby | Aneme Wake | Yareban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ace | Achinese | Austronesian | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| acf | Saint Lucian Creole French | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| acm | Mesopotamian Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| acq | Ta'izzi-Adeni Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| acr | Achi | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| acu | Achuar-Shiwiar | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| adz | Adzera | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aeb | Tunisian Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| aer | Eastern Arrernte | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aey | Amele | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| afr | Afrikaans | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 10 |
-| agd | Agarabi | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| agg | Angor | Senagi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| agm | Angaataha | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| agn | Agutaynen | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| agr | Aguaruna | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| agt | Central Cagayan Agta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| agu | Aguacateco | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aia | Arosi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aii | Assyrian Neo-Aramaic | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ajp | South Levantine Arabic | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| aka | Akan | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ake | Akawaio | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| alp | Alune | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| alq | Algonquin | Algic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| als | Tosk Albanian | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| aly | Alyawarr | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ame | Yanesha' | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amf | Hamer-Banna | South Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amh | Amharic | Afro-Asiatic | 0 | 0 | 0 | 3 | 6 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 14 |
-| amk | Ambai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amm | Ama (Papua New Guinea) | Left May | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amn | Amanab | Border | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amo | Amo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amp | Alamblak | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amr | Amarakaeri | Harakmbut | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amu | Guerrero Amuzgo | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| amx | Anmatyerre | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ang | Old English (ca. 450-1100) | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| anh | Nend | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| anp | Angika | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| anv | Denya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aoi | Anindilyakwa | Gunwinyguan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aoj | Mufian | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aom | Ömie | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aon | Bumbita Arapesh | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apb | Sa'a | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apc | Levantine Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ape | Bukiyip | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apn | Apinayé | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apr | Arop-Lokep | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apu | Apurinã | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apw | Western Apache | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| apz | Safeyoka | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ara | Arabic | Unclassified | 0 | 2 | 0 | 2 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 9 | 2 | 0 | 0 | 1 | 0 | 32 |
-| arb | Standard Arabic | Afro-Asiatic | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 8 |
-| are | Western Arrarnta | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| arl | Arabela | Zaparoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| arn | Mapudungun | Araucanian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| arp | Arapaho | Algic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| arq | Algerian Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 |
-| ars | Najdi Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ary | Moroccan Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 7 |
-| arz | Egyptian Arabic | Afro-Asiatic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| asm | Assamese | Indo-European | 0 | 0 | 0 | 5 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 14 |
-| aso | Dano | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ast | Asturian | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ata | Pele-Ata | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| atb | Zaiwa | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| atd | Ata Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| atg | Ivbie North-Okpela-Arhe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| att | Pamplona Atta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| auc | Waorani | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| aui | Anuki | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| auy | Awiyaana | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| avt | Au | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| awa | Awadhi | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| awb | Awa (Papua New Guinea) | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| awk | Awabakal | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| awx | Awara | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ayr | Central Aymara | Aymaran | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| azb | South Azerbaijani | Turkic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| aze | Azerbaijani | Unclassified | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| azg | San Pedro Amuzgos Amuzgo | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| azj | North Azerbaijani | Turkic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| azz | Highland Puebla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bak | Bashkir | Turkic | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| bam | Bambara | Mande | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| ban | Balinese | Austronesian | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| bao | Waimaha | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bba | Baatonum | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bbb | Barai | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bbc | Batak Toba | Austronesian | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| bbr | Girawa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bch | Bariai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bco | Kaluli | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bdd | Bunama | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bea | Beaver | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bef | Benabena | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bel | Belarusian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| bem | Bemba (Zambia) | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ben | Bengali | Indo-European | 0 | 1 | 0 | 7 | 9 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 6 | 1 | 0 | 0 | 0 | 0 | 29 |
-| beo | Beami | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ber | Berber (Other) | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| beu | Blagar | Timor-Alor-Pantar | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bew | Betawi | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| bgc | Haryanvi | Indo-European | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| bgs | Tagabawa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bgt | Bughotu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bhb | Bhili | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bhd | Bhadrawahi | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bhg | Binandere | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bhl | Bimin | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bho | Bhojpuri | Indo-European | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| bhp | Bima | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| big | Biangai | Kunimaipan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bjj | Kanauji | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bjk | Barok | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bjn | Banjar | Austronesian | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| bjp | Fanamaket | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bjr | Binumarien | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bjv | Bedjond | Central Sudanic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bjz | Baruga | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bkd | Binukid | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bki | Baki | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bkq | Bakairí | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bkx | Baikeno | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| blw | Balangao | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| blz | Balantak | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bmh | Kein | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bmk | Ghayavi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bmr | Muinane | Boran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bmu | Somba-Siawari | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bnp | Bola | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bns | Bundeli | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| boa | Bora | Boran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bod | Tibetan | Sino-Tibetan | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| boj | Anjam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bon | Bine | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bos | Bosnian | Indo-European | 0 | 0 | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| box | Buamu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| boy | Bodo (Central African Republic) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bpr | Koronadal Blaan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bps | Sarangani Blaan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bqc | Boko (Benin) | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bqp | Busa | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bra | Braj | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bre | Breton | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| brx | Bodo (India) | Sino-Tibetan | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| bsj | Bangwinji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bsn | Barasana-Eduria | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bsp | Baga Sitemu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bss | Akoose | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bug | Buginese | Austronesian | 0 | 0 | 0 | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
-| buk | Bugawac | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bul | Bulgarian | Indo-European | 0 | 1 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 14 |
-| bus | Bokobaru | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bvd | Baeggu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bvr | Burarra | Maningrida | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bxh | Buhutu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| byr | Baruya | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| byx | Qaqet | Baining | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bzd | Bribri | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bzh | Mapos Buang | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| bzj | Belize Kriol English | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| caa | Chortí | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cab | Garifuna | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cac | Chuj | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| caf | Southern Carrier | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cak | Kaqchikel | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cao | Chácobo | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cap | Chipaya | Uru-Chipaya | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| car | Galibi Carib | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cat | Catalan | Indo-European | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| cav | Cavineña | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cax | Chiquitano | Chiquitano | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbc | Carapana | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbi | Chachi | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbk | Chavacano | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| cbr | Cashibo-Cacataibo | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbs | Cashinahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbt | Chayahuita | Cahuapanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbu | Candoshi-Shapra | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cbv | Cacua | Kakua-Nukak | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cco | Comaltepec Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ceb | Cebuano | Austronesian | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| cek | Eastern Khumi Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ces | Czech | Indo-European | 0 | 1 | 0 | 4 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 18 |
-| cgc | Kagayanen | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cha | Chamorro | Austronesian | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| chd | Highland Oaxaca Chontal | Tequistlatecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| chf | Tabasco Chontal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| chk | Chuukese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| chq | Quiotepec Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| chv | Chuvash | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| chz | Ozumacín Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cjk | Chokwe | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| cjo | Ashéninka Pajonal | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cjv | Chuave | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ckb | Central Kurdish | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| cle | Lealao Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| clu | Caluyanun | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cme | Cerma | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cmn | Mandarin Chinese | Sino-Tibetan | 0 | 0 | 0 | 4 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 10 | 9 | 0 | 0 | 1 | 0 | 45 |
-| cmo | Central Mnong | Austroasiatic | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| cni | Asháninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cnl | Lalana Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cnt | Tepetotutla Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| code | unknown | Unclassified | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 37 | 0 | 0 | 0 | 0 | 0 | 41 |
-| cof | Colorado | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| con | Cofán | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cop | Coptic | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cor | Cornish | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cot | Caquinte | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cpa | Palantla Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cpb | Ucayali-Yurúa Ashéninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cpc | Ajyíninka Apurucayali | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cpu | Pichis Ashéninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cpy | South Ucayali Ashéninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| crh | Crimean Tatar | Turkic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| crn | El Nayar Cora | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| crx | Carrier | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| csb | Kashubian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cso | Sochiapam Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| csy | Siyin Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cta | Tataltepec Chatino | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cth | Thaiphum Chin | Bookkeeping | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ctp | Western Highland Chatino | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ctu | Chol | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cub | Cubeo | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cuc | Usila Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cui | Cuiba | Guahiboan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cuk | San Blas Kuna | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cut | Teutila Cuicatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cux | Tepeuxila Cuicatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cwe | Kwere | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cya | Nopala Chatino | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| cym | Welsh | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
-| daa | Dangaléat | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dad | Marik | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dah | Gwahatike | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dan | Danish | Indo-European | 0 | 2 | 0 | 5 | 9 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 25 |
-| ded | Dedua | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| deu | German | Indo-European | 0 | 2 | 0 | 6 | 14 | 7 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 2 | 18 | 4 | 0 | 0 | 2 | 0 | 63 |
-| dgc | Casiguran Dumagat Agta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dgr | Dogrib | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dgz | Daga | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dhg | Dhangu-Djangu | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dif | Dieri | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dik | Southwestern Dinka | Nilotic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| div | Dhivehi | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dji | Djinang | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| djk | Eastern Maroon Creole | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| djr | Djambarrpuyngu | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dob | Dobu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| doi | Dogri (macrolanguage) | Unclassified | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| dop | Lukpa | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dov | Dombe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dsb | Lower Sorbian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dtp | Kadazan Dusun | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dwr | Dawro | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dww | Dawawa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dwy | Dhuwaya | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dyu | Dyula | Mande | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| dza | Tunzu | Atlantic-Congo | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| dzo | Dzongkha | Sino-Tibetan | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ebk | Eastern Bontok | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| eko | Koti | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ell | Modern Greek (1453-) | Indo-European | 0 | 2 | 0 | 3 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 18 |
-| emi | Mussau-Emira | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| emp | Northern Emberá | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| eng | English | Indo-European | 9 | 62 | 4 | 17 | 160 | 20 | 21 | 5 | 1 | 6 | 3 | 1 | 13 | 9 | 110 | 13 | 2 | 1 | 7 | 24 | 488 |
-| enq | Enga | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| epo | Esperanto | Artificial Language | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| eri | Ogea | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ese | Ese Ejja | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| esk | Northwest Alaska Inupiatun | Eskimo-Aleut | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| est | Estonian | Uralic | 0 | 1 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 9 |
-| etr | Edolo | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| eus | Basque | Unclassified | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| ewe | Ewe | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| faa | Fasu | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fai | Faiwol | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fao | Faroese | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
-| far | Fataleka | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fas | Persian | Indo-European | 0 | 1 | 0 | 4 | 28 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 2 | 40 | 3 | 0 | 0 | 0 | 0 | 91 |
-| ffm | Maasina Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fij | Fijian | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| fil | Filipino | Austronesian | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| fin | Finnish | Uralic | 0 | 1 | 0 | 3 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 5 | 1 | 0 | 0 | 0 | 0 | 20 |
-| fon | Fon | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| for | Fore | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fra | French | Indo-European | 0 | 1 | 0 | 7 | 13 | 8 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 3 | 15 | 4 | 0 | 1 | 2 | 0 | 61 |
-| fry | Western Frisian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fuc | Pulaar | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fue | Borgu Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fuf | Pular | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fuh | Western Niger Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fur | Friulian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| fuv | Nigerian Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| gah | Alekano | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gai | Borei | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gam | Kandawo | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gaw | Nobonob | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gaz | West Central Oromo | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| gbm | Garhwali | Indo-European | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| gdn | Umanakaina | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gdr | Wipi | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| geb | Kire | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gfk | Patpatar | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ghs | Guhu-Samane | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gla | Scottish Gaelic | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| gle | Irish | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| glg | Galician | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| glk | Gilaki | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| glv | Manx | Indo-European | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gmv | Gamo | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gng | Ngangam | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gnn | Gumatj | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gnw | Western Bolivian Guaraní | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gof | Gofa | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gom | Goan Konkani | Indo-European | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| grc | Ancient Greek (to 1453) | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| grn | Guarani | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| gsw | Swiss German | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gub | Guajajára | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| guh | Guahibo | Guahiboan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gui | Eastern Bolivian Guaraní | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| guj | Gujarati | Indo-European | 0 | 0 | 0 | 6 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 18 |
-| gul | Sea Island Creole English | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gum | Guambiano | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gun | Mbyá Guaraní | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| guo | Guayabero | Guahiboan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gup | Gunwinggu | Gunwinyguan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gux | Gourmanchéma | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gvc | Guanano | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gvf | Golin | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gvn | Kuku-Yalanji | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gvs | Gumawana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gwi | Gwichʼin | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gym | Ngäbere | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| gyr | Guarayu | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hat | Haitian | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| hau | Hausa | Afro-Asiatic | 0 | 0 | 0 | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 14 |
-| haw | Hawaiian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hbo | Ancient Hebrew | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hch | Huichol | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| heb | Hebrew | Afro-Asiatic | 0 | 1 | 0 | 4 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 12 |
-| heg | Helong | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hin | Hindi | Indo-European | 0 | 1 | 0 | 9 | 12 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 10 | 2 | 0 | 0 | 0 | 0 | 40 |
-| hix | Hixkaryána | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hla | Halia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hlt | Matu Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hmn | Hmong | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hmo | Hiri Motu | Pidgin | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hne | Chhattisgarhi | Indo-European | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| hns | Caribbean Hindustani | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hop | Hopi | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hot | Hote | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hrv | Croatian | Indo-European | 0 | 1 | 0 | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 11 |
-| hsb | Upper Sorbian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hto | Minica Huitoto | Huitotoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hub | Huambisa | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hui | Huli | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hun | Hungarian | Uralic | 0 | 1 | 0 | 5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 13 |
-| hus | Huastec | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| huu | Murui Huitoto | Huitotoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| huv | San Mateo Del Mar Huave | Huavean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hvn | Sabu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hye | Armenian | Indo-European | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| ian | Iatmul | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ibo | Igbo | Atlantic-Congo | 0 | 0 | 0 | 3 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 12 |
-| ido | Ido | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ign | Ignaciano | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ikk | Ika | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ikw | Ikwere | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ile | Interlingue | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ilo | Iloko | Austronesian | 0 | 0 | 0 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| imo | Imbongu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ina | Interlingua (International Auxiliary Language Association) | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| inb | Inga | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ind | Indonesian | Austronesian | 0 | 3 | 0 | 6 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 24 |
-| ino | Inoke-Yate | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| iou | Tuma-Irumu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ipi | Ipili | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| isl | Icelandic | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| isn | Isanzu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ita | Italian | Indo-European | 0 | 1 | 0 | 5 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 5 | 3 | 0 | 0 | 2 | 0 | 30 |
-| iws | Sepik Iwam | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ixl | Ixil | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jac | Popti' | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jae | Yabem | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jao | Yanyuwa | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jav | Javanese | Austronesian | 0 | 0 | 0 | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 13 |
-| jic | Tol | Jicaquean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jid | Bu (Kaduna State) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jiv | Shuar | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jni | Janji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jpn | Japanese | Japonic | 0 | 3 | 0 | 5 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 13 | 2 | 0 | 0 | 0 | 0 | 39 |
-| jvn | Caribbean Javanese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kab | Kabyle | Afro-Asiatic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| kac | Kachin | Sino-Tibetan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| kam | Kamba (Kenya) | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kan | Kannada | Dravidian | 0 | 0 | 0 | 6 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 19 |
-| kaq | Capanahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kas | Kashmiri | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| kat | Georgian | Kartvelian | 0 | 0 | 0 | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 10 |
-| kaz | Kazakh | Turkic | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| kbc | Kadiwéu | Guaicuruan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kbh | Camsá | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kbm | Iwal | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kbp | Kabiyè | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kbq | Kamano | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kdc | Kutu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kde | Makonde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kdl | Tsikimba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kea | Kabuverdianu | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| kek | Kekchí | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ken | Kenyang | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kew | West Kewa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kfg | Kudiya | Dravidian | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kfy | Kumaoni | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kgf | Kube | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kgk | Kaiwá | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kgp | Kaingang | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| khk | Halh Mongolian | Mongolic-Khitan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| khm | Khmer | Austroasiatic | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| khs | Kasua | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| khz | Keapara | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kik | Kikuyu | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| kin | Kinyarwanda | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 8 |
-| kir | Kirghiz | Turkic | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| kiw | Northeast Kiwai | Kiwaian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kiz | Kisi | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kje | Kisar | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kjs | East Kewa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kkc | Odoodee | East Strickland | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kkl | Kosarek Yale | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| klt | Nukna | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| klv | Maskelynes | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kmb | Kimbundu | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kmg | Kâte | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kmh | Kalam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kmk | Limos Kalinga | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kmo | Kwoma | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kmr | Northern Kurdish | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| kms | Kamasau | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kmu | Kanite | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| knc | Central Kanuri | Saharan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kne | Kankanaey | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| knf | Mankanya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| knj | Western Kanjobal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| knv | Tabo | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kon | Kongo | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kor | Korean | Koreanic | 0 | 2 | 0 | 4 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 9 | 3 | 0 | 0 | 1 | 0 | 33 |
-| kos | Kosraean | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kpf | Komba | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kpg | Kapingamarangi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kpj | Karajá | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kpr | Korafe-Yegha | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kpw | Kobon | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kpx | Mountain Koiali | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kqa | Mum | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kqc | Doromu-Koki | Manubaran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kqf | Kakabai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kql | Kyenele | Yuat | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kqw | Kandas | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| krc | Karachay-Balkar | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ksd | Kuanua | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ksj | Uare | Kwalean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ksr | Borong | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ktm | Kurti | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kto | Kuot | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kud | 'Auhelawa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kue | Kuman (Papua New Guinea) | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kup | Kunimaipa | Kunimaipan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kur | Kurdish | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kvg | Kuni-Boazi | Anim | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kvn | Border Kuna | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kwd | Kwaio | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kwf | Kwara'ae | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kwi | Awa-Cuaiquer | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kwj | Kwanga | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kyc | Kyaka | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kyf | Kouya | Kru | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kyg | Keyagana | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kyq | Kenga | Central Sudanic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kyz | Kayabí | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kze | Kosena | Bookkeeping | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| kzj | Coastal Kadazan | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lac | Lacandon | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lao | Lao | Tai-Kadai | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| lat | Latin | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| lav | Latvian | Indo-European | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| lbb | Label | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lbk | Central Bontok | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lcm | Tungag | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| leu | Kara (Papua New Guinea) | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lex | Luang | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lfn | Lingua Franca Nova | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lgl | Wala | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lid | Nyindrou | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lif | Limbu | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lij | Ligurian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| lim | Limburgan | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| lin | Lingala | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| lit | Lithuanian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| llg | Lole | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| lmo | Lombard | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| ltg | Latgalian | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| ltz | Luxembourgish | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| lua | Luba-Lulua | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| lug | Ganda | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| luo | Luo (Kenya and Tanzania) | Nilotic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| lus | Lushai | Sino-Tibetan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| lvs | Standard Latvian | Unclassified | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| lww | Lewo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| maa | San Jerónimo Tecóatl Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mad | Madurese | Austronesian | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| mag | Magahi | Indo-European | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| mai | Maithili | Indo-European | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
-| maj | Jalapa De Díaz Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mak | Makasar | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| mal | Malayalam | Dravidian | 0 | 0 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 19 |
-| mam | Mam | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| maq | Chiquihuitlán Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mar | Marathi | Indo-European | 0 | 0 | 0 | 7 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 20 |
-| mau | Huautla Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mav | Sateré-Mawé | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| max | North Moluccan Malay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| maz | Central Mazahua | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbb | Western Bukidnon Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbc | Macushi | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbh | Mangseng | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbj | Nadëb | Naduhup | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbl | Maxakalí | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbs | Sarangani Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mbt | Matigsalug Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mca | Maca | Mataguayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mcb | Machiguenga | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mcd | Sharanahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mcf | Matsés | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mco | Coatlán Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mcp | Makaa | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mcq | Ese | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mcr | Menya | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mdy | Male (Ethiopia) | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| med | Melpa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mee | Mengen | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mek | Mekeo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| meq | Merey | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| met | Mato | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| meu | Motu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mey | Hassaniyya | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mgc | Morokodo | Central Sudanic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mgh | Makhuwa-Meetto | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mgw | Matumbi | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mhl | Mauwake | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mhr | Eastern Mari | Uralic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mib | Atatláhuca Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mic | Mi'kmaq | Algic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mie | Ocotepec Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mig | San Miguel El Grande Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mih | Chayuco Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mil | Peñoles Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| min | Minangkabau | Austronesian | 0 | 0 | 0 | 3 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
-| mio | Pinotepa Nacional Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mir | Isthmus Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mit | Southern Puebla Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| miz | Coatzospan Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mjc | San Juan Colorado Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mkd | Macedonian | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| mkj | Mokilese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mkl | Mokole | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mkn | Kupang Malay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mks | Silacayoapan Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mle | Manambu | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mlg | Malagasy | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mlh | Mape | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mlp | Bargam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mlt | Maltese | Afro-Asiatic | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| mmo | Mangga Buang | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mmx | Madak | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mna | Mbula | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mni | Manipuri | Sino-Tibetan | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
-| mon | Mongolian | Unclassified | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| mop | Mopán Maya | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mos | Mossi | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| mox | Molima | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mph | Maung | Iwaidjan Proper | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mpj | Martu Wangka | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mpm | Yosondúa Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mpp | Migabac | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mps | Dadibi | Teberan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mpt | Mian | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mpx | Misima-Panaeati | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mqb | Mbuko | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mqj | Mamasa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mri | Maori | Austronesian | 0 | 1 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| msa | Malay (macrolanguage) | Unclassified | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| msb | Masbatenyo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| msc | Sankaran Maninka | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| msk | Mansaka | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| msm | Agusan Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| msy | Aruamu | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mti | Maiwa (Papua New Guinea) | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mto | Totontepec Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mui | Musi | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| mup | Malvi | Indo-European | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| mux | Bo-Ung | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| muy | Muyang | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mva | Manam | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mvn | Minaveha | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mwc | Are | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mwe | Mwera (Chimwera) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mwf | Murrinh-Patha | Southern Daly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mwp | Kala Lagaw Ya | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mwr | Marwari | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mxb | Tezoatlán Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mxp | Tlahuitoltepec Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mxq | Juquila Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mxt | Jamiltepec Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mya | Burmese | Sino-Tibetan | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| myk | Mamara Senoufo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| myu | Mundurukú | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| myw | Muyuw | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| myy | Macuna | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| mzz | Maiadomu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nab | Southern Nambikuára | Nambiquaran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| naf | Nabak | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nak | Nakanai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nas | Naasioi | South Bougainville | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nbl | South Ndebele | Unclassified | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nbq | Nggem | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nca | Iyo | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nch | Central Huasteca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ncj | Northern Puebla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ncl | Michoacán Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ncu | Chumburung | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nde | North Ndebele | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ndg | Ndengereko | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ndj | Ndamba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nds | Low German | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nep | Nepali (macrolanguage) | Unclassified | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| nfa | Dhao | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ngp | Ngulu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ngu | Guerrero Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhe | Eastern Huasteca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhg | Tetelcingo Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhi | Zacatlán-Ahuacatlán-Tepetzintla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nho | Takuu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhr | Naro | Khoe-Kwadi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhu | Noone | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhw | Western Huasteca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nhy | Northern Oaxaca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nif | Nek | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nii | Nii | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nij | Ngaju | Austronesian | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| nin | Ninzo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nko | Nkonya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nld | Dutch | Indo-European | 0 | 1 | 0 | 6 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 29 | 2 | 0 | 0 | 2 | 0 | 50 |
-| nlg | Gela | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nna | Nyangumarta | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nno | Norwegian Nynorsk | Unclassified | 0 | 0 | 0 | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
-| nnq | Ngindo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| noa | Woun Meu | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nob | Norwegian Bokmål | Unclassified | 0 | 0 | 0 | 4 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 19 |
-| noe | Nimadi | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nop | Numanggang | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nor | Norwegian | Indo-European | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| not | Nomatsiguenga | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nou | Ewage-Notu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nov | Novial | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| npi | Nepali (individual language) | Indo-European | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| npl | Southeastern Puebla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nqo | N'Ko | Artificial Language | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| nsn | Nehan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nso | Pedi | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| nss | Nali | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ntj | Ngaanyatjarra | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ntp | Northern Tepehuan | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ntu | Natügu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nus | Nuer | Nilotic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| nuy | Nunggubuyu | Gunwinyguan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nvm | Namiae | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nwi | Southwest Tanna | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nya | Nyanja | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| nys | Nyungar | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| nyu | Nyungwe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| obo | Obo Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| oci | Occitan (post 1500) | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| okv | Orokaiva | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| omw | South Tairora | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ong | Olo | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ons | Ono | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ood | Tohono O'odham | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| opm | Oksapmin | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ori | Oriya (macrolanguage) | Unclassified | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| orm | Oromo | Unclassified | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| orv | Old Russian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ory | Odia | Indo-European | 0 | 0 | 0 | 5 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 15 |
-| ote | Mezquital Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| otm | Eastern Highland Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| otn | Tenango Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| otq | Querétaro Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ots | Estado de México Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pab | Parecís | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pad | Paumarí | Arawan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pag | Pangasinan | Austronesian | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| pah | Tenharim | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pam | Pampanga | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pan | Panjabi | Indo-European | 0 | 0 | 0 | 6 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 18 |
-| pao | Northern Paiute | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pap | Papiamento | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| pbt | Southern Pashto | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| pcm | Nigerian Pidgin | Indo-European | 0 | 0 | 0 | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
-| pes | Iranian Persian | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| pib | Yine | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pio | Piapoco | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pir | Piratapuyo | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| piu | Pintupi-Luritja | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pjt | Pitjantjatjara | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pls | San Marcos Tlacoyalco Popoloca | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| plt | Plateau Malagasy | Austronesian | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| plu | Palikúr | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pma | Paama | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pms | Piemontese | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| poe | San Juan Atzingo Popoloca | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| poh | Poqomchi' | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| poi | Highland Popoluca | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pol | Polish | Indo-European | 0 | 1 | 0 | 4 | 11 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 18 | 4 | 0 | 0 | 1 | 0 | 48 |
-| pon | Pohnpeian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| por | Portuguese | Indo-European | 0 | 1 | 0 | 4 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 5 | 3 | 0 | 0 | 1 | 0 | 30 |
-| poy | Pogolo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ppo | Folopa | Teberan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| prf | Paranan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pri | Paicî | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| prs | Dari | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ptp | Patep | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ptu | Bambam | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| pus | Pushto | Unclassified | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| pwg | Gapapaiwa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qub | Huallaga Huánuco Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| quc | K'iche' | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| quf | Lambayeque Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| quh | South Bolivian Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qul | North Bolivian Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qup | Southern Pastaza Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| quy | Ayacucho Quechua | Quechuan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| quz | Cusco Quechua | Quechuan | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvc | Cajamarca Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qve | Eastern Apurímac Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvh | Huamalíes-Dos de Mayo Huánuco Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvm | Margos-Yarowilca-Lauricocha Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvn | North Junín Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvs | San Martín Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvw | Huaylla Wanca Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qvz | Northern Pastaza Quichua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qwh | Huaylas Ancash Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qxh | Panao Huánuco Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qxn | Northern Conchucos Ancash Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| qxo | Southern Conchucos Ancash Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rai | Ramoaaina | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| raj | Rajasthani | Unclassified | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| reg | Kara (Tanzania) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rej | Rejang | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| rgu | Ringgou | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rkb | Rikbaktsa | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rmc | Carpathian Romani | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rmy | Vlax Romani | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rom | Romany | Unclassified | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| ron | Romanian | Indo-European | 0 | 1 | 0 | 5 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 19 |
-| roo | Rotokas | North Bougainville | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rop | Kriol | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| row | Dela-Oenale | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rro | Waima | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ruf | Luguru | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| rug | Roviana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| run | Rundi | Atlantic-Congo | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| rus | Russian | Indo-European | 0 | 2 | 0 | 5 | 13 | 6 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 16 | 4 | 0 | 0 | 1 | 0 | 55 |
-| rwo | Rawa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sab | Buglere | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sag | Sango | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| sah | Yakut | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| san | Sanskrit | Indo-European | 0 | 0 | 0 | 5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| sat | Santali | Austroasiatic | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
-| sbe | Saliba | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sbk | Safwa | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sbs | Subiya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| scn | Sicilian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| sco | Scots | Indo-European | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| seh | Sena | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sey | Secoya | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sgb | Mag-antsi Ayta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sgz | Sursurunga | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| shi | Tachelhit | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| shj | Shatt | Dajuic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| shn | Shan | Tai-Kadai | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| shp | Shipibo-Conibo | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sim | Mende (Papua New Guinea) | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sin | Sinhala | Indo-European | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| sja | Epena | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| slk | Slovak | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 12 |
-| sll | Salt-Yui | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| slv | Slovenian | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
-| smk | Bolinao | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| smo | Samoan | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| sna | Shona | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| snc | Sinaugoro | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| snd | Sindhi | Indo-European | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| snn | Siona | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| snp | Siane | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| snx | Sam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sny | Saniyo-Hiyewe | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| som | Somali | Afro-Asiatic | 0 | 0 | 0 | 3 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| soq | Kanasi | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sot | Southern Sotho | Atlantic-Congo | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| soy | Miyobe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| spa | Spanish | Indo-European | 0 | 2 | 0 | 4 | 13 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 13 | 4 | 0 | 0 | 2 | 0 | 48 |
-| spl | Selepet | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| spm | Akukem | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| spp | Supyire Senoufo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sps | Saposa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| spy | Sabaot | Nilotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sqi | Albanian | Unclassified | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| srd | Sardinian | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| sri | Siriano | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| srm | Saramaccan | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| srn | Sranan Tongo | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| srp | Serbian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 9 |
-| srq | Sirionó | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ssd | Siroi | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ssg | Seimat | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ssw | Swati | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| ssx | Samberigi | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| stp | Southeastern Tepehuan | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sua | Sulka | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sue | Suena | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sun | Sundanese | Austronesian | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| sus | Susu | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| suz | Sunwar | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| svk | Slovakian Sign Language | Sign Language | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| swa | Swahili (macrolanguage) | Atlantic-Congo | 0 | 1 | 0 | 1 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 16 |
-| swe | Swedish | Indo-European | 0 | 1 | 0 | 4 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 23 |
-| swg | Swabian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| swh | Swahili (individual language) | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| swp | Suau | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| sxb | Suba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| szl | Silesian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| tac | Lowland Tarahumara | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tah | Tahitian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| taj | Eastern Tamang | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tam | Tamil | Dravidian | 0 | 0 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 21 |
-| taq | Tamasheq | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| tat | Tatar | Turkic | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| tav | Tatuyo | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| taw | Tai | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tbc | Takia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tbf | Mandara | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tbg | North Tairora | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tbo | Tawala | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tbz | Ditammari | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tca | Ticuna | Ticuna-Yuri | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tcs | Torres Strait Creole | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tcz | Thado Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tdt | Tetun Dili | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tee | Huehuetla Tepehua | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tel | Telugu | Dravidian | 0 | 1 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 | 0 | 0 | 25 |
-| ter | Tereno | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tet | Tetum | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tew | Tewa (USA) | Kiowa-Tanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tfr | Teribe | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tgk | Tajik | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| tgl | Tagalog | Austronesian | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| tgo | Sudest | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tgp | Tangoa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tha | Thai | Tai-Kadai | 0 | 1 | 0 | 4 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 22 |
-| tif | Tifal | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tim | Timbe | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tir | Tigrinya | Afro-Asiatic | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
-| tiw | Tiwi | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tiy | Tiruray | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tke | Takwane | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tku | Upper Necaxa Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tlf | Telefol | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tmd | Haruai | Piawi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tna | Tacana | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tnc | Tanimuca-Retuarã | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tnk | Kwamera | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tnn | North Tanna | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tnp | Whitesands | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| toc | Coyutla Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tod | Toma | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tof | Gizrra | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| toj | Tojolabal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ton | Tonga (Tonga Islands) | Austronesian | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| too | Xicotepec De Juárez Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| top | Papantla Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tos | Highland Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tpa | Taupota | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tpi | Tok Pisin | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| tpt | Tlachichilco Tepehua | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tpz | Tinputz | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| trc | Copala Triqui | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tsn | Tswana | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| tso | Tsonga | Atlantic-Congo | 0 | 0 | 0 | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| tsw | Tsishingini | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ttc | Tektiteko | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tte | Bwanabwana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tuc | Mutu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tue | Tuyuca | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tuf | Central Tunebo | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tuk | Turkmen | Turkic | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
-| tum | Tumbuka | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| tuo | Tucano | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tur | Turkish | Turkic | 0 | 3 | 0 | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 24 |
-| tvk | Southeast Ambrym | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| twi | Twi | Unclassified | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| txq | Tii | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| txu | Kayapó | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tyv | Tuvinian | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tzj | Tz'utujil | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tzl | Talossan | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tzm | Central Atlas Tamazight | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| tzo | Tzotzil | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ubr | Ubir | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ubu | Umbu-Ungu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| udu | Uduk | Koman | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| uig | Uighur | Turkic | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
-| ukr | Ukrainian | Indo-European | 0 | 1 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 9 |
-| uli | Ulithian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ulk | Meriam Mir | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| umb | Umbundu | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| upv | Uripiv-Wala-Rano-Atchin | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ura | Urarina | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| urb | Urubú-Kaapor | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| urd | Urdu | Indo-European | 0 | 0 | 0 | 7 | 8 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 19 |
-| uri | Urim | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| urt | Urat | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| urw | Sop | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| usa | Usarufa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| usp | Uspanteco | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| uvh | Uri | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| uvl | Lote | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| uzb | Uzbek | Unclassified | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| uzn | Northern Uzbek | Turkic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
-| vec | Venetian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| ven | Venda | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| vid | Vidunda | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| vie | Vietnamese | Austroasiatic | 0 | 2 | 0 | 5 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 20 |
-| viv | Iduna | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| vmy | Ayautla Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| waj | Waffa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wal | Wolaytta | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wap | Wapishana | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| war | Waray (Philippines) | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| wat | Kaninuwa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wbi | Vwanji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wbp | Warlpiri | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wed | Wedau | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wer | Weri | Kunimaipan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wim | Wik-Mungkan | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wiu | Wiru | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wiv | Vitu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wln | Walloon | Indo-European | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wmt | Walmajarri | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wmw | Mwani | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wnc | Wantoat | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wnu | Usan | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wol | Wolof | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
-| wos | Hanga Hundi | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wrk | Garrwa | Garrwan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wro | Worrorra | Worrorran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wrs | Waris | Border | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wsk | Waskia | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wuu | Wu Chinese | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| wuv | Wuvulu-Aua | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xav | Xavánte | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xbi | Kombio | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xed | Hdi | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xho | Xhosa | Atlantic-Congo | 0 | 0 | 0 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
-| xla | Kamula | Kamula-Elevala | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xnn | Northern Kankanay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xon | Konkomba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xsi | Sio | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xtd | Diuxi-Tilantongo Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| xtm | Magdalena Peñasco Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yaa | Yaminahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yad | Yagua | Peba-Yagua | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yal | Yalunka | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yap | Yapese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yaq | Yaqui | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yby | Yaweyuha | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ycn | Yucuna | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ydd | Eastern Yiddish | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| yid | Yiddish | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yka | Yakan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yle | Yele | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yml | Iamalele | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yon | Yongkom | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yor | Yoruba | Atlantic-Congo | 0 | 0 | 0 | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 16 |
-| yrb | Yareba | Yareban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yre | Yaouré | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yss | Yessan-Mayo | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yue | Yue Chinese | Sino-Tibetan | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| yuj | Karkar-Yuri | Pauwasi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yut | Yopno | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yuw | Yau (Morobe Province) | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yva | Yawa | Yawa-Saweru | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zaa | Sierra de Juárez Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zab | Western Tlacolula Valley Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zac | Ocotlán Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zad | Cajonos Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zai | Isthmus Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zaj | Zaramo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zam | Miahuatlán Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zao | Ozolotepec Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zap | Zapotec | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zar | Rincón Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zas | Santo Domingo Albarradas Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zat | Tabaa Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zav | Yatzachi Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zaw | Mitla Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zca | Coatecas Altas Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zga | Kinga | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zho | Chinese | Unclassified | 0 | 2 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 13 | 0 | 0 | 0 | 0 | 0 | 23 |
-| zia | Zia | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ziw | Zigula | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zlm | Malay (individual language) | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zos | Francisco León Zoque | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpc | Choapan Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpl | Lachixío Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpm | Mixtepec Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpo | Amatlán Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpq | Zoogocho Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpu | Yalálag Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpv | Chichicapan Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zpz | Texmelucan Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zsm | Standard Malay | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
-| zsr | Southern Rincon Zapotec | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ztq | Quioquitani-Quierí Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zty | Yatee Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zul | Zulu | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
-| zyp | Zyphe Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| Total | None | None | None | 9 | 114 | 4 | 1398 | 836 | 313 | 21 | 5 | 1 | 6 | 3 | 28 | 91 | 56 | 536 | 88 | 2 | 2 | 24 | 24 |
+| ISO Code | Language | Family | Any2AnyMultiChoice | Any2AnyMultilingualRetrieval | Any2AnyRetrieval | BitextMining | Classification | Clustering | Compositionality | DocumentUnderstanding | ImageClassification | ImageClustering | ImageMultilabelClassification | InstructionRetrieval | MultilabelClassification | PairClassification | Reranking | Retrieval | STS | Speed | Summarization | VisionCentric | VisualSTS(eng) | VisualSTS(multi) | ZeroShotClassification | Sum |
+|---|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|---|
+| aai | Arifama-Miniafia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aak | Ankave | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aau | Abau | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aaz | Amarasi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abs | Ambonese Malay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abt | Ambulas | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abx | Inabaknon | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aby | Aneme Wake | Yareban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ace | Achinese | Austronesian | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| acf | Saint Lucian Creole French | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| acm | Mesopotamian Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| acq | Ta'izzi-Adeni Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| acr | Achi | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| acu | Achuar-Shiwiar | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| adz | Adzera | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aeb | Tunisian Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| aer | Eastern Arrernte | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aey | Amele | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| afr | Afrikaans | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| agd | Agarabi | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agg | Angor | Senagi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agm | Angaataha | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agn | Agutaynen | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agr | Aguaruna | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agt | Central Cagayan Agta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agu | Aguacateco | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aia | Arosi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aii | Assyrian Neo-Aramaic | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ajp | South Levantine Arabic | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| aka | Akan | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ake | Akawaio | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| alp | Alune | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| alq | Algonquin | Algic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| als | Tosk Albanian | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| aly | Alyawarr | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ame | Yanesha' | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amf | Hamer-Banna | South Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amh | Amharic | Afro-Asiatic | 0 | 0 | 0 | 3 | 6 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
+| amk | Ambai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amm | Ama (Papua New Guinea) | Left May | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amn | Amanab | Border | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amo | Amo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amp | Alamblak | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amr | Amarakaeri | Harakmbut | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amu | Guerrero Amuzgo | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amx | Anmatyerre | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ang | Old English (ca. 450-1100) | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anh | Nend | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anp | Angika | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anv | Denya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aoi | Anindilyakwa | Gunwinyguan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aoj | Mufian | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aom | Ömie | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aon | Bumbita Arapesh | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apb | Sa'a | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apc | Levantine Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ape | Bukiyip | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apn | Apinayé | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apr | Arop-Lokep | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apu | Apurinã | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apw | Western Apache | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apz | Safeyoka | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ara | Arabic | Unclassified | 0 | 2 | 0 | 2 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 9 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 33 |
+| arb | Standard Arabic | Afro-Asiatic | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| are | Western Arrarnta | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arl | Arabela | Zaparoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arn | Mapudungun | Araucanian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arp | Arapaho | Algic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arq | Algerian Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ars | Najdi Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ary | Moroccan Arabic | Afro-Asiatic | 0 | 0 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| arz | Egyptian Arabic | Afro-Asiatic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| asm | Assamese | Indo-European | 0 | 0 | 0 | 5 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
+| aso | Dano | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ast | Asturian | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ata | Pele-Ata | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atb | Zaiwa | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atd | Ata Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atg | Ivbie North-Okpela-Arhe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| att | Pamplona Atta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| auc | Waorani | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aui | Anuki | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| auy | Awiyaana | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| avt | Au | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awa | Awadhi | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| awb | Awa (Papua New Guinea) | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awk | Awabakal | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awx | Awara | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ayr | Central Aymara | Aymaran | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| azb | South Azerbaijani | Turkic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| aze | Azerbaijani | Unclassified | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| azg | San Pedro Amuzgos Amuzgo | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| azj | North Azerbaijani | Turkic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| azz | Highland Puebla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bak | Bashkir | Turkic | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bam | Bambara | Mande | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| ban | Balinese | Austronesian | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bao | Waimaha | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bba | Baatonum | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bbb | Barai | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bbc | Batak Toba | Austronesian | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bbr | Girawa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bch | Bariai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bco | Kaluli | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bdd | Bunama | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bea | Beaver | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bef | Benabena | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bel | Belarusian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bem | Bemba (Zambia) | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ben | Bengali | Indo-European | 0 | 1 | 0 | 7 | 9 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 29 |
+| beo | Beami | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ber | Berber (Other) | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| beu | Blagar | Timor-Alor-Pantar | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bew | Betawi | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| bgc | Haryanvi | Indo-European | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| bgs | Tagabawa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bgt | Bughotu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhb | Bhili | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhd | Bhadrawahi | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhg | Binandere | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhl | Bimin | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bho | Bhojpuri | Indo-European | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bhp | Bima | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| big | Biangai | Kunimaipan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjj | Kanauji | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjk | Barok | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjn | Banjar | Austronesian | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bjp | Fanamaket | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjr | Binumarien | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjv | Bedjond | Central Sudanic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjz | Baruga | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkd | Binukid | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bki | Baki | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkq | Bakairí | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkx | Baikeno | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| blw | Balangao | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| blz | Balantak | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmh | Kein | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmk | Ghayavi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmr | Muinane | Boran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmu | Somba-Siawari | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bnp | Bola | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bns | Bundeli | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| boa | Bora | Boran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bod | Tibetan | Sino-Tibetan | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| boj | Anjam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bon | Bine | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bos | Bosnian | Indo-European | 0 | 0 | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| box | Buamu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| boy | Bodo (Central African Republic) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bpr | Koronadal Blaan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bps | Sarangani Blaan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bqc | Boko (Benin) | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bqp | Busa | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bra | Braj | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bre | Breton | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| brx | Bodo (India) | Sino-Tibetan | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bsj | Bangwinji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bsn | Barasana-Eduria | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bsp | Baga Sitemu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bss | Akoose | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bug | Buginese | Austronesian | 0 | 0 | 0 | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| buk | Bugawac | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bul | Bulgarian | Indo-European | 0 | 1 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
+| bus | Bokobaru | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bvd | Baeggu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bvr | Burarra | Maningrida | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bxh | Buhutu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| byr | Baruya | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| byx | Qaqet | Baining | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzd | Bribri | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzh | Mapos Buang | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzj | Belize Kriol English | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| caa | Chortí | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cab | Garifuna | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cac | Chuj | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| caf | Southern Carrier | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cak | Kaqchikel | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cao | Chácobo | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cap | Chipaya | Uru-Chipaya | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| car | Galibi Carib | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cat | Catalan | Indo-European | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| cav | Cavineña | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cax | Chiquitano | Chiquitano | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbc | Carapana | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbi | Chachi | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbk | Chavacano | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cbr | Cashibo-Cacataibo | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbs | Cashinahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbt | Chayahuita | Cahuapanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbu | Candoshi-Shapra | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbv | Cacua | Kakua-Nukak | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cco | Comaltepec Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ceb | Cebuano | Austronesian | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| cek | Eastern Khumi Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ces | Czech | Indo-European | 0 | 1 | 0 | 4 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
+| cgc | Kagayanen | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cha | Chamorro | Austronesian | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| chd | Highland Oaxaca Chontal | Tequistlatecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chf | Tabasco Chontal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chk | Chuukese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chq | Quiotepec Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chv | Chuvash | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chz | Ozumacín Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cjk | Chokwe | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| cjo | Ashéninka Pajonal | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cjv | Chuave | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ckb | Central Kurdish | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| cle | Lealao Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| clu | Caluyanun | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cme | Cerma | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cmn | Mandarin Chinese | Sino-Tibetan | 0 | 0 | 0 | 4 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 10 | 9 | 0 | 0 | 0 | 0 | 2 | 0 | 46 |
+| cmo | Central Mnong | Austroasiatic | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cni | Asháninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cnl | Lalana Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cnt | Tepetotutla Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| code | unknown | Unclassified | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 |
+| cof | Colorado | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| con | Cofán | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cop | Coptic | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cor | Cornish | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cot | Caquinte | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpa | Palantla Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpb | Ucayali-Yurúa Ashéninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpc | Ajyíninka Apurucayali | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpu | Pichis Ashéninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpy | South Ucayali Ashéninka | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| crh | Crimean Tatar | Turkic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| crn | El Nayar Cora | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| crx | Carrier | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| csb | Kashubian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cso | Sochiapam Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| csy | Siyin Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cta | Tataltepec Chatino | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cth | Thaiphum Chin | Bookkeeping | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ctp | Western Highland Chatino | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ctu | Chol | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cub | Cubeo | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cuc | Usila Chinantec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cui | Cuiba | Guahiboan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cuk | San Blas Kuna | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cut | Teutila Cuicatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cux | Tepeuxila Cuicatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cwe | Kwere | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cya | Nopala Chatino | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cym | Welsh | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| daa | Dangaléat | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dad | Marik | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dah | Gwahatike | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dan | Danish | Indo-European | 0 | 2 | 0 | 5 | 9 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
+| ded | Dedua | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| deu | German | Indo-European | 0 | 2 | 0 | 6 | 14 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 2 | 18 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 65 |
+| dgc | Casiguran Dumagat Agta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dgr | Dogrib | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dgz | Daga | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dhg | Dhangu-Djangu | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dif | Dieri | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dik | Southwestern Dinka | Nilotic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| div | Dhivehi | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dji | Djinang | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| djk | Eastern Maroon Creole | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| djr | Djambarrpuyngu | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dob | Dobu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| doi | Dogri (macrolanguage) | Unclassified | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| dop | Lukpa | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dov | Dombe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dsb | Lower Sorbian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dtp | Kadazan Dusun | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dwr | Dawro | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dww | Dawawa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dwy | Dhuwaya | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dyu | Dyula | Mande | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| dza | Tunzu | Atlantic-Congo | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dzo | Dzongkha | Sino-Tibetan | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ebk | Eastern Bontok | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eko | Koti | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ell | Modern Greek (1453-) | Indo-European | 0 | 2 | 0 | 3 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
+| emi | Mussau-Emira | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| emp | Northern Emberá | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eng | English | Indo-European | 0 | 3 | 55 | 17 | 160 | 20 | 7 | 10 | 22 | 5 | 0 | 3 | 1 | 13 | 9 | 110 | 13 | 2 | 1 | 6 | 7 | 3 | 24 | 491 |
+| enq | Enga | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| epo | Esperanto | Artificial Language | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| eri | Ogea | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ese | Ese Ejja | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| esk | Northwest Alaska Inupiatun | Eskimo-Aleut | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| est | Estonian | Uralic | 0 | 1 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| etr | Edolo | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eus | Basque | Unclassified | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| ewe | Ewe | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| faa | Fasu | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fai | Faiwol | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fao | Faroese | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| far | Fataleka | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fas | Persian | Indo-European | 0 | 1 | 0 | 4 | 28 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 2 | 40 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 91 |
+| ffm | Maasina Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fij | Fijian | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| fil | Filipino | Austronesian | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| fin | Finnish | Uralic | 0 | 1 | 0 | 3 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
+| fon | Fon | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| for | Fore | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fra | French | Indo-European | 0 | 1 | 0 | 7 | 13 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 3 | 15 | 4 | 0 | 1 | 0 | 0 | 4 | 0 | 63 |
+| fry | Western Frisian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuc | Pulaar | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fue | Borgu Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuf | Pular | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuh | Western Niger Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fur | Friulian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| fuv | Nigerian Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| gah | Alekano | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gai | Borei | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gam | Kandawo | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gaw | Nobonob | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gaz | West Central Oromo | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| gbm | Garhwali | Indo-European | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| gdn | Umanakaina | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gdr | Wipi | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| geb | Kire | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gfk | Patpatar | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ghs | Guhu-Samane | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gla | Scottish Gaelic | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| gle | Irish | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| glg | Galician | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| glk | Gilaki | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| glv | Manx | Indo-European | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gmv | Gamo | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gng | Ngangam | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gnn | Gumatj | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gnw | Western Bolivian Guaraní | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gof | Gofa | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gom | Goan Konkani | Indo-European | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| grc | Ancient Greek (to 1453) | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| grn | Guarani | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| gsw | Swiss German | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gub | Guajajára | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guh | Guahibo | Guahiboan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gui | Eastern Bolivian Guaraní | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guj | Gujarati | Indo-European | 0 | 0 | 0 | 6 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
+| gul | Sea Island Creole English | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gum | Guambiano | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gun | Mbyá Guaraní | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guo | Guayabero | Guahiboan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gup | Gunwinggu | Gunwinyguan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gux | Gourmanchéma | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvc | Guanano | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvf | Golin | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvn | Kuku-Yalanji | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvs | Gumawana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gwi | Gwichʼin | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gym | Ngäbere | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gyr | Guarayu | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hat | Haitian | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| hau | Hausa | Afro-Asiatic | 0 | 0 | 0 | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
+| haw | Hawaiian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hbo | Ancient Hebrew | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hch | Huichol | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| heb | Hebrew | Afro-Asiatic | 0 | 1 | 0 | 4 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
+| heg | Helong | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hin | Hindi | Indo-European | 0 | 1 | 0 | 9 | 12 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 10 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 40 |
+| hix | Hixkaryána | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hla | Halia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hlt | Matu Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hmn | Hmong | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hmo | Hiri Motu | Pidgin | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hne | Chhattisgarhi | Indo-European | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| hns | Caribbean Hindustani | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hop | Hopi | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hot | Hote | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hrv | Croatian | Indo-European | 0 | 1 | 0 | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
+| hsb | Upper Sorbian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hto | Minica Huitoto | Huitotoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hub | Huambisa | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hui | Huli | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hun | Hungarian | Uralic | 0 | 1 | 0 | 5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
+| hus | Huastec | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| huu | Murui Huitoto | Huitotoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| huv | San Mateo Del Mar Huave | Huavean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hvn | Sabu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hye | Armenian | Indo-European | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| ian | Iatmul | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ibo | Igbo | Atlantic-Congo | 0 | 0 | 0 | 3 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
+| ido | Ido | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ign | Ignaciano | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ikk | Ika | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ikw | Ikwere | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ile | Interlingue | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ilo | Iloko | Austronesian | 0 | 0 | 0 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| imo | Imbongu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ina | Interlingua (International Auxiliary Language Association) | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| inb | Inga | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ind | Indonesian | Austronesian | 0 | 3 | 0 | 6 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 24 |
+| ino | Inoke-Yate | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| iou | Tuma-Irumu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ipi | Ipili | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| isl | Icelandic | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| isn | Isanzu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ita | Italian | Indo-European | 0 | 1 | 0 | 5 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 5 | 3 | 0 | 0 | 0 | 0 | 4 | 0 | 32 |
+| iws | Sepik Iwam | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ixl | Ixil | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jac | Popti' | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jae | Yabem | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jao | Yanyuwa | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jav | Javanese | Austronesian | 0 | 0 | 0 | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
+| jic | Tol | Jicaquean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jid | Bu (Kaduna State) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jiv | Shuar | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jni | Janji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jpn | Japanese | Japonic | 0 | 3 | 0 | 5 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 13 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 39 |
+| jvn | Caribbean Javanese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kab | Kabyle | Afro-Asiatic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kac | Kachin | Sino-Tibetan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kam | Kamba (Kenya) | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kan | Kannada | Dravidian | 0 | 0 | 0 | 6 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
+| kaq | Capanahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kas | Kashmiri | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| kat | Georgian | Kartvelian | 0 | 0 | 0 | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| kaz | Kazakh | Turkic | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| kbc | Kadiwéu | Guaicuruan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbh | Camsá | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbm | Iwal | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbp | Kabiyè | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kbq | Kamano | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kdc | Kutu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kde | Makonde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kdl | Tsikimba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kea | Kabuverdianu | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kek | Kekchí | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ken | Kenyang | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kew | West Kewa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kfg | Kudiya | Dravidian | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kfy | Kumaoni | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgf | Kube | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgk | Kaiwá | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgp | Kaingang | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| khk | Halh Mongolian | Mongolic-Khitan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| khm | Khmer | Austroasiatic | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| khs | Kasua | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| khz | Keapara | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kik | Kikuyu | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kin | Kinyarwanda | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| kir | Kirghiz | Turkic | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| kiw | Northeast Kiwai | Kiwaian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kiz | Kisi | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kje | Kisar | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kjs | East Kewa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kkc | Odoodee | East Strickland | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kkl | Kosarek Yale | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| klt | Nukna | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| klv | Maskelynes | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmb | Kimbundu | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kmg | Kâte | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmh | Kalam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmk | Limos Kalinga | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmo | Kwoma | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmr | Northern Kurdish | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kms | Kamasau | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmu | Kanite | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knc | Central Kanuri | Saharan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kne | Kankanaey | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knf | Mankanya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knj | Western Kanjobal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knv | Tabo | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kon | Kongo | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kor | Korean | Koreanic | 0 | 2 | 0 | 4 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 9 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 34 |
+| kos | Kosraean | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpf | Komba | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpg | Kapingamarangi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpj | Karajá | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpr | Korafe-Yegha | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpw | Kobon | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpx | Mountain Koiali | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqa | Mum | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqc | Doromu-Koki | Manubaran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqf | Kakabai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kql | Kyenele | Yuat | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqw | Kandas | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| krc | Karachay-Balkar | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksd | Kuanua | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksj | Uare | Kwalean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksr | Borong | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ktm | Kurti | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kto | Kuot | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kud | 'Auhelawa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kue | Kuman (Papua New Guinea) | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kup | Kunimaipa | Kunimaipan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kur | Kurdish | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kvg | Kuni-Boazi | Anim | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kvn | Border Kuna | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwd | Kwaio | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwf | Kwara'ae | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwi | Awa-Cuaiquer | Barbacoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwj | Kwanga | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyc | Kyaka | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyf | Kouya | Kru | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyg | Keyagana | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyq | Kenga | Central Sudanic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyz | Kayabí | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kze | Kosena | Bookkeeping | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kzj | Coastal Kadazan | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lac | Lacandon | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lao | Lao | Tai-Kadai | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| lat | Latin | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| lav | Latvian | Indo-European | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| lbb | Label | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lbk | Central Bontok | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lcm | Tungag | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| leu | Kara (Papua New Guinea) | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lex | Luang | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lfn | Lingua Franca Nova | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lgl | Wala | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lid | Nyindrou | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lif | Limbu | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lij | Ligurian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lim | Limburgan | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lin | Lingala | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| lit | Lithuanian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| llg | Lole | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lmo | Lombard | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ltg | Latgalian | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ltz | Luxembourgish | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| lua | Luba-Lulua | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lug | Ganda | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| luo | Luo (Kenya and Tanzania) | Nilotic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| lus | Lushai | Sino-Tibetan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lvs | Standard Latvian | Unclassified | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| lww | Lewo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maa | San Jerónimo Tecóatl Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mad | Madurese | Austronesian | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| mag | Magahi | Indo-European | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| mai | Maithili | Indo-European | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| maj | Jalapa De Díaz Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mak | Makasar | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mal | Malayalam | Dravidian | 0 | 0 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
+| mam | Mam | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maq | Chiquihuitlán Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mar | Marathi | Indo-European | 0 | 0 | 0 | 7 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
+| mau | Huautla Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mav | Sateré-Mawé | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| max | North Moluccan Malay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maz | Central Mazahua | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbb | Western Bukidnon Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbc | Macushi | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbh | Mangseng | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbj | Nadëb | Naduhup | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbl | Maxakalí | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbs | Sarangani Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbt | Matigsalug Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mca | Maca | Mataguayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcb | Machiguenga | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcd | Sharanahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcf | Matsés | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mco | Coatlán Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcp | Makaa | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcq | Ese | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcr | Menya | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mdy | Male (Ethiopia) | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| med | Melpa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mee | Mengen | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mek | Mekeo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| meq | Merey | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| met | Mato | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| meu | Motu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mey | Hassaniyya | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgc | Morokodo | Central Sudanic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgh | Makhuwa-Meetto | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgw | Matumbi | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mhl | Mauwake | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mhr | Eastern Mari | Uralic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mib | Atatláhuca Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mic | Mi'kmaq | Algic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mie | Ocotepec Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mig | San Miguel El Grande Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mih | Chayuco Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mil | Peñoles Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| min | Minangkabau | Austronesian | 0 | 0 | 0 | 3 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| mio | Pinotepa Nacional Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mir | Isthmus Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mit | Southern Puebla Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| miz | Coatzospan Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mjc | San Juan Colorado Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkd | Macedonian | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| mkj | Mokilese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkl | Mokole | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkn | Kupang Malay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mks | Silacayoapan Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mle | Manambu | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlg | Malagasy | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlh | Mape | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlp | Bargam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlt | Maltese | Afro-Asiatic | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| mmo | Mangga Buang | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mmx | Madak | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mna | Mbula | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mni | Manipuri | Sino-Tibetan | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| mon | Mongolian | Unclassified | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| mop | Mopán Maya | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mos | Mossi | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mox | Molima | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mph | Maung | Iwaidjan Proper | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpj | Martu Wangka | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpm | Yosondúa Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpp | Migabac | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mps | Dadibi | Teberan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpt | Mian | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpx | Misima-Panaeati | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mqb | Mbuko | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mqj | Mamasa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mri | Maori | Austronesian | 0 | 1 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| msa | Malay (macrolanguage) | Unclassified | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| msb | Masbatenyo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msc | Sankaran Maninka | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msk | Mansaka | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msm | Agusan Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msy | Aruamu | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mti | Maiwa (Papua New Guinea) | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mto | Totontepec Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mui | Musi | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mup | Malvi | Indo-European | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| mux | Bo-Ung | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| muy | Muyang | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mva | Manam | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mvn | Minaveha | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwc | Are | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwe | Mwera (Chimwera) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwf | Murrinh-Patha | Southern Daly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwp | Kala Lagaw Ya | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwr | Marwari | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxb | Tezoatlán Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxp | Tlahuitoltepec Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxq | Juquila Mixe | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxt | Jamiltepec Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mya | Burmese | Sino-Tibetan | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| myk | Mamara Senoufo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myu | Mundurukú | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myw | Muyuw | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myy | Macuna | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mzz | Maiadomu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nab | Southern Nambikuára | Nambiquaran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| naf | Nabak | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nak | Nakanai | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nas | Naasioi | South Bougainville | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nbl | South Ndebele | Unclassified | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nbq | Nggem | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nca | Iyo | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nch | Central Huasteca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncj | Northern Puebla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncl | Michoacán Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncu | Chumburung | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nde | North Ndebele | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ndg | Ndengereko | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ndj | Ndamba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nds | Low German | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nep | Nepali (macrolanguage) | Unclassified | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| nfa | Dhao | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ngp | Ngulu | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ngu | Guerrero Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhe | Eastern Huasteca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhg | Tetelcingo Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhi | Zacatlán-Ahuacatlán-Tepetzintla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nho | Takuu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhr | Naro | Khoe-Kwadi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhu | Noone | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhw | Western Huasteca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhy | Northern Oaxaca Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nif | Nek | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nii | Nii | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nij | Ngaju | Austronesian | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| nin | Ninzo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nko | Nkonya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nld | Dutch | Indo-European | 0 | 1 | 0 | 6 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 29 | 2 | 0 | 0 | 0 | 0 | 4 | 0 | 52 |
+| nlg | Gela | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nna | Nyangumarta | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nno | Norwegian Nynorsk | Unclassified | 0 | 0 | 0 | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| nnq | Ngindo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| noa | Woun Meu | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nob | Norwegian Bokmål | Unclassified | 0 | 0 | 0 | 4 | 7 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
+| noe | Nimadi | Indo-European | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nop | Numanggang | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nor | Norwegian | Indo-European | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| not | Nomatsiguenga | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nou | Ewage-Notu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nov | Novial | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| npi | Nepali (individual language) | Indo-European | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| npl | Southeastern Puebla Nahuatl | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nqo | N'Ko | Artificial Language | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| nsn | Nehan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nso | Pedi | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| nss | Nali | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntj | Ngaanyatjarra | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntp | Northern Tepehuan | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntu | Natügu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nus | Nuer | Nilotic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| nuy | Nunggubuyu | Gunwinyguan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nvm | Namiae | Koiarian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nwi | Southwest Tanna | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nya | Nyanja | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| nys | Nyungar | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nyu | Nyungwe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| obo | Obo Manobo | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| oci | Occitan (post 1500) | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| okv | Orokaiva | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| omw | South Tairora | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ong | Olo | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ons | Ono | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ood | Tohono O'odham | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| opm | Oksapmin | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ori | Oriya (macrolanguage) | Unclassified | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| orm | Oromo | Unclassified | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| orv | Old Russian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ory | Odia | Indo-European | 0 | 0 | 0 | 5 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 15 |
+| ote | Mezquital Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otm | Eastern Highland Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otn | Tenango Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otq | Querétaro Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ots | Estado de México Otomi | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pab | Parecís | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pad | Paumarí | Arawan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pag | Pangasinan | Austronesian | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| pah | Tenharim | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pam | Pampanga | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pan | Panjabi | Indo-European | 0 | 0 | 0 | 6 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
+| pao | Northern Paiute | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pap | Papiamento | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| pbt | Southern Pashto | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| pcm | Nigerian Pidgin | Indo-European | 0 | 0 | 0 | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| pes | Iranian Persian | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| pib | Yine | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pio | Piapoco | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pir | Piratapuyo | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| piu | Pintupi-Luritja | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pjt | Pitjantjatjara | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pls | San Marcos Tlacoyalco Popoloca | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| plt | Plateau Malagasy | Austronesian | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| plu | Palikúr | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pma | Paama | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pms | Piemontese | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poe | San Juan Atzingo Popoloca | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poh | Poqomchi' | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poi | Highland Popoluca | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pol | Polish | Indo-European | 0 | 1 | 0 | 4 | 11 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 18 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 49 |
+| pon | Pohnpeian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| por | Portuguese | Indo-European | 0 | 1 | 0 | 4 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 5 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 31 |
+| poy | Pogolo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ppo | Folopa | Teberan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| prf | Paranan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pri | Paicî | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| prs | Dari | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ptp | Patep | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ptu | Bambam | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pus | Pushto | Unclassified | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| pwg | Gapapaiwa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qub | Huallaga Huánuco Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quc | K'iche' | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quf | Lambayeque Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quh | South Bolivian Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qul | North Bolivian Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qup | Southern Pastaza Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quy | Ayacucho Quechua | Quechuan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| quz | Cusco Quechua | Quechuan | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvc | Cajamarca Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qve | Eastern Apurímac Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvh | Huamalíes-Dos de Mayo Huánuco Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvm | Margos-Yarowilca-Lauricocha Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvn | North Junín Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvs | San Martín Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvw | Huaylla Wanca Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvz | Northern Pastaza Quichua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qwh | Huaylas Ancash Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxh | Panao Huánuco Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxn | Northern Conchucos Ancash Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxo | Southern Conchucos Ancash Quechua | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rai | Ramoaaina | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| raj | Rajasthani | Unclassified | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| reg | Kara (Tanzania) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rej | Rejang | Austronesian | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| rgu | Ringgou | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rkb | Rikbaktsa | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rmc | Carpathian Romani | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rmy | Vlax Romani | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rom | Romany | Unclassified | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| ron | Romanian | Indo-European | 0 | 1 | 0 | 5 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
+| roo | Rotokas | North Bougainville | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rop | Kriol | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| row | Dela-Oenale | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rro | Waima | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ruf | Luguru | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rug | Roviana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| run | Rundi | Atlantic-Congo | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| rus | Russian | Indo-European | 0 | 2 | 0 | 5 | 13 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 16 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 56 |
+| rwo | Rawa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sab | Buglere | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sag | Sango | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sah | Yakut | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| san | Sanskrit | Indo-European | 0 | 0 | 0 | 5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| sat | Santali | Austroasiatic | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| sbe | Saliba | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sbk | Safwa | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sbs | Subiya | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| scn | Sicilian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sco | Scots | Indo-European | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| seh | Sena | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sey | Secoya | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sgb | Mag-antsi Ayta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sgz | Sursurunga | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shi | Tachelhit | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shj | Shatt | Dajuic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shn | Shan | Tai-Kadai | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| shp | Shipibo-Conibo | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sim | Mende (Papua New Guinea) | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sin | Sinhala | Indo-European | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| sja | Epena | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| slk | Slovak | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
+| sll | Salt-Yui | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| slv | Slovenian | Indo-European | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| smk | Bolinao | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| smo | Samoan | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| sna | Shona | Atlantic-Congo | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| snc | Sinaugoro | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snd | Sindhi | Indo-European | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| snn | Siona | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snp | Siane | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snx | Sam | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sny | Saniyo-Hiyewe | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| som | Somali | Afro-Asiatic | 0 | 0 | 0 | 3 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| soq | Kanasi | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sot | Southern Sotho | Atlantic-Congo | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| soy | Miyobe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spa | Spanish | Indo-European | 0 | 2 | 0 | 4 | 13 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 13 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 50 |
+| spl | Selepet | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spm | Akukem | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spp | Supyire Senoufo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sps | Saposa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spy | Sabaot | Nilotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sqi | Albanian | Unclassified | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| srd | Sardinian | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sri | Siriano | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| srm | Saramaccan | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| srn | Sranan Tongo | Indo-European | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| srp | Serbian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| srq | Sirionó | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssd | Siroi | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssg | Seimat | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssw | Swati | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| ssx | Samberigi | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| stp | Southeastern Tepehuan | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sua | Sulka | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sue | Suena | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sun | Sundanese | Austronesian | 0 | 0 | 0 | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| sus | Susu | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| suz | Sunwar | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| svk | Slovakian Sign Language | Sign Language | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| swa | Swahili (macrolanguage) | Atlantic-Congo | 0 | 1 | 0 | 1 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
+| swe | Swedish | Indo-European | 0 | 1 | 0 | 4 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 |
+| swg | Swabian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| swh | Swahili (individual language) | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| swp | Suau | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sxb | Suba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| szl | Silesian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tac | Lowland Tarahumara | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tah | Tahitian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| taj | Eastern Tamang | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tam | Tamil | Dravidian | 0 | 0 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 21 |
+| taq | Tamasheq | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tat | Tatar | Turkic | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| tav | Tatuyo | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| taw | Tai | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbc | Takia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbf | Mandara | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbg | North Tairora | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbo | Tawala | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbz | Ditammari | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tca | Ticuna | Ticuna-Yuri | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tcs | Torres Strait Creole | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tcz | Thado Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tdt | Tetun Dili | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tee | Huehuetla Tepehua | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tel | Telugu | Dravidian | 0 | 1 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
+| ter | Tereno | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tet | Tetum | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tew | Tewa (USA) | Kiowa-Tanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tfr | Teribe | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tgk | Tajik | Indo-European | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| tgl | Tagalog | Austronesian | 0 | 0 | 0 | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| tgo | Sudest | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tgp | Tangoa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tha | Thai | Tai-Kadai | 0 | 1 | 0 | 4 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 |
+| tif | Tifal | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tim | Timbe | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tir | Tigrinya | Afro-Asiatic | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| tiw | Tiwi | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tiy | Tiruray | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tke | Takwane | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tku | Upper Necaxa Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tlf | Telefol | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tmd | Haruai | Piawi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tna | Tacana | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnc | Tanimuca-Retuarã | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnk | Kwamera | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnn | North Tanna | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnp | Whitesands | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| toc | Coyutla Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tod | Toma | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tof | Gizrra | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| toj | Tojolabal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ton | Tonga (Tonga Islands) | Austronesian | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| too | Xicotepec De Juárez Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| top | Papantla Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tos | Highland Totonac | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpa | Taupota | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpi | Tok Pisin | Indo-European | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| tpt | Tlachichilco Tepehua | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpz | Tinputz | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| trc | Copala Triqui | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tsn | Tswana | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| tso | Tsonga | Atlantic-Congo | 0 | 0 | 0 | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| tsw | Tsishingini | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ttc | Tektiteko | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tte | Bwanabwana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuc | Mutu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tue | Tuyuca | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuf | Central Tunebo | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuk | Turkmen | Turkic | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| tum | Tumbuka | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tuo | Tucano | Tucanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tur | Turkish | Turkic | 0 | 3 | 0 | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 25 |
+| tvk | Southeast Ambrym | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| twi | Twi | Unclassified | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| txq | Tii | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| txu | Kayapó | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tyv | Tuvinian | Turkic | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzj | Tz'utujil | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzl | Talossan | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzm | Central Atlas Tamazight | Afro-Asiatic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tzo | Tzotzil | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ubr | Ubir | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ubu | Umbu-Ungu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| udu | Uduk | Koman | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uig | Uighur | Turkic | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| ukr | Ukrainian | Indo-European | 0 | 1 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| uli | Ulithian | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ulk | Meriam Mir | Eastern Trans-Fly | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| umb | Umbundu | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| upv | Uripiv-Wala-Rano-Atchin | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ura | Urarina | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urb | Urubú-Kaapor | Tupian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urd | Urdu | Indo-European | 0 | 0 | 0 | 7 | 8 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
+| uri | Urim | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urt | Urat | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urw | Sop | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| usa | Usarufa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| usp | Uspanteco | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uvh | Uri | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uvl | Lote | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uzb | Uzbek | Unclassified | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| uzn | Northern Uzbek | Turkic | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| vec | Venetian | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ven | Venda | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| vid | Vidunda | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| vie | Vietnamese | Austroasiatic | 0 | 2 | 0 | 5 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
+| viv | Iduna | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| vmy | Ayautla Mazatec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| waj | Waffa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wal | Wolaytta | Ta-Ne-Omotic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wap | Wapishana | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| war | Waray (Philippines) | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| wat | Kaninuwa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wbi | Vwanji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wbp | Warlpiri | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wed | Wedau | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wer | Weri | Kunimaipan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wim | Wik-Mungkan | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wiu | Wiru | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wiv | Vitu | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wln | Walloon | Indo-European | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wmt | Walmajarri | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wmw | Mwani | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wnc | Wantoat | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wnu | Usan | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wol | Wolof | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| wos | Hanga Hundi | Ndu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wrk | Garrwa | Garrwan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wro | Worrorra | Worrorran | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wrs | Waris | Border | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wsk | Waskia | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wuu | Wu Chinese | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wuv | Wuvulu-Aua | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xav | Xavánte | Nuclear-Macro-Je | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xbi | Kombio | Nuclear Torricelli | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xed | Hdi | Afro-Asiatic | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xho | Xhosa | Atlantic-Congo | 0 | 0 | 0 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| xla | Kamula | Kamula-Elevala | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xnn | Northern Kankanay | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xon | Konkomba | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xsi | Sio | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xtd | Diuxi-Tilantongo Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xtm | Magdalena Peñasco Mixtec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yaa | Yaminahua | Pano-Tacanan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yad | Yagua | Peba-Yagua | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yal | Yalunka | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yap | Yapese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yaq | Yaqui | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yby | Yaweyuha | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ycn | Yucuna | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ydd | Eastern Yiddish | Indo-European | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| yid | Yiddish | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yka | Yakan | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yle | Yele | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yml | Iamalele | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yon | Yongkom | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yor | Yoruba | Atlantic-Congo | 0 | 0 | 0 | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
+| yrb | Yareba | Yareban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yre | Yaouré | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yss | Yessan-Mayo | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yue | Yue Chinese | Sino-Tibetan | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| yuj | Karkar-Yuri | Pauwasi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yut | Yopno | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yuw | Yau (Morobe Province) | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yva | Yawa | Yawa-Saweru | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaa | Sierra de Juárez Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zab | Western Tlacolula Valley Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zac | Ocotlán Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zad | Cajonos Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zai | Isthmus Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaj | Zaramo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zam | Miahuatlán Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zao | Ozolotepec Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zap | Zapotec | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zar | Rincón Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zas | Santo Domingo Albarradas Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zat | Tabaa Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zav | Yatzachi Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaw | Mitla Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zca | Coatecas Altas Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zga | Kinga | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zho | Chinese | Unclassified | 0 | 2 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 |
+| zia | Zia | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ziw | Zigula | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zlm | Malay (individual language) | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zos | Francisco León Zoque | Mixe-Zoque | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpc | Choapan Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpl | Lachixío Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpm | Mixtepec Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpo | Amatlán Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpq | Zoogocho Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpu | Yalálag Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpv | Chichicapan Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpz | Texmelucan Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zsm | Standard Malay | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| zsr | Southern Rincon Zapotec | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ztq | Quioquitani-Quierí Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zty | Yatee Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zul | Zulu | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| zyp | Zyphe Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| Total | None | None | None | 0 | 55 | 55 | 1398 | 836 | 313 | 7 | 10 | 22 | 5 | 0 | 3 | 28 | 91 | 56 | 536 | 88 | 2 | 2 | 6 | 7 | 37 | 24 |
 <!-- TASK LANG TABLE END -->
 
 </details>
diff --git a/mteb/abstasks/Image/AbsTaskAny2AnyMultiChoice.py b/mteb/abstasks/Image/AbsTaskAny2AnyMultiChoice.py
index 1e78fb8107..5c6b102331 100644
--- a/mteb/abstasks/Image/AbsTaskAny2AnyMultiChoice.py
+++ b/mteb/abstasks/Image/AbsTaskAny2AnyMultiChoice.py
@@ -15,6 +15,7 @@
 from mteb.abstasks.AbsTask import AbsTask, ScoresDict
 
 from ...evaluation.evaluators import Any2AnyMultiChoiceEvaluator
+from ..TaskMetadata import DescriptiveStatistics
 
 logger = logging.getLogger(__name__)
 
@@ -186,6 +187,95 @@ def _load_qrels(self, split):
         self.qrels = qrels_ds
 
 
+class Any2AnyMutipleChoiceDescriptiveStatistics(DescriptiveStatistics):
+    """Descriptive statistics for Any2TextMutipleChoice
+
+    Attributes:
+        num_samples: Number of queries and documents
+        num_queries: number of queries in the dataset
+        num_documents: Number of documents
+        number_of_characters: Total number of text characters in the dataset
+
+        For text only:
+        min_document_length: Minimum length of documents
+        average_document_length: Average length of documents
+        max_document_length: Maximum length of documents
+        unique_documents: Number of unique documents
+
+        For text only:
+        min_query_length: Minimum length of queries
+        average_query_length: Average length of queries
+        max_query_length: Maximum length of queries
+        unique_queries: Number of unique queries
+
+        For images:
+        num_query_images: Number of query images
+        num_document_images: Number of document images
+
+        For images:
+        min_document_image_width: Minimum width of document images
+        average_document_image_width: Average width of document images
+        max_document_image_width: Maximum width of document images
+        min_document_image_height: Minimum height of document images
+        average_document_image_height: Average height of document images
+        max_document_image_height: Maximum height of document images
+
+        For images:
+        min_query_image_width: Minimum width of query images
+        average_query_image_width: Average width of query images
+        max_query_image_width: Maximum width of query images
+        min_query_image_height: Minimum height of query images
+        average_query_image_height: Average height of query images
+        max_query_image_height: Maximum height of query images
+
+        min_relevant_docs_per_query: Minimum number of relevant documents per query
+        average_relevant_docs_per_query: Average number of relevant documents per query
+        max_relevant_docs_per_query: Maximum number of relevant documents per query
+        unique_relevant_docs: Number of unique relevant documents
+
+        min_irrelevant_docs_per_query: Minimum number of irrelevant documents per query
+        average_irrelevant_docs_per_query: Average number of irrelevant documents per query
+        max_irrelevant_docs_per_query: Maximum number of irrelevant documents per query
+        unique_irrelevant_docs: Number of unique irrelevant documents
+    """
+
+    num_samples: int
+    num_queries: int
+    num_documents: int
+    number_of_characters: int
+
+    min_document_length: int
+    average_document_length: float
+    max_document_length: int
+    unique_documents: int
+    num_document_images: int
+
+    min_document_image_width: float
+    average_document_image_width: float
+    max_document_image_width: float
+    min_document_image_height: float
+    average_document_image_height: float
+    max_document_image_height: float
+
+    min_query_length: int
+    average_query_length: float
+    max_query_length: int
+    unique_queries: int
+    num_query_images: int
+
+    min_query_image_width: float
+    average_query_image_width: float
+    max_query_image_width: float
+    min_query_image_height: float
+    average_query_image_height: float
+    max_query_image_height: float
+
+    min_relevant_docs_per_query: int
+    average_relevant_docs_per_query: float
+    max_relevant_docs_per_query: int
+    unique_relevant_docs: int
+
+
 class AbsTaskAny2AnyMultiChoice(AbsTask):
     """Abstract class for Any2Any multiple choice experiments
 
@@ -376,39 +466,124 @@ def _add_main_score(self, scores: ScoresDict) -> None:
 
     def _calculate_metrics_from_split(
         self, split: str, hf_subset: str | None = None, compute_overall: bool = False
-    ):
-        pass
-
-    def calculate_metadata_metrics(self) -> None:
-        self.load_data()
-
-        all_details = {}
-        pbar_split = tqdm.tqdm(self.metadata.eval_splits, desc="Processing Splits...")
-        for split in pbar_split:
-            pbar_split.set_postfix_str(f"Split: {split}")
-            logger.info(f"Processing metadata for split {split}")
-            all_details[split] = {}
-            if self.metadata.is_multilingual:
-                pbar_lang = tqdm.tqdm(
-                    self.relevant_docs.keys(), desc="Processing Languages..."
+    ) -> Any2AnyMutipleChoiceDescriptiveStatistics:
+        if hf_subset:
+            queries = self.queries[hf_subset][split]
+            corpus = self.corpus[hf_subset][split]
+            relevant_docs = self.relevant_docs[hf_subset][split]
+        elif compute_overall:
+            queries = {}
+            corpus = {}
+            relevant_docs = {}
+            for hf_subset in self.metadata.eval_langs:
+                queries.update(process_docs(self.queries, hf_subset, split))
+                corpus.update(process_docs(self.corpus, hf_subset, split))
+                relevant_docs.update(
+                    process_relevant_docs(self.relevant_docs, hf_subset, split)
                 )
-                for lang in pbar_lang:
-                    pbar_lang.set_postfix_str(f"Language: {lang}")
-                    logger.info(f"Processing metadata for language {lang}")
-                    split_details = process_language(
-                        self.relevant_docs[lang][split],
-                        self.queries[lang][split],
-                        self.corpus[lang][split],
-                        lang,
-                    )
-                    all_details[split][lang] = split_details
-            else:
-                split_details = process_language(
-                    self.relevant_docs[split], self.queries[split], self.corpus[split]
-                )
-                all_details[split] = split_details
-
-        return all_details
+        else:
+            queries = self.queries[split]
+            corpus = self.corpus[split]
+            relevant_docs = self.relevant_docs[split]
+
+        queries_lens, doc_lens = [], []
+        num_query_images = 0
+        num_document_images = 0
+
+        q_modality = queries[0]["modality"]
+        unique_queries = len(set(queries["text"])) if "text" in q_modality else 0
+
+        for query in tqdm.tqdm(queries, desc="queries:"):
+            if "text" in q_modality:
+                text_query = query["text"]
+                queries_lens.append(len(text_query))
+            if "image" in q_modality:
+                num_query_images += 1
+
+        d_modality = corpus[0]["modality"]
+        unique_documents = len(set(corpus["text"])) if "text" in d_modality else 0
+
+        for doc in tqdm.tqdm(corpus, desc="docs:"):
+            if "text" in d_modality:
+                text_doc = doc["text"]
+                doc_lens.append(len(text_doc))
+            if "image" in d_modality:
+                num_document_images += 1
+
+        total_doc_len = sum(doc_lens)
+        total_query_len = sum(queries_lens)
+        num_documents = len(corpus)
+        num_queries = len(queries)
+
+        d_modality = corpus[0]["modality"]
+        imgs = [doc["image"] for doc in corpus if "image" in d_modality]
+        d_img_widths, d_img_heights = [], []
+        for img in imgs:
+            width, height = img.size
+            d_img_widths.append(height)
+            d_img_heights.append(width)
+
+        q_modality = queries[0]["modality"]
+        imgs = [query["image"] for query in queries if "image" in q_modality]
+        q_img_widths, q_img_heights = [], []
+        for img in imgs:
+            width, height = img.size
+            q_img_widths.append(height)
+            q_img_heights.append(width)
+
+        # create a list of number of relevant docs per query
+        queries_set = set(queries["id"])
+        qrels_lengths = [
+            len(relevant_docs[qid])
+            for qid in tqdm.tqdm(relevant_docs.keys(), desc="qrels:")
+            if qid in queries_set
+        ]
+        num_qrels = sum(qrels_lengths)
+        qrels_per_doc = num_qrels / len(relevant_docs) if num_queries else 0
+        unique_qrels = len({doc for qid in relevant_docs for doc in relevant_docs[qid]})
+
+        return Any2AnyMutipleChoiceDescriptiveStatistics(
+            number_of_characters=total_query_len + total_doc_len,
+            num_samples=num_documents + num_queries,
+            num_queries=num_queries,
+            num_documents=num_documents,
+            min_document_length=min(doc_lens) if doc_lens else 0,
+            average_document_length=total_doc_len / len(doc_lens) if doc_lens else 0,
+            max_document_length=max(doc_lens) if doc_lens else 0,
+            unique_documents=unique_documents,
+            min_document_image_width=min(d_img_widths) if d_img_widths else 0,
+            average_document_image_width=sum(d_img_widths) / len(d_img_widths)
+            if d_img_widths
+            else 0,
+            max_document_image_width=max(d_img_widths) if d_img_widths else 0,
+            min_document_image_height=min(d_img_heights) if d_img_heights else 0,
+            average_document_image_height=sum(d_img_heights) / len(d_img_heights)
+            if d_img_heights
+            else 0,
+            max_document_image_height=max(d_img_heights) if d_img_heights else 0,
+            num_document_images=num_document_images,
+            min_query_length=min(queries_lens) if queries_lens else 0,
+            average_query_length=total_query_len / len(queries_lens)
+            if queries_lens
+            else 0,
+            max_query_length=max(queries_lens) if queries_lens else 0,
+            unique_queries=unique_queries,
+            num_query_images=num_query_images,
+            min_query_image_width=min(q_img_widths) if q_img_widths else 0,
+            average_query_image_width=sum(q_img_widths) / len(q_img_widths)
+            if q_img_widths
+            else 0,
+            max_query_image_width=max(q_img_widths) if q_img_widths else 0,
+            min_query_image_height=min(q_img_heights) if q_img_heights else 0,
+            average_query_image_height=sum(q_img_heights) / len(q_img_heights)
+            if q_img_heights
+            else 0,
+            max_query_image_height=max(q_img_heights) if q_img_heights else 0,
+            min_relevant_docs_per_query=min(qrels_lengths),
+            average_relevant_docs_per_query=qrels_per_doc,
+            max_relevant_docs_per_query=max(qrels_lengths),
+            unique_relevant_docs=unique_qrels,
+        )
 
 
 def process_language(relevant_docs, queries, corpus, lang=None):
@@ -448,13 +623,36 @@ def process_language(relevant_docs, queries, corpus, lang=None):
 def calculate_length(queries, corpus):
     queries_lens = []
     doc_lens = []
-    for query in queries.values():
+    for query in queries:
         queries_lens.append(len(query))
 
-    for doc in corpus.values():
+    for doc in corpus:
         if isinstance(doc, Image.Image):
             doc_lens.append(1.0)  # for image append 1. Can perhaps be removed.
 
     doc_len = sum(doc_lens) / len(doc_lens) if doc_lens else 0
     query_len = sum(queries_lens) / len(queries_lens) if queries_lens else 0
     return query_len, doc_len
+
+
+def process_relevant_docs(
+    collection: dict[str, dict[str, dict[str, dict[str, int]]]],
+    hf_subset: str,
+    split: str,
+) -> dict[str, dict[str, int]]:
+    """Collections can contain overlapping ids in different splits. Prepend split to avoid this"""
+    return_collection = {}
+    for query_id, relevant in collection[hf_subset][split].items():
+        return_collection[f"{split}_{hf_subset}_{query_id}"] = {
+            f"{split}_{hf_subset}_{doc_id}": value for doc_id, value in relevant.items()
+        }
+    return return_collection
+
+
+def process_docs(
+    collection: dict[str, dict[str, dict[str, str] | str]], hf_subset: str, split: str
+) -> dict[str, str]:
+    """Collections can contain overlapping ids in different splits. Prepend split to avoid this"""
+    return {
+        f"{split}_{hf_subset}_{k}": v for k, v in collection[hf_subset][split].items()
+    }
diff --git a/mteb/abstasks/Image/AbsTaskAny2AnyRetrieval.py b/mteb/abstasks/Image/AbsTaskAny2AnyRetrieval.py
index 46a029d928..5a0e25e48e 100644
--- a/mteb/abstasks/Image/AbsTaskAny2AnyRetrieval.py
+++ b/mteb/abstasks/Image/AbsTaskAny2AnyRetrieval.py
@@ -5,7 +5,7 @@
 import os
 from collections import defaultdict
 from pathlib import Path
-from time import time
+from time import asctime, time
 from typing import Any
 
 import tqdm
@@ -14,6 +14,7 @@
 
 from ...evaluation.evaluators import Any2AnyRetrievalEvaluator
 from ..AbsTask import AbsTask, ScoresDict
+from ..TaskMetadata import DescriptiveStatistics
 
 logger = logging.getLogger(__name__)
 
@@ -95,11 +96,16 @@ def load(
         self._load_qrels(split)
         # filter queries with no qrels
         qrels_dict = defaultdict(dict)
+        logger.info(f"{asctime()} - Done load qrels, before map qrels_dict")
 
-        def qrels_dict_init(row):
-            qrels_dict[row["query-id"]][row["corpus-id"]] = int(row["score"])
+        df = self.qrels.to_pandas()
+        query_ids = df["query-id"].to_numpy()
+        corpus_ids = df["corpus-id"].to_numpy()
+        scores = df["score"].to_numpy()
 
-        self.qrels.map(qrels_dict_init)
+        for q, c, s in zip(query_ids, corpus_ids, scores):
+            qrels_dict[q][c] = int(s)
+        logger.info(f"{asctime()} - Done qrels_dict")
         self.qrels = qrels_dict
         self.queries = self.queries.filter(lambda x: x["id"] in self.qrels)
         logger.info("Loaded %d %s Queries.", len(self.queries), split.upper())
@@ -185,6 +191,60 @@ def _load_qrels(self, split):
         self.qrels = qrels_ds
 
 
+class Any2AnyRetrievalDescriptiveStatistics(DescriptiveStatistics):
+    """Descriptive statistics for Retrieval
+
+    Attributes:
+        num_samples: Number of queries and documents
+        num_queries: number of queries in the dataset
+        num_documents: Number of documents
+        number_of_characters: Total number of text characters in the dataset
+
+        For text only:
+        min_document_length: Minimum length of documents
+        average_document_length: Average length of documents
+        max_document_length: Maximum length of documents
+        unique_documents: Number of unique documents
+
+        For text only:
+        min_query_length: Minimum length of queries
+        average_query_length: Average length of queries
+        max_query_length: Maximum length of queries
+        unique_queries: Number of unique queries
+
+        For images:
+        num_query_images: Number of query images
+        num_document_images: Number of document images
+
+        min_relevant_docs_per_query: Minimum number of relevant documents per query
+        average_relevant_docs_per_query: Average number of relevant documents per query
+        max_relevant_docs_per_query: Maximum number of relevant documents per query
+        unique_relevant_docs: Number of unique relevant documents
+    """
+
+    num_samples: int
+    num_queries: int
+    num_documents: int
+    number_of_characters: int
+
+    min_document_length: int
+    average_document_length: float
+    max_document_length: int
+    unique_documents: int
+    num_document_images: int
+
+    min_query_length: int
+    average_query_length: float
+    max_query_length: int
+    unique_queries: int
+    num_query_images: int
+
+    min_relevant_docs_per_query: int
+    average_relevant_docs_per_query: float
+    max_relevant_docs_per_query: int
+    unique_relevant_docs: int
+
+
 class AbsTaskAny2AnyRetrieval(AbsTask):
     """Abstract class for retrieval experiments.
 
@@ -371,85 +431,108 @@ def _add_main_score(self, scores: ScoresDict) -> None:
 
     def _calculate_metrics_from_split(
         self, split: str, hf_subset: str | None = None, compute_overall: bool = False
-    ):
-        pass
-
-    def calculate_metadata_metrics(self) -> None:
-        self.load_data()
-
-        all_details = {}
-        pbar_split = tqdm.tqdm(self.metadata.eval_splits, desc="Processing Splits...")
-        for split in pbar_split:
-            pbar_split.set_postfix_str(f"Split: {split}")
-            logger.info(f"Processing metadata for split {split}")
-            all_details[split] = {}
-            if self.metadata.is_multilingual:
-                pbar_lang = tqdm.tqdm(
-                    self.relevant_docs.keys(), desc="Processing Languages..."
+    ) -> Any2AnyRetrievalDescriptiveStatistics:
+        if hf_subset:
+            queries = self.queries[hf_subset][split]
+            corpus = self.corpus[hf_subset][split]
+            relevant_docs = self.relevant_docs[hf_subset][split]
+        elif compute_overall:
+            queries = {}
+            corpus = {}
+            relevant_docs = {}
+            for hf_subset in self.metadata.eval_langs:
+                queries.update(process_docs(self.queries, hf_subset, split))
+                corpus.update(process_docs(self.corpus, hf_subset, split))
+                relevant_docs.update(
+                    process_relevant_docs(self.relevant_docs, hf_subset, split)
                 )
-                for lang in pbar_lang:
-                    pbar_lang.set_postfix_str(f"Language: {lang}")
-                    logger.info(f"Processing metadata for language {lang}")
-                    split_details = process_language(
-                        self.relevant_docs[lang][split],
-                        self.queries[lang][split],
-                        self.corpus[lang][split],
-                        lang,
-                    )
-                    all_details[split][lang] = split_details
-            else:
-                split_details = process_language(
-                    self.relevant_docs[split], self.queries[split], self.corpus[split]
-                )
-                all_details[split] = split_details
+        else:
+            queries = self.queries[split]
+            corpus = self.corpus[split]
+            relevant_docs = self.relevant_docs[split]
+
+        queries_lens, doc_lens = [], []
+        num_query_images = 0
+        num_document_images = 0
+
+        q_modality = queries[0]["modality"]
+        unique_queries = len(set(queries["text"])) if "text" in q_modality else 0
+
+        for query in tqdm.tqdm(queries, desc="queries:"):
+            if "text" in q_modality:
+                text_query = query["text"]
+                queries_lens.append(len(text_query))
+            if "image" in q_modality:
+                num_query_images += 1
+
+        d_modality = corpus[0]["modality"]
+        unique_documents = len(set(corpus["text"])) if "text" in d_modality else 0
+
+        for doc in tqdm.tqdm(corpus, desc="docs:"):
+            if "text" in d_modality:
+                text_doc = doc["text"]
+                doc_lens.append(len(text_doc))
+            if "image" in d_modality:
+                num_document_images += 1
+
+        total_doc_len = sum(doc_lens)
+        total_query_len = sum(queries_lens)
+        num_documents = len(corpus)
+        num_queries = len(queries)
+
+        # create a list of number of relevant docs per query
+        queries_set = set(queries["id"])
+        qrels_lengths = [
+            len(relevant_docs[qid])
+            for qid in tqdm.tqdm(relevant_docs.keys(), desc="qrels:")
+            if qid in queries_set
+        ]
+        num_qrels = sum(qrels_lengths)
+        qrels_per_doc = num_qrels / len(relevant_docs) if num_queries else 0
+        unique_qrels = len({doc for qid in relevant_docs for doc in relevant_docs[qid]})
+
+        return Any2AnyRetrievalDescriptiveStatistics(
+            number_of_characters=total_query_len + total_doc_len,
+            num_samples=num_documents + num_queries,
+            num_queries=num_queries,
+            num_documents=num_documents,
+            min_document_length=min(doc_lens) if doc_lens else 0,
+            average_document_length=total_doc_len / len(doc_lens) if doc_lens else 0,
+            max_document_length=max(doc_lens) if doc_lens else 0,
+            unique_documents=unique_documents,
+            num_document_images=num_document_images,
+            min_query_length=min(queries_lens) if queries_lens else 0,
+            average_query_length=total_query_len / len(queries_lens)
+            if queries_lens
+            else 0,
+            max_query_length=max(queries_lens) if queries_lens else 0,
+            unique_queries=unique_queries,
+            num_query_images=num_query_images,
+            min_relevant_docs_per_query=min(qrels_lengths),
+            average_relevant_docs_per_query=qrels_per_doc,
+            max_relevant_docs_per_query=max(qrels_lengths),
+            unique_relevant_docs=unique_qrels,
+        )
+
 
-        return all_details
+def process_relevant_docs(
+    collection: dict[str, dict[str, dict[str, dict[str, int]]]],
+    hf_subset: str,
+    split: str,
+) -> dict[str, dict[str, int]]:
+    """Collections can contain overlapping ids in different splits. Prepend split to avoid this"""
+    return_collection = {}
+    for query_id, relevant in collection[hf_subset][split].items():
+        return_collection[f"{split}_{hf_subset}_{query_id}"] = {
+            f"{split}_{hf_subset}_{doc_id}": value for doc_id, value in relevant.items()
+        }
+    return return_collection
 
 
-def process_language(relevant_docs, queries, corpus, lang=None):
-    """We want to get three pieces of information:
-    - the number of documents (and their char length) in the corpus
-    - the number of queries (and their char length)
-    - the average number of relevant documents per query
-    """
-    query_len, doc_len = calculate_length(queries, corpus)
-    num_documents = len(corpus)
-    num_queries = len(queries)
-
-    # number of qrels that are not 0
-    num_qrels_non_zero = sum(
-        sum(1 for doc_id in docs if docs[doc_id] != 0)
-        for docs in relevant_docs.values()
-    )
-    qrels_per_doc = num_qrels_non_zero / num_queries if num_queries else 0
-
-    language_description = f" for language {lang}" if lang else ""
-    logger.info(f"Average document character length{language_description} is {doc_len}")
-    logger.info(f"Average query character length{language_description} is {query_len}")
-    logger.info(f"Number of documents{language_description} is {num_documents}")
-    logger.info(f"Number of queries{language_description} is {num_queries}")
-    logger.info(
-        f"Average number of relevant documents per query{language_description} is {qrels_per_doc}"
-    )
+def process_docs(
+    collection: dict[str, dict[str, dict[str, str] | str]], hf_subset: str, split: str
+) -> dict[str, str]:
+    """Collections can contain overlapping ids in different splits. Prepend split to avoid this"""
     return {
-        "average_document_length": doc_len,
-        "average_query_length": query_len,
-        "num_documents": num_documents,
-        "num_queries": num_queries,
-        "average_relevant_docs_per_query": qrels_per_doc,
+        f"{split}_{hf_subset}_{k}": v for k, v in collection[hf_subset][split].items()
     }
-
-
-def calculate_length(queries, corpus):
-    queries_lens = []
-    doc_lens = []
-    for query in queries.values():
-        queries_lens.append(len(query))
-
-    for doc in corpus.values():
-        if isinstance(doc, Image.Image):
-            doc_lens.append(1.0)  # for image append 1. Can perhaps be removed.
-
-    doc_len = sum(doc_lens) / len(doc_lens) if doc_lens else 0
-    query_len = sum(queries_lens) / len(queries_lens) if queries_lens else 0
-    return query_len, doc_len
diff --git a/mteb/abstasks/Image/AbsTaskImageTextPairClassification.py b/mteb/abstasks/Image/AbsTaskImageTextPairClassification.py
index 93d2681f85..6b18c192f3 100644
--- a/mteb/abstasks/Image/AbsTaskImageTextPairClassification.py
+++ b/mteb/abstasks/Image/AbsTaskImageTextPairClassification.py
@@ -8,10 +8,35 @@
 from ...encoder_interface import Encoder
 from ...evaluation.evaluators import ImageTextPairClassificationEvaluator
 from ..AbsTask import AbsTask, ScoresDict
+from ..TaskMetadata import DescriptiveStatistics
 
 logger = logging.getLogger(__name__)
 
 
+class ImageTextPairClassificationDescriptiveStatistics(DescriptiveStatistics):
+    """Descriptive statistics for ImageTextPairClassification
+
+    Attributes:
+        num_samples: number of samples in the dataset.
+        num_images: number of images in the dataset.
+        num_texts: number of texts in the dataset.
+        num_unique_texts: number of unique texts in the dataset.
+
+        min_text_length: Minimum length of texts
+        average_text_length: Average length of texts
+        max_text_length: Maximum length of texts
+    """
+
+    num_samples: int
+    num_images: int
+    num_texts: int
+    num_unique_texts: int
+
+    min_text_length: int
+    average_text_length: float
+    max_text_length: int
+
+
 class AbsTaskImageTextPairClassification(AbsTask):
     """Abstract class for Image Text Pair Classification tasks,
     e.g. Compositionality evaluation.
@@ -35,8 +60,41 @@ def _add_main_score(self, scores) -> None:
 
     def _calculate_metrics_from_split(
         self, split: str, hf_subset: str | None = None, compute_overall: bool = False
-    ):
-        pass
+    ) -> ImageTextPairClassificationDescriptiveStatistics:
+        dataset = (
+            self.dataset[split] if hf_subset is None else self.dataset[hf_subset][split]
+        )
+        num_samples = len(dataset)
+
+        if isinstance(self.images_column_names, str):
+            num_images = list(dataset[self.images_column_names])
+        elif isinstance(self.images_column_names, list):
+            num_images = sum(
+                [len(dataset[img_column]) for img_column in self.images_column_names]
+            )
+
+        if isinstance(self.texts_column_names, str):
+            texts = list(dataset[self.texts_column_names])
+            unique_texts = set(texts)
+            text_lengths = [len(text) for text in texts]
+        elif isinstance(self.texts_column_names, list):
+            texts = [
+                text
+                for text_column in self.texts_column_names
+                for text in dataset[text_column]
+            ]
+            unique_texts = set(texts)
+            text_lengths = [len(text) for text in texts]
+
+        return ImageTextPairClassificationDescriptiveStatistics(
+            num_samples=num_samples,
+            num_images=num_images,
+            num_texts=len(texts),
+            num_unique_texts=len(unique_texts),
+            min_text_length=min(text_lengths),
+            average_text_length=sum(text_lengths) / len(text_lengths),
+            max_text_length=max(text_lengths),
+        )
 
     def _evaluate_subset(
         self,
diff --git a/mteb/abstasks/TaskMetadata.py b/mteb/abstasks/TaskMetadata.py
index bea221ab50..149ce389e0 100644
--- a/mteb/abstasks/TaskMetadata.py
+++ b/mteb/abstasks/TaskMetadata.py
@@ -60,6 +60,7 @@
     "Activity recognition",
     "Tumor detection",
     "Duplicate Detection",
+    "Rendered semantic textual similarity",
 ]
 
 TASK_DOMAIN = Literal[
@@ -105,13 +106,16 @@
 MIEB_TASK_TYPE = (
     "Any2AnyMultiChoice",
     "Any2AnyRetrieval",
-    "Any2TextMutipleChoice",
+    "Any2AnyMultilingualRetrieval",
+    "VisionCentric",
     "ImageClustering",
     "ImageClassification",
     "ImageMultilabelClassification",
-    "ImageTextPairClassification",
-    "VisualSTS",
+    "DocumentUnderstanding",
+    "VisualSTS(eng)",
+    "VisualSTS(multi)",
     "ZeroShotClassification",
+    "Compositionality",
 )
 
 _task_types = (
@@ -287,6 +291,7 @@ class TaskMetadata(BaseModel):
             "machine-translated and localized".
         prompt: The prompt used for the task. Can be a string or a dictionary containing the query and passage prompts.
         bibtex_citation: The BibTeX citation for the dataset. Should be an empty string if no citation is available.
+        adapted_from: Datasets adapted (translated, sampled from, etc.) from other datasets.
     """
 
     model_config = ConfigDict(extra="forbid")
@@ -315,6 +320,7 @@ class TaskMetadata(BaseModel):
 
     sample_creation: SAMPLE_CREATION_METHOD | None = None
     bibtex_citation: str | None = None
+    adapted_from: list[str] | None = None
 
     def validate_metadata(self) -> None:
         self.dataset_path_is_specified(self.dataset)
@@ -438,7 +444,7 @@ def is_filled(self) -> bool:
         return all(
             getattr(self, field_name) is not None
             for field_name in self.model_fields
-            if field_name != "prompt"
+            if field_name not in ["prompt", "adapted_from"]
         )
 
     @property
diff --git a/mteb/abstasks/aggregate_task_metadata.py b/mteb/abstasks/aggregate_task_metadata.py
index 106419b752..8967703dcb 100644
--- a/mteb/abstasks/aggregate_task_metadata.py
+++ b/mteb/abstasks/aggregate_task_metadata.py
@@ -60,6 +60,8 @@ class AggregateTaskMetadata(TaskMetadata):
     @property
     def hf_subsets_to_langscripts(self) -> dict[HFSubset, list[ISO_LANGUAGE_SCRIPT]]:
         """Return a dictionary mapping huggingface subsets to languages."""
+        if isinstance(self.eval_langs, dict):
+            return self.eval_langs
         return {"default": self.eval_langs}  # type: ignore
 
     @model_validator(mode="after")  # type: ignore
diff --git a/mteb/abstasks/aggregated_task.py b/mteb/abstasks/aggregated_task.py
index 255df2000f..4c79db01ae 100644
--- a/mteb/abstasks/aggregated_task.py
+++ b/mteb/abstasks/aggregated_task.py
@@ -35,15 +35,27 @@ def task_results_to_scores(
     ) -> dict[str, dict[HFSubset, ScoresDict]]:
         """The function that aggregated scores. Can be redefined to allow for custom aggregations."""
         scores = {}
+        subsets = (
+            self.metadata.eval_langs.keys()
+            if isinstance(self.metadata.eval_langs, dict)
+            else None
+        )
+        eval_langs = (
+            self.metadata.eval_langs.values()
+            if isinstance(self.metadata.eval_langs, dict)
+            else [self.metadata.eval_langs]
+        )
         for split in self.metadata.eval_splits:
             main_scores = []
             for task_res in task_results:
-                main_scores.append(
-                    task_res.get_score_fast(
-                        languages=None,
-                        splits=self.metadata.eval_splits,
+                for langs in eval_langs:
+                    main_scores.append(
+                        task_res.get_score_fast(
+                            languages=[lang.split("-")[0] for lang in langs],
+                            splits=self.metadata.eval_splits,
+                            subsets=subsets,
+                        )
                     )
-                )
             main_score = np.mean(main_scores)
             scores[split] = {
                 "default": {
@@ -64,7 +76,7 @@ def combine_task_results(self, task_results: list[TaskResult]) -> TaskResult:
         eval_times = [tr.evaluation_time for tr in task_results if tr.evaluation_time]
         if len(eval_times) != len(task_results):
             logger.info(
-                f"Loaded results does not include runtime. Therefor evaluation of {self.metadata.name} "
+                f"Loaded results does not include runtime. Therefore evaluation of {self.metadata.name} "
                 + "can't be computed. Setting it to None."
             )
             eval_time = np.nan
@@ -76,7 +88,7 @@ def combine_task_results(self, task_results: list[TaskResult]) -> TaskResult:
         ]
         if len(kg_co2_emissions_) != len(task_results):
             logger.info(
-                f"Loaded results does not include co2-eq emissions. Therefor evaluation of {self.metadata.name} "
+                f"Loaded results does not include co2-eq emissions. Therefore evaluation of {self.metadata.name} "
                 + "can't be computed. Setting it to None."
             )
             kg_co2_emissions = np.nan
diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py
index f147505f54..9ca870ee85 100644
--- a/mteb/benchmarks/benchmarks.py
+++ b/mteb/benchmarks/benchmarks.py
@@ -343,7 +343,7 @@
             "SwedishSentimentClassification",
             "SweRecClassification",
             # Retrieval
-            "DanFEVER",
+            "DanFeverRetrieval",
             "NorQuadRetrieval",
             "SNLRetrieval",
             "SwednRetrieval",
@@ -1400,6 +1400,254 @@
 }""",
 )
 
+MIEB_common_tasks = [
+    # Image Classification
+    "Birdsnap",  # fine
+    "Caltech101",  # fine
+    "CIFAR10",  # coarse
+    "CIFAR100",  # fine
+    "Country211",  # fine
+    "DTD",  # coarse
+    "EuroSAT",  # coarse
+    "FER2013",  # coarse
+    "FGVCAircraft",  # fine
+    "Food101Classification",  # fine
+    "GTSRB",  # coarse
+    "Imagenet1k",  # fine
+    "MNIST",  # coarse
+    "OxfordFlowersClassification",  # fine
+    "OxfordPets",  # fine
+    "PatchCamelyon",  # coarse
+    "RESISC45",  # fine
+    "StanfordCars",  # fine
+    "STL10",  # coarse
+    "SUN397",  # fine
+    "UCF101",  # fine
+    # ImageMultiLabelClassification
+    "VOC2007",  # coarse
+    # Clustering
+    "CIFAR10Clustering",
+    "CIFAR100Clustering",
+    "ImageNetDog15Clustering",
+    "ImageNet10Clustering",
+    "TinyImageNetClustering",
+    # ZeroShotClassification
+    "BirdsnapZeroShot",
+    "Caltech101ZeroShot",
+    "CIFAR10ZeroShot",
+    "CIFAR100ZeroShot",
+    "CLEVRZeroShot",
+    "CLEVRCountZeroShot",
+    "Country211ZeroShot",
+    "DTDZeroShot",
+    "EuroSATZeroShot",
+    "FER2013ZeroShot",
+    "FGVCAircraftZeroShot",
+    "Food101ZeroShot",
+    "GTSRBZeroShot",
+    "Imagenet1kZeroShot",
+    "MNISTZeroShot",
+    "OxfordPetsZeroShot",
+    "PatchCamelyonZeroShot",
+    "RenderedSST2",
+    "RESISC45ZeroShot",
+    "StanfordCarsZeroShot",
+    "STL10ZeroShot",
+    "SUN397ZeroShot",
+    "UCF101ZeroShot",
+    # Any2TextMutipleChoice
+    "CVBenchCount",
+    "CVBenchRelation",
+    "CVBenchDepth",
+    "CVBenchDistance",
+    # Any2AnyMultipleChoice
+    "BLINKIT2IMultiChoice",
+    "BLINKIT2TMultiChoice",
+    # Compositionality
+    "ImageCoDeT2IMultiChoice",
+    "AROCocoOrder",
+    "AROFlickrOrder",
+    "AROVisualAttribution",
+    "AROVisualRelation",
+    "SugarCrepe",
+    "Winoground",
+    # VisualSTS
+    "STS12VisualSTS",
+    "STS13VisualSTS",
+    "STS14VisualSTS",
+    "STS15VisualSTS",
+    "STS16VisualSTS",
+    # Any2AnyRetrieval
+    "BLINKIT2IRetrieval",
+    "BLINKIT2TRetrieval",
+    "CIRRIT2IRetrieval",
+    "CUB200I2IRetrieval",
+    "EDIST2ITRetrieval",
+    "Fashion200kI2TRetrieval",
+    "Fashion200kT2IRetrieval",
+    "FashionIQIT2IRetrieval",
+    "Flickr30kI2TRetrieval",
+    "Flickr30kT2IRetrieval",
+    "FORBI2IRetrieval",
+    "GLDv2I2IRetrieval",
+    "GLDv2I2TRetrieval",
+    "HatefulMemesI2TRetrieval",
+    "HatefulMemesT2IRetrieval",
+    "ImageCoDeT2IRetrieval",
+    "InfoSeekIT2ITRetrieval",
+    "InfoSeekIT2TRetrieval",
+    "MemotionI2TRetrieval",
+    "MemotionT2IRetrieval",
+    "METI2IRetrieval",
+    "MSCOCOI2TRetrieval",
+    "MSCOCOT2IRetrieval",
+    "NIGHTSI2IRetrieval",
+    "OVENIT2ITRetrieval",
+    "OVENIT2TRetrieval",
+    "ROxfordEasyI2IMultiChoice",
+    "ROxfordMediumI2IMultiChoice",
+    "ROxfordHardI2IMultiChoice",
+    "RP2kI2IRetrieval",
+    "RParisEasyI2IMultiChoice",
+    "RParisMediumI2IMultiChoice",
+    "RParisHardI2IMultiChoice",
+    "SciMMIRI2TRetrieval",
+    "SciMMIRT2IRetrieval",
+    "SketchyI2IRetrieval",
+    "SOPI2IRetrieval",
+    "StanfordCarsI2IRetrieval",
+    "TUBerlinT2IRetrieval",
+    "VidoreArxivQARetrieval",
+    "VidoreDocVQARetrieval",
+    "VidoreInfoVQARetrieval",
+    "VidoreTabfquadRetrieval",
+    "VidoreTatdqaRetrieval",
+    "VidoreShiftProjectRetrieval",
+    "VidoreSyntheticDocQAAIRetrieval",
+    "VidoreSyntheticDocQAEnergyRetrieval",
+    "VidoreSyntheticDocQAGovernmentReportsRetrieval",
+    "VidoreSyntheticDocQAHealthcareIndustryRetrieval",
+    "VisualNewsI2TRetrieval",
+    "VisualNewsT2IRetrieval",
+    "VizWizIT2TRetrieval",
+    "VQA2IT2TRetrieval",
+    "WebQAT2ITRetrieval",
+    "WebQAT2TRetrieval",
+]
+
+MIEB_ENG = Benchmark(
+    name="MIEB(eng)",
+    tasks=get_tasks(
+        tasks=MIEB_common_tasks
+        + [
+            "VisualSTS17Eng",
+            "VisualSTS-b-Eng",
+        ],
+    ),
+    description="""MIEB(eng) is a comprehensive image embeddings benchmark, spanning 8 task types, covering 125 tasks. 
+    In addition to image classification (zero shot and linear probing), clustering, retrieval, MIEB includes tasks in compositionality evaluation, 
+    document undestanding, visual STS, and CV-centric tasks.""",
+    reference="",
+    contacts=["gowitheflow-1998", "isaac-chung"],
+    citation="",
+)
+
+MIEB_MULTILINGUAL = Benchmark(
+    name="MIEB(Multilingual)",
+    tasks=get_tasks(
+        tasks=MIEB_common_tasks
+        + [
+            "WITT2IRetrieval",
+            "XFlickr30kCoT2IRetrieval",
+            "XM3600T2IRetrieval",
+            "VisualSTS17Eng",
+            "VisualSTS-b-Eng",
+            "VisualSTS17Multilingual",
+            "VisualSTS-b-Multilingual",
+        ],
+    ),
+    description="""MIEB(Multilingual) is a comprehensive image embeddings benchmark, spanning 10 task types, covering 130 tasks and a total of 39 languages. 
+    In addition to image classification (zero shot and linear probing), clustering, retrieval, MIEB includes tasks in compositionality evaluation, 
+    document undestanding, visual STS, and CV-centric tasks. This benchmark consists of MIEB(eng) + 3 multilingual retrieval 
+    datasets + the multilingual parts of VisualSTS-b and VisualSTS-16.""",
+    reference="",
+    contacts=["gowitheflow-1998", "isaac-chung"],
+    citation="",
+)
+
+MIEB_LITE = Benchmark(
+    name="MIEB(lite)",
+    tasks=get_tasks(
+        tasks=[
+            # Image Classification
+            "Country211",
+            "DTD",
+            "EuroSAT",
+            "GTSRB",
+            "OxfordPets",
+            "PatchCamelyon",
+            "RESISC45",
+            "SUN397",
+            # Clustering
+            "ImageNetDog15Clustering",
+            "TinyImageNetClustering",
+            # ZeroShotClassification
+            "CIFAR100ZeroShot",
+            "Country211ZeroShot",
+            "FER2013ZeroShot",
+            "FGVCAircraftZeroShot",
+            "Food101ZeroShot",
+            "OxfordPetsZeroShot",
+            "StanfordCarsZeroShot",
+            # Any2TextMutipleChoice
+            "CVBenchCount",
+            "CVBenchRelation",
+            "CVBenchDepth",
+            "CVBenchDistance",
+            # Any2AnyMultipleChoice
+            "BLINKIT2IMultiChoice",
+            "ImageCoDeT2IMultiChoice",
+            # ImageTextPairClassification
+            "AROCocoOrder",
+            "AROFlickrOrder",
+            "AROVisualAttribution",
+            "AROVisualRelation",
+            "Winoground",
+            # VisualSTS
+            "STS13VisualSTS",
+            "STS15VisualSTS",
+            "STS17MultilingualVisualSTS",
+            "STSBenchmarkMultilingualVisualSTS",
+            # Any2AnyRetrieval
+            "CIRRIT2IRetrieval",
+            "CUB200I2IRetrieval",
+            "Fashion200kI2TRetrieval",
+            "HatefulMemesI2TRetrieval",
+            "InfoSeekIT2TRetrieval",
+            "NIGHTSI2IRetrieval",
+            "OVENIT2TRetrieval",
+            "RP2kI2IRetrieval",
+            "VidoreDocVQARetrieval",
+            "VidoreInfoVQARetrieval",
+            "VidoreTabfquadRetrieval",
+            "VidoreTatdqaRetrieval",
+            "VidoreShiftProjectRetrieval",
+            "VidoreSyntheticDocQAAIRetrieval",
+            "VisualNewsI2TRetrieval",
+            "VQA2IT2TRetrieval",
+            "WebQAT2ITRetrieval",
+            "WITT2IRetrieval",
+            "XM3600T2IRetrieval",
+        ],
+    ),
+    description="""MIEB(lite) is a comprehensive image embeddings benchmark, spanning 10 task types, covering 51 tasks. 
+    This is a lite version of MIEB(Multilingual), designed to be run at a fraction of the cost while maintaining
+    relative rank of models.""",
+    reference="",
+    contacts=["gowitheflow-1998", "isaac-chung"],
+    citation="",
+)
+
 BEIR_PL = Benchmark(
     name="BEIR-PL",
     tasks=get_tasks(
diff --git a/mteb/descriptive_stats/Image/Any2AnyRetrieval/FashionIQIT2IRetrieval.json b/mteb/descriptive_stats/Image/Any2AnyRetrieval/FashionIQIT2IRetrieval.json
new file mode 100644
index 0000000000..618a7d6e51
--- /dev/null
+++ b/mteb/descriptive_stats/Image/Any2AnyRetrieval/FashionIQIT2IRetrieval.json
@@ -0,0 +1,22 @@
+{
+    "test": {
+        "number_of_characters": 361250,
+        "num_samples": 80384,
+        "num_queries": 6003,
+        "num_documents": 74381,
+        "min_document_length": 0,
+        "average_document_length": 0,
+        "max_document_length": 0,
+        "unique_documents": 0,
+        "num_document_images": 74381,
+        "min_query_length": 18,
+        "average_query_length": 60.17824421122772,
+        "max_query_length": 138,
+        "unique_queries": 5973,
+        "num_query_images": 6003,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.001832417124771,
+        "max_relevant_docs_per_query": 4,
+        "unique_relevant_docs": 6003
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Image/Compositionality/SugarCrepe.json b/mteb/descriptive_stats/Image/Compositionality/SugarCrepe.json
new file mode 100644
index 0000000000..5e0b8d0caa
--- /dev/null
+++ b/mteb/descriptive_stats/Image/Compositionality/SugarCrepe.json
@@ -0,0 +1,11 @@
+{
+    "test": {
+        "num_samples": 7511,
+        "num_images": 7511,
+        "num_texts": 15022,
+        "num_unique_texts": 11844,
+        "min_text_length": 24,
+        "average_text_length": 56.48681933164692,
+        "max_text_length": 210
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Image/Compositionality/Winoground.json b/mteb/descriptive_stats/Image/Compositionality/Winoground.json
new file mode 100644
index 0000000000..b048d354f2
--- /dev/null
+++ b/mteb/descriptive_stats/Image/Compositionality/Winoground.json
@@ -0,0 +1,11 @@
+{
+    "test": {
+        "num_samples": 400,
+        "num_images": 800,
+        "num_texts": 800,
+        "num_unique_texts": 800,
+        "min_text_length": 8,
+        "average_text_length": 45.46875,
+        "max_text_length": 151
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Image/VisionCentric/BLINKIT2IMultiChoice.json b/mteb/descriptive_stats/Image/VisionCentric/BLINKIT2IMultiChoice.json
new file mode 100644
index 0000000000..8cb4311cfe
--- /dev/null
+++ b/mteb/descriptive_stats/Image/VisionCentric/BLINKIT2IMultiChoice.json
@@ -0,0 +1,34 @@
+{
+    "test": {
+        "number_of_characters": 21204,
+        "num_samples": 1206,
+        "num_queries": 402,
+        "num_documents": 804,
+        "min_document_length": 0,
+        "average_document_length": 0,
+        "max_document_length": 0,
+        "unique_documents": 0,
+        "min_document_image_width": 83,
+        "average_document_image_width": 788.4415422885572,
+        "max_document_image_width": 5087,
+        "min_document_image_height": 127,
+        "average_document_image_height": 813.9539800995025,
+        "max_document_image_height": 3230,
+        "num_document_images": 804,
+        "min_query_length": 51,
+        "average_query_length": 52.74626865671642,
+        "max_query_length": 57,
+        "unique_queries": 3,
+        "num_query_images": 402,
+        "min_query_image_width": 166,
+        "average_query_image_width": 815.1293532338309,
+        "max_query_image_width": 2733,
+        "min_query_image_height": 254,
+        "average_query_image_height": 875.3781094527363,
+        "max_query_image_height": 5687,
+        "min_relevant_docs_per_query": 2,
+        "average_relevant_docs_per_query": 2.0,
+        "max_relevant_docs_per_query": 2,
+        "unique_relevant_docs": 804
+    }
+}
\ No newline at end of file
diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py
index 3966ffba03..02e9ff500c 100644
--- a/mteb/leaderboard/app.py
+++ b/mteb/leaderboard/app.py
@@ -6,7 +6,7 @@
 import tempfile
 import time
 from pathlib import Path
-from typing import Literal
+from typing import Literal, get_args
 from urllib.parse import urlencode
 
 import cachetools
@@ -15,7 +15,9 @@
 from gradio_rangeslider import RangeSlider
 
 import mteb
+from mteb.abstasks.TaskMetadata import TASK_DOMAIN, TASK_TYPE
 from mteb.benchmarks.benchmarks import MTEB_multilingual
+from mteb.languages import ISO_TO_LANGUAGE
 from mteb.leaderboard.figures import performance_size_plot, radar_chart
 from mteb.leaderboard.table import scores_to_tables
 
@@ -43,20 +45,6 @@
 We also thank the following companies which provide API credits to evaluate their models: [OpenAI](https://openai.com/), [Voyage AI](https://www.voyageai.com/)
 """
 
-MMTEB_TASK_TYPES = [  # TEMPORARY FIX: when adding MIEB to the leaderboard, this can probably be replaced with TASK_TYPE
-    "BitextMining",
-    "Classification",
-    "MultilabelClassification",
-    "Clustering",
-    "PairClassification",
-    "Reranking",
-    "Retrieval",
-    "STS",
-    "Summarization",
-    "InstructionRetrieval",
-    "Speed",
-]
-
 
 ALL_MODELS = {meta.name for meta in mteb.get_model_metas()}
 
@@ -170,7 +158,7 @@ def filter_models(
     compatibility: list[str],
     instructions: bool | None,
     model_size: tuple[int | None, int | None],
-    zero_shot_setting: Literal["hard", "soft", "off"],
+    zero_shot_setting: Literal["only_zero_shot", "allow_all", "remove_unknown"],
 ):
     lower, upper = model_size
     # Setting to None, when the user doesn't specify anything
@@ -194,10 +182,10 @@ def filter_models(
     for model_meta in model_metas:
         is_model_zero_shot = model_meta.is_zero_shot_on(task_select)
         if is_model_zero_shot is None:
-            if zero_shot_setting == "hard":
+            if zero_shot_setting in ["remove_unknown", "only_zero_shot"]:
                 continue
         elif not is_model_zero_shot:
-            if zero_shot_setting != "off":
+            if zero_shot_setting == "only_zero_shot":
                 continue
         models_to_keep.add(model_meta.name)
     return list(models_to_keep)
@@ -224,7 +212,7 @@ def filter_models(
     compatibility=[],
     instructions=None,
     model_size=(MIN_MODEL_SIZE, MAX_MODEL_SIZE),
-    zero_shot_setting="soft",
+    zero_shot_setting="allow_all",
 )
 
 summary_table, per_task_table = scores_to_tables(
@@ -238,28 +226,29 @@ def filter_models(
     info="Select one of our expert-selected benchmarks from MTEB publications.",
 )
 lang_select = gr.Dropdown(
-    all_results.languages,
+    ISO_TO_LANGUAGE,
     value=sorted(default_results.languages),
+    allow_custom_value=True,
     multiselect=True,
     label="Language",
     info="Select languages to include.",
 )
 type_select = gr.Dropdown(
-    all_results.task_types,
-    value=sorted(MMTEB_TASK_TYPES),
+    sorted(get_args(TASK_TYPE)),
+    value=sorted(default_results.task_types),
     multiselect=True,
     label="Task Type",
     info="Select task types to include.",
 )
 domain_select = gr.Dropdown(
-    all_results.domains,
+    sorted(get_args(TASK_DOMAIN)),
     value=sorted(default_results.domains),
     multiselect=True,
     label="Domain",
     info="Select domains to include.",
 )
 task_select = gr.Dropdown(
-    all_results.task_names,
+    sorted(all_results.task_names),
     value=sorted(default_results.task_names),
     allow_custom_value=True,
     multiselect=True,
@@ -354,12 +343,12 @@ def filter_models(
                             [
                                 (
                                     "Only Zero-shot",
-                                    "hard",
+                                    "only_zero_shot",
                                 ),
-                                ("Allow Unknown", "soft"),
-                                ("Allow all", "off"),
+                                ("Remove Unknown", "remove_unknown"),
+                                ("Allow All", "allow_all"),
                             ],
-                            value="soft",
+                            value="allow_all",
                             label="Zero-shot",
                             interactive=True,
                         )
@@ -392,13 +381,6 @@ def filter_models(
                     "*We only display models that have been run on all task types in the benchmark*"
                 )
     with gr.Tab("Summary"):
-        gr.Markdown(
-            """
-            ✅ - Model is zero-shot on the benchmark <br>
-            ⚠️  - Training data unknown <br>
-            ❌ - Model is **NOT** zero-shot on the benchmark
-        """
-        )
         summary_table.render()
         download_summary = gr.DownloadButton("Download Table")
         download_summary.click(
@@ -425,7 +407,9 @@ def filter_models(
             gr.Markdown(
                 """
 A model is considered zero-shot if it is not trained on any splits of the datasets used to derive the tasks.
-E.g., if a model is trained on Natural Questions, it cannot be considered zero-shot on benchmarks containing the task “NQ” which is derived from Natural Questions.
+The percentages in the table indicate what portion of the benchmark can be considered out-of-distribution for a given model.
+100% means the model has not been trained on any of the datasets in a given benchmark, and therefore the benchmark score can be interpreted as the model's overall generalization performance,
+while 50% means the model has been finetuned on half of the tasks in the benchmark, thereby indicating that the benchmark results should be interpreted with a pinch of salt.
 This definition creates a few edge cases. For instance, multiple models are typically trained on Wikipedia title and body pairs, but we do not define this as leakage on, e.g., “WikipediaRetrievalMultilingual” and “WikiClusteringP2P” as these datasets are not based on title-body pairs.
 Distilled, further fine-tunes, or in other ways, derivative models inherit the datasets of their parent models.
 Based on community feedback and research findings, this definition may change in the future. Please open a PR if you notice any mistakes or want to help us refine annotations, see [GitHub](https://github.com/embeddings-benchmark/mteb/blob/06489abca007261c7e6b11f36d4844c5ed5efdcb/mteb/models/bge_models.py#L91).
@@ -600,7 +584,7 @@ def update_models(
         compatibility: list[str],
         instructions: bool | None,
         model_size: tuple[int, int],
-        zero_shot: Literal["hard", "soft", "off"],
+        zero_shot: Literal["allow_all", "remove_unknown", "only_zero_shot"],
     ):
         start_time = time.time()
         model_names = list({entry["model_name"] for entry in scores})
@@ -788,7 +772,7 @@ def update_tables(
         compatibility=[],
         instructions=None,
         model_size=(MIN_MODEL_SIZE, MAX_MODEL_SIZE),
-        zero_shot="soft",
+        zero_shot="allow_all",
     )
     # We have to call this both on the filtered and unfiltered task because the callbacks
     # also gets called twice for some reason
diff --git a/mteb/leaderboard/figures.py b/mteb/leaderboard/figures.py
index a883b043b3..30beed066d 100644
--- a/mteb/leaderboard/figures.py
+++ b/mteb/leaderboard/figures.py
@@ -1,10 +1,14 @@
 from __future__ import annotations
 
+from typing import get_args
+
 import numpy as np
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 
+from mteb.abstasks.TaskMetadata import TASK_TYPE
+
 
 def text_plot(text: str):
     """Returns empty scatter plot with text added, this can be great for error messages."""
@@ -56,6 +60,10 @@ def parse_float(value) -> float:
     "GritLM-7B",
     "LaBSE",
     "multilingual-e5-large-instruct",
+    "EVA02-CLIP-bigE-14-plus",
+    "voyage-multimodal-3",
+    "e5-v",
+    "VLM2Vec-Full",
 ]
 
 
@@ -165,21 +173,10 @@ def performance_size_plot(df: pd.DataFrame) -> go.Figure:
 
 
 TOP_N = 5
-task_types = [
-    "BitextMining",
-    "Classification",
-    "MultilabelClassification",
-    "Clustering",
-    "PairClassification",
-    "Reranking",
-    "Retrieval",
-    "STS",
-    "Summarization",
-    # "InstructionRetrieval",
-    # Not displayed, because the scores are negative,
-    # doesn't work well with the radar chart.
-    "Speed",
-]
+task_types = sorted(get_args(TASK_TYPE))
+task_types.remove("InstructionRetrieval")
+# Not displayed, because the scores are negative,
+# doesn't work well with the radar chart.
 
 line_colors = [
     "#EE4266",
diff --git a/mteb/leaderboard/table.py b/mteb/leaderboard/table.py
index 237b7627c1..ff44abc2e7 100644
--- a/mteb/leaderboard/table.py
+++ b/mteb/leaderboard/table.py
@@ -92,15 +92,10 @@ def format_max_tokens(max_tokens: float | None) -> str:
     return str(int(max_tokens))
 
 
-def get_zero_shot_emoji(model_meta, tasks):
-    if model_meta is None:
-        return "⚠️"
-    is_zero_shot = model_meta.is_zero_shot_on(tasks)
-    if is_zero_shot is None:
-        return "⚠️"
-    if is_zero_shot:
-        return "✅"
-    return "❌"
+def format_zero_shot(zero_shot_percentage: int):
+    if zero_shot_percentage == -1:
+        return "⚠️ NA"
+    return f"{zero_shot_percentage:.0f}%"
 
 
 def scores_to_tables(
@@ -164,8 +159,10 @@ def scores_to_tables(
     )
     tasks = get_tasks(tasks=list(data["task_name"].unique()))
     joint_table.insert(
-        1, "Zero-shot", model_metas.map(lambda m: get_zero_shot_emoji(m, tasks))
+        1, "Zero-shot", model_metas.map(lambda m: m.zero_shot_percentage(tasks))
     )
+    joint_table["Zero-shot"] = joint_table["Zero-shot"].fillna(-1)
+    # joint_table = joint_table[joint_table["Zero-shot"].notna()]
     # Removing HF organization from model
     joint_table["model_name"] = joint_table["model_name"].map(
         lambda name: name.split("/")[-1]
@@ -205,11 +202,18 @@ def scores_to_tables(
             {
                 **{column: "{:.2f}" for column in score_columns},
                 "Rank (Borda)": "{:.0f}",
+                "Zero-shot": format_zero_shot,
             },
             na_rep="",
         )
         .highlight_min("Rank (Borda)", props="font-weight: bold")
         .highlight_max(subset=score_columns, props="font-weight: bold")
+        .background_gradient(
+            cmap="RdYlGn",
+            subset=["Zero-shot"],
+            vmin=50,
+            vmax=100,
+        )
     )
     task_score_columns = per_task.select_dtypes("number").columns
     per_task[task_score_columns] *= 100
diff --git a/mteb/load_results/task_results.py b/mteb/load_results/task_results.py
index de3de02c4d..650e549d11 100644
--- a/mteb/load_results/task_results.py
+++ b/mteb/load_results/task_results.py
@@ -461,7 +461,10 @@ def get_score(
         return aggregation(values)
 
     def get_score_fast(
-        self, splits: Iterable[str] | None = None, languages: str | None = None
+        self,
+        splits: Iterable[str] | None = None,
+        languages: str | None = None,
+        subsets: Iterable[str] | None = None,
     ) -> float:
         """Sped up version of get_score that will be used if no aggregation, script or getter needs to be specified."""
         if splits is None:
@@ -478,6 +481,13 @@ def get_score_fast(
                 main_score = scores.get("main_score", None)
                 if main_score is None:
                     raise ValueError(f"Missing main score for subset: {hf_subset}")
+                if subsets and hf_subset not in subsets:
+                    continue
+                elif subsets:
+                    val_sum += main_score
+                    n_val += 1
+                    continue
+
                 if languages is None:
                     val_sum += main_score
                     n_val += 1
@@ -486,6 +496,7 @@ def get_score_fast(
                     if lang.split("-")[0] in languages:
                         val_sum += main_score
                         n_val += 1
+                        logger.info(f"{val_sum=}, {n_val=}")
                         break
         if n_val == 0:
             raise ValueError("No splits had scores for the specified languages.")
diff --git a/mteb/model_meta.py b/mteb/model_meta.py
index 046e143629..b41b10e9c6 100644
--- a/mteb/model_meta.py
+++ b/mteb/model_meta.py
@@ -189,9 +189,14 @@ def is_zero_shot_on(self, tasks: Sequence[AbsTask] | Sequence[str]) -> bool | No
         zero-shot or not on the given tasks.
         Returns None if no training data is specified on the model.
         """
-        if self.training_datasets is None:
+        # If no tasks were specified, we're obviously zero-shot
+        if not tasks:
+            return True
+        training_datasets = self.get_training_datasets()
+        # If no tasks were specified, we're obviously zero-shot
+        if training_datasets is None:
             return None
-        model_datasets = {ds_name for ds_name, splits in self.training_datasets.items()}
+        model_datasets = {ds_name for ds_name, splits in training_datasets.items()}
         if isinstance(tasks[0], str):
             benchmark_datasets = set(tasks)
         else:
@@ -202,6 +207,51 @@ def is_zero_shot_on(self, tasks: Sequence[AbsTask] | Sequence[str]) -> bool | No
         intersection = model_datasets & benchmark_datasets
         return len(intersection) == 0
 
+    def get_training_datasets(self) -> dict[str, list[str]] | None:
+        """Returns all training datasets of the model including similar tasks."""
+        import mteb
+
+        if self.training_datasets is None:
+            return None
+
+        training_datasets = self.training_datasets.copy()
+        if self.adapted_from is not None:
+            try:
+                adapted_from_model = mteb.get_model_meta(
+                    self.adapted_from, fetch_from_hf=True
+                )
+                adapted_training_datasets = adapted_from_model.get_training_datasets()
+                if adapted_training_datasets is not None:
+                    training_datasets |= adapted_training_datasets
+            except ValueError as e:
+                logger.warning(f"Could not get source model: {e} in MTEB")
+
+        return_dataset = training_datasets.copy()
+        visited = set()
+
+        for dataset in training_datasets:
+            similar_tasks = collect_similar_tasks(dataset, visited)
+            return_dataset |= {task: [] for task in similar_tasks}
+
+        return return_dataset
+
+    def zero_shot_percentage(
+        self, tasks: Sequence[AbsTask] | Sequence[str]
+    ) -> int | None:
+        """Indicates how out-of-domain the selected tasks are for the given model."""
+        training_datasets = self.get_training_datasets()
+        if (training_datasets is None) or (not tasks):
+            return None
+        model_datasets = {ds_name for ds_name, splits in training_datasets.items()}
+        if isinstance(tasks[0], str):
+            benchmark_datasets = set(tasks)
+        else:
+            tasks = cast(Sequence[AbsTask], tasks)
+            benchmark_datasets = {task.metadata.name for task in tasks}
+        overlap = model_datasets & benchmark_datasets
+        perc_overlap = 100 * (len(overlap) / len(benchmark_datasets))
+        return int(100 - perc_overlap)
+
     def calculate_memory_usage_mb(self) -> int | None:
         """Calculates the memory usage (in FP32) of the model in MB."""
         if "API" in self.framework:
@@ -239,3 +289,28 @@ def calculate_memory_usage_mb(self) -> int | None:
         # Convert to MB
         model_memory_mb = model_memory_bytes / MB
         return round(model_memory_mb)
+
+
+def collect_similar_tasks(dataset: str, visited: set[str]) -> set[str]:
+    """Recursively collect all similar tasks for a given dataset."""
+    from .overview import SIMILAR_TASKS
+
+    if dataset in visited:
+        return set()
+
+    visited.add(dataset)
+    similar = set()
+
+    # Check if dataset is a key in SIMILAR_TASKS
+    if dataset in SIMILAR_TASKS:
+        for similar_task in SIMILAR_TASKS[dataset]:
+            similar.add(similar_task)
+            similar.update(collect_similar_tasks(similar_task, visited))
+
+    # Check if dataset appears as a value in SIMILAR_TASKS
+    for parent, children in SIMILAR_TASKS.items():
+        if dataset in children:
+            similar.add(parent)
+            similar.update(collect_similar_tasks(parent, visited))
+
+    return similar
diff --git a/mteb/models/bge_models.py b/mteb/models/bge_models.py
index 23cccfbbfe..ab98100a63 100644
--- a/mteb/models/bge_models.py
+++ b/mteb/models/bge_models.py
@@ -671,40 +671,6 @@
     training_datasets=bge_m3_training_data,
 )
 
-bge_multilingual_gemma2 = ModelMeta(
-    loader=partial(  # type: ignore
-        sentence_transformers_loader,
-        model_name="BAAI/bge-multilingual-gemma2",
-        revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
-    ),
-    name="BAAI/bge-multilingual-gemma2",
-    languages=[
-        "eng_Latn",
-        "zho_Hans",
-        "kor_Hang",
-        "kor_Latn",
-        "fra_Latn",
-        "jpn_Jpan",
-        "jpn_Latn",
-    ],  # This list is incomlete. Their description says "and more".
-    # I'm also unsure about the scripts.
-    open_weights=True,
-    revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
-    release_date="2024-07-25",  # initial commit of hf model.
-    n_parameters=9.24 * 1e9,
-    memory_usage_mb=35254,
-    embed_dim=3584,  # from old C-MTEB leaderboard
-    license="gemma",
-    max_tokens=8192,  # from old C-MTEB leaderboard
-    reference="https://huggingface.co/BAAI/bge-multilingual-gemma2",
-    similarity_fn_name=ScoringFunction.COSINE,
-    framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
-    public_training_code=None,
-    public_training_data=None,
-    training_datasets=None,  # not disclosed
-)
-
 # Contents of cfli/bge-full-data
 bge_full_data = {
     # source: https://arxiv.org/pdf/2409.15700
@@ -761,6 +727,46 @@
     "STSBenchmark": ["train"],
 }
 
+
+bge_multilingual_gemma2 = ModelMeta(
+    loader=partial(  # type: ignore
+        sentence_transformers_loader,
+        model_name="BAAI/bge-multilingual-gemma2",
+        revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
+    ),
+    name="BAAI/bge-multilingual-gemma2",
+    languages=[
+        "eng_Latn",
+        "zho_Hans",
+        "kor_Hang",
+        "kor_Latn",
+        "fra_Latn",
+        "jpn_Jpan",
+        "jpn_Latn",
+    ],  # This list is incomlete. Their description says "and more".
+    # I'm also unsure about the scripts.
+    open_weights=True,
+    revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
+    release_date="2024-07-25",  # initial commit of hf model.
+    n_parameters=9.24 * 1e9,
+    memory_usage_mb=35254,
+    embed_dim=3584,  # from old C-MTEB leaderboard
+    license="gemma",
+    max_tokens=8192,  # from old C-MTEB leaderboard
+    reference="https://huggingface.co/BAAI/bge-multilingual-gemma2",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=False,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets={
+        **bge_full_data,
+        **bge_m3_training_data,
+        "MIRACLReranking": ["train"],
+        "MrTidyRetrieval": ["train"],
+    },
+)
+
 bge_en_icl = ModelMeta(
     loader=partial(
         sentence_transformers_loader,
diff --git a/mteb/models/e5_instruct.py b/mteb/models/e5_instruct.py
index 06bc64d650..130d89a364 100644
--- a/mteb/models/e5_instruct.py
+++ b/mteb/models/e5_instruct.py
@@ -55,6 +55,7 @@
     embed_dim=1024,
     license="mit",
     max_tokens=514,
+    adapted_from="FacebookAI/xlm-roberta-large",
     citation="""@article{wang2024multilingual,
       title={Multilingual E5 Text Embeddings: A Technical Report},
       author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
@@ -111,6 +112,7 @@
     public_training_code=None,
     public_training_data=None,
     training_datasets=E5_MISTRAL_TRAINING_DATA,
+    adapted_from="mistralai/Mistral-7B-v0.1",
 )
 
 zeta_alpha_ai__Zeta_Alpha_E5_Mistral = ModelMeta(
diff --git a/mteb/models/e5_models.py b/mteb/models/e5_models.py
index f0de0396ca..8236c1e1e4 100644
--- a/mteb/models/e5_models.py
+++ b/mteb/models/e5_models.py
@@ -187,10 +187,11 @@
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=True,
-    citation=MULTILINGUAL_E5_CITATION,
     public_training_code=None,  # couldn't find
-    training_datasets=ME5_TRAINING_DATA,
     public_training_data=None,
+    training_datasets=ME5_TRAINING_DATA,
+    adapted_from="microsoft/Multilingual-MiniLM-L12-H384",
+    citation=MULTILINGUAL_E5_CITATION,
 )
 
 e5_mult_base = ModelMeta(
@@ -215,6 +216,7 @@
     use_instructions=True,
     public_training_code=None,
     public_training_data=None,
+    adapted_from="FacebookAI/xlm-roberta-base",
     training_datasets=ME5_TRAINING_DATA,
     citation=MULTILINGUAL_E5_CITATION,
 )
@@ -243,6 +245,7 @@
     public_training_code=None,
     public_training_data=None,
     training_datasets=ME5_TRAINING_DATA,
+    adapted_from="FacebookAI/xlm-roberta-large",
     citation=MULTILINGUAL_E5_CITATION,
 )
 
@@ -268,6 +271,7 @@
     use_instructions=True,
     public_training_code=None,
     public_training_data=None,
+    adapted_from="intfloat/e5-small",
     training_datasets=E5_TRAINING_DATA,
     citation=E5_CITATION,
 )
@@ -296,6 +300,7 @@
     public_training_code=None,
     public_training_data=None,
     training_datasets=E5_TRAINING_DATA,
+    adapted_from="sentence-transformers/all-MiniLM-L6-v2",
     citation=E5_CITATION,
 )
 
@@ -321,7 +326,7 @@
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=True,
     superseded_by=None,
-    adapted_from=None,
+    adapted_from="intfloat/e5-base",
     citation=E5_CITATION,
     public_training_code=None,
     public_training_data=None,
@@ -350,7 +355,7 @@
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=True,
     superseded_by=None,
-    adapted_from=None,
+    adapted_from="intfloat/e5-large",
     public_training_code=None,
     public_training_data=None,
     training_datasets=E5_TRAINING_DATA,
@@ -379,7 +384,7 @@
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=True,
     superseded_by="intfloat/e5-large-v2",
-    adapted_from=None,
+    adapted_from="google-bert/bert-large-uncased-whole-word-masking",
     public_training_code=None,
     public_training_data=None,
     training_datasets=E5_TRAINING_DATA,
@@ -408,7 +413,7 @@
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=True,
     superseded_by="intfloat/e5-base-v2",
-    adapted_from=None,
+    adapted_from="google-bert/bert-base-uncased",
     public_training_code=None,
     public_training_data=None,
     training_datasets=E5_TRAINING_DATA,
diff --git a/mteb/models/gte_models.py b/mteb/models/gte_models.py
index 5a0e9cb3b1..cd71e2cd61 100644
--- a/mteb/models/gte_models.py
+++ b/mteb/models/gte_models.py
@@ -63,7 +63,7 @@ def instruction_template(
     public_training_code=None,
     public_training_data=None,
     training_datasets=None,
-    max_tokens=131072,
+    max_tokens=32_768,
 )
 
 gte_Qwen1_5_7B_instruct = ModelMeta(
@@ -87,7 +87,7 @@ def instruction_template(
     memory_usage_mb=29449,
     embed_dim=4096,
     license="apache-2.0",
-    max_tokens=32768,
+    max_tokens=32_768,
     reference="https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct",
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch"],
@@ -118,7 +118,7 @@ def instruction_template(
     memory_usage_mb=6776,
     embed_dim=8960,
     license="apache-2.0",
-    max_tokens=131072,
+    max_tokens=32_768,
     reference="https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct",
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch"],
diff --git a/mteb/models/jina_models.py b/mteb/models/jina_models.py
index f2b7a9ebfa..6af92262a4 100644
--- a/mteb/models/jina_models.py
+++ b/mteb/models/jina_models.py
@@ -278,8 +278,39 @@ def encode(
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=False,
     superseded_by=None,
-    adapted_from=None,
-    training_datasets=None,
+    adapted_from="jina-bert-base-en-v1",  # pretrained on C4 with Alibi to support longer context.
+    training_datasets={
+        "PAQ": ["train"],
+        "GooAQ": ["train"],
+        "WikiAnswers": ["train"],
+        "AmazonQA": ["train"],
+        "ELI5": ["train"],
+        "SentenceCompression": ["train"],
+        "SimpleWikipedia": ["train"],
+        "Specter": ["train"],
+        "Squad2": ["train"],
+        "Tmdb": ["train"],
+        "TrivialQA": ["train"],
+        "TweetQA": ["train"],
+        "WikiHow": ["train"],
+        "Xmarket": [],  # adopted from Cross-Market Recommendation (XMRec).
+        "S2ORC": [],  # title abstract pair.
+        "YahooAnswers": [],  # question answer pair.
+        "MSMARCO": ["train"],  # pairs and mined hard negative.
+        "StackExchange": [],  # title body pair.
+        "QuoraQA": ["train"],  # duplicate question pairs.
+        "MsCocoCaptions": ["train"],  # pairs describe the same image.
+        "Flickr30k": ["train"],  # pairs describe the same image.
+        "SNLI": ["train"],  # random negative.
+        "ESCI": ["train"],  # exact match as positive match and mined hard negative.
+        "NegationDataset": [
+            "train"
+        ],  # synthetically generated negation dataset https://huggingface.co/datasets/jinaai/negation-dataset
+        "NQ": ["train"],  # mined hard negative.
+        "HotpotQA": ["train"],  # mined hard negative.
+        "FEVER": ["train"],  # mined hard negative.
+        "CC-NEWS": [],  # title-content with random negative.
+    },
     public_training_code=None,
     public_training_data=None,
 )
@@ -288,13 +319,13 @@ def encode(
     loader=partial(
         SentenceTransformerWrapper,
         model_name="jinaai/jina-embeddings-v2-small-en",
-        revision="796cff318cdd4e5fbe8b7303a1ef8cbec36996ef",
+        revision="44e7d1d6caec8c883c2d4b207588504d519788d0",
         trust_remote_code=True,
     ),
     name="jinaai/jina-embeddings-v2-small-en",
     languages=["eng-Latn"],
     open_weights=True,
-    revision="796cff318cdd4e5fbe8b7303a1ef8cbec36996ef",
+    revision="44e7d1d6caec8c883c2d4b207588504d519788d0",
     release_date="2023-09-27",
     n_parameters=32_700_000,
     memory_usage_mb=62,
@@ -306,8 +337,39 @@ def encode(
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=False,
     superseded_by=None,
-    adapted_from=None,
-    training_datasets=None,
+    adapted_from="jina-bert-smalll-en-v1",  # pretrained on C4 with Alibi to support longer context
+    training_datasets={
+        "PAQ": ["train"],
+        "GooAQ": ["train"],
+        "WikiAnswers": ["train"],
+        "AmazonQA": ["train"],
+        "ELI5": ["train"],
+        "SentenceCompression": ["train"],
+        "SimpleWikipedia": ["train"],
+        "Specter": ["train"],
+        "Squad2": ["train"],
+        "Tmdb": ["train"],
+        "TrivialQA": ["train"],
+        "TweetQA": ["train"],
+        "WikiHow": ["train"],
+        "Xmarket": [],  # adopted from Cross-Market Recommendation (XMRec).
+        "S2ORC": [],  # title abstract pair.
+        "YahooAnswers": [],  # question answer pair.
+        "MSMARCO": ["train"],  # pairs and mined hard negative.
+        "StackExchange": [],  # title body pair.
+        "QuoraQA": ["train"],  # duplicate question pairs.
+        "MsCocoCaptions": ["train"],  # pairs describe the same image.
+        "Flickr30k": ["train"],  # pairs describe the same image.
+        "SNLI": ["train"],  # random negative.
+        "ESCI": ["train"],  # exact match as positive match and mined hard negative.
+        "NegationDataset": [
+            "train"
+        ],  # synthetically generated negation dataset https://huggingface.co/datasets/jinaai/negation-dataset
+        "NQ": ["train"],  # mined hard negative.
+        "HotpotQA": ["train"],  # mined hard negative.
+        "FEVER": ["train"],  # mined hard negative.
+        "CC-NEWS": [],  # title content with random negative.
+    },
     public_training_code=None,
     public_training_data=None,
 )
@@ -316,13 +378,12 @@ def encode(
     loader=partial(
         SentenceTransformerWrapper,
         model_name="jinaai/jina-embedding-b-en-v1",
-        revision="aa0645035294a8c0607ce5bb700aba982cdff32c",
-        trust_remote_code=True,
+        revision="32aa658e5ceb90793454d22a57d8e3a14e699516",
     ),
     name="jinaai/jina-embedding-b-en-v1",
     languages=["eng-Latn"],
     open_weights=True,
-    revision="aa0645035294a8c0607ce5bb700aba982cdff32c",
+    revision="32aa658e5ceb90793454d22a57d8e3a14e699516",
     release_date="2023-07-07",
     n_parameters=110_000_000,
     memory_usage_mb=420,
@@ -334,8 +395,35 @@ def encode(
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=False,
     superseded_by="jinaai/jina-embeddings-v2-base-en",
-    adapted_from=None,
-    training_datasets=None,
+    adapted_from="google-t5/t5-base",
+    training_datasets={
+        "PAQ": ["train"],
+        "GooAQ": ["train"],
+        "WikiAnswers": ["train"],
+        "AmazonQA": ["train"],
+        "ELI5": ["train"],
+        "SentenceCompression": ["train"],
+        "SimpleWikipedia": ["train"],
+        "Specter": ["train"],
+        "Squad2": ["train"],
+        "Tmdb": ["train"],
+        "TrivialQA": ["train"],
+        "TweetQA": ["train"],
+        "WikiHow": ["train"],
+        "Xmarket": [],  # adopted from Cross-Market Recommendation (XMRec).
+        "S2ORC": [],  # title abstract pair.
+        "YahooAnswers": [],  # question answer pair.
+        "MSMARCO": ["train"],  # pairs and mined hard negative.
+        "StackExchange": [],  # title body pair.
+        "QuoraQA": ["train"],  # duplicate question pairs.
+        "MsCocoCaptions": ["train"],  # pairs describe the same image.
+        "Flickr30k": ["train"],  # pairs describe the same image.
+        "SNLI": ["train"],  # random negative.
+        "ESCI": ["train"],  # exact match as positive match and mined hard negative.
+        "NegationDataset": [
+            "train"
+        ],  # synthetically generated negation dataset https://huggingface.co/datasets/jinaai/negation-dataset
+    },
     public_training_code=None,
     public_training_data=None,
 )
@@ -344,13 +432,12 @@ def encode(
     loader=partial(
         SentenceTransformerWrapper,
         model_name="jinaai/jina-embedding-s-en-v1",
-        revision="c1fed70aa4823a640f1a7150a276e4d3b08dce08",
-        trust_remote_code=True,
+        revision="5ac6cd473e2324c6d5f9e558a6a9f65abb57143e",
     ),
     name="jinaai/jina-embedding-s-en-v1",
     languages=["eng-Latn"],
     open_weights=True,
-    revision="c1fed70aa4823a640f1a7150a276e4d3b08dce08",
+    revision="5ac6cd473e2324c6d5f9e558a6a9f65abb57143e",
     release_date="2023-07-07",
     n_parameters=35_000_000,
     memory_usage_mb=134,
@@ -362,8 +449,35 @@ def encode(
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=False,
     superseded_by="jinaai/jina-embeddings-v2-small-en",
-    adapted_from=None,
-    training_datasets=None,
+    adapted_from="google-t5/t5-small",
+    training_datasets={
+        "PAQ": ["train"],
+        "GooAQ": ["train"],
+        "WikiAnswers": ["train"],
+        "AmazonQA": ["train"],
+        "ELI5": ["train"],
+        "SentenceCompression": ["train"],
+        "SimpleWikipedia": ["train"],
+        "Specter": ["train"],
+        "Squad2": ["train"],
+        "Tmdb": ["train"],
+        "TrivialQA": ["train"],
+        "TweetQA": ["train"],
+        "WikiHow": ["train"],
+        "Xmarket": [],  # adopted from Cross-Market Recommendation (XMRec).
+        "S2ORC": [],  # title abstract pair.
+        "YahooAnswers": [],  # question answer pair.
+        "MSMARCO": ["train"],  # pairs and mined hard negative.
+        "StackExchange": [],  # title body pair.
+        "QuoraQA": ["train"],  # duplicate question pairs.
+        "MsCocoCaptions": ["train"],  # pairs describe the same image.
+        "Flickr30k": ["train"],  # pairs describe the same image.
+        "SNLI": ["train"],  # random negative.
+        "ESCI": ["train"],  # exact match as positive match and mined hard negative.
+        "NegationDataset": [
+            "train"
+        ],  # synthetically generated negation dataset https://huggingface.co/datasets/jinaai/negation-dataset
+    },
     public_training_code=None,
     public_training_data=None,
 )
diff --git a/mteb/models/linq_models.py b/mteb/models/linq_models.py
index 71c3dc21fa..d693781432 100644
--- a/mteb/models/linq_models.py
+++ b/mteb/models/linq_models.py
@@ -6,6 +6,7 @@
 
 from mteb.encoder_interface import PromptType
 from mteb.model_meta import ModelMeta, ScoringFunction
+from mteb.models.e5_instruct import E5_MISTRAL_TRAINING_DATA
 from mteb.models.instruct_wrapper import instruct_wrapper
 
 
@@ -42,5 +43,6 @@ def instruction_template(
     use_instructions=True,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    adapted_from="intfloat/e5-mistral-7b-instruct",
+    training_datasets=E5_MISTRAL_TRAINING_DATA,
 )
diff --git a/mteb/models/llm2clip_models.py b/mteb/models/llm2clip_models.py
new file mode 100644
index 0000000000..25ed3c6808
--- /dev/null
+++ b/mteb/models/llm2clip_models.py
@@ -0,0 +1,292 @@
+from __future__ import annotations
+
+from functools import partial
+from pathlib import Path
+from typing import Any
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import AutoConfig, AutoModel, AutoTokenizer, CLIPImageProcessor
+
+from mteb.encoder_interface import PromptType
+from mteb.model_meta import ModelMeta
+
+MODEL2PROCESSOR = {
+    "microsoft/LLM2CLIP-Openai-L-14-336": "openai/clip-vit-large-patch14-336",
+    "microsoft/LLM2CLIP-Openai-B-16": "openai/clip-vit-base-patch16",
+    "microsoft/LLM2CLIP-Openai-L-14-224": "openai/clip-vit-large-patch14",
+}
+
+
+def llm2clip_loader(**kwargs):
+    try:
+        from llm2vec import LLM2Vec
+    except ImportError:
+        # https://github.com/baaivision/EVA/tree/master/EVA-CLIP#setup
+        raise ImportError(
+            "To use the LLM2CLIP models `llm2vec` is required. Please install it with `pip install llm2vec`."
+        )
+
+    class LLM2CLIPWrapper:
+        def __init__(
+            self,
+            model_name: str = "microsoft/LLM2CLIP-Openai-L-14-336",
+            device: str = "cuda" if torch.cuda.is_available() else "cpu",
+            **kwargs: Any,
+        ):
+            if model_name not in MODEL2PROCESSOR:
+                raise Exception(
+                    f"This model {model_name} is not in the supported mode list: {list(MODEL2PROCESSOR.keys())}."
+                )
+
+            self.device = device
+            from huggingface_hub import snapshot_download
+
+            model_folder_path = snapshot_download(
+                repo_id=model_name, allow_patterns=["*.json", "*.safetensors", "*.py"]
+            )
+            snapshot_download(
+                repo_id=MODEL2PROCESSOR[model_name],
+                allow_patterns=["*.json", "*.safetensors", "*.py"],
+            )
+            model_name_or_path = Path(model_folder_path)
+            self.processor = CLIPImageProcessor.from_pretrained(
+                MODEL2PROCESSOR[model_name]
+            )
+            self.model = (
+                AutoModel.from_pretrained(
+                    model_name_or_path,
+                    torch_dtype=torch.float16,
+                    trust_remote_code=True,
+                )
+                .to(self.device)
+                .eval()
+            )
+
+            llm_model_name = (
+                "microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned"  # constant
+            )
+            config = AutoConfig.from_pretrained(llm_model_name, trust_remote_code=True)
+            llm_model = AutoModel.from_pretrained(
+                llm_model_name,
+                torch_dtype=torch.bfloat16,
+                config=config,
+                trust_remote_code=True,
+            )
+            tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
+            llm_model.config._name_or_path = "meta-llama/Meta-Llama-3-8B-Instruct"  #  Workaround for LLM2VEC. constant.
+            self.l2v = LLM2Vec(
+                llm_model,
+                tokenizer,
+                pooling_mode="mean",
+                max_length=512,
+                doc_max_length=512,
+            )
+
+        def get_text_embeddings(
+            self,
+            texts: list[str],
+            *,
+            task_name: str | None = None,
+            prompt_type: PromptType | None = None,
+            batch_size: int = 32,
+            **kwargs: Any,
+        ):
+            all_text_embeddings = []
+
+            with torch.no_grad(), torch.amp.autocast("cuda"):
+                for i in tqdm(range(0, len(texts), batch_size)):
+                    batch_texts = texts[i : i + batch_size]
+                    text_features = self.l2v.encode(
+                        batch_texts, convert_to_tensor=True
+                    ).to(self.device)
+                    text_features = self.model.get_text_features(text_features)
+                    text_features /= text_features.norm(dim=-1, keepdim=True)
+                    all_text_embeddings.append(text_features.cpu().to(torch.float32))
+
+            all_text_embeddings = torch.cat(all_text_embeddings, dim=0)
+
+            return all_text_embeddings
+
+        def get_image_embeddings(
+            self,
+            images: list[Image.Image] | DataLoader,
+            *,
+            task_name: str | None = None,
+            prompt_type: PromptType | None = None,
+            batch_size: int = 32,
+            **kwargs: Any,
+        ):
+            all_image_embeddings = []
+            if isinstance(images, DataLoader):
+                import torchvision.transforms.functional as F
+
+                with torch.no_grad(), torch.amp.autocast("cuda"):
+                    for batch in tqdm(images):
+                        input_pixels = self.processor(
+                            images=[F.to_pil_image(b) for b in batch],
+                            return_tensors="pt",
+                        ).pixel_values.to(self.device)
+                        image_features = self.model.get_image_features(input_pixels)
+                        image_features /= image_features.norm(dim=-1, keepdim=True)
+                        all_image_embeddings.append(
+                            image_features.cpu().to(torch.float32)
+                        )
+            else:
+                with torch.no_grad(), torch.cuda.amp.autocast():
+                    for i in tqdm(range(0, len(images), batch_size)):
+                        batch_images = images[i : i + batch_size]
+                        input_pixels = self.processor(
+                            images=batch_images, return_tensors="pt"
+                        ).pixel_values.to(self.device)
+                        image_features = self.model.get_image_features(input_pixels)
+                        image_features /= image_features.norm(dim=-1, keepdim=True)
+                        all_image_embeddings.append(
+                            image_features.cpu().to(torch.float32)
+                        )
+
+            all_image_embeddings = torch.cat(all_image_embeddings, dim=0)
+            return all_image_embeddings
+
+        def calculate_probs(self, text_embeddings, image_embeddings):
+            text_embeddings = text_embeddings / text_embeddings.norm(
+                dim=-1, keepdim=True
+            )
+            image_embeddings = image_embeddings / image_embeddings.norm(
+                dim=-1, keepdim=True
+            )
+            logits = torch.matmul(image_embeddings, text_embeddings.T)
+            probs = (logits * 100).softmax(dim=-1)
+            return probs
+
+        def get_fused_embeddings(
+            self,
+            texts: list[str] = None,
+            images: list[Image.Image] | DataLoader = None,
+            fusion_mode="sum",
+            **kwargs: Any,
+        ):
+            if texts is None and images is None:
+                raise ValueError("Either texts or images must be provided")
+
+            text_embeddings = None
+            image_embeddings = None
+
+            if texts is not None:
+                text_embeddings = self.get_text_embeddings(texts, **kwargs)
+
+            if images is not None:
+                image_embeddings = self.get_image_embeddings(images, **kwargs)
+
+            if text_embeddings is not None and image_embeddings is not None:
+                if len(text_embeddings) != len(image_embeddings):
+                    raise ValueError(
+                        "The number of texts and images must have the same length"
+                    )
+                if fusion_mode == "sum":
+                    fused_embeddings = text_embeddings + image_embeddings
+                else:
+                    # to do: add other fusion mode
+                    raise ValueError(
+                        f"fusion mode {fusion_mode} hasn't been implemented"
+                    )
+                return fused_embeddings
+            elif text_embeddings is not None:
+                return text_embeddings
+            elif image_embeddings is not None:
+                return image_embeddings
+
+    return LLM2CLIPWrapper(**kwargs)
+
+
+llm2clip_training_sets = {
+    # CC3M
+    # CC12M
+    # YFCC15M
+    # Recap-DataComp-1B(30M subset)
+}
+
+llm2clip_openai_l_14_336 = ModelMeta(
+    loader=partial(
+        llm2clip_loader,
+        model_name="microsoft/LLM2CLIP-Openai-L-14-336",
+    ),
+    name="microsoft/LLM2CLIP-Openai-L-14-336",
+    languages=["eng_Latn"],
+    revision="92512331f393a003c3d98404677f991c188162c9",
+    release_date="2024-11-07",
+    modalities=["image", "text"],
+    n_parameters=579_000_000,
+    memory_usage_mb=None,
+    max_tokens=None,
+    embed_dim=1280,
+    license="apache-2.0",
+    open_weights=True,
+    public_training_code="https://github.com/microsoft/LLM2CLIP",
+    public_training_data=None,
+    framework=["PyTorch"],
+    reference="https://huggingface.co/microsoft/LLM2CLIP-Openai-L-14-336",
+    similarity_fn_name=None,
+    use_instructions=True,
+    training_datasets=llm2clip_training_sets,
+)
+
+## NOTE: https://huggingface.co/microsoft/LLM2CLIP-Openai-L-14-224/discussions/1
+llm2clip_openai_l_14_224 = ModelMeta(
+    loader=partial(
+        llm2clip_loader,
+        model_name="microsoft/LLM2CLIP-Openai-L-14-224",
+    ),
+    name="microsoft/LLM2CLIP-Openai-L-14-224",
+    languages=["eng_Latn"],
+    revision="6b8a11a94ff380fa220dfefe73ac9293d2677575",
+    release_date="2024-11-07",
+    modalities=["image", "text"],
+    n_parameters=578_000_000,
+    memory_usage_mb=None,
+    max_tokens=None,
+    embed_dim=1280,
+    license="apache-2.0",
+    open_weights=True,
+    public_training_code="https://github.com/microsoft/LLM2CLIP",
+    public_training_data=None,
+    framework=["PyTorch"],
+    reference="https://huggingface.co/microsoft/LLM2CLIP-Openai-L-14-224",
+    similarity_fn_name=None,
+    use_instructions=True,
+    training_datasets=llm2clip_training_sets,
+)
+
+llm2clip_openai_b_16 = ModelMeta(
+    loader=partial(
+        llm2clip_loader,
+        model_name="microsoft/LLM2CLIP-Openai-B-16",
+    ),
+    name="microsoft/LLM2CLIP-Openai-B-16",
+    languages=["eng_Latn"],
+    revision="ecfb347eb3dcfeb2fbc2a2eae7de6ac5a001aaf8",
+    release_date="2024-11-07",
+    modalities=["image", "text"],
+    n_parameters=361_000_000,
+    memory_usage_mb=None,
+    max_tokens=None,
+    embed_dim=1280,
+    license="apache-2.0",
+    open_weights=True,
+    public_training_code="https://github.com/microsoft/LLM2CLIP",
+    public_training_data=None,
+    framework=["PyTorch"],
+    reference="https://huggingface.co/microsoft/LLM2CLIP-Openai-B-16",
+    similarity_fn_name=None,
+    use_instructions=True,
+    training_datasets=llm2clip_training_sets,
+)
+
+
+if __name__ == "__main__":
+    m = llm2clip_loader()
+    emb = m.get_text_embeddings(
+        texts=["what is going on blah?", "this is a test for this model."]
+    )
diff --git a/mteb/models/misc_models.py b/mteb/models/misc_models.py
index a5a262370a..fbfdae2d37 100644
--- a/mteb/models/misc_models.py
+++ b/mteb/models/misc_models.py
@@ -10,6 +10,7 @@
     sentence_transformers_loader,
 )
 from mteb.models.bge_models import bge_m3_training_data, bge_training_data
+from mteb.models.e5_instruct import E5_MISTRAL_TRAINING_DATA
 from mteb.models.e5_models import E5_TRAINING_DATA
 from mteb.models.sentence_transformers_models import sent_trf_training_dataset
 
@@ -53,10 +54,181 @@
     reference="https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={
+        # Source: https://huggingface.co/datasets/Gameselo/monolingual-wideNLI
+        # https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2/discussions/2
+        # SNLI,
+        # MNLI,
+        # QNLI,
+        # WNLI,
+        # SciTail
+        # Vitamin C
+        # Trains on all of MTEB
+        "AlphaNLI": ["train"],
+        "RTE3": ["train"],
+        "AmazonPolarityClassification": ["train"],
+        "AmazonReviewsClassification": ["train"],
+        "ArguAna": ["train"],
+        "ArxivClusteringP2P": ["train"],
+        "ArxivClusteringS2S": ["train"],
+        "AskUbuntuDupQuestions": ["train"],
+        "BIOSSES": ["train"],
+        "Banking77Classification": ["train"],
+        "BiorxivClusteringP2P": ["train"],
+        "BiorxivClusteringS2S": ["train"],
+        "CQADupstackRetrieval": ["train"],
+        "ClimateFEVER": ["train"],
+        "DBPedia": ["train"],
+        "EmotionClassification": ["train"],
+        "FEVER": ["train"],
+        "FiQA2018": ["train"],
+        "HotpotQA": ["train"],
+        "ImdbClassification": ["train"],
+        "MTOPDomainClassification": ["train"],
+        "MTOPIntentClassification": ["train"],
+        "MassiveIntentClassification": ["train"],
+        "MassiveScenarioClassification": ["train"],
+        "MedrxivClusteringP2P": ["train"],
+        "MedrxivClusteringS2S": ["train"],
+        "MindSmallReranking": ["train"],
+        "NFCorpus": ["train"],
+        "NQ": ["train"],
+        "QuoraRetrieval": ["train"],
+        "RedditClustering": ["train"],
+        "RedditClusteringP2P": ["train"],
+        "SCIDOCS": ["train"],
+        "SICK-R": ["train"],
+        "STS12": ["train"],
+        "STS13": ["train"],
+        "STS14": ["train"],
+        "STS15": ["train"],
+        "STS16": ["train"],
+        "STSBenchmark": ["train"],
+        "SciDocsRR": ["train"],
+        "SciFact": ["train"],
+        "SprintDuplicateQuestions": ["train"],
+        "StackExchangeClustering": ["train"],
+        "StackExchangeClusteringP2P": ["train"],
+        "StackOverflowDupQuestions": ["train"],
+        "SummEval": ["train"],
+        "TRECCOVID": ["train"],
+        "Touche2020": ["train"],
+        "ToxicConversationsClassification": ["train"],
+        "TweetSentimentExtractionClassification": ["train"],
+        "TwentyNewsgroupsClustering": ["train"],
+        "TwitterSemEval2015": ["train"],
+        "TwitterURLCorpus": ["train"],
+        "MSMARCO": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+        "STS17": ["train"],
+        "STS22": ["train"],
+    },
     adapted_from="sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
     superseded_by=None,
 )
+
+kalm_training_data = {
+    # from technical report
+    # not in MTEB:
+    # ExpertQA
+    # MEDI2BGE
+    # OpenOrca
+    # PAQ
+    # PubMedQA
+    # SearchQA
+    # arxiv_qa
+    # rag-dataset-12000
+    # CC-News
+    # SQuAD 2.0
+    # TriviaQA
+    # WebGPT Comparisons
+    # MultiNLI
+    # NLLB
+    # WikiAnswers
+    # SimCSE NLI
+    # SNLI
+    # Aya Dataset
+    # eli5
+    # ----
+    # in MTEB:
+    "CodeFeedbackMT": ["train"],
+    "CodeFeedbackST": ["train"],
+    "ArxivClusteringP2P": ["train"],
+    "ArxivClusteringS2S": ["train"],
+    "ArxivClusteringP2P.v2": ["train"],
+    "TRECCOVID": ["train"],
+    "DBPedia": ["train"],
+    "ESCIReranking": ["train"],
+    "FEVER": ["train"],
+    "FiQA2018": ["train"],
+    "FEVERHardNegatives": ["train"],
+    "NanoFEVERRetrieval": ["train"],
+    "FEVER-NL": ["train"],  # translation not trained on
+    "FiQA2018-NL": ["train"],  # translation not trained on
+    "HotpotQA-PL": ["train"],  # translation not trained on
+    "HotpotQA-NL": ["train"],  # translation not trained on
+    "HotpotQAHardNegatives": ["train"],
+    "MultiLongDocRetrieval": ["train"],
+    "MSMARCO": ["train"],
+    "MSMARCOHardNegatives": ["train"],
+    "NanoMSMARCORetrieval": ["train"],
+    "MSMARCO-PL": ["train"],  # translation not trained on
+    "mMARCO-NL": ["train"],  # translation not trained on
+    "MSMARCOv2": ["train"],
+    "NFCorpus": ["train"],
+    "SciFact": ["train"],
+    "NQ": ["train"],
+    "NQHardNegatives": ["train"],
+    "NanoNQRetrieval": ["train"],
+    "NQ-PL": ["train"],  # translation not trained on
+    "NQ-NL": ["train"],  # translation not trained on
+    "YahooAnswersTopicsClassification": ["train"],
+    "ContractNLIConfidentialityOfAgreementLegalBenchClassification": ["train"],
+    "ContractNLIExplicitIdentificationLegalBenchClassification": ["train"],
+    "ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification": [
+        "train"
+    ],
+    "ContractNLILimitedUseLegalBenchClassification": ["train"],
+    "ContractNLINoLicensingLegalBenchClassification": ["train"],
+    "ContractNLINoticeOnCompelledDisclosureLegalBenchClassification": ["train"],
+    "ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification": [
+        "train"
+    ],
+    "ContractNLIPermissibleCopyLegalBenchClassification": ["train"],
+    "ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification": [
+        "train"
+    ],
+    "ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification": ["train"],
+    "ContractNLIReturnOfConfidentialInformationLegalBenchClassification": ["train"],
+    "ContractNLISharingWithEmployeesLegalBenchClassification": ["train"],
+    "ContractNLISharingWithThirdPartiesLegalBenchClassification": ["train"],
+    "ContractNLISurvivalOfObligationsLegalBenchClassification": ["train"],
+    "QuoraRetrieval": ["train"],
+    "NanoQuoraRetrieval": ["train"],
+    "BiorxivClusteringP2P.v2": ["train"],
+    "BiorxivClusteringS2S.v2": ["train"],
+    "MedrxivClusteringP2P.v2": ["train"],
+    "MedrxivClusteringS2S.v2": ["train"],
+    "Banking77Classification": ["train"],
+    "AmazonPolarityClassification": ["train"],
+    "ImdbClassification": ["train"],
+    "EmotionClassification": ["train"],
+    "TweetSentimentExtractionClassification": ["train"],
+    "ToxicConversationsClassification": ["train"],
+    "MIRACLRetrieval": ["train"],
+    "MIRACLRetrievalHardNegatives": ["train"],
+    "MIRACLReranking": ["train"],
+    "MrTidyRetrieval": ["train"],
+    "PawsXPairClassification": ["train"],
+    "AmazonReviewsClassification": ["train"],
+    "AmazonCounterfactualClassification": ["train"],
+    "MultilingualSentiment": ["train"],
+    "MassiveIntentClassification": ["train"],
+    "MassiveScenarioClassification": ["train"],
+    "MTOPDomainClassification": ["train"],
+    "MTOPIntentClassification": ["train"],
+}
+
 HIT_TMG__KaLM_embedding_multilingual_mini_instruct_v1 = ModelMeta(
     name="HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1",
     revision="45e42c89990c40aca042659133fc8b13c28634b5",
@@ -65,7 +237,7 @@
     loader=None,
     n_parameters=494032768,
     memory_usage_mb=1885,
-    max_tokens=131072.0,
+    max_tokens=512,
     embed_dim=896,
     license="mit",
     open_weights=True,
@@ -75,7 +247,7 @@
     reference="https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=kalm_training_data,
     adapted_from="/mnt/shgeminicephfs/wx-dc-plt-hpc/xinshuohu/Output/Embedding/Qwen2-0.5B-eos_mean_pretrain_0806_1e-4_uen_sft_1022_filtered_v2_inst_3node_g8_1e-5_sin-0.1_mrl",
     superseded_by=None,
 )
@@ -87,7 +259,7 @@
     loader=None,
     n_parameters=494032768,
     memory_usage_mb=1885,
-    max_tokens=131072.0,
+    max_tokens=512,
     embed_dim=896,
     license="mit",
     open_weights=True,
@@ -97,7 +269,7 @@
     reference="https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=kalm_training_data,
     adapted_from="/mnt/shgeminicephfs/wx-dc-plt-hpc/xinshuohu/Output/Embedding/Qwen2-0.5B-eos_mean_pretrain_0806_1e-4_uen_sft_0902_filtered_v2_3node_g8_1e-5_sin-0.1",
     superseded_by=None,
 )
@@ -207,11 +379,20 @@
     reference="https://huggingface.co/BeastyZ/e5-R-mistral-7b",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=E5_TRAINING_DATA,
+    training_datasets=E5_MISTRAL_TRAINING_DATA,
     # not MTEB: {"BeastyZ/E5-R": ["train"]},
-    adapted_from="/ConRetriever/public_weight_mistral",
+    adapted_from="intfloat/e5-mistral-7b-instruct",
     superseded_by=None,
 )
+
+bilingual_embedding_training_data = {
+    "STSBenchmark": ["train"],
+    "STSBenchmarkMultilingualSTS": ["train"],
+    "XNLI": ["train"],
+    # not in mteb
+    # SNLI
+}
+
 Lajavaness__bilingual_embedding_base = ModelMeta(
     name="Lajavaness/bilingual-embedding-base",
     revision="0bfc54bb2aa2666dd84715289c7ef58a95eb4d8d",
@@ -235,7 +416,7 @@
     reference="https://huggingface.co/Lajavaness/bilingual-embedding-base",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=bilingual_embedding_training_data,
     adapted_from="dangvantuan/bilingual_impl",
     superseded_by=None,
 )
@@ -262,7 +443,7 @@
     reference="https://huggingface.co/Lajavaness/bilingual-embedding-large",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=bilingual_embedding_training_data,
     adapted_from="dangvantuan/bilingual_impl",
     superseded_by=None,
 )
@@ -289,7 +470,7 @@
     reference="https://huggingface.co/Lajavaness/bilingual-embedding-small",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=bilingual_embedding_training_data,
     adapted_from="dangvantuan/bilingual_impl",
     superseded_by=None,
 )
@@ -681,6 +862,14 @@
     superseded_by=None,
 )
 
+SENTENCE_CROISSANT_TRAINING_DATA = {
+    "STS12": ["train"],
+    "STSBenchmark": ["train"],
+    "STSBenchmarkMultilingualSTS": ["train"],
+    "QuoraRetrieval": ["train"],
+    "MSMARCO": ["train"],
+    "STSB": ["train"],
+}
 manu__sentence_croissant_alpha_v0_2 = ModelMeta(
     name="manu/sentence_croissant_alpha_v0.2",
     revision="4610b8cea65d7dd59e0b04af50753933fe5b29b2",
@@ -699,7 +888,7 @@
     reference="https://huggingface.co/manu/sentence_croissant_alpha_v0.2",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=SENTENCE_CROISSANT_TRAINING_DATA,
     adapted_from="croissantllm/CroissantCool",
     superseded_by="manu/sentence_croissant_alpha_v0.3",
 )
@@ -721,7 +910,7 @@
     reference="https://huggingface.co/manu/sentence_croissant_alpha_v0.3",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=SENTENCE_CROISSANT_TRAINING_DATA,
     adapted_from="croissantllm/CroissantCool-v0.2",
     superseded_by="manu/sentence_croissant_alpha_v0.4",
 )
@@ -743,7 +932,7 @@
     reference="https://huggingface.co/manu/sentence_croissant_alpha_v0.4",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=SENTENCE_CROISSANT_TRAINING_DATA,
     # Not in MTEB: {"manu/embedding_data_v2_100k": ["train"]},
     adapted_from="croissantllm/CroissantCool-v0.2",
     superseded_by=None,
@@ -876,7 +1065,7 @@
     reference="https://huggingface.co/sdadas/mmlw-e5-base",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=E5_TRAINING_DATA,
     adapted_from="intfloat/multilingual-e5-base",
     superseded_by=None,
 )
@@ -898,8 +1087,10 @@
     reference="https://huggingface.co/dwzhu/e5-base-4k",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
-    adapted_from="/mnt/default/longembed/models/intfloat/e5-base-v2",
+    training_datasets={
+        **E5_TRAINING_DATA,
+    },
+    adapted_from="intfloat/e5-base-v2",
     superseded_by=None,
 )
 sdadas__mmlw_e5_large = ModelMeta(
@@ -920,7 +1111,7 @@
     reference="https://huggingface.co/sdadas/mmlw-e5-large",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=E5_TRAINING_DATA,
     adapted_from="intfloat/multilingual-e5-large",
     superseded_by=None,
 )
@@ -942,7 +1133,7 @@
     reference="https://huggingface.co/sdadas/mmlw-e5-small",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=E5_TRAINING_DATA,
     adapted_from="intfloat/multilingual-e5-small",
     superseded_by=None,
 )
@@ -964,7 +1155,7 @@
     reference="https://huggingface.co/sdadas/mmlw-roberta-base",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={"MSMARCO": ["train"]},
     adapted_from="sdadas/polish-roberta-base-v2",
     superseded_by=None,
 )
@@ -986,61 +1177,70 @@
     reference="https://huggingface.co/sdadas/mmlw-roberta-large",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={"MSMARCO": ["train"]},
     adapted_from="sdadas/polish-roberta-large-v2",
     superseded_by=None,
 )
+
+udever_dataset = {  # discussed here: https://github.com/embeddings-benchmark/mteb/issues/2193
+    "MSMARCO": [],
+    # SNLI
+    # MultiNLI
+}
+
+udever_langauges = [
+    "aka_Latn",
+    "ara_Arab",
+    "asm_Beng",
+    "bam_Latn",
+    "ben_Beng",
+    "cat_Latn",
+    "eng_Latn",
+    "spa_Latn",
+    "eus_Latn",
+    "fon_Latn",
+    "fra_Latn",
+    "guj_Gujr",
+    "hin_Deva",
+    "ind_Latn",
+    "ibo_Latn",
+    "kik_Latn",
+    "kan_Knda",
+    "lug_Latn",
+    "lin_Latn",
+    "mal_Mlym",
+    "mar_Deva",
+    "nep_Deva",
+    "nso_Latn",
+    "nya_Latn",
+    "ori_Orya",
+    "pan_Guru",
+    "por_Latn",
+    "run_Latn",
+    "kin_Latn",
+    "sna_Latn",
+    "sot_Latn",
+    "swa_Latn",
+    "tam_Taml",
+    "tel_Telu",
+    "tsn_Latn",
+    "tso_Latn",
+    "tum_Latn",
+    "twi_Latn",
+    "urd_Arab",
+    "vie_Latn",
+    "wol_Latn",
+    "xho_Latn",
+    "yor_Latn",
+    "zho_Hans",
+    "zul_Latn",
+]
+
 izhx__udever_bloom_1b1 = ModelMeta(
     name="izhx/udever-bloom-1b1",
     revision="7bf1ee29878cb040b2708a691aa4b61f27eaa252",
     release_date="2023-10-24",
-    languages=[
-        "aka_Latn",
-        "ara_Arab",
-        "asm_Beng",
-        "bam_Latn",
-        "ben_Beng",
-        "cat_Latn",
-        "eng_Latn",
-        "spa_Latn",
-        "eus_Latn",
-        "fon_Latn",
-        "fra_Latn",
-        "guj_Gujr",
-        "hin_Deva",
-        "ind_Latn",
-        "ibo_Latn",
-        "kik_Latn",
-        "kan_Knda",
-        "lug_Latn",
-        "lin_Latn",
-        "mal_Mlym",
-        "mar_Deva",
-        "nep_Deva",
-        "nso_Latn",
-        "nya_Latn",
-        "ori_Orya",
-        "pan_Guru",
-        "por_Latn",
-        "run_Latn",
-        "kin_Latn",
-        "sna_Latn",
-        "sot_Latn",
-        "swa_Latn",
-        "tam_Taml",
-        "tel_Telu",
-        "tsn_Latn",
-        "tso_Latn",
-        "tum_Latn",
-        "twi_Latn",
-        "urd_Arab",
-        "vie_Latn",
-        "wol_Latn",
-        "xho_Latn",
-        "yor_Latn",
-        "zho_Hans",
-        "zul_Latn",
-    ],
+    languages=udever_langauges,
     loader=None,
     n_parameters=None,
     memory_usage_mb=None,
@@ -1054,7 +1254,7 @@
     reference="https://huggingface.co/izhx/udever-bloom-1b1",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=udever_dataset,
     adapted_from="bigscience/bloom-1b1",
     superseded_by=None,
 )
@@ -1062,53 +1262,7 @@
     name="izhx/udever-bloom-3b",
     revision="4edd8affe80ca89ba0f6b6ba4103fc7f25fc57b2",
     release_date="2023-10-24",
-    languages=[
-        "aka_Latn",
-        "ara_Arab",
-        "asm_Beng",
-        "bam_Latn",
-        "ben_Beng",
-        "cat_Latn",
-        "eng_Latn",
-        "spa_Latn",
-        "eus_Latn",
-        "fon_Latn",
-        "fra_Latn",
-        "guj_Gujr",
-        "hin_Deva",
-        "ind_Latn",
-        "ibo_Latn",
-        "kik_Latn",
-        "kan_Knda",
-        "lug_Latn",
-        "lin_Latn",
-        "mal_Mlym",
-        "mar_Deva",
-        "nep_Deva",
-        "nso_Latn",
-        "nya_Latn",
-        "ori_Orya",
-        "pan_Guru",
-        "por_Latn",
-        "run_Latn",
-        "kin_Latn",
-        "sna_Latn",
-        "sot_Latn",
-        "swa_Latn",
-        "tam_Taml",
-        "tel_Telu",
-        "tsn_Latn",
-        "tso_Latn",
-        "tum_Latn",
-        "twi_Latn",
-        "urd_Arab",
-        "vie_Latn",
-        "wol_Latn",
-        "xho_Latn",
-        "yor_Latn",
-        "zho_Hans",
-        "zul_Latn",
-    ],
+    languages=udever_langauges,
     loader=None,
     n_parameters=None,
     memory_usage_mb=None,
@@ -1122,7 +1276,7 @@
     reference="https://huggingface.co/izhx/udever-bloom-3b",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=udever_dataset,
     adapted_from="bigscience/bloom-3b",
     superseded_by=None,
 )
@@ -1130,53 +1284,7 @@
     name="izhx/udever-bloom-560m",
     revision="b2a723e355946ec5a5c5fbed3459766627ded2bb",
     release_date="2023-10-24",
-    languages=[
-        "aka_Latn",
-        "ara_Arab",
-        "asm_Beng",
-        "bam_Latn",
-        "ben_Beng",
-        "cat_Latn",
-        "eng_Latn",
-        "spa_Latn",
-        "eus_Latn",
-        "fon_Latn",
-        "fra_Latn",
-        "guj_Gujr",
-        "hin_Deva",
-        "ind_Latn",
-        "ibo_Latn",
-        "kik_Latn",
-        "kan_Knda",
-        "lug_Latn",
-        "lin_Latn",
-        "mal_Mlym",
-        "mar_Deva",
-        "nep_Deva",
-        "nso_Latn",
-        "nya_Latn",
-        "ori_Orya",
-        "pan_Guru",
-        "por_Latn",
-        "run_Latn",
-        "kin_Latn",
-        "sna_Latn",
-        "sot_Latn",
-        "swa_Latn",
-        "tam_Taml",
-        "tel_Telu",
-        "tsn_Latn",
-        "tso_Latn",
-        "tum_Latn",
-        "twi_Latn",
-        "urd_Arab",
-        "vie_Latn",
-        "wol_Latn",
-        "xho_Latn",
-        "yor_Latn",
-        "zho_Hans",
-        "zul_Latn",
-    ],
+    languages=udever_langauges,
     loader=None,
     n_parameters=None,
     memory_usage_mb=None,
@@ -1190,7 +1298,7 @@
     reference="https://huggingface.co/izhx/udever-bloom-560m",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=udever_dataset,
     adapted_from="bigscience/bloom-560m",
     superseded_by=None,
 )
@@ -1198,53 +1306,7 @@
     name="izhx/udever-bloom-7b1",
     revision="18e8d3e6dbd94868584877f2e72a105a17df22ef",
     release_date="2023-10-24",
-    languages=[
-        "aka_Latn",
-        "ara_Arab",
-        "asm_Beng",
-        "bam_Latn",
-        "ben_Beng",
-        "cat_Latn",
-        "eng_Latn",
-        "spa_Latn",
-        "eus_Latn",
-        "fon_Latn",
-        "fra_Latn",
-        "guj_Gujr",
-        "hin_Deva",
-        "ind_Latn",
-        "ibo_Latn",
-        "kik_Latn",
-        "kan_Knda",
-        "lug_Latn",
-        "lin_Latn",
-        "mal_Mlym",
-        "mar_Deva",
-        "nep_Deva",
-        "nso_Latn",
-        "nya_Latn",
-        "ori_Orya",
-        "pan_Guru",
-        "por_Latn",
-        "run_Latn",
-        "kin_Latn",
-        "sna_Latn",
-        "sot_Latn",
-        "swa_Latn",
-        "tam_Taml",
-        "tel_Telu",
-        "tsn_Latn",
-        "tso_Latn",
-        "tum_Latn",
-        "twi_Latn",
-        "urd_Arab",
-        "vie_Latn",
-        "wol_Latn",
-        "xho_Latn",
-        "yor_Latn",
-        "zho_Hans",
-        "zul_Latn",
-    ],
+    languages=udever_langauges,
     loader=None,
     n_parameters=None,
     memory_usage_mb=None,
@@ -1258,7 +1320,7 @@
     reference="https://huggingface.co/izhx/udever-bloom-7b1",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets=udever_dataset,
     adapted_from="bigscience/bloom-7b1",
     superseded_by=None,
 )
@@ -1280,8 +1342,25 @@
     reference="https://huggingface.co/avsolatorio/GIST-Embedding-v0",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
-    adapted_from=None,
+    training_datasets={
+        **bge_training_data,
+        # not in mteb:
+        # MEDI
+        # all MTEB CLF datasets that has a train split:
+        "AmazonPolarityClassification": ["train"],
+        "AmazonReviewsClassification": ["train"],
+        "EmotionClassification": ["train"],
+        "ImdbClassification": ["train"],
+        "MTOPDomainClassification": ["train"],
+        "MTOPIntentClassification": ["train"],
+        "MassiveIntentClassification": ["train"],
+        "MassiveScenarioClassification": ["train"],
+        "ToxicConversationsClassification": ["train"],
+        "TweetSentimentExtractionClassification": ["train"],
+        "Banking77Classification": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+    },
+    adapted_from="BAAI/bge-large-en-v1.5",
     superseded_by=None,
 )
 avsolatorio__GIST_all_MiniLM_L6_v2 = ModelMeta(
@@ -1302,7 +1381,24 @@
     reference="https://huggingface.co/avsolatorio/GIST-all-MiniLM-L6-v2",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={
+        **bge_training_data,
+        # not in mteb:
+        # MEDI
+        # all MTEB CLF datasets that has a train split:
+        "AmazonPolarityClassification": ["train"],
+        "AmazonReviewsClassification": ["train"],
+        "EmotionClassification": ["train"],
+        "ImdbClassification": ["train"],
+        "MTOPDomainClassification": ["train"],
+        "MTOPIntentClassification": ["train"],
+        "MassiveIntentClassification": ["train"],
+        "MassiveScenarioClassification": ["train"],
+        "ToxicConversationsClassification": ["train"],
+        "TweetSentimentExtractionClassification": ["train"],
+        "Banking77Classification": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+    },
     adapted_from=None,
     superseded_by=None,
 )
@@ -1324,7 +1420,24 @@
     reference="https://huggingface.co/avsolatorio/GIST-large-Embedding-v0",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={
+        **bge_training_data,
+        # not in mteb:
+        # MEDI
+        # all MTEB CLF datasets that has a train split:
+        "AmazonPolarityClassification": ["train"],
+        "AmazonReviewsClassification": ["train"],
+        "EmotionClassification": ["train"],
+        "ImdbClassification": ["train"],
+        "MTOPDomainClassification": ["train"],
+        "MTOPIntentClassification": ["train"],
+        "MassiveIntentClassification": ["train"],
+        "MassiveScenarioClassification": ["train"],
+        "ToxicConversationsClassification": ["train"],
+        "TweetSentimentExtractionClassification": ["train"],
+        "Banking77Classification": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+    },
     adapted_from=None,
     superseded_by=None,
 )
@@ -1346,7 +1459,24 @@
     reference="https://huggingface.co/avsolatorio/GIST-small-Embedding-v0",
     similarity_fn_name=ScoringFunction.COSINE,
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={
+        **bge_training_data,
+        # not in mteb:
+        # MEDI
+        # all MTEB CLF datasets that has a train split:
+        "AmazonPolarityClassification": ["train"],
+        "AmazonReviewsClassification": ["train"],
+        "EmotionClassification": ["train"],
+        "ImdbClassification": ["train"],
+        "MTOPDomainClassification": ["train"],
+        "MTOPIntentClassification": ["train"],
+        "MassiveIntentClassification": ["train"],
+        "MassiveScenarioClassification": ["train"],
+        "ToxicConversationsClassification": ["train"],
+        "TweetSentimentExtractionClassification": ["train"],
+        "Banking77Classification": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+    },
     adapted_from=None,
     superseded_by=None,
 )
diff --git a/mteb/models/mxbai_models.py b/mteb/models/mxbai_models.py
index 1d5f83f22d..e6cbaa0479 100644
--- a/mteb/models/mxbai_models.py
+++ b/mteb/models/mxbai_models.py
@@ -8,6 +8,15 @@
     sentence_transformers_loader,
 )
 
+mixedbread_training_data = {
+    # from correspondance:
+    # as mentioned in our blog post
+    # (https://www.mixedbread.com/blog/mxbai-embed-large-v1#built-for-rag-and-real-world-use-cases:~:text=During%20the%20whole,related%20use%20cases.)
+    # We do not train on any data (except the MSMarco training split) of MTEB. We have a strong filtering process to ensure the OOD setting. That's true
+    # for all of our models. Keep up the good work and let me know if you have any questions.
+    "MSMARCO": [],
+}
+
 mxbai_embed_large_v1 = ModelMeta(
     loader=partial(  # type: ignore
         sentence_transformers_loader,
@@ -95,7 +104,7 @@
     superseded_by=None,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    training_datasets=mixedbread_training_data,
 )
 
 mxbai_embed_2d_large_v1 = ModelMeta(
@@ -118,7 +127,7 @@
     superseded_by=None,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    training_datasets=mixedbread_training_data,
 )
 
 
@@ -142,5 +151,5 @@
     superseded_by=None,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    training_datasets=mixedbread_training_data,
 )
diff --git a/mteb/models/nvidia_models.py b/mteb/models/nvidia_models.py
index f09b1c21dc..078aa0fcd9 100644
--- a/mteb/models/nvidia_models.py
+++ b/mteb/models/nvidia_models.py
@@ -41,7 +41,6 @@ def instruction_template(
     "FEVERHardNegatives": ["train"],
     "NanoFEVERRetrieval": ["train"],
     "FiQA2018": ["train"],
-    "FiQA2018-PL": ["train"],  # translation not trained on
     "FiQA2018-NL": ["train"],  # translation not trained on
     "STS12": ["train"],
     "STS22": ["train"],
@@ -56,7 +55,6 @@ def instruction_template(
     "ArxivClusteringP2P": ["train"],
     "ArxivClusteringP2P.v2": ["train"],
     "ArxivClusteringS2S": ["train"],
-    "ArxivClusteringS2S.v2": ["train"],
     "BiorxivClusteringP2P": ["train"],
     "BiorxivClusteringP2P.v2": ["train"],
     "BiorxivClusteringS2S": ["train"],
diff --git a/mteb/models/overview.py b/mteb/models/overview.py
index 8c9a8d72d7..8a1505cf5f 100644
--- a/mteb/models/overview.py
+++ b/mteb/models/overview.py
@@ -42,6 +42,7 @@
     jina_models,
     lens_models,
     linq_models,
+    llm2clip_models,
     llm2vec_models,
     misc_models,
     moco_models,
@@ -106,6 +107,7 @@
     jina_clip,
     lens_models,
     linq_models,
+    llm2clip_models,
     llm2vec_models,
     misc_models,
     model2vec_models,
@@ -239,12 +241,15 @@ def get_model(model_name: str, revision: str | None = None, **kwargs: Any) -> En
     return model
 
 
-def get_model_meta(model_name: str, revision: str | None = None) -> ModelMeta:
+def get_model_meta(
+    model_name: str, revision: str | None = None, fetch_from_hf: bool = True
+) -> ModelMeta:
     """A function to fetch a model metadata object by name.
 
     Args:
         model_name: Name of the model to fetch
         revision: Revision of the model to fetch
+        fetch_from_hf: Whether to fetch the model from HuggingFace Hub if not found in the registry
 
     Returns:
         A model metadata object
@@ -256,6 +261,10 @@ def get_model_meta(model_name: str, revision: str | None = None) -> ModelMeta:
             )
         return MODEL_REGISTRY[model_name]
     else:  # assume it is a sentence-transformers model
+        if not fetch_from_hf:
+            raise ValueError(
+                f"Model {model_name} not found in MTEB registry. Please set fetch_from_hf=False to load it from HuggingFace Hub."
+            )
         logger.info(
             "Model not found in model registry, assuming it is on HF Hub model."
         )
diff --git a/mteb/models/qodo_models.py b/mteb/models/qodo_models.py
index 5437aabc43..fb87612335 100644
--- a/mteb/models/qodo_models.py
+++ b/mteb/models/qodo_models.py
@@ -2,7 +2,7 @@
 
 from mteb.model_meta import ModelMeta
 
-Qodo_Embed = ModelMeta(
+Qodo_Embed_1_1_5B = ModelMeta(
     name="Qodo/Qodo-Embed-1-1.5B",
     languages=[
         "python-Code",
@@ -32,3 +32,34 @@
     training_datasets=None,
     adapted_from="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
 )
+
+Qodo_Embed_1_7B = ModelMeta(
+    name="Qodo/Qodo-Embed-1-7B",
+    languages=[
+        "python-Code",
+        "c++-Code",
+        "c#-Code",
+        "go-Code",
+        "java-Code",
+        "Javascript-Code",
+        "php-Code",
+        "ruby-Code",
+        "typescript-Code",
+    ],
+    open_weights=True,
+    revision="f9edd9bf7f687c0e832424058e265120f603cd81",
+    release_date="2025-02-24",
+    n_parameters=7_613_000_000,
+    memory_usage_mb=29040,
+    embed_dim=3584,
+    license="Qodo-Model",
+    max_tokens=32768,
+    reference="https://huggingface.co/Qodo/Qodo-Embed-1-7B",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=False,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=None,
+    adapted_from="Alibaba-NLP/gte-Qwen2-7B-instruct",
+)
diff --git a/mteb/models/ru_sentence_models.py b/mteb/models/ru_sentence_models.py
index 9244cec6af..845fc3e52c 100644
--- a/mteb/models/ru_sentence_models.py
+++ b/mteb/models/ru_sentence_models.py
@@ -12,7 +12,6 @@
     ScoringFunction,
     sentence_transformers_loader,
 )
-from mteb.models.bge_models import bge_m3_training_data
 from mteb.models.instruct_wrapper import InstructSentenceTransformerWrapper
 
 rubert_tiny = ModelMeta(
@@ -81,7 +80,11 @@
     use_instructions=False,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    adapted_from="google/bert_uncased_L-12_H-768_A-12",
+    training_datasets={
+        # SNLI
+        # MNLI
+    },
 )
 
 sbert_large_mt_nlu_ru = ModelMeta(
@@ -247,7 +250,7 @@
     reference="https://huggingface.co/deepvk/USER-base",
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch"],
-    adapted_from="https://huggingface.co/BAAI/bge-m3",
+    adapted_from="BAAI/bge-m3",
     use_instructions=False,
     training_datasets={
         "BibleNLPBitextMining": ["train"],
@@ -255,7 +258,6 @@
         "MLSUMClusteringP2P.v2": ["train"],
         "MLSUMClusteringS2S": ["train"],
         "MLSUMClusteringS2S.v2": ["train"],
-        **bge_m3_training_data,
         # not MTEB:
         # "deepvk/ru-HNP": ["train"],
         # "deepvk/ru-WANLI": ["train"],
@@ -294,7 +296,15 @@
     # Wikipedia, Books, Twitter comments, Pikabu, Proza.ru, Film subtitles, News websites, and Social corpus
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    training_datasets={
+        # 400 GB of filtered and deduplicated texts in total.
+        # A mix of the following data: Wikipedia, Books, Twitter comments, Pikabu, Proza.ru,
+        # Film subtitles, News websites, and Social corpus.
+        # wikipedia
+        "WikipediaRetrievalMultilingual": [],
+        "WikipediaRerankingMultilingual": [],
+        "RiaNewsRetrieval": [],  # probably
+    },
 )
 
 rubert_base_cased = ModelMeta(
@@ -314,7 +324,12 @@
     use_instructions=False,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    adapted_from="google/bert_uncased_L-12_H-768_A-12",
+    training_datasets={
+        # wikipedia
+        "WikipediaRetrievalMultilingual": [],
+        "WikipediaRerankingMultilingual": [],
+    },
     citation="""@misc{kuratov2019adaptationdeepbidirectionalmultilingual,
       title={Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language}, 
       author={Yuri Kuratov and Mikhail Arkhipov},
@@ -343,7 +358,10 @@
     use_instructions=False,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
+    adapted_from="DeepPavlov/distilrubert-base-cased-conversational",
+    training_datasets={
+        # OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus
+    },
     citation="""@misc{https://doi.org/10.48550/arxiv.2205.02340,
       doi = {10.48550/ARXIV.2205.02340},
       url = {https://arxiv.org/abs/2205.02340},
@@ -396,10 +414,15 @@
     use_instructions=False,
     public_training_code="https://colab.research.google.com/drive/1dnPRn0-ugj3vZgSpyCC9sgslM2SuSfHy?usp=sharing",
     public_training_data=None,
-    training_datasets=None,
+    training_datasets={
+        # https://translate.yandex.ru/corpus
+    },
     adapted_from="sentence-transformers/LaBSE",
 )
 
+turbo_models_datasets = {
+    # Not MTEB: {"IlyaGusev/gazeta": ["train"], "zloelias/lenta-ru": ["train"]},
+}
 rubert_tiny_turbo = ModelMeta(
     name="sergeyzh/rubert-tiny-turbo",
     languages=["rus_Cyrl"],
@@ -417,8 +440,7 @@
     use_instructions=False,
     public_training_code=None,
     public_training_data=None,
-    training_datasets=None,
-    # Not MTEB: {"IlyaGusev/gazeta": ["train"], "zloelias/lenta-ru": ["train"]},
+    training_datasets=turbo_models_datasets,
     adapted_from="cointegrated/rubert-tiny2",
 )
 
@@ -437,8 +459,7 @@
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=False,
-    training_datasets=None,
-    # not MTEB: {"IlyaGusev/gazeta": ["train"], "zloelias/lenta-ru": ["train"]},
+    training_datasets=turbo_models_datasets,
     public_training_code=None,
     adapted_from="cointegrated/LaBSE-en-ru",
     public_training_data=None,
diff --git a/mteb/models/salesforce_models.py b/mteb/models/salesforce_models.py
index 5d89ce872c..6eb3b8d0ac 100644
--- a/mteb/models/salesforce_models.py
+++ b/mteb/models/salesforce_models.py
@@ -21,7 +21,6 @@ def instruction_template(
     **E5_MISTRAL_TRAINING_DATA,
     # From previously released blogpost which now have been taken down:
     "FiQA2018": ["train"],
-    "FiQA2018-PL": ["train"],
     "FiQA2018-NL": ["train"],  # translation not trained on
     "FEVER": ["train"],
     "FEVERHardNegatives": ["train"],
diff --git a/mteb/models/sentence_transformers_models.py b/mteb/models/sentence_transformers_models.py
index 7d9109b47d..0372d7d9e7 100644
--- a/mteb/models/sentence_transformers_models.py
+++ b/mteb/models/sentence_transformers_models.py
@@ -233,7 +233,11 @@
     use_instructions=False,
     superseded_by=None,
     adapted_from=None,
-    training_datasets=None,
+    training_datasets={
+        # CommonCrawl
+        # wiki  05-21-2020 dump
+        # The translation corpus is constructed from web pages using a bitext mining system
+    },
     # scraped and mined webdata including CC, wiki, see section 3.1 https://aclanthology.org/2022.acl-long.62.pdf
     public_training_code="https://www.kaggle.com/models/google/labse/tensorFlow2/labse/2?tfhub-redirect=true",
     citation="""@misc{feng2022languageagnosticbertsentenceembedding,
diff --git a/mteb/models/stella_models.py b/mteb/models/stella_models.py
index 202d185515..7da433c688 100644
--- a/mteb/models/stella_models.py
+++ b/mteb/models/stella_models.py
@@ -4,6 +4,7 @@
 
 from mteb.model_meta import ModelMeta, ScoringFunction
 from mteb.models.instruct_wrapper import instruct_wrapper
+from mteb.models.nvidia_models import nvidia_training_datasets
 
 stella_en_400M = ModelMeta(
     # https://huggingface.co/dunzhang/stella_en_400M_v5/discussions/21#671a6205ac1e2416090f2bf4
@@ -29,7 +30,7 @@
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch", "GritLM"],
     reference="https://huggingface.co/dunzhang/stella_en_400M_v5",
-    training_datasets=None,
+    training_datasets=nvidia_training_datasets,  # also distilled from gte-qwen (but training data is unknown) #2164
     public_training_code="https://github.com/NovaSearch-Team/RAG-Retrieval/blob/c40f4638b705eb77d88305d2056901ed550f9f4b/rag_retrieval/train/embedding/README.md",
     public_training_data=None,
 )
@@ -57,7 +58,7 @@
     similarity_fn_name=ScoringFunction.COSINE,
     framework=["Sentence Transformers", "PyTorch", "GritLM"],
     reference="https://huggingface.co/dunzhang/stella_en_1.5B_v5",
-    training_datasets=None,
+    training_datasets=nvidia_training_datasets,  # also distilled from gte-qwen (but training data is unknown) #2164
     public_training_code="https://github.com/NovaSearch-Team/RAG-Retrieval/blob/c40f4638b705eb77d88305d2056901ed550f9f4b/rag_retrieval/train/embedding/README.md",
     public_training_data=None,
 )
@@ -121,7 +122,7 @@
     open_weights=True,
     revision="17bb1c32a93a8fc5f6fc9e91d5ea86da99983cfe",
     release_date="2024-02-27",
-    n_parameters=326 * 1e6,
+    n_parameters=int(326 * 1e6),
     memory_usage_mb=1242,
     embed_dim=1792,
     license="mit",
@@ -143,7 +144,7 @@
     open_weights=True,
     revision="b1075144f440ab4409c05622c1179130ebd57d03",
     release_date="2024-06-04",
-    n_parameters=326 * 1e6,
+    n_parameters=int(326 * 1e6),
     memory_usage_mb=1242,
     embed_dim=1792,
     license="mit",
diff --git a/mteb/models/voyage_models.py b/mteb/models/voyage_models.py
index 0e4711caf5..679b6adcf4 100644
--- a/mteb/models/voyage_models.py
+++ b/mteb/models/voyage_models.py
@@ -262,7 +262,7 @@ def _batched_encode(
     similarity_fn_name="cosine",
     framework=["API"],
     use_instructions=True,
-    training_datasets=None,  # Not known
+    training_datasets=VOYAGE_TRAINING_DATA,  # src: private communication with Voyage
     public_training_code=None,
     public_training_data=None,
 )
@@ -405,6 +405,7 @@ def _batched_encode(
     max_tokens=32000,
     embed_dim=2048,
     open_weights=False,
+    # from their card https://huggingface.co/voyageai/voyage-3-m-exp#model-information
     n_parameters=int(6918 * 1e6),
     memory_usage_mb=None,
     license=None,
@@ -416,85 +417,62 @@ def _batched_encode(
         # MTEB(eng, v1) training data:
         "AmazonPolarityClassification": ["train"],
         "AmazonReviewsClassification": ["train"],
-        "ArguAna": ["train"],
-        "ArxivClusteringP2P": ["train"],
-        "ArxivClusteringS2S": ["train"],
-        "AskUbuntuDupQuestions": ["train"],
-        "BIOSSES": ["train"],
-        "Banking77Classification": ["train"],
-        "BiorxivClusteringP2P": ["train"],
-        "BiorxivClusteringS2S": ["train"],
-        "CQADupstackRetrieval": ["train"],
-        "ClimateFEVER": ["train"],
-        "DBPedia": ["train"],
         "EmotionClassification": ["train"],
-        "FEVER": ["train"],
-        "FiQA2018": ["train"],
         "HotpotQA": ["train"],
         "ImdbClassification": ["train"],
         "MTOPDomainClassification": ["train"],
         "MTOPIntentClassification": ["train"],
+        "MindSmallReranking": ["train"],
         "MassiveIntentClassification": ["train"],
         "MassiveScenarioClassification": ["train"],
         "MedrxivClusteringP2P": ["train"],
         "MedrxivClusteringS2S": ["train"],
-        "MindSmallReranking": ["train"],
-        "NFCorpus": ["train"],
-        "NQ": ["train"],
-        "QuoraRetrieval": ["train"],
-        "RedditClustering": ["train"],
-        "RedditClusteringP2P": ["train"],
-        "SCIDOCS": ["train"],
-        "SICK-R": ["train"],
         "STS12": ["train"],
-        "STS13": ["train"],
-        "STS14": ["train"],
-        "STS15": ["train"],
-        "STS16": ["train"],
         "STSBenchmark": ["train"],
-        "SciDocsRR": ["train"],
-        "SciFact": ["train"],
-        "SprintDuplicateQuestions": ["train"],
-        "StackExchangeClustering": ["train"],
-        "StackExchangeClusteringP2P": ["train"],
         "StackOverflowDupQuestions": ["train"],
-        "SummEval": ["train"],
-        "TRECCOVID": ["train"],
-        "Touche2020": ["train"],
         "ToxicConversationsClassification": ["train"],
         "TweetSentimentExtractionClassification": ["train"],
-        "TwentyNewsgroupsClustering": ["train"],
-        "TwitterSemEval2015": ["train"],
-        "TwitterURLCorpus": ["train"],
+        "BiorxivClusteringP2P": ["train"],
+        "BiorxivClusteringS2S": ["train"],
+        "Banking77Classification": ["train"],
+        "ArguAna": ["train"],
         "ArguAna-PL": ["train"],
         "ArguAna-NL": ["train"],  # translation not trained on
         "NanoArguAnaRetrieval": ["train"],
-        "HotpotQA-PL": ["train"],  # translation not trained on
-        "HotpotQA-NL": ["train"],  # translation not trained on
-        "HotpotQAHardNegatives": ["train"],
-        "MSMARCO": ["train"],
-        "MSMARCOHardNegatives": ["train"],
-        "NanoMSMARCORetrieval": ["train"],
-        "MSMARCO-PL": ["train"],  # translation not trained on
-        "mMARCO-NL": ["train"],  # translation not trained on
+        "STS22": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+        "ArxivClusteringP2P": ["train"],
+        "ArxivClusteringS2S": ["train"],
+        "NQ": ["train"],
+        "SciFact": ["train"],
+        "QuoraRetrieval": ["train"],
+        "NanoQuoraRetrieval": ["train"],
         "NQHardNegatives": ["train"],
         "NanoNQRetrieval": ["train"],
         "NQ-PL": ["train"],  # translation not trained on
         "NQ-NL": ["train"],  # translation not trained on
+        "NFCorpus": ["train"],
         "FEVERHardNegatives": ["train"],
         "NanoFEVERRetrieval": ["train"],
         "FEVER-NL": ["train"],  # translation not trained on
-        "FiQA2018-PL": ["train"],  # translation not trained on
         "FiQA2018-NL": ["train"],  # translation not trained on
-        "STS22": ["train"],
-        "AmazonCounterfactualClassification": ["train"],
-        "ArxivClusteringP2P.v2": ["train"],
-        "ArxivClusteringS2S.v2": ["train"],
         "BiorxivClusteringP2P.v2": ["train"],
         "BiorxivClusteringS2S.v2": ["train"],
         "MedrxivClusteringP2P.v2": ["train"],
         "MedrxivClusteringS2S.v2": ["train"],
-        "TwentyNewsgroupsClustering.v2": ["train"],
+        "MSMARCO": ["train"],
+        "MSMARCOHardNegatives": ["train"],
+        "NanoMSMARCORetrieval": ["train"],
+        "MSMARCO-PL": ["train"],  # translation not trained on
+        "mMARCO-NL": ["train"],  # translation not trained on
+        "HotpotQA-PL": ["train"],  # translation not trained on
+        "HotpotQA-NL": ["train"],  # translation not trained on
+        "HotpotQAHardNegatives": ["train"],
+        "FEVER": ["train"],
+        "FiQA2018": ["train"],
+        "DBPedia": ["train"],
+        "TRECCOVID": ["train"],
+        "ArxivClusteringP2P.v2": ["train"],
         "STSBenchmarkMultilingualSTS": ["train"],  # translated, not trained on
     },
     public_training_code=None,
diff --git a/mteb/models/voyage_v.py b/mteb/models/voyage_v.py
index d607d809f0..fc880347c5 100644
--- a/mteb/models/voyage_v.py
+++ b/mteb/models/voyage_v.py
@@ -248,16 +248,16 @@ def get_fused_embeddings(
     release_date="2024-11-10",
     n_parameters=None,
     memory_usage_mb=None,
-    max_tokens=None,
+    max_tokens=32768,
     embed_dim=1024,
-    license=None,
+    license="mit",
     similarity_fn_name="cosine",
-    framework=[],
+    framework=["API"],
     modalities=["image", "text"],
-    open_weights=None,
+    open_weights=False,
     public_training_code=None,
     public_training_data=None,
-    reference=None,
+    reference="https://huggingface.co/voyageai/voyage-multimodal-3",
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={},  # No overlap with MTEB according to Voyage, could overlap with MIEB, didn't ask
 )
diff --git a/mteb/overview.py b/mteb/overview.py
index d17b679651..c92a83310a 100644
--- a/mteb/overview.py
+++ b/mteb/overview.py
@@ -4,7 +4,7 @@
 
 import difflib
 import logging
-from collections import Counter
+from collections import Counter, defaultdict
 
 import pandas as pd
 
@@ -56,7 +56,23 @@ def create_name_to_task_mapping() -> dict[str, type[AbsTask]]:
     return metadata_names
 
 
+def create_similar_tasks() -> dict[str, list[str]]:
+    """Create a dictionary of similar tasks.
+
+    Returns:
+        Dict with key is parent task and value is list of similar tasks.
+    """
+    tasks = create_task_list()
+    similar_tasks = defaultdict(list)
+    for task in tasks:
+        if task.metadata.adapted_from:
+            for similar_task in task.metadata.adapted_from:
+                similar_tasks[similar_task].append(task.metadata.name)
+    return similar_tasks
+
+
 TASKS_REGISTRY = create_name_to_task_mapping()
+SIMILAR_TASKS = create_similar_tasks()
 
 
 def check_is_valid_script(script: str) -> None:
diff --git a/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py b/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py
index 5df787e1ca..fa533805f7 100644
--- a/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py
+++ b/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py
@@ -55,4 +55,5 @@ class BUCCBitextMiningFast(AbsTaskBitextMining):
     pages = "60--67",
     abstract = "This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0.84 (German-English), 0.80 (French-English), and 0.43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard. Adding them to the gold standard leads to revised estimates for the French-English F-scores of at most +1.5pt. This suggests that the BUCC 2017 datasets provide a reasonable approximate evaluation of the parallel sentence spotting task.",
 }""",
+        adapted_from=["BUCC"],
     )
diff --git a/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py b/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py
index 0f122842ed..cef28959d1 100644
--- a/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py
+++ b/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py
@@ -78,6 +78,7 @@ class BlurbsClusteringP2PFast(AbsTaskClusteringFast):
   year={2019},
   url={https://api.semanticscholar.org/CorpusID:208334484}
 }""",
+        adapted_from=["BlurbsClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py b/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py
index bebd2295b8..548e74ae3a 100644
--- a/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py
+++ b/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py
@@ -87,6 +87,7 @@ class BlurbsClusteringS2SFast(AbsTaskClusteringFast):
   year={2019},
   url={https://api.semanticscholar.org/CorpusID:208334484}
 }""",
+        adapted_from=["BlurbsClusteringS2S"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py b/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py
index 163a481f83..a5a5e6300b 100644
--- a/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py
+++ b/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py
@@ -63,6 +63,7 @@ class TenKGnadClusteringP2PFast(AbsTaskClusteringFast):
         sample_creation="found",
         bibtex_citation=None,  # none found
         # due to duplicates
+        adapted_from=["TenKGnadClusteringP2P"],
     )
 
     def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py b/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py
index 5fe67c330a..264665f1d5 100644
--- a/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py
+++ b/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py
@@ -63,6 +63,7 @@ class TenKGnadClusteringS2SFast(AbsTaskClusteringFast):
         sample_creation="found",
         bibtex_citation=None,  # none found
         # due to duplicates
+        adapted_from=["TenKGnadClusteringS2S"],
     )
 
     def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py b/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py
index 384fdc6d10..d3bccd923a 100644
--- a/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py
@@ -79,6 +79,7 @@ class ArxivClusteringP2PFast(AbsTaskClustering):
     year={2024}
 }""",  # None found
         prompt="Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
+        adapted_from=["ArxivClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/BigPatentClustering.py b/mteb/tasks/Clustering/eng/BigPatentClustering.py
index db96152d69..f9ef1815d7 100644
--- a/mteb/tasks/Clustering/eng/BigPatentClustering.py
+++ b/mteb/tasks/Clustering/eng/BigPatentClustering.py
@@ -97,6 +97,7 @@ class BigPatentClusteringFast(AbsTaskClusteringFast):
   biburl    = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }""",
+        adapted_from=["BigPatentClustering"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py b/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py
index c50e29e67b..eab2e893fc 100644
--- a/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py
@@ -32,6 +32,7 @@ class BiorxivClusteringP2PFast(AbsTaskClusteringFast):
         sample_creation="created",
         bibtex_citation="",
         prompt="Identify the main category of Biorxiv papers based on the titles and abstracts",
+        adapted_from=["BiorxivClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py b/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py
index 339880d88d..3f50904449 100644
--- a/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py
+++ b/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py
@@ -32,6 +32,7 @@ class BiorxivClusteringS2SFast(AbsTaskClusteringFast):
         sample_creation="created",
         bibtex_citation="",
         prompt="Identify the main category of Biorxiv papers based on the titles",
+        adapted_from=["BiorxivClusteringS2S"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py b/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py
index 3ad04945d9..8fd1073e78 100644
--- a/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py
@@ -36,6 +36,7 @@ class MedrxivClusteringP2PFast(AbsTaskClusteringFast):
         sample_creation="created",
         bibtex_citation="",
         prompt="Identify the main category of Medrxiv papers based on the titles and abstracts",
+        adapted_from=["MedrxivClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py b/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py
index 2e1e144833..a969278d8b 100644
--- a/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py
+++ b/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py
@@ -36,6 +36,7 @@ class MedrxivClusteringS2SFast(AbsTaskClusteringFast):
         sample_creation="created",
         bibtex_citation="",
         prompt="Identify the main category of Medrxiv papers based on the titles",
+        adapted_from=["MedrxivClusteringS2S"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/RedditClustering.py b/mteb/tasks/Clustering/eng/RedditClustering.py
index 70a0d80d42..d6091a3f62 100644
--- a/mteb/tasks/Clustering/eng/RedditClustering.py
+++ b/mteb/tasks/Clustering/eng/RedditClustering.py
@@ -48,6 +48,7 @@ class RedditFastClusteringS2S(AbsTaskClusteringFast):
         eprint    = {2104.07081}
         }""",
         prompt="Identify the topic or theme of Reddit posts based on the titles",
+        adapted_from=["RedditClustering"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/RedditClusteringP2P.py b/mteb/tasks/Clustering/eng/RedditClusteringP2P.py
index 8c4865c6d1..fc2cd5a021 100644
--- a/mteb/tasks/Clustering/eng/RedditClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/RedditClusteringP2P.py
@@ -89,6 +89,7 @@ class RedditFastClusteringP2P(AbsTaskClusteringFast):
         eprint    = {2104.07081}
         }""",
         prompt="Identify the topic or theme of Reddit posts based on the titles and posts",
+        adapted_from=["RedditClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/StackExchangeClustering.py b/mteb/tasks/Clustering/eng/StackExchangeClustering.py
index b7950b2819..5927dc1679 100644
--- a/mteb/tasks/Clustering/eng/StackExchangeClustering.py
+++ b/mteb/tasks/Clustering/eng/StackExchangeClustering.py
@@ -48,6 +48,7 @@ class StackExchangeClusteringFast(AbsTaskClusteringFast):
         eprint    = {2104.07081}
         }""",
         prompt="Identify the topic or theme of StackExchange posts based on the titles",
+        adapted_from=["StackExchangeClustering"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py b/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py
index d1d0ba54ae..62ea4bf9ac 100644
--- a/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py
@@ -50,6 +50,7 @@ class StackExchangeClusteringP2PFast(AbsTaskClusteringFast):
         eprint    = {2104.07081}
         }""",
         prompt="Identify the topic or theme of StackExchange posts based on the given paragraphs",
+        adapted_from=["StackExchangeClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py b/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py
index 2f12b2e93b..ed96dabdac 100644
--- a/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py
+++ b/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py
@@ -90,6 +90,7 @@ class TwentyNewsgroupsClusteringFast(AbsTaskClusteringFast):
         }
         """,
         prompt="Identify the topic or theme of the given news articles",
+        adapted_from=["TwentyNewsgroupsClustering"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py b/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py
index 7846b6b2c0..72c07b4400 100644
--- a/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py
+++ b/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py
@@ -106,6 +106,7 @@ class AlloProfClusteringP2PFast(AbsTaskClusteringFast):
   copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
 }
 """,
+        adapted_from=["AlloProfClusteringP2P"],
     )
 
     def create_description(self, example):
diff --git a/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py b/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py
index 96e1cbf7b4..01393656ee 100644
--- a/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py
+++ b/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py
@@ -103,6 +103,7 @@ class AlloProfClusteringS2SFast(AbsTaskClusteringFast):
   copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
 }
 """,
+        adapted_from=["AlloProfClusteringS2S"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/fra/HALClusteringS2S.py b/mteb/tasks/Clustering/fra/HALClusteringS2S.py
index 5143b5c678..6f9a916f6b 100644
--- a/mteb/tasks/Clustering/fra/HALClusteringS2S.py
+++ b/mteb/tasks/Clustering/fra/HALClusteringS2S.py
@@ -94,6 +94,7 @@ class HALClusteringS2SFast(AbsTaskClusteringFast):
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }""",
+        adapted_from=["HALClusteringS2S"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py b/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py
index aa34b84f96..fac13d633c 100644
--- a/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py
+++ b/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py
@@ -32,6 +32,7 @@ class LivedoorNewsClusteringv2(AbsTaskClusteringFast):
         dialect=[],
         sample_creation="found",
         bibtex_citation="",
+        adapted_from=["LivedoorNewsClustering"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py b/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py
index 23e0f0f8cc..a262a1b878 100644
--- a/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py
+++ b/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py
@@ -119,6 +119,7 @@ class MLSUMClusteringP2PFast(AbsTaskClusteringFast):
         journal={arXiv preprint arXiv:2004.14900},
         year={2020}
         }""",
+        adapted_from=["MLSUMClusteringP2P"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py b/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py
index 640299dc98..63cf87e436 100644
--- a/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py
+++ b/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py
@@ -114,6 +114,7 @@ class MLSUMClusteringS2SFast(AbsTaskClusteringFast):
         journal={arXiv preprint arXiv:2004.14900},
         year={2020}
         }""",
+        adapted_from=["MLSUMClusteringS2S"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py b/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py
index 5c13455d78..4842a7f55c 100644
--- a/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py
+++ b/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py
@@ -80,6 +80,7 @@ class WikiClusteringFastP2P(AbsTaskClusteringFast):
         dialect=[],
         sample_creation="created",
         bibtex_citation="",  # None exists
+        adapted_from=["WikiClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/pol/PolishClustering.py b/mteb/tasks/Clustering/pol/PolishClustering.py
index 6593f453e9..0edd6ecd97 100644
--- a/mteb/tasks/Clustering/pol/PolishClustering.py
+++ b/mteb/tasks/Clustering/pol/PolishClustering.py
@@ -128,6 +128,7 @@ class EightTagsClusteringFast(AbsTaskClusteringFast):
             language = "English",
             ISBN = "979-10-95546-34-4",
         }""",
+        adapted_from=["EightTagsClustering"],
     )
 
     def dataset_transform(self):
@@ -200,6 +201,7 @@ class PlscClusteringS2SFast(AbsTaskClusteringFast):
         dialect=[],
         sample_creation="found",
         bibtex_citation="",
+        adapted_from=["PlscClusteringS2S"],
     )
 
     def dataset_transform(self):
@@ -281,6 +283,7 @@ class PlscClusteringP2PFast(AbsTaskClusteringFast):
         dialect=[],
         sample_creation="found",
         bibtex_citation="",
+        adapted_from=["PlscClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/zho/CMTEBClustering.py b/mteb/tasks/Clustering/zho/CMTEBClustering.py
index e03d922374..cb34e59d99 100644
--- a/mteb/tasks/Clustering/zho/CMTEBClustering.py
+++ b/mteb/tasks/Clustering/zho/CMTEBClustering.py
@@ -48,6 +48,7 @@ class CLSClusteringFastS2S(AbsTaskClusteringFast):
             primaryClass={cs.CL}
         }""",
         prompt="Identify the main category of scholar papers based on the titles",
+        adapted_from=["CLSClusteringS2S"],
     )
 
     def dataset_transform(self):
@@ -104,6 +105,7 @@ class CLSClusteringFastP2P(AbsTaskClusteringFast):
             primaryClass={cs.CL}
         }""",
         prompt="Identify the main category of scholar papers based on the titles and abstracts",
+        adapted_from=["CLSClusteringP2P"],
     )
 
     def dataset_transform(self):
@@ -228,6 +230,7 @@ class ThuNewsClusteringFastS2S(AbsTaskClusteringFast):
   url = {https://github.com/thunlp/THUCTC}
 }""",
         prompt="Identify the topic or theme of the given news articles based on the titles",
+        adapted_from=["ThuNewsClusteringS2S"],
     )
 
     def dataset_transform(self):
@@ -284,6 +287,7 @@ class ThuNewsClusteringFastP2P(AbsTaskClusteringFast):
   url = {https://github.com/thunlp/THUCTC}
 }""",
         prompt="Identify the topic or theme of the given news articles based on the titles and contents",
+        adapted_from=["ThuNewsClusteringP2P"],
     )
 
     def dataset_transform(self):
diff --git a/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2IMultiChoice.py b/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2IMultiChoice.py
index ccf443fe18..2ecdd6fc05 100644
--- a/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2IMultiChoice.py
+++ b/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2IMultiChoice.py
@@ -14,7 +14,7 @@ class BLINKIT2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "revision": "a9f994925551c14503d00d86f1307bac6e2ead6a",
             "trust_remote_code": True,
         },
-        type="Any2AnyMultiChoice",
+        type="VisionCentric",
         category="it2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2TMultiChoice.py b/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2TMultiChoice.py
index 82368dd016..719a381876 100644
--- a/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2TMultiChoice.py
+++ b/mteb/tasks/Image/Any2AnyMultiChoice/eng/BLINKIT2TMultiChoice.py
@@ -13,7 +13,7 @@ class BLINKIT2TMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/blink-it2t-multi",
             "revision": "bc8f4c7f62450a4ceb737c8339061cf87aea42d5",
         },
-        type="Any2AnyMultiChoice",
+        type="VisionCentric",
         category="it2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/Any2AnyMultiChoice/eng/ImageCoDeT2IMultiChoice.py b/mteb/tasks/Image/Any2AnyMultiChoice/eng/ImageCoDeT2IMultiChoice.py
index a3cb17483d..a41f77ab0e 100644
--- a/mteb/tasks/Image/Any2AnyMultiChoice/eng/ImageCoDeT2IMultiChoice.py
+++ b/mteb/tasks/Image/Any2AnyMultiChoice/eng/ImageCoDeT2IMultiChoice.py
@@ -13,7 +13,7 @@ class ImageCoDeT2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/imagecode-multi",
             "revision": "d28adfd8b34fefa546fdf94bdc352622b2575f6c",
         },
-        type="Any2AnyMultiChoice",
+        type="Compositionality",
         category="it2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/Any2AnyMultiChoice/eng/ROxfordI2IMultiChoice.py b/mteb/tasks/Image/Any2AnyMultiChoice/eng/ROxfordI2IMultiChoice.py
index e1a4ef6e12..215200d6df 100644
--- a/mteb/tasks/Image/Any2AnyMultiChoice/eng/ROxfordI2IMultiChoice.py
+++ b/mteb/tasks/Image/Any2AnyMultiChoice/eng/ROxfordI2IMultiChoice.py
@@ -4,6 +4,8 @@
 from mteb.abstasks.TaskMetadata import TaskMetadata
 
 
+# NOTE: These tasks are marked as Any2AnyRetrieval types they are the correct implementations of ROxford retrieval and RParis retrieval
+# (as it requires masking out the different docs in corpus for every query). This aligns with the MIEB papeer.
 class ROxfordEasyI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
     metadata = TaskMetadata(
         name="ROxfordEasyI2IMultiChoice",
@@ -13,7 +15,7 @@ class ROxfordEasyI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/r-oxford-easy-multi",
             "revision": "4c167c3ce529f19457c9b8e694258cc6cf8e7cc7",
         },
-        type="Any2AnyMultiChoice",
+        type="Any2AnyRetrieval",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -47,7 +49,7 @@ class ROxfordMediumI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/r-oxford-medium-multi",
             "revision": "83bd440268e200a4f60313070618e3f45000fa94",
         },
-        type="Any2AnyMultiChoice",
+        type="Any2AnyRetrieval",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -81,7 +83,7 @@ class ROxfordHardI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/r-oxford-hard-multi",
             "revision": "fc7c4ae6655b1e6b132f3b262a359acef42dfce8",
         },
-        type="Any2AnyMultiChoice",
+        type="Any2AnyRetrieval",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/Any2AnyMultiChoice/eng/RParisI2IMultiChoice.py b/mteb/tasks/Image/Any2AnyMultiChoice/eng/RParisI2IMultiChoice.py
index 6f7333fbb0..a759689d3d 100644
--- a/mteb/tasks/Image/Any2AnyMultiChoice/eng/RParisI2IMultiChoice.py
+++ b/mteb/tasks/Image/Any2AnyMultiChoice/eng/RParisI2IMultiChoice.py
@@ -4,6 +4,8 @@
 from mteb.abstasks.TaskMetadata import TaskMetadata
 
 
+# NOTE: These tasks are marked as Any2AnyRetrieval types they are the correct implementations of ROxford retrieval and RParis retrieval
+# (as it requires masking out the different docs in corpus for every query). This aligns with the MIEB papeer.
 class RParisEasyI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
     metadata = TaskMetadata(
         name="RParisEasyI2IMultiChoice",
@@ -13,7 +15,7 @@ class RParisEasyI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/r-paris-easy-multi",
             "revision": "db94b5afd0014ab8c978f20a0fbcc52da1612a08",
         },
-        type="Any2AnyMultiChoice",
+        type="Any2AnyRetrieval",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -47,7 +49,7 @@ class RParisMediumI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/r-paris-medium-multi",
             "revision": "372c79fc823e1cebc1d55f8e0039aa239285e177",
         },
-        type="Any2AnyMultiChoice",
+        type="Any2AnyRetrieval",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -81,7 +83,7 @@ class RParisHardI2IMultiChoice(AbsTaskAny2AnyMultiChoice):
             "path": "JamieSJS/r-paris-hard-multi",
             "revision": "4e5997e48fb2f2f8bf1c8973851dedeb17e09a83",
         },
-        type="Any2AnyMultiChoice",
+        type="Any2AnyRetrieval",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/Any2AnyRetrieval/eng/VidoreBenchRetrieval.py b/mteb/tasks/Image/Any2AnyRetrieval/eng/VidoreBenchRetrieval.py
index 496b08719c..d38b59b0b9 100644
--- a/mteb/tasks/Image/Any2AnyRetrieval/eng/VidoreBenchRetrieval.py
+++ b/mteb/tasks/Image/Any2AnyRetrieval/eng/VidoreBenchRetrieval.py
@@ -79,7 +79,7 @@ class VidoreArxivQARetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/arxivqa_test_subsampled_beir",
             "revision": "7d94d570960eac2408d3baa7a33f9de4822ae3e4",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -121,7 +121,7 @@ class VidoreDocVQARetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/docvqa_test_subsampled_beir",
             "revision": "162ba2fc1a8437eda8b6c37b240bc1c0f0deb092",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -163,7 +163,7 @@ class VidoreInfoVQARetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/infovqa_test_subsampled_beir",
             "revision": "b802cc5fd6c605df2d673a963667d74881d2c9a4",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -205,7 +205,7 @@ class VidoreTabfquadRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/tabfquad_test_subsampled_beir",
             "revision": "61a2224bcd29b7b261a4892ff4c8bea353527a31",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -247,7 +247,7 @@ class VidoreTatdqaRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/tatdqa_test_beir",
             "revision": "5feb5630fdff4d8d189ffedb2dba56862fdd45c0",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -289,7 +289,7 @@ class VidoreShiftProjectRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/shiftproject_test_beir",
             "revision": "84a382e05c4473fed9cff2bbae95fe2379416117",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -331,7 +331,7 @@ class VidoreSyntheticDocQAAIRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/syntheticDocQA_artificial_intelligence_test_beir",
             "revision": "2d9ebea5a1c6e9ef4a3b902a612f605dca11261c",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -373,7 +373,7 @@ class VidoreSyntheticDocQAEnergyRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/syntheticDocQA_energy_test_beir",
             "revision": "9935aadbad5c8deec30910489db1b2c7133ae7a7",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -415,7 +415,7 @@ class VidoreSyntheticDocQAGovernmentReportsRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/syntheticDocQA_government_reports_test_beir",
             "revision": "b4909afa930f81282fd20601e860668073ad02aa",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -457,7 +457,7 @@ class VidoreSyntheticDocQAHealthcareIndustryRetrieval(AbsTaskAny2AnyRetrieval):
             "path": "vidore/syntheticDocQA_healthcare_industry_test_beir",
             "revision": "f9e25d5b6e13e1ad9f5c3cce202565031b3ab164",
         },
-        type="Any2AnyRetrieval",
+        type="DocumentUnderstanding",
         category="t2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/WITT2IRetrieval.py b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/WITT2IRetrieval.py
index b87d1513a5..d3cf1a146d 100644
--- a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/WITT2IRetrieval.py
+++ b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/WITT2IRetrieval.py
@@ -100,7 +100,7 @@ class WITT2IRetrieval(AbsTaskAny2AnyRetrieval):
             "revision": "91ac153f1371a98b209ed763205e25e115ecd06e",
             # "trust_remote_code": True,
         },
-        type="Any2AnyRetrieval",
+        type="Any2AnyMultilingualRetrieval",
         category="t2i",
         eval_splits=["test"],
         eval_langs=_LANGUAGES,
diff --git a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XFlickr30kCoT2IRetrieval.py b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XFlickr30kCoT2IRetrieval.py
index b17db60bf3..13fef09c41 100644
--- a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XFlickr30kCoT2IRetrieval.py
+++ b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XFlickr30kCoT2IRetrieval.py
@@ -83,7 +83,7 @@ class XFlickr30kCoT2IRetrieval(AbsTaskAny2AnyRetrieval):
             "revision": "0af2c2eba58b27a71898787e286be04befdd7a20",
             # "trust_remote_code": True,
         },
-        type="Any2AnyRetrieval",
+        type="Any2AnyMultilingualRetrieval",
         category="t2i",
         eval_splits=["test"],
         eval_langs=_LANGUAGES,
diff --git a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XM3600T2IRetrieval.py b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XM3600T2IRetrieval.py
index 56c5fbdba8..0b91efb0cf 100644
--- a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XM3600T2IRetrieval.py
+++ b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/XM3600T2IRetrieval.py
@@ -128,7 +128,7 @@ class XM3600T2IRetrieval(AbsTaskAny2AnyRetrieval):
             "revision": "8d3e5665526c55a5855cd6ddfbaba2032bc7cee4",
             # "trust_remote_code": True,
         },
-        type="Any2AnyRetrieval",
+        type="Any2AnyMultilingualRetrieval",
         category="t2i",
         eval_splits=["test"],
         eval_langs=_LANGUAGES,
diff --git a/mteb/tasks/Image/Any2TextMultipleChoice/eng/CVBench.py b/mteb/tasks/Image/Any2TextMultipleChoice/eng/CVBench.py
index c8f5d8702e..847d583b58 100644
--- a/mteb/tasks/Image/Any2TextMultipleChoice/eng/CVBench.py
+++ b/mteb/tasks/Image/Any2TextMultipleChoice/eng/CVBench.py
@@ -23,7 +23,7 @@ class CVBenchCount(AbsTaskAny2TextMultipleChoice):
             "path": "nyu-visionx/CV-Bench",
             "revision": "22409a927ab5cf68e3655023d51694587455fc99",
         },
-        type="Any2TextMutipleChoice",
+        type="VisionCentric",
         category="it2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -75,7 +75,7 @@ class CVBenchRelation(AbsTaskAny2TextMultipleChoice):
             "path": "nyu-visionx/CV-Bench",
             "revision": "22409a927ab5cf68e3655023d51694587455fc99",
         },
-        type="Any2TextMutipleChoice",
+        type="VisionCentric",
         category="it2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -129,7 +129,7 @@ class CVBenchDepth(AbsTaskAny2TextMultipleChoice):
             "path": "nyu-visionx/CV-Bench",
             "revision": "22409a927ab5cf68e3655023d51694587455fc99",
         },
-        type="Any2TextMutipleChoice",
+        type="VisionCentric",
         category="it2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -181,7 +181,7 @@ class CVBenchDistance(AbsTaskAny2TextMultipleChoice):
             "path": "nyu-visionx/CV-Bench",
             "revision": "22409a927ab5cf68e3655023d51694587455fc99",
         },
-        type="Any2TextMutipleChoice",
+        type="VisionCentric",
         category="it2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/ImageClassification/eng/OxfordFlowersClassification.py b/mteb/tasks/Image/ImageClassification/eng/OxfordFlowersClassification.py
index 113aa74544..9f259a1a88 100644
--- a/mteb/tasks/Image/ImageClassification/eng/OxfordFlowersClassification.py
+++ b/mteb/tasks/Image/ImageClassification/eng/OxfordFlowersClassification.py
@@ -29,5 +29,14 @@ class OxfordFlowersClassification(AbsTaskImageClassification):
         dialect=[],
         modalities=["image"],
         sample_creation="found",
-        bibtex_citation="""d""",
+        bibtex_citation="""@INPROCEEDINGS{4756141,
+  author={Nilsback, Maria-Elena and Zisserman, Andrew},
+  booktitle={2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing}, 
+  title={Automated Flower Classification over a Large Number of Classes}, 
+  year={2008},
+  volume={},
+  number={},
+  pages={722-729},
+  keywords={Shape;Kernel;Distributed computing;Support vector machines;Support vector machine classification;object classification;segmentation},
+  doi={10.1109/ICVGIP.2008.47}}""",
     )
diff --git a/mteb/tasks/Image/ImageClassification/eng/OxfordPetsClassification.py b/mteb/tasks/Image/ImageClassification/eng/OxfordPetsClassification.py
index e95ffdf0f8..3927f91349 100644
--- a/mteb/tasks/Image/ImageClassification/eng/OxfordPetsClassification.py
+++ b/mteb/tasks/Image/ImageClassification/eng/OxfordPetsClassification.py
@@ -8,7 +8,7 @@ class OxfordPetsClassification(AbsTaskImageClassification):
     metadata = TaskMetadata(
         name="OxfordPets",
         description="Classifying animal images.",
-        reference="https://arxiv.org/abs/1306.5151",
+        reference="https://ieeexplore.ieee.org/abstract/document/6248092",
         dataset={
             "path": "isaacchung/OxfordPets",
             "revision": "557b480fae8d69247be74d9503b378a09425096f",
@@ -29,14 +29,15 @@ class OxfordPetsClassification(AbsTaskImageClassification):
         dialect=[],
         modalities=["image"],
         sample_creation="created",
-        bibtex_citation="""@misc{maji2013finegrainedvisualclassificationaircraft,
-            title={Fine-Grained Visual Classification of Aircraft}, 
-            author={Subhransu Maji and Esa Rahtu and Juho Kannala and Matthew Blaschko and Andrea Vedaldi},
-            year={2013},
-            eprint={1306.5151},
-            archivePrefix={arXiv},
-            primaryClass={cs.CV},
-            url={https://arxiv.org/abs/1306.5151}, 
-        }
+        bibtex_citation="""@INPROCEEDINGS{6248092,
+  author={Parkhi, Omkar M and Vedaldi, Andrea and Zisserman, Andrew and Jawahar, C. V.},
+  booktitle={2012 IEEE Conference on Computer Vision and Pattern Recognition}, 
+  title={Cats and dogs}, 
+  year={2012},
+  volume={},
+  number={},
+  pages={3498-3505},
+  keywords={Positron emission tomography;Image segmentation;Cats;Dogs;Layout;Deformable models;Head},
+  doi={10.1109/CVPR.2012.6248092}}
         """,
     )
diff --git a/mteb/tasks/Image/ImageMultilabelClassification/eng/PascalVOC2007.py b/mteb/tasks/Image/ImageMultilabelClassification/eng/PascalVOC2007.py
index f2554a4ab2..9d401c24ba 100644
--- a/mteb/tasks/Image/ImageMultilabelClassification/eng/PascalVOC2007.py
+++ b/mteb/tasks/Image/ImageMultilabelClassification/eng/PascalVOC2007.py
@@ -6,6 +6,8 @@
 from mteb.abstasks.TaskMetadata import TaskMetadata
 
 
+# NOTE: In the paper, this is grouped with linear probe tasks.
+# See https://github.com/embeddings-benchmark/mteb/pull/2035#issuecomment-2661626309.
 class VOC2007Classification(AbsTaskImageMultilabelClassification):
     metadata = TaskMetadata(
         name="VOC2007",
@@ -17,7 +19,7 @@ class VOC2007Classification(AbsTaskImageMultilabelClassification):
             "revision": "dbafdb9e1506c9c419c5c4672e409463cd21ba50",
             "trust_remote_code": True,
         },
-        type="ImageMultilabelClassification",
+        type="ImageClassification",
         category="i2i",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/ImageTextPairClassification/AROCocoOrder.py b/mteb/tasks/Image/ImageTextPairClassification/AROCocoOrder.py
index 6d01e98861..7392eb56b7 100644
--- a/mteb/tasks/Image/ImageTextPairClassification/AROCocoOrder.py
+++ b/mteb/tasks/Image/ImageTextPairClassification/AROCocoOrder.py
@@ -20,12 +20,12 @@ class AROCocoOrder(AbsTaskImageTextPairClassification):
         name="AROCocoOrder",
         description="Compositionality Evaluation of images to their captions."
         + "Each capation has four hard negatives created by order permutations.",
-        reference="https://proceedings.neurips.cc/paper_files/paper/2023/hash/63461de0b4cb760fc498e85b18a7fe81-Abstract-Datasets_and_Benchmarks.html",
+        reference="https://openreview.net/forum?id=KRLUvxh8uaX",
         dataset={
             "path": "gowitheflow/ARO-COCO-order",
             "revision": "853ec8757226585a38a80886c51fe0f3f268787c",
         },
-        type="ImageTextPairClassification",
+        type="Compositionality",
         category="i2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -41,11 +41,10 @@ class AROCocoOrder(AbsTaskImageTextPairClassification):
         dialect=[],
         modalities=["text", "image"],
         sample_creation="created",
-        bibtex_citation="""@article{hsieh2024sugarcrepe,
-  title={Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality},
-  author={Hsieh, Cheng-Yu and Zhang, Jieyu and Ma, Zixian and Kembhavi, Aniruddha and Krishna, Ranjay},
-  journal={Advances in neural information processing systems},
-  volume={36},
-  year={2024}
+        bibtex_citation="""@inproceedings{yuksekgonul2023and,
+  title={When and why vision-language models behave like bags-of-words, and what to do about it?},
+  author={Yuksekgonul, Mert and Bianchi, Federico and Kalluri, Pratyusha and Jurafsky, Dan and Zou, James},
+  booktitle={The Eleventh International Conference on Learning Representations},
+  year={2023}
 }""",
     )
diff --git a/mteb/tasks/Image/ImageTextPairClassification/AROFlickrOrder.py b/mteb/tasks/Image/ImageTextPairClassification/AROFlickrOrder.py
index 95c5bc6641..941c8da184 100644
--- a/mteb/tasks/Image/ImageTextPairClassification/AROFlickrOrder.py
+++ b/mteb/tasks/Image/ImageTextPairClassification/AROFlickrOrder.py
@@ -20,12 +20,12 @@ class AROFlickrOrder(AbsTaskImageTextPairClassification):
         name="AROFlickrOrder",
         description="Compositionality Evaluation of images to their captions."
         + "Each capation has four hard negatives created by order permutations.",
-        reference="https://proceedings.neurips.cc/paper_files/paper/2023/hash/63461de0b4cb760fc498e85b18a7fe81-Abstract-Datasets_and_Benchmarks.html",
+        reference="https://openreview.net/forum?id=KRLUvxh8uaX",
         dataset={
             "path": "gowitheflow/ARO-Flickr-Order",
             "revision": "1f9485f69c87947812378a1aedf86410c86a0aa8",
         },
-        type="ImageTextPairClassification",
+        type="Compositionality",
         category="i2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
@@ -41,11 +41,10 @@ class AROFlickrOrder(AbsTaskImageTextPairClassification):
         dialect=[],
         modalities=["text", "image"],
         sample_creation="created",
-        bibtex_citation="""@article{hsieh2024sugarcrepe,
-  title={Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality},
-  author={Hsieh, Cheng-Yu and Zhang, Jieyu and Ma, Zixian and Kembhavi, Aniruddha and Krishna, Ranjay},
-  journal={Advances in neural information processing systems},
-  volume={36},
-  year={2024}
+        bibtex_citation="""@inproceedings{yuksekgonul2023and,
+  title={When and why vision-language models behave like bags-of-words, and what to do about it?},
+  author={Yuksekgonul, Mert and Bianchi, Federico and Kalluri, Pratyusha and Jurafsky, Dan and Zou, James},
+  booktitle={The Eleventh International Conference on Learning Representations},
+  year={2023}
 }""",
     )
diff --git a/mteb/tasks/Image/ImageTextPairClassification/AROVisualAttribution.py b/mteb/tasks/Image/ImageTextPairClassification/AROVisualAttribution.py
index f334686f56..4cb8273ee1 100644
--- a/mteb/tasks/Image/ImageTextPairClassification/AROVisualAttribution.py
+++ b/mteb/tasks/Image/ImageTextPairClassification/AROVisualAttribution.py
@@ -18,7 +18,7 @@ class AROVisualAttribution(AbsTaskImageTextPairClassification):
             "path": "gowitheflow/ARO-Visual-Attribution",
             "revision": "18f7e01358d91df599d723f00e16a18640e19398",
         },
-        type="ImageTextPairClassification",
+        type="Compositionality",
         category="i2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/ImageTextPairClassification/AROVisualRelation.py b/mteb/tasks/Image/ImageTextPairClassification/AROVisualRelation.py
index 0eaf9a2925..b74d7b67f8 100644
--- a/mteb/tasks/Image/ImageTextPairClassification/AROVisualRelation.py
+++ b/mteb/tasks/Image/ImageTextPairClassification/AROVisualRelation.py
@@ -18,7 +18,7 @@ class AROVisualRelation(AbsTaskImageTextPairClassification):
             "path": "gowitheflow/ARO-Visual-Relation",
             "revision": "3867ad4f46a1ac2e63be034d1fc77dd8c2ef7209",
         },
-        type="ImageTextPairClassification",
+        type="Compositionality",
         category="i2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/ImageTextPairClassification/SugarCrepe.py b/mteb/tasks/Image/ImageTextPairClassification/SugarCrepe.py
index 608ff312f6..c321f02c36 100644
--- a/mteb/tasks/Image/ImageTextPairClassification/SugarCrepe.py
+++ b/mteb/tasks/Image/ImageTextPairClassification/SugarCrepe.py
@@ -20,7 +20,7 @@ class SugarCrepe(AbsTaskImageTextPairClassification):
             "path": "yjkimstats/SUGARCREPE_fmt",
             "revision": "134abf9ade6a32f9fdae0e89022ff227a70b87e5",
         },
-        type="ImageTextPairClassification",
+        type="Compositionality",
         category="i2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/ImageTextPairClassification/Winoground.py b/mteb/tasks/Image/ImageTextPairClassification/Winoground.py
index 55111e4929..c37f83dd69 100644
--- a/mteb/tasks/Image/ImageTextPairClassification/Winoground.py
+++ b/mteb/tasks/Image/ImageTextPairClassification/Winoground.py
@@ -18,7 +18,7 @@ class Winoground(AbsTaskImageTextPairClassification):
             "path": "facebook/winoground",
             "revision": "b400e173549071916ad1b3d449293bc8d8b4b763",
         },
-        type="ImageTextPairClassification",
+        type="Compositionality",
         category="i2t",
         eval_splits=["test"],
         eval_langs=["eng-Latn"],
diff --git a/mteb/tasks/Image/VisualSTS/en/STS12VisualSTS.py b/mteb/tasks/Image/VisualSTS/en/STS12VisualSTS.py
index 710f81f4c3..6a371b7daf 100644
--- a/mteb/tasks/Image/VisualSTS/en/STS12VisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/en/STS12VisualSTS.py
@@ -13,7 +13,7 @@ class STS12VisualSTS(AbsTaskVisualSTS):
         },
         description="SemEval-2012 Task 6." + "then rendered into images.",
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(eng)",
         category="i2i",
         modalities=["image"],
         eval_splits=["test"],
diff --git a/mteb/tasks/Image/VisualSTS/en/STS13VisualSTS.py b/mteb/tasks/Image/VisualSTS/en/STS13VisualSTS.py
index d103ecfbab..c9e9140fc4 100644
--- a/mteb/tasks/Image/VisualSTS/en/STS13VisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/en/STS13VisualSTS.py
@@ -13,7 +13,7 @@ class STS13VisualSTS(AbsTaskVisualSTS):
         },
         description="SemEval STS 2013 dataset." + "then rendered into images.",
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(eng)",
         category="i2i",
         modalities=["image"],
         eval_splits=["test"],
diff --git a/mteb/tasks/Image/VisualSTS/en/STS14VisualSTS.py b/mteb/tasks/Image/VisualSTS/en/STS14VisualSTS.py
index a1ba4ac9c3..9813d12533 100644
--- a/mteb/tasks/Image/VisualSTS/en/STS14VisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/en/STS14VisualSTS.py
@@ -14,7 +14,7 @@ class STS14VisualSTS(AbsTaskVisualSTS):
         description="SemEval STS 2014 dataset. Currently only the English dataset."
         + "rendered into images.",
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(eng)",
         category="i2i",
         modalities=["image"],
         eval_splits=["test"],
diff --git a/mteb/tasks/Image/VisualSTS/en/STS15VisualSTS.py b/mteb/tasks/Image/VisualSTS/en/STS15VisualSTS.py
index 1c24fc9be5..0bbb1f4fcc 100644
--- a/mteb/tasks/Image/VisualSTS/en/STS15VisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/en/STS15VisualSTS.py
@@ -13,7 +13,7 @@ class STS15VisualSTS(AbsTaskVisualSTS):
         },
         description="SemEval STS 2015 dataset" + "rendered into images.",
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(eng)",
         category="i2i",
         modalities=["image"],
         eval_splits=["test"],
diff --git a/mteb/tasks/Image/VisualSTS/en/STS16VisualSTS.py b/mteb/tasks/Image/VisualSTS/en/STS16VisualSTS.py
index a78612f386..baae2f62d5 100644
--- a/mteb/tasks/Image/VisualSTS/en/STS16VisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/en/STS16VisualSTS.py
@@ -13,7 +13,7 @@ class STS16VisualSTS(AbsTaskVisualSTS):
         },
         description="SemEval STS 2016 dataset" + "rendered into images.",
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(eng)",
         category="i2i",
         modalities=["image"],
         eval_splits=["test"],
diff --git a/mteb/tasks/Image/VisualSTS/multilingual/STS17MultilingualVisualSTS.py b/mteb/tasks/Image/VisualSTS/multilingual/STS17MultilingualVisualSTS.py
index 831395c92f..413314f085 100644
--- a/mteb/tasks/Image/VisualSTS/multilingual/STS17MultilingualVisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/multilingual/STS17MultilingualVisualSTS.py
@@ -32,7 +32,7 @@ class STS17MultilingualVisualSTS(AbsTaskVisualSTS):
             + "rendered into images."
         ),
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(multi)",
         category="i2i",
         modalities=["image"],
         eval_splits=_SPLITS,
@@ -40,7 +40,7 @@ class STS17MultilingualVisualSTS(AbsTaskVisualSTS):
         main_score="cosine_spearman",
         date=("2012-01-01", "2017-12-31"),
         domains=["News", "Social", "Web", "Spoken", "Written"],
-        task_subtypes=[],
+        task_subtypes=["Rendered semantic textual similarity"],
         license="not specified",
         annotations_creators="human-annotated",
         dialect=[],
diff --git a/mteb/tasks/Image/VisualSTS/multilingual/STSBenchmarkMultilingualVisualSTS.py b/mteb/tasks/Image/VisualSTS/multilingual/STSBenchmarkMultilingualVisualSTS.py
index bdf942aef4..01d4a071b2 100644
--- a/mteb/tasks/Image/VisualSTS/multilingual/STSBenchmarkMultilingualVisualSTS.py
+++ b/mteb/tasks/Image/VisualSTS/multilingual/STSBenchmarkMultilingualVisualSTS.py
@@ -33,7 +33,7 @@ class STSBenchmarkMultilingualVisualSTS(AbsTaskVisualSTS):
             + "built upon multi-sts created by Philip May"
         ),
         reference="https://arxiv.org/abs/2402.08183/",
-        type="VisualSTS",
+        type="VisualSTS(multi)",
         category="i2i",
         modalities=["image"],
         eval_splits=_SPLITS,
@@ -41,7 +41,7 @@ class STSBenchmarkMultilingualVisualSTS(AbsTaskVisualSTS):
         main_score="cosine_spearman",
         date=("2012-01-01", "2017-12-31"),
         domains=["News", "Social", "Web", "Spoken", "Written"],
-        task_subtypes=[],
+        task_subtypes=["Rendered semantic textual similarity"],
         license="not specified",
         annotations_creators="human-annotated",
         dialect=[],
diff --git a/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py b/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py
index e79de63476..106baeb2dc 100644
--- a/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py
+++ b/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py
@@ -13,7 +13,7 @@ class MultiEURLEXMultilabelClassification(AbsTaskMultilabelClassification):
             "path": "mteb/eurlex-multilingual",
             "revision": "2aea5a6dc8fdcfeca41d0fb963c0a338930bde5c",
         },
-        description="EU laws in 23 EU languages containing gold labels.",
+        description="EU laws in 23 EU languages containing annotated labels for 21 EUROVOC concepts.",
         reference="https://huggingface.co/datasets/coastalcph/multi_eurlex",
         category="t2t",
         modalities=["text"],
diff --git a/mteb/tasks/Reranking/eng/SciDocsReranking.py b/mteb/tasks/Reranking/eng/SciDocsReranking.py
index 3bcc0b3967..c027a65316 100644
--- a/mteb/tasks/Reranking/eng/SciDocsReranking.py
+++ b/mteb/tasks/Reranking/eng/SciDocsReranking.py
@@ -51,4 +51,5 @@ class SciDocsReranking(AbsTaskRetrieval):
     abstract = "Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.",
 }
 """,
+        adapted_from=["SCIDOCS"],
     )
diff --git a/mteb/tasks/Reranking/multilingual/MIRACLReranking.py b/mteb/tasks/Reranking/multilingual/MIRACLReranking.py
index 5c20d45b17..dbf9517615 100644
--- a/mteb/tasks/Reranking/multilingual/MIRACLReranking.py
+++ b/mteb/tasks/Reranking/multilingual/MIRACLReranking.py
@@ -69,4 +69,5 @@ class MIRACLReranking(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve Wikipedia passages that answer the question"
         },
+        adapted_from=["MIRACLRetrieval"],
     )
diff --git a/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py b/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py
index 25cf5312c7..5a4ee7bb38 100644
--- a/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py
@@ -71,6 +71,7 @@ class ClimateFEVERHardNegatives(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }""",
+        adapted_from=["ClimateFEVER"],
     )
 
 
@@ -107,4 +108,5 @@ class ClimateFEVERRetrievalv2(AbsTaskRetrieval):
         prompt={
             "query": "Given a claim about climate change, retrieve documents that support or refute the claim"
         },
+        adapted_from=["ClimateFEVER"],
     )
diff --git a/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py b/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py
index 5e5dc25a41..31420a0a53 100644
--- a/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py
@@ -75,4 +75,5 @@ class DBPediaHardNegatives(AbsTaskRetrieval):
  doi =       {10.1145/3077136.3080751},
  publisher = {ACM}
 }""",
+        adapted_from=["DBPedia"],
     )
diff --git a/mteb/tasks/Retrieval/eng/FEVERRetrieval.py b/mteb/tasks/Retrieval/eng/FEVERRetrieval.py
index 794155f1e9..73f35781e1 100644
--- a/mteb/tasks/Retrieval/eng/FEVERRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/FEVERRetrieval.py
@@ -105,4 +105,5 @@ class FEVERHardNegatives(AbsTaskRetrieval):
     pages = "809--819",
     abstract = "In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss kappa. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87{\%}, while if we ignore the evidence we achieve 50.91{\%}. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.",
 }""",
+        adapted_from=["FEVER"],
     )
diff --git a/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py b/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py
index 9980781203..5ff0403aa9 100644
--- a/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py
@@ -107,4 +107,5 @@ class HotpotQAHardNegatives(AbsTaskRetrieval):
     pages = "2369--2380",
     abstract = "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.",
 }""",
+        adapted_from=["HotpotQA"],
     )
diff --git a/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py b/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py
index ac7070ba05..e4ad927143 100644
--- a/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py
+++ b/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py
@@ -121,4 +121,5 @@ class MSMARCOHardNegatives(AbsTaskRetrieval):
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }
 }""",
+        adapted_from=["MSMARCO"],
     )
diff --git a/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py b/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py
index 5029ff0c60..4972d718eb 100644
--- a/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py
@@ -58,4 +58,5 @@ class MSMARCOv2(AbsTaskRetrieval):
           bibsource = {dblp computer science bibliography, https://dblp.org}
         }
         }""",
+        adapted_from=["MSMARCO"],
     )
diff --git a/mteb/tasks/Retrieval/eng/NQRetrieval.py b/mteb/tasks/Retrieval/eng/NQRetrieval.py
index 8a39148b17..e41c7b6c8a 100644
--- a/mteb/tasks/Retrieval/eng/NQRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NQRetrieval.py
@@ -70,4 +70,5 @@ class NQHardNegatives(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve Wikipedia passages that answer the question"
         },
+        adapted_from=["NQ"],
     )
diff --git a/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py b/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py
index 57bc330ef8..f8aaf68bd5 100644
--- a/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py
@@ -41,6 +41,7 @@ class NanoArguAnaRetrieval(AbsTaskRetrieval):
   url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf}
 }""",
         prompt={"query": "Given a claim, find documents that refute the claim"},
+        adapted_from=["ArguAna"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py b/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py
index cc66fbd352..083f4ea644 100644
--- a/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py
@@ -41,6 +41,7 @@ class NanoClimateFeverRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a claim about climate change, retrieve documents that support or refute the claim"
         },
+        adapted_from=["ClimateFEVER"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py b/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py
index 420d7f5017..1c252b3565 100644
--- a/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py
@@ -31,6 +31,7 @@ class NanoDBPediaRetrieval(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation="""@article{lehmann2015dbpedia, title={DBpedia: A large-scale, multilingual knowledge base extracted from Wikipedia}, author={Lehmann, Jens and et al.}, journal={Semantic Web}, year={2015}}""",
+        adapted_from=["DBPedia"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py b/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py
index bfc1e3256d..3213992d06 100644
--- a/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py
@@ -55,6 +55,7 @@ class NanoFEVERRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a claim, retrieve documents that support or refute the claim"
         },
+        adapted_from=["FEVER"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py b/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py
index 0049f9dd13..571d95ce59 100644
--- a/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py
@@ -41,6 +41,7 @@ class NanoFiQA2018Retrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a financial question, retrieve user replies that best answer the question"
         },
+        adapted_from=["FiQA2018"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py b/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py
index 6e1048a79b..fb62746315 100644
--- a/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py
@@ -58,6 +58,7 @@ class NanoHotpotQARetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a multi-hop question, retrieve documents that can help answer the question"
         },
+        adapted_from=["HotpotQA"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py b/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py
index e88522d383..518fccc729 100644
--- a/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py
@@ -53,6 +53,7 @@ class NanoMSMARCORetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a web search query, retrieve relevant passages that answer the query"
         },
+        adapted_from=["MSMARCO"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py b/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py
index c226a8a057..4d9d0ac68a 100644
--- a/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py
@@ -43,6 +43,7 @@ class NanoNFCorpusRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve relevant documents that best answer the question"
         },
+        adapted_from=["NFCorpus"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py b/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py
index 559d651de6..ac990d4ba4 100644
--- a/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py
@@ -39,6 +39,7 @@ class NanoNQRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve Wikipedia passages that answer the question"
         },
+        adapted_from=["NQ"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py b/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py
index 18ab80dc02..007e6a192b 100644
--- a/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py
@@ -42,6 +42,7 @@ class NanoQuoraRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve questions that are semantically equivalent to the given question"
         },
+        adapted_from=["QuoraRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py b/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py
index 97841e4746..1123104497 100644
--- a/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py
@@ -41,6 +41,7 @@ class NanoSCIDOCSRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper"
         },
+        adapted_from=["SCIDOCS"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py b/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py
index e63bc651dd..625c4b853f 100644
--- a/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py
@@ -39,6 +39,7 @@ class NanoSciFactRetrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a scientific claim, retrieve documents that support or refute the claim"
         },
+        adapted_from=["SciFact"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py b/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py
index 3e8a3a9463..5b1ff910e5 100644
--- a/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py
@@ -50,6 +50,7 @@ class NanoTouche2020Retrieval(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve detailed and persuasive arguments that answer the question"
         },
+        adapted_from=["Touche2020"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/QuoraRetrieval.py b/mteb/tasks/Retrieval/eng/QuoraRetrieval.py
index d69fe1d6f9..071d8e3478 100644
--- a/mteb/tasks/Retrieval/eng/QuoraRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/QuoraRetrieval.py
@@ -79,4 +79,5 @@ class QuoraRetrievalHardNegatives(AbsTaskRetrieval):
     year = {2017},
     url = {https://kaggle.com/competitions/quora-question-pairs}
 }""",
+        adapted_from=["QuoraRetrieval"],
     )
diff --git a/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py b/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py
index 42370457c1..0e6e7d198e 100644
--- a/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py
@@ -134,4 +134,5 @@ class TopiOCQARetrievalHardNegatives(AbsTaskRetrieval):
       primaryClass={cs.CL}
         }
         """,
+        adapted_from=["TopiOCQA"],
     )
diff --git a/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py b/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py
index 359eedad4b..831caf8f92 100644
--- a/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py
@@ -48,6 +48,7 @@ class Touche2020(AbsTaskRetrieval):
         prompt={
             "query": "Given a question, retrieve detailed and persuasive arguments that answer the question"
         },
+        adapted_from=["Touche2020"],
     )
 
 
@@ -80,4 +81,5 @@ class Touche2020v3Retrieval(AbsTaskRetrieval):
    year = 2024,
    address_ = "Washington, D.C."
 }""",
+        adapted_from=["Touche2020"],
     )
diff --git a/mteb/tasks/Retrieval/fas/BEIRFa.py b/mteb/tasks/Retrieval/fas/BEIRFa.py
index 1d44b25ce9..fdbda0c8b8 100644
--- a/mteb/tasks/Retrieval/fas/BEIRFa.py
+++ b/mteb/tasks/Retrieval/fas/BEIRFa.py
@@ -29,6 +29,7 @@ class ArguAnaFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["ArguAna"],
     )
 
 
@@ -55,6 +56,7 @@ class ClimateFEVERFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["ClimateFEVER"],
     )
 
 
@@ -81,6 +83,7 @@ class CQADupstackAndroidRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackAndroid"],
     )
 
 
@@ -107,6 +110,7 @@ class CQADupstackEnglishRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackEnglish"],
     )
 
 
@@ -133,6 +137,7 @@ class CQADupstackGamingRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackGamingRetrieval"],
     )
 
 
@@ -159,6 +164,7 @@ class CQADupstackGisRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackGisRetrieval"],
     )
 
 
@@ -185,6 +191,7 @@ class CQADupstackMathematicaRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackMathematicaRetrieval"],
     )
 
 
@@ -211,6 +218,7 @@ class CQADupstackPhysicsRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackPhysicsRetrieval"],
     )
 
 
@@ -237,6 +245,7 @@ class CQADupstackProgrammersRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackProgrammersRetrieval"],
     )
 
 
@@ -263,6 +272,7 @@ class CQADupstackStatsRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackStatsRetrieval"],
     )
 
 
@@ -289,6 +299,7 @@ class CQADupstackTexRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackTexRetrieval"],
     )
 
 
@@ -315,6 +326,7 @@ class CQADupstackUnixRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackUnixRetrieval"],
     )
 
 
@@ -341,6 +353,7 @@ class CQADupstackWebmastersRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackWebmasters"],
     )
 
 
@@ -367,6 +380,7 @@ class CQADupstackWordpressRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackWordpressRetrieval"],
     )
 
 
@@ -393,6 +407,7 @@ class DBPediaFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["DBPedia"],
     )
 
 
@@ -421,6 +436,7 @@ class FiQA2018Fa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["FiQA2018"],
     )
 
 
@@ -447,6 +463,7 @@ class HotpotQAFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["HotpotQA"],
     )
 
 
@@ -475,6 +492,7 @@ class MSMARCOFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["MSMARCO"],
     )
 
 
@@ -501,6 +519,7 @@ class NFCorpusFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["NFCorpus"],
     )
 
 
@@ -527,6 +546,7 @@ class NQFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["NQ"],
     )
 
 
@@ -555,6 +575,7 @@ class QuoraRetrievalFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["QuoraRetrieval"],
     )
 
 
@@ -581,6 +602,7 @@ class SCIDOCSFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["SCIDOCS"],
     )
 
 
@@ -607,6 +629,7 @@ class SciFactFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["SciFact"],
     )
 
 
@@ -633,6 +656,7 @@ class TRECCOVIDFa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["TRECCOVID"],
     )
 
 
@@ -659,4 +683,5 @@ class Touche2020Fa(AbsTaskRetrieval):
         dialect=[],
         sample_creation="found",
         bibtex_citation=""" """,
+        adapted_from=["Touche2020"],
     )
diff --git a/mteb/tasks/Retrieval/fas/FaMTEBRetrieval.py b/mteb/tasks/Retrieval/fas/FaMTEBRetrieval.py
index c331823bd6..9e5fab52cb 100644
--- a/mteb/tasks/Retrieval/fas/FaMTEBRetrieval.py
+++ b/mteb/tasks/Retrieval/fas/FaMTEBRetrieval.py
@@ -10,7 +10,7 @@ class SynPerQARetrieval(AbsTaskRetrieval):
     metadata = TaskMetadata(
         name="SynPerQARetrieval",
         description="Synthetic Persian QA Retrieval",
-        reference="https://huggingface.co/datasets/MCINext/synthetic-persian-qa-retrieval/settings",
+        reference="https://huggingface.co/datasets/MCINext/synthetic-persian-qa-retrieval/",
         dataset={
             "path": "MCINext/synthetic-persian-qa-retrieval",
             "revision": "e85114f13f42dc1edc456d58931cc38d44d697cf",
diff --git a/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py b/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py
index 447efe1be6..f0752efb06 100644
--- a/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py
@@ -320,6 +320,7 @@ class MIRACLRetrievalHardNegatives(AbsTaskRetrieval):
     url = {https://doi.org/10.1162/tacl\_a\_00595},
     eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00595/2157340/tacl\_a\_00595.pdf},
 }""",
+        adapted_from=["MIRACLRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py b/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py
index 7cee98d1d0..8bde72f325 100644
--- a/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py
@@ -198,6 +198,7 @@ class NeuCLIR2022RetrievalHardNegatives(AbsTaskRetrieval):
   journal={arXiv preprint arXiv:2304.12367},
   year={2023}
 }""",
+        adapted_from=["NeuCLIR2022Retrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py b/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py
index 53e887425b..7165c39eae 100644
--- a/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py
@@ -201,6 +201,7 @@ class NeuCLIR2023RetrievalHardNegatives(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["NeuCLIR2022Retrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/ArguAnaNLRetrieval.py b/mteb/tasks/Retrieval/nld/ArguAnaNLRetrieval.py
index 95587d705c..aefc51ba7b 100644
--- a/mteb/tasks/Retrieval/nld/ArguAnaNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/ArguAnaNLRetrieval.py
@@ -39,4 +39,5 @@ class ArguAnaNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["ArguAna"],
     )
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackAndroidNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackAndroidNLRetrieval.py
index cb98d93ff9..dd96906d7d 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackAndroidNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackAndroidNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackAndroidNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackAndroid"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackEnglishNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackEnglishNLRetrieval.py
index 080fac0f4a..b797d3d125 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackEnglishNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackEnglishNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackEnglishNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackEnglish"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackGamingNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackGamingNLRetrieval.py
index 2b9145cf0b..0fde017f78 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackGamingNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackGamingNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackGamingNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackGamingRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackGisNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackGisNLRetrieval.py
index 615915cbef..7bf6dc26bd 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackGisNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackGisNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackGisNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackGisRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackMathematicaNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackMathematicaNLRetrieval.py
index b00ca870ee..5b0a942e33 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackMathematicaNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackMathematicaNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackMathematicaNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackMathematicaRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackPhysicsNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackPhysicsNLRetrieval.py
index 373845968b..de5f9c628a 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackPhysicsNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackPhysicsNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackPhysicsNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackPhysicsRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackProgrammersNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackProgrammersNLRetrieval.py
index 53ae13624f..6bf3fc2a34 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackProgrammersNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackProgrammersNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackProgrammersNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackProgrammersRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackStatsNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackStatsNLRetrieval.py
index e7db588440..45fd950747 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackStatsNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackStatsNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackStatsNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackStatsRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackTexNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackTexNLRetrieval.py
index 43f2057cda..486414849f 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackTexNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackTexNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackTexNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackTexRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackUnixNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackUnixNLRetrieval.py
index 6e31d88bd2..d3191590c2 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackUnixNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackUnixNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackUnixNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackUnixRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackWebmastersNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackWebmastersNLRetrieval.py
index 0e94115f78..ae1e3f1ff0 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackWebmastersNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackWebmastersNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackWebmastersNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackWebmasters"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/CQADupstackWordpressNLRetrieval.py b/mteb/tasks/Retrieval/nld/CQADupstackWordpressNLRetrieval.py
index 5541dcd9dd..0dc6cd90ff 100644
--- a/mteb/tasks/Retrieval/nld/CQADupstackWordpressNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/CQADupstackWordpressNLRetrieval.py
@@ -40,6 +40,7 @@ class CQADupstackWordpressNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackWordpressRetrieval"],
     )
 
     def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nld/ClimateFEVERNLRetrieval.py b/mteb/tasks/Retrieval/nld/ClimateFEVERNLRetrieval.py
index 57d5a6baee..32b968636e 100644
--- a/mteb/tasks/Retrieval/nld/ClimateFEVERNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/ClimateFEVERNLRetrieval.py
@@ -37,4 +37,5 @@ class ClimateFEVERNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["ClimateFEVER"],
     )
diff --git a/mteb/tasks/Retrieval/nld/DBPediaNLRetrieval.py b/mteb/tasks/Retrieval/nld/DBPediaNLRetrieval.py
index 70af70f571..efaf438ab7 100644
--- a/mteb/tasks/Retrieval/nld/DBPediaNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/DBPediaNLRetrieval.py
@@ -39,4 +39,5 @@ class DBPediaNL(AbsTaskRetrieval):
         prompt={
             "query": "Given a query, retrieve relevant entity descriptions from DBPedia"
         },
+        adapted_from=["DBPedia"],
     )
diff --git a/mteb/tasks/Retrieval/nld/FEVERNLRetrieval.py b/mteb/tasks/Retrieval/nld/FEVERNLRetrieval.py
index d8d8715850..e9cd6fa297 100644
--- a/mteb/tasks/Retrieval/nld/FEVERNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/FEVERNLRetrieval.py
@@ -42,4 +42,5 @@ class FEVERNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["FEVER"],
     )
diff --git a/mteb/tasks/Retrieval/nld/FiQA2018NLRetrieval.py b/mteb/tasks/Retrieval/nld/FiQA2018NLRetrieval.py
index 2238470594..04b122c996 100644
--- a/mteb/tasks/Retrieval/nld/FiQA2018NLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/FiQA2018NLRetrieval.py
@@ -38,4 +38,5 @@ class FiQA2018NL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["FiQA2018"],
     )
diff --git a/mteb/tasks/Retrieval/nld/HotpotQANLRetrieval.py b/mteb/tasks/Retrieval/nld/HotpotQANLRetrieval.py
index 24b86ee9d3..910b23b62d 100644
--- a/mteb/tasks/Retrieval/nld/HotpotQANLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/HotpotQANLRetrieval.py
@@ -40,4 +40,5 @@ class HotpotQANL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["HotpotQA"],
     )
diff --git a/mteb/tasks/Retrieval/nld/NFCorpusNLRetrieval.py b/mteb/tasks/Retrieval/nld/NFCorpusNLRetrieval.py
index 8c2bd7f4f2..a67be7a5ff 100644
--- a/mteb/tasks/Retrieval/nld/NFCorpusNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/NFCorpusNLRetrieval.py
@@ -37,4 +37,5 @@ class NFCorpusNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["NFCorpus"],
     )
diff --git a/mteb/tasks/Retrieval/nld/NQNLRetrieval.py b/mteb/tasks/Retrieval/nld/NQNLRetrieval.py
index b816a7da0e..c40bbd0c35 100644
--- a/mteb/tasks/Retrieval/nld/NQNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/NQNLRetrieval.py
@@ -36,4 +36,5 @@ class NQNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["NQ"],
     )
diff --git a/mteb/tasks/Retrieval/nld/QuoraNLRetrieval.py b/mteb/tasks/Retrieval/nld/QuoraNLRetrieval.py
index 40ee7185f2..a567bde781 100644
--- a/mteb/tasks/Retrieval/nld/QuoraNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/QuoraNLRetrieval.py
@@ -41,4 +41,5 @@ class QuoraNLRetrieval(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["QuoraRetrieval"],
     )
diff --git a/mteb/tasks/Retrieval/nld/SCIDOCSNLRetrieval.py b/mteb/tasks/Retrieval/nld/SCIDOCSNLRetrieval.py
index 02235e8120..1e00c545bb 100644
--- a/mteb/tasks/Retrieval/nld/SCIDOCSNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/SCIDOCSNLRetrieval.py
@@ -39,4 +39,5 @@ class SCIDOCSNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["SCIDOCS"],
     )
diff --git a/mteb/tasks/Retrieval/nld/SciFactNLRetrieval.py b/mteb/tasks/Retrieval/nld/SciFactNLRetrieval.py
index 8c28edcfe3..9cdefea606 100644
--- a/mteb/tasks/Retrieval/nld/SciFactNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/SciFactNLRetrieval.py
@@ -36,4 +36,5 @@ class SciFactNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["SciFact"],
     )
diff --git a/mteb/tasks/Retrieval/nld/TRECCOVIDNLRetrieval.py b/mteb/tasks/Retrieval/nld/TRECCOVIDNLRetrieval.py
index 102174830a..67440c1c41 100644
--- a/mteb/tasks/Retrieval/nld/TRECCOVIDNLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/TRECCOVIDNLRetrieval.py
@@ -40,4 +40,5 @@ class TRECCOVIDNL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329},
 }""",
+        adapted_from=["TRECCOVID"],
     )
diff --git a/mteb/tasks/Retrieval/nld/Touche2020NLRetrieval.py b/mteb/tasks/Retrieval/nld/Touche2020NLRetrieval.py
index d4896b438f..6457cbbbd4 100644
--- a/mteb/tasks/Retrieval/nld/Touche2020NLRetrieval.py
+++ b/mteb/tasks/Retrieval/nld/Touche2020NLRetrieval.py
@@ -35,4 +35,5 @@ class Touche2020NL(AbsTaskRetrieval):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["Touche2020"],
     )
diff --git a/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py b/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py
index 56c207141c..2182829d8e 100644
--- a/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py
@@ -37,4 +37,5 @@ class ArguAnaPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["ArguAna"],
     )
diff --git a/mteb/tasks/Retrieval/pol/CqadupstackPLRetrieval.py b/mteb/tasks/Retrieval/pol/CqadupstackPLRetrieval.py
index f56c8f5d98..112aec2727 100644
--- a/mteb/tasks/Retrieval/pol/CqadupstackPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/CqadupstackPLRetrieval.py
@@ -34,6 +34,7 @@ class CQADupstackWordpressRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackWordpressRetrieval"],
     )
 
 
@@ -67,6 +68,7 @@ class CQADupstackWebmastersRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackWebmastersRetrieval"],
     )
 
 
@@ -100,6 +102,7 @@ class CQADupstackUnixRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackUnixRetrieval"],
     )
 
 
@@ -133,6 +136,7 @@ class CQADupstackTexRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackTexRetrieval"],
     )
 
 
@@ -166,6 +170,7 @@ class CQADupstackStatsRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackStatsRetrieval"],
     )
 
 
@@ -199,6 +204,7 @@ class CQADupstackProgrammersRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackProgrammersRetrieval"],
     )
 
 
@@ -232,6 +238,7 @@ class CQADupstackPhysicsRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackPhysicsRetrieval"],
     )
 
 
@@ -265,6 +272,7 @@ class CQADupstackMathematicaRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackMathematicaRetrieval"],
     )
 
 
@@ -298,6 +306,7 @@ class CQADupstackGisRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackGisRetrieval"],
     )
 
 
@@ -331,6 +340,7 @@ class CQADupstackGamingRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackGamingRetrieval"],
     )
 
 
@@ -364,6 +374,7 @@ class CQADupstackEnglishRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackEnglishRetrieval"],
     )
 
 
@@ -397,4 +408,5 @@ class CQADupstackAndroidRetrievalPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackAndroidRetrieval"],
     )
diff --git a/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py b/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py
index db194ef42f..c4b0f01b77 100644
--- a/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py
@@ -37,6 +37,7 @@ class DBPediaPL(AbsTaskRetrieval):
  doi =       {10.1145/3077136.3080751},
  publisher = {ACM}
 }""",
+        adapted_from=["DBPedia"],
     )
 
 
@@ -73,4 +74,5 @@ class DBPediaPLHardNegatives(AbsTaskRetrieval):
  doi =       {10.1145/3077136.3080751},
  publisher = {ACM}
 }""",
+        adapted_from=["DBPedia"],
     )
diff --git a/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py b/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py
index 66fee69785..3cf5fc1c4b 100644
--- a/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py
@@ -37,4 +37,5 @@ class FiQAPLRetrieval(AbsTaskRetrieval):
 year={2021},
 url={https://openreview.net/forum?id=wCu6T5xFjeJ}
 }""",
+        adapted_from=["FiQA2018"],
     )
diff --git a/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py b/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py
index 422cef0bb1..333ee9df82 100644
--- a/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py
@@ -35,6 +35,7 @@ class HotpotQAPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["HotpotQA"],
     )
 
 
@@ -69,4 +70,5 @@ class HotpotQAPLHardNegatives(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["HotpotQA"],
     )
diff --git a/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py b/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py
index 91cacf53ac..a3dedeba15 100644
--- a/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py
@@ -37,6 +37,7 @@ class MSMARCOPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["MSMARCO"],
     )
 
 
@@ -73,4 +74,5 @@ class MSMARCOPLHardNegatives(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["MSMARCO"],
     )
diff --git a/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py b/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py
index 20a60d11ad..f41e09d877 100644
--- a/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py
@@ -35,4 +35,5 @@ class NFCorpusPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["NFCorpus"],
     )
diff --git a/mteb/tasks/Retrieval/pol/NQPLRetrieval.py b/mteb/tasks/Retrieval/pol/NQPLRetrieval.py
index b059e1954f..15e6522d93 100644
--- a/mteb/tasks/Retrieval/pol/NQPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/NQPLRetrieval.py
@@ -35,6 +35,7 @@ class NQPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["NQ"],
     )
 
 
@@ -69,4 +70,5 @@ class NQPLHardNegatives(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["NQ"],
     )
diff --git a/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py b/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py
index 3d3e3b575d..f9e8accc07 100644
--- a/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py
@@ -35,6 +35,7 @@ class QuoraPLRetrieval(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["QuoraRetrieval"],
     )
 
 
@@ -69,4 +70,5 @@ class QuoraPLRetrievalHardNegatives(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["QuoraRetrieval"],
     )
diff --git a/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py b/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py
index 9922edfece..ef689ccec8 100644
--- a/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py
@@ -35,4 +35,5 @@ class SCIDOCSPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["SCIDOCS"],
     )
diff --git a/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py b/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py
index 88783f2a09..2919c10af4 100644
--- a/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py
@@ -35,4 +35,5 @@ class SciFactPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["SciFact"],
     )
diff --git a/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py b/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py
index dd90d1f167..37d2410cce 100644
--- a/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py
@@ -38,4 +38,5 @@ class TRECCOVIDPL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["TRECCOVID"],
     )
diff --git a/mteb/tasks/Retrieval/pol/Touche2020PLRetrieval.py b/mteb/tasks/Retrieval/pol/Touche2020PLRetrieval.py
index 9b816b9aa7..c619a58f33 100644
--- a/mteb/tasks/Retrieval/pol/Touche2020PLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/Touche2020PLRetrieval.py
@@ -38,4 +38,5 @@ class Touche2020PL(AbsTaskRetrieval):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["Touche2020"],
     )
diff --git a/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py b/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py
index d62ed32c7d..027cc0a85f 100644
--- a/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py
+++ b/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py
@@ -69,4 +69,5 @@ class RiaNewsRetrievalHardNegatives(AbsTaskRetrieval):
         booktitle={Proceedings of the 41st European Conference on Information Retrieval},
         year={2019}
         }""",
+        adapted_from=["RiaNewsRetrieval"],
     )
diff --git a/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py b/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py
index 87ef59173a..2d33e72a25 100644
--- a/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py
+++ b/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py
@@ -76,6 +76,7 @@ class STS22CrosslingualSTSv2(AbsTaskSTS):
     doi = "10.18653/v1/2022.semeval-1.155",
     pages = "1094--1106",
 }""",
+        adapted_from=["STS22"],
     )
 
     min_score = 1
diff --git a/mteb/tasks/Summarization/eng/SummEvalSummarization.py b/mteb/tasks/Summarization/eng/SummEvalSummarization.py
index ab2cc5ed16..ad536d4824 100644
--- a/mteb/tasks/Summarization/eng/SummEvalSummarization.py
+++ b/mteb/tasks/Summarization/eng/SummEvalSummarization.py
@@ -72,6 +72,7 @@ class SummEvalSummarizationv2(AbsTaskSummarization):
   journal={arXiv preprint arXiv:2007.12626},
   year={2020}
 }""",
+        adapted_from=["SummEvalSummarization"],
     )
 
     min_score = 0
diff --git a/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py b/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py
index 4a099fdbba..d0352c75c8 100644
--- a/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py
+++ b/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py
@@ -71,6 +71,7 @@ class SummEvalFrSummarizationv2(AbsTaskSummarization):
   journal={arXiv preprint arXiv:2007.12626},
   year={2020}
 }""",
+        adapted_from=["SummEvalFrSummarization"],
     )
 
     min_score = 0
diff --git a/mteb/tasks/aggregated_tasks/CQADupStackNLRetrieval.py b/mteb/tasks/aggregated_tasks/CQADupStackNLRetrieval.py
index 7ca0611e5a..22a8cde7e4 100644
--- a/mteb/tasks/aggregated_tasks/CQADupStackNLRetrieval.py
+++ b/mteb/tasks/aggregated_tasks/CQADupStackNLRetrieval.py
@@ -62,4 +62,5 @@ class CQADupstackNLRetrieval(AbsTaskAggregate):
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.08329}, 
 }""",
+        adapted_from=["CQADupstackRetrieval"],
     )
diff --git a/mteb/tasks/aggregated_tasks/CQADupStackRetrievalFa.py b/mteb/tasks/aggregated_tasks/CQADupStackRetrievalFa.py
index 6a60f4b000..7e86162fdd 100644
--- a/mteb/tasks/aggregated_tasks/CQADupStackRetrievalFa.py
+++ b/mteb/tasks/aggregated_tasks/CQADupStackRetrievalFa.py
@@ -43,4 +43,5 @@ class CQADupstackRetrievalFa(AbsTaskAggregate):
         type="Retrieval",  # since everything is retrieval - otherwise it would be "Aggregated"
         eval_splits=["test"],
         bibtex_citation=""" """,
+        adapted_from=["CQADupstackRetrieval"],
     )
diff --git a/mteb/tasks/aggregated_tasks/CQADupStackRetrievalPl.py b/mteb/tasks/aggregated_tasks/CQADupStackRetrievalPl.py
index ac2e5edc96..2dec6a3a34 100644
--- a/mteb/tasks/aggregated_tasks/CQADupStackRetrievalPl.py
+++ b/mteb/tasks/aggregated_tasks/CQADupStackRetrievalPl.py
@@ -50,4 +50,5 @@ class CQADupstackRetrievalPL(AbsTaskAggregate):
       archivePrefix={arXiv},
       primaryClass={cs.IR}
 }""",
+        adapted_from=["CQADupstackRetrieval"],
     )
diff --git a/mteb/tasks/aggregated_tasks/STS17MultilingualVisualSTS.py b/mteb/tasks/aggregated_tasks/STS17MultilingualVisualSTS.py
new file mode 100644
index 0000000000..563f09cbe6
--- /dev/null
+++ b/mteb/tasks/aggregated_tasks/STS17MultilingualVisualSTS.py
@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+from mteb.abstasks.AbsTask import AbsTask
+from mteb.abstasks.aggregated_task import AbsTaskAggregate, AggregateTaskMetadata
+from mteb.tasks.Image.VisualSTS import STS17MultilingualVisualSTS
+
+task_list_sts17: list[AbsTask] = [
+    STS17MultilingualVisualSTS().filter_languages(
+        languages=["eng"], hf_subsets=["en-en"]
+    )
+]
+
+
+class STS17MultilingualVisualSTSEng(AbsTaskAggregate):
+    metadata = AggregateTaskMetadata(
+        name="VisualSTS17Eng",
+        description="STS17MultilingualVisualSTS English only.",
+        reference="https://arxiv.org/abs/2402.08183/",
+        tasks=task_list_sts17,
+        category="i2i",
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[""],
+        eval_langs={
+            "en-en": ["eng-Latn"]
+        },  # rely on subsets to filter scores in TaskResults.get_score_fast().
+        sample_creation="rendered",
+        main_score="cosine_spearman",
+        type="VisualSTS(eng)",
+        eval_splits=["test"],
+        bibtex_citation="""@article{xiao2024pixel,
+  title={Pixel Sentence Representation Learning},
+  author={Xiao, Chenghao and Huang, Zhuoxu and Chen, Danlu and Hudson, G Thomas and Li, Yizhi and Duan, Haoran and Lin, Chenghua and Fu, Jie and Han, Jungong and Moubayed, Noura Al},
+  journal={arXiv preprint arXiv:2402.08183},
+  year={2024}
+}""",
+    )
+
+
+task_list_sts17_multi: list[AbsTask] = [
+    STS17MultilingualVisualSTS().filter_languages(
+        languages=["ara", "eng", "spa", "kor"],
+        hf_subsets=[
+            "ko-ko",
+            "ar-ar",
+            "en-ar",
+            "en-de",
+            "en-tr",
+            "es-en",
+            "es-es",
+            "fr-en",
+            "it-en",
+            "nl-en",
+        ],
+    )
+]
+
+
+class STS17MultilingualVisualSTSMultilingual(AbsTaskAggregate):
+    metadata = AggregateTaskMetadata(
+        name="VisualSTS17Multilingual",
+        description="STS17MultilingualVisualSTS multilingual.",
+        reference="https://arxiv.org/abs/2402.08183/",
+        tasks=task_list_sts17_multi,
+        category="i2i",
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[""],
+        sample_creation="rendered",
+        main_score="cosine_spearman",
+        type="VisualSTS(multi)",
+        eval_splits=["test"],
+        eval_langs={  # rely on subsets to filter scores in TaskResults.get_score_fast().
+            "ko-ko": ["kor-Hang"],
+            "ar-ar": ["ara-Arab"],
+            "en-ar": ["eng-Latn", "ara-Arab"],
+            "en-de": ["eng-Latn", "deu-Latn"],
+            "en-tr": ["eng-Latn", "tur-Latn"],
+            "es-en": ["spa-Latn", "eng-Latn"],
+            "es-es": ["spa-Latn"],
+            "fr-en": ["fra-Latn", "eng-Latn"],
+            "it-en": ["ita-Latn", "eng-Latn"],
+            "nl-en": ["nld-Latn", "eng-Latn"],
+        },
+        bibtex_citation="""@article{xiao2024pixel,
+  title={Pixel Sentence Representation Learning},
+  author={Xiao, Chenghao and Huang, Zhuoxu and Chen, Danlu and Hudson, G Thomas and Li, Yizhi and Duan, Haoran and Lin, Chenghua and Fu, Jie and Han, Jungong and Moubayed, Noura Al},
+  journal={arXiv preprint arXiv:2402.08183},
+  year={2024}
+}""",
+    )
diff --git a/mteb/tasks/aggregated_tasks/STSBenchmarkMultilingualVisualSTS.py b/mteb/tasks/aggregated_tasks/STSBenchmarkMultilingualVisualSTS.py
new file mode 100644
index 0000000000..74c5f9feb6
--- /dev/null
+++ b/mteb/tasks/aggregated_tasks/STSBenchmarkMultilingualVisualSTS.py
@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+from mteb.abstasks.AbsTask import AbsTask
+from mteb.abstasks.aggregated_task import AbsTaskAggregate, AggregateTaskMetadata
+from mteb.tasks.Image.VisualSTS import STSBenchmarkMultilingualVisualSTS
+
+task_list_stsb: list[AbsTask] = [
+    STSBenchmarkMultilingualVisualSTS().filter_languages(
+        languages=["eng"], hf_subsets=["en"]
+    )
+]
+
+
+class STSBenchmarkMultilingualVisualSTSEng(AbsTaskAggregate):
+    metadata = AggregateTaskMetadata(
+        name="VisualSTS-b-Eng",
+        description="STSBenchmarkMultilingualVisualSTS English only.",
+        reference="https://arxiv.org/abs/2402.08183/",
+        tasks=task_list_stsb,
+        category="i2i",
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[""],
+        sample_creation="rendered",
+        main_score="cosine_spearman",
+        type="VisualSTS(eng)",
+        eval_splits=["test"],
+        eval_langs=["eng-Latn"],
+        bibtex_citation="""@article{xiao2024pixel,
+  title={Pixel Sentence Representation Learning},
+  author={Xiao, Chenghao and Huang, Zhuoxu and Chen, Danlu and Hudson, G Thomas and Li, Yizhi and Duan, Haoran and Lin, Chenghua and Fu, Jie and Han, Jungong and Moubayed, Noura Al},
+  journal={arXiv preprint arXiv:2402.08183},
+  year={2024}
+}""",
+    )
+
+
+task_list_multi: list[AbsTask] = [
+    STSBenchmarkMultilingualVisualSTS().filter_languages(
+        languages=[
+            "deu",
+            "spa",
+            "fra",
+            "ita",
+            "nld",
+            "pol",
+            "por",
+            "rus",
+            "cmn",
+        ],
+        hf_subsets=[
+            "de",
+            "es",
+            "fr",
+            "it",
+            "nl",
+            "pl",
+            "pt",
+            "ru",
+            "zh",
+        ],
+    )
+]
+
+
+class STSBenchmarkMultilingualVisualSTSMultilingual(AbsTaskAggregate):
+    metadata = AggregateTaskMetadata(
+        name="VisualSTS-b-Multilingual",
+        description="STSBenchmarkMultilingualVisualSTS multilingual.",
+        reference="https://arxiv.org/abs/2402.08183/",
+        tasks=task_list_multi,
+        category="i2i",
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[""],
+        sample_creation="rendered",
+        main_score="cosine_spearman",
+        type="VisualSTS(multi)",
+        eval_splits=["test"],
+        eval_langs=[
+            "deu-Latn",
+            "spa-Latn",
+            "fra-Latn",
+            "ita-Latn",
+            "nld-Latn",
+            "pol-Latn",
+            "por-Latn",
+            "rus-Cyrl",
+            "cmn-Hans",
+        ],
+        bibtex_citation="""@article{xiao2024pixel,
+  title={Pixel Sentence Representation Learning},
+  author={Xiao, Chenghao and Huang, Zhuoxu and Chen, Danlu and Hudson, G Thomas and Li, Yizhi and Duan, Haoran and Lin, Chenghua and Fu, Jie and Han, Jungong and Moubayed, Noura Al},
+  journal={arXiv preprint arXiv:2402.08183},
+  year={2024}
+}""",
+    )
diff --git a/mteb/tasks/aggregated_tasks/__init__.py b/mteb/tasks/aggregated_tasks/__init__.py
index 0cfa47da5e..faaf6eb996 100644
--- a/mteb/tasks/aggregated_tasks/__init__.py
+++ b/mteb/tasks/aggregated_tasks/__init__.py
@@ -4,12 +4,24 @@
 from .CQADupStackRetrieval import CQADupstackRetrieval
 from .CQADupStackRetrievalFa import CQADupstackRetrievalFa
 from .CQADupStackRetrievalPl import CQADupstackRetrievalPL
+from .STS17MultilingualVisualSTS import (
+    STS17MultilingualVisualSTSEng,
+    STS17MultilingualVisualSTSMultilingual,
+)
+from .STSBenchmarkMultilingualVisualSTS import (
+    STSBenchmarkMultilingualVisualSTSEng,
+    STSBenchmarkMultilingualVisualSTSMultilingual,
+)
 from .SynPerChatbotConvSAClassification import SynPerChatbotConvSAClassification
 
 __all__ = [
     "CQADupstackRetrieval",
     "CQADupstackRetrievalFa",
     "CQADupstackNLRetrieval",
+    "STS17MultilingualVisualSTSEng",
+    "STS17MultilingualVisualSTSMultilingual",
+    "STSBenchmarkMultilingualVisualSTSEng",
+    "STSBenchmarkMultilingualVisualSTSMultilingual",
     "CQADupstackRetrievalPL",
     "SynPerChatbotConvSAClassification",
 ]
diff --git a/pyproject.toml b/pyproject.toml
index b50b124e6a..3cb8365b44 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "mteb"
-version = "1.34.29"
+version = "1.36.8"
 description = "Massive Text Embedding Benchmark"
 readme = "README.md"
 authors = [
diff --git a/scripts/run_mieb.py b/scripts/run_mieb.py
index b3c55b26d5..a0d444617e 100644
--- a/scripts/run_mieb.py
+++ b/scripts/run_mieb.py
@@ -60,13 +60,14 @@
         task_types=[
             "Any2AnyRetrieval",
             "Any2AnyMultiChoice",
-            "Any2TextMutipleChoice",
+            "VisionCentric",
             "ImageClustering",
             "ImageClassification",
             "ImageMultilabelClassification",
-            "ImageTextPairClassification",
+            "Compositionality",
             "VisualSTS",
             "ZeroShotClassification",
+            "DocumentUnderstanding",
         ]
     )
     # get i-only tasks for i-only models.
diff --git a/scripts/run_mieb_agg_task.py b/scripts/run_mieb_agg_task.py
new file mode 100644
index 0000000000..b59f989efc
--- /dev/null
+++ b/scripts/run_mieb_agg_task.py
@@ -0,0 +1,66 @@
+from __future__ import annotations
+
+import mteb
+
+for model_name in [
+    "openai/clip-vit-base-patch32",
+    "openai/clip-vit-base-patch16",
+    "openai/clip-vit-large-patch14",
+    "royokong/e5-v",
+    "BAAI/bge-visualized-base",
+    "BAAI/bge-visualized-m3",
+    "kakaobrain/align-base",
+    "jinaai/jina-clip-v1",
+    "nomic-ai/nomic-embed-vision-v1.5",
+    "Salesforce/blip-image-captioning-large",
+    "Salesforce/blip-image-captioning-base",
+    "Salesforce/blip2-opt-2.7b",
+    "Salesforce/blip2-opt-6.7b-coco",
+    "facebook/dinov2-small",
+    "facebook/dinov2-base",
+    "facebook/dinov2-large",
+    "facebook/dinov2-giant",
+    "nyu-visionx/moco-v3-vit-b",
+    "nyu-visionx/moco-v3-vit-l",
+    "google/siglip-so400m-patch14-224",
+    "google/siglip-so400m-patch14-384",
+    "google/siglip-so400m-patch16-256-i18n",
+    "google/siglip-base-patch16-256-multilingual",
+    "google/siglip-base-patch16-256",
+    "google/siglip-base-patch16-512",
+    "google/siglip-base-patch16-384",
+    "google/siglip-base-patch16-224",
+    "google/siglip-large-patch16-256",
+    "google/siglip-large-patch16-384",
+    "laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K",
+    "laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K",
+    "laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K",
+    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
+    "laion/CLIP-ViT-g-14-laion2B-s34B-b88K",
+    "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
+    "laion/CLIP-ViT-L-14-laion2B-s32B-b82K",
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K",
+    "TIGER-Lab/VLM2Vec-Full",
+    "TIGER-Lab/VLM2Vec-LoRA",
+    "Salesforce/blip-itm-base-coco",
+    "Salesforce/blip-itm-large-coco",
+    "Salesforce/blip-itm-base-flickr",
+    "Salesforce/blip-itm-large-flickr",
+    "QuanSun/EVA02-CLIP-B-16",
+    "QuanSun/EVA02-CLIP-L-14",
+    "QuanSun/EVA02-CLIP-bigE-14",
+    "QuanSun/EVA02-CLIP-bigE-14-plus",
+    "voyageai/voyage-multimodal-3",
+]:
+    model = mteb.get_model(model_name)
+    tasks = mteb.get_tasks(
+        tasks=[
+            "VisualSTS-b-Eng",
+            "VisualSTS-b-Multilingual",
+            "VisualSTS17Eng",
+            "VisualSTS17Multilingual",
+        ]
+    )
+
+    evaluation = mteb.MTEB(tasks=tasks)
+    results = evaluation.run(model, output_folder="/home/.cache/mteb/results/results")
diff --git a/scripts/run_mieb_rerun_siglip.py b/scripts/run_mieb_rerun_siglip.py
index 539a31e2e7..5c7bad9f27 100644
--- a/scripts/run_mieb_rerun_siglip.py
+++ b/scripts/run_mieb_rerun_siglip.py
@@ -17,13 +17,14 @@
         task_types=[
             "Any2AnyRetrieval",
             "Any2AnyMultiChoice",
-            "Any2TextMutipleChoice",
+            "VisionCentric",
             "ImageClustering",
             "ImageClassification",
             "ImageMultilabelClassification",
-            "ImageTextPairClassification",
+            "Compositionality",
             # "VisualSTS",  # visual sts does not need rerun as will be the same after fixed.
             "ZeroShotClassification",
+            "DocumentUnderstanding",
         ]
     )
     evaluation = mteb.MTEB(tasks=tasks)
diff --git a/tests/test_TaskMetadata.py b/tests/test_TaskMetadata.py
index 1ac891b422..9e28cfae9f 100644
--- a/tests/test_TaskMetadata.py
+++ b/tests/test_TaskMetadata.py
@@ -184,6 +184,8 @@
     "CQADupstackRetrieval-PL",
     "WikiClusteringP2P",
     "VGClustering",
+    "VisualSTS17Eng",
+    "VisualSTS17Multilingual",
 ]
 
 
diff --git a/tests/test_benchmark/mock_tasks.py b/tests/test_benchmark/mock_tasks.py
index 4d25fa19d6..4b2023e9c7 100644
--- a/tests/test_benchmark/mock_tasks.py
+++ b/tests/test_benchmark/mock_tasks.py
@@ -2609,7 +2609,7 @@ class MockTextMultipleChoiceTask(AbsTaskAny2TextMultipleChoice):
     }
 
     metadata = TaskMetadata(
-        type="Any2TextMutipleChoice",
+        type="VisionCentric",
         name="MockTextMultipleChoice",
         main_score="accuracy",
         **general_args,  # type: ignore
@@ -3057,7 +3057,7 @@ class MockImageTextPairClassificationTask(AbsTaskImageTextPairClassification):
     }
 
     metadata = TaskMetadata(
-        type="ImageTextPairClassification",
+        type="Compositionality",
         name="MockImageTextPairClassification",
         main_score="text_acc",
         **general_args,  # type: ignore
@@ -3115,7 +3115,7 @@ class MockMultilingualImageTextPairClassificationTask(
     }
 
     metadata = TaskMetadata(
-        type="ImageTextPairClassification",
+        type="Compositionality",
         name="MockMultilingualImageTextPairClassification",
         main_score="accuracy",
         **general_args,  # type: ignore
@@ -3160,7 +3160,7 @@ class MockVisualSTSTask(AbsTaskVisualSTS):
     }
 
     metadata = TaskMetadata(
-        type="VisualSTS",
+        type="VisualSTS(eng)",
         name="MockVisualSTS",
         main_score="cosine_spearman",
         **general_args,  # type: ignore
diff --git a/tests/test_model_meta/test_model_meta_.py b/tests/test_model_meta/test_model_meta_.py
deleted file mode 100644
index 168c1d3653..0000000000
--- a/tests/test_model_meta/test_model_meta_.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-
-import pytest
-from sentence_transformers import CrossEncoder, SentenceTransformer
-
-import mteb
-from mteb import MTEB
-from mteb.abstasks import AbsTask
-from mteb.model_meta import ScoringFunction
-from tests.test_benchmark.mock_tasks import MockRetrievalTask
-
-
-def test_create_model_meta_from_sentence_transformers():
-    model_name = "sentence-transformers/average_word_embeddings_levy_dependency"
-    revision = "6d9c09a789ad5dd126b476323fccfeeafcd90509"
-    model = SentenceTransformer(model_name, revision=revision)
-
-    meta = MTEB.create_model_meta(model)
-
-    assert meta.similarity_fn_name == ScoringFunction.COSINE
-    assert meta.similarity_fn_name == "cosine"
-    assert meta.embed_dim == model.get_sentence_embedding_dimension()
-    assert type(meta.framework) is list
-    assert meta.framework[0] == "Sentence Transformers"
-    assert meta.name == model_name
-    assert meta.revision == revision
-
-
-def test_create_model_meta_from_cross_encoder():
-    model_name = "cross-encoder/ms-marco-TinyBERT-L-2-v2"
-    revision = "841d331b6f34b15d6ac0ab366ae3a3b36eeac691"
-    model = CrossEncoder(model_name, revision=revision)
-
-    meta = MTEB.create_model_meta(model)
-
-    assert meta.name == model_name
-    assert meta.revision == revision
-
-
-@pytest.mark.parametrize("task", [MockRetrievalTask()])
-def test_output_folder_model_meta(task: AbsTask, tmp_path: Path):
-    mteb = MTEB(tasks=[task])
-    model_name = "cross-encoder/ms-marco-TinyBERT-L-2-v2"
-    model = CrossEncoder(model_name)
-    meta = mteb.create_model_meta(model)
-    output_path = mteb.create_output_folder(
-        model_meta=meta, output_folder=tmp_path.as_posix()
-    )
-
-    output_path = Path(output_path)
-    assert output_path.exists()
-    assert output_path.is_dir()
-    assert output_path.name == model.config._commit_hash
-    assert output_path.parent.name == "cross-encoder__ms-marco-TinyBERT-L-2-v2"
-    assert output_path.parent.parent == tmp_path
-
-
-def test_model_meta_colbert():
-    model_name = "colbert-ir/colbertv2.0"
-    colbert_model = pytest.importorskip("pylate.models", reason="pylate not installed")
-    revision = "c1e84128e85ef755c096a95bdb06b47793b13acf"
-    model = colbert_model.ColBERT(model_name_or_path=model_name, revision=revision)
-
-    meta = MTEB.create_model_meta(model)
-
-    assert meta.similarity_fn_name == "MaxSim"
-    assert meta.similarity_fn_name == ScoringFunction.MAX_SIM
-    assert type(meta.framework) is list
-    assert meta.framework[0] == "Sentence Transformers"
-    assert meta.name == model_name
-    assert meta.revision == revision
-
-
-@pytest.mark.parametrize(
-    ("model_name", "expected_memory"),
-    [
-        ("intfloat/e5-mistral-7b-instruct", 13563),  # multiple safetensors
-        ("infgrad/jasper_en_vision_language_v1", 3802),  # bf16
-        ("intfloat/multilingual-e5-small", 449),  # safetensors
-        ("BAAI/bge-m3", 2167),  # pytorch_model.bin
-    ],
-)
-def test_model_memory_usage(model_name: str, expected_memory: int | None):
-    meta = mteb.get_model_meta(model_name)
-    assert meta.memory_usage_mb is not None
-    used_memory = round(meta.memory_usage_mb)
-    assert used_memory == expected_memory
-
-
-def test_model_memory_usage_api_model():
-    meta = mteb.get_model_meta("openai/text-embedding-3-large")
-    assert meta.memory_usage_mb is None
diff --git a/tests/test_models/__init__.py b/tests/test_models/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/test_models/test_model_meta.py b/tests/test_models/test_model_meta.py
index 89e85dae7e..03a8267017 100644
--- a/tests/test_models/test_model_meta.py
+++ b/tests/test_models/test_model_meta.py
@@ -1,8 +1,63 @@
 from __future__ import annotations
 
+from pathlib import Path
+
 import pytest
+from sentence_transformers import CrossEncoder, SentenceTransformer
 
 import mteb
+from mteb import MTEB, AbsTask
+from mteb.model_meta import ModelMeta, ScoringFunction
+from tests.test_benchmark.mock_tasks import MockRetrievalTask
+
+
+@pytest.mark.parametrize(
+    "training_datasets",
+    [
+        {"Touche2020": []},  # parent task
+        {"Touche2020-NL": []},  # child task
+    ],
+)
+def test_model_similar_tasks(training_datasets):
+    dummy_model_meta = ModelMeta(
+        name="test_model",
+        revision="test",
+        release_date=None,
+        languages=None,
+        loader=None,
+        n_parameters=None,
+        memory_usage_mb=None,
+        max_tokens=None,
+        embed_dim=None,
+        license=None,
+        open_weights=None,
+        public_training_code=None,
+        public_training_data=None,
+        framework=[],
+        reference=None,
+        similarity_fn_name=None,
+        use_instructions=None,
+        training_datasets=training_datasets,
+        adapted_from=None,
+        superseded_by=None,
+    )
+    expected = [
+        "NanoTouche2020Retrieval",
+        "Touche2020",
+        "Touche2020-Fa",
+        "Touche2020-NL",
+        "Touche2020-PL",
+        "Touche2020Retrieval.v3",
+    ]
+    assert sorted(dummy_model_meta.get_training_datasets().keys()) == expected
+
+
+def test_model_training_dataset_adapted():
+    model_meta = mteb.get_model_meta("deepvk/USER-bge-m3")
+    assert model_meta.adapted_from == "BAAI/bge-m3"
+    # MIRACLRetrieval not in training_datasets of deepvk/USER-bge-m3, but in
+    # training_datasets of BAAI/bge-m3
+    assert "MIRACLRetrieval" in model_meta.get_training_datasets()
 
 
 @pytest.mark.parametrize(
@@ -24,3 +79,64 @@ def test_model_memory_usage(model_name: str, expected_memory: int | None):
 def test_model_memory_usage_api_model():
     meta = mteb.get_model_meta("openai/text-embedding-3-large")
     assert meta.memory_usage_mb is None
+
+
+def test_create_model_meta_from_sentence_transformers():
+    model_name = "sentence-transformers/average_word_embeddings_levy_dependency"
+    revision = "6d9c09a789ad5dd126b476323fccfeeafcd90509"
+    model = SentenceTransformer(model_name, revision=revision)
+
+    meta = MTEB.create_model_meta(model)
+
+    assert meta.similarity_fn_name == ScoringFunction.COSINE
+    assert meta.similarity_fn_name == "cosine"
+    assert meta.embed_dim == model.get_sentence_embedding_dimension()
+    assert type(meta.framework) is list
+    assert meta.framework[0] == "Sentence Transformers"
+    assert meta.name == model_name
+    assert meta.revision == revision
+
+
+def test_create_model_meta_from_cross_encoder():
+    model_name = "cross-encoder/ms-marco-TinyBERT-L-2-v2"
+    revision = "841d331b6f34b15d6ac0ab366ae3a3b36eeac691"
+    model = CrossEncoder(model_name, revision=revision)
+
+    meta = MTEB.create_model_meta(model)
+
+    assert meta.name == model_name
+    assert meta.revision == revision
+
+
+@pytest.mark.parametrize("task", [MockRetrievalTask()])
+def test_output_folder_model_meta(task: AbsTask, tmp_path: Path):
+    mteb = MTEB(tasks=[task])
+    model_name = "cross-encoder/ms-marco-TinyBERT-L-2-v2"
+    model = CrossEncoder(model_name)
+    meta = mteb.create_model_meta(model)
+    output_path = mteb.create_output_folder(
+        model_meta=meta, output_folder=tmp_path.as_posix()
+    )
+
+    output_path = Path(output_path)
+    assert output_path.exists()
+    assert output_path.is_dir()
+    assert output_path.name == model.config._commit_hash
+    assert output_path.parent.name == "cross-encoder__ms-marco-TinyBERT-L-2-v2"
+    assert output_path.parent.parent == tmp_path
+
+
+def test_model_meta_colbert():
+    model_name = "colbert-ir/colbertv2.0"
+    colbert_model = pytest.importorskip("pylate.models", reason="pylate not installed")
+    revision = "c1e84128e85ef755c096a95bdb06b47793b13acf"
+    model = colbert_model.ColBERT(model_name_or_path=model_name, revision=revision)
+
+    meta = MTEB.create_model_meta(model)
+
+    assert meta.similarity_fn_name == "MaxSim"
+    assert meta.similarity_fn_name == ScoringFunction.MAX_SIM
+    assert type(meta.framework) is list
+    assert meta.framework[0] == "Sentence Transformers"
+    assert meta.name == model_name
+    assert meta.revision == revision
diff --git a/tests/test_overview.py b/tests/test_overview.py
index e5b8db9097..aeb7e238e2 100644
--- a/tests/test_overview.py
+++ b/tests/test_overview.py
@@ -76,11 +76,16 @@ def test_get_tasks_filtering():
     tasks = get_tasks(languages=["eng"])
 
     for task in tasks:
-        if task.metadata.is_multilingual:
+        if (
+            task.metadata.is_multilingual
+            and task.metadata.name != "STS17MultilingualVisualSTSEng"
+        ):
             assert isinstance(task.metadata.eval_langs, dict)
 
             for hf_subset in task.hf_subsets:
-                assert "eng-Latn" in task.metadata.eval_langs[hf_subset]
+                assert "eng-Latn" in task.metadata.eval_langs[hf_subset], (
+                    f"{task.metadata.name}"
+                )
 
 
 @pytest.mark.parametrize("script", [["Latn"], ["Cyrl"], None])
diff --git a/tests/test_tasks/test_all_abstasks.py b/tests/test_tasks/test_all_abstasks.py
index 0377540b78..d40c793988 100644
--- a/tests/test_tasks/test_all_abstasks.py
+++ b/tests/test_tasks/test_all_abstasks.py
@@ -3,8 +3,8 @@
 import logging
 from unittest.mock import Mock, patch
 
+import huggingface_hub
 import pytest
-import requests
 
 import mteb
 from mteb.abstasks import AbsTask
@@ -42,6 +42,17 @@
 )
 
 
+dataset_revisions = list(
+    {  # deduplicate as multiple tasks rely on the same dataset (save us at least 100 test cases)
+        (t.metadata.dataset["path"], t.metadata.dataset["revision"])
+        for t in mteb.get_tasks(exclude_superseded=False)
+        if not isinstance(t, (AbsTaskAggregate, AbsTaskSpeedTask))
+        and t.metadata.name != "AfriSentiLangClassification"
+        and t.metadata.name not in ALL_MOCK_TASKS
+    }
+)
+
+
 @pytest.mark.parametrize("task", tasks)
 @patch("datasets.load_dataset")
 @patch("datasets.concatenate_datasets")
@@ -74,13 +85,16 @@ def test_load_data(
 )
 @pytest.mark.parametrize("dataset_revision", dataset_revisions)
 def test_dataset_on_hf(dataset_revision: tuple[str, str]):
-    dataset, revision = dataset_revision
-    url = f"https://huggingface.co/datasets/{dataset}/tree/{revision}"
-    response = requests.head(url)
-
-    assert response.status_code == 200, (
-        f"Dataset {dataset} - {revision} not available. Status code: {response.status_code}"
-    )
+    repo_id, revision = dataset_revision
+    try:
+        huggingface_hub.dataset_info(repo_id, revision=revision)
+    except (
+        huggingface_hub.errors.RepositoryNotFoundError,
+        huggingface_hub.errors.RevisionNotFoundError,
+    ):
+        assert False, f"Dataset {repo_id} - {revision} not available"
+    except Exception as e:
+        assert False, f"Dataset {repo_id} - {revision} failed with {e}"
 
 
 def test_superseded_dataset_exists():