diff --git a/README.md b/README.md
index 06b9d817a7..3d859ce8c3 100644
--- a/README.md
+++ b/README.md
@@ -210,6 +210,21 @@ Note that the public leaderboard uses the test splits for all datasets except MS
 
 </details>
 
+
+<details>
+  <summary> Selecting evaluation subset </summary>
+
+### Selecting evaluation subset
+You can evaluate only on selected subsets. For example, if you want to evaluate only the `subset_name_to_run` subset of all tasks, do the following:
+
+```python
+evaluation.run(model, eval_subsets=["subset_name_to_run"])
+```
+
+Monolingual tasks have `default` subset, other tasks have subsets that are specific to the dataset.
+
+</details>
+
 <details>
   <summary>  Using a custom model </summary>
 
@@ -315,6 +330,34 @@ evaluation.run(
 )
 ```
 
+</details>
+
+<details>
+  <summary> Late Interaction (ColBERT) </summary>
+
+### Using Late Interaction models for retrieval
+
+```python
+from mteb import MTEB
+import mteb
+
+
+colbert = mteb.get_model("colbert-ir/colbertv2.0")
+tasks = mteb.get_tasks(tasks=["NFCorpus"], languages=["eng"])
+
+eval_splits = ["test"]
+
+evaluation = MTEB(tasks=tasks)
+
+evaluation.run(
+    colbert,
+    eval_splits=eval_splits,
+    corpus_chunk_size=500,
+)
+```
+This implementation employs the MaxSim operation to compute the similarity between sentences. While MaxSim provides high-quality results, it processes a larger number of embeddings, potentially leading to increased resource usage. To manage resource consumption, consider lowering the `corpus_chunk_size` parameter.
+
+
 </details>
 
 <details>
diff --git a/docs/tasks.md b/docs/tasks.md
index b7be0d58cb..f4ec3c792e 100644
--- a/docs/tasks.md
+++ b/docs/tasks.md
@@ -1,7 +1,6 @@
-# Task Overview
+## Available tasks
+The following tables give you an overview of the tasks in MTEB.
 
-The following tables give you an overview of the tasks within MTEB.
-    
 <details>
 
 <!-- This allows the table to be autogenerated in the future: -->
@@ -64,7 +63,7 @@ The following tables give you an overview of the tasks within MTEB.
 | [CLSClusteringP2P.v2](https://arxiv.org/abs/2209.05034) (Yudong Li, 2022) | ['cmn'] | Clustering | p2p | [Academic, Written] | None | None |
 | [CLSClusteringS2S.v2](https://arxiv.org/abs/2209.05034) (Yudong Li, 2022) | ['cmn'] | Clustering | s2s | [Academic, Written] | None | None |
 | [CMedQAv1-reranking](https://github.com/zhangsheng93/cMedQA) (Zhang et al., 2017) | ['cmn'] | Reranking | s2s | [Medical, Written] | None | None |
-| [CMedQAv2-reranking](https://github.com/zhangsheng93/cMedQA2) (S. Zhang, 2018) | ['cmn'] | Reranking | s2s |  | None | None |
+| [CMedQAv2-reranking](https://github.com/zhangsheng93/cMedQA2) (S. Zhang, 2018) | ['cmn'] | Reranking | s2s | [Medical, Written] | None | None |
 | [COIRCodeSearchNetRetrieval](https://huggingface.co/datasets/code_search_net/) (Husain et al., 2019) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 1056326} | {'test': {'number_of_characters': 36843313, 'num_samples': 1056326, 'num_queries': 52561, 'num_documents': 1003765, 'min_document_length': 54, 'average_document_length': 34.71, 'max_document_length': 334374, 'unique_documents': 1003765, 'min_query_length': 2, 'average_query_length': 38.19, 'max_query_length': 2, 'unique_queries': 52561, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 52561, 'hf_subset_descriptive_stats': {'python': {'number_of_characters': 14574651, 'num_samples': 295228, 'num_queries': 14918, 'num_documents': 280310, 'min_document_length': 95, 'average_document_length': 49.99, 'max_document_length': 14008, 'unique_documents': 280310, 'min_query_length': 2, 'average_query_length': 37.58, 'max_query_length': 2, 'unique_queries': 14918, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 14918}, 'javascript': {'number_of_characters': 2587540, 'num_samples': 68145, 'num_queries': 3291, 'num_documents': 64854, 'min_document_length': 87, 'average_document_length': 37.9, 'max_document_length': 334374, 'unique_documents': 64854, 'min_query_length': 2, 'average_query_length': 39.41, 'max_query_length': 2, 'unique_queries': 3291, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 3291}, 'go': {'number_of_characters': 3641108, 'num_samples': 190562, 'num_queries': 8122, 'num_documents': 182440, 'min_document_length': 54, 'average_document_length': 17.96, 'max_document_length': 5280, 'unique_documents': 182440, 'min_query_length': 2, 'average_query_length': 44.92, 'max_query_length': 2, 'unique_queries': 8122, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 8122}, 'ruby': {'number_of_characters': 629446, 'num_samples': 28831, 'num_queries': 1261, 'num_documents': 27570, 'min_document_length': 83, 'average_document_length': 20.83, 'max_document_length': 3992, 'unique_documents': 27570, 'min_query_length': 2, 'average_query_length': 43.73, 'max_query_length': 2, 'unique_queries': 1261, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1261}, 'java': {'number_of_characters': 6791137, 'num_samples': 191821, 'num_queries': 10955, 'num_documents': 180866, 'min_document_length': 77, 'average_document_length': 35.55, 'max_document_length': 7615, 'unique_documents': 180866, 'min_query_length': 2, 'average_query_length': 33.02, 'max_query_length': 2, 'unique_queries': 10955, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 10955}, 'php': {'number_of_characters': 8619431, 'num_samples': 281739, 'num_queries': 14014, 'num_documents': 267725, 'min_document_length': 94, 'average_document_length': 30.2, 'max_document_length': 4904, 'unique_documents': 267725, 'min_query_length': 2, 'average_query_length': 38.21, 'max_query_length': 2, 'unique_queries': 14014, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 14014}}}} |
 | [CPUSpeedTask](https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/c8376f967d1294419be1d3eb41217d04cd3a65d3/src/seb/registered_tasks/speed.py#L83-L96) | ['eng'] | Speed | s2s | [Fiction, Written] | None | None |
 | [CQADupstackAndroidRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p |  | None | None |
@@ -120,11 +119,12 @@ The following tables give you an overview of the tasks within MTEB.
 | [CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
 | [CUADVolumeRestrictionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
 | [CUADWarrantyDurationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUREv1](https://huggingface.co/datasets/clinia/CUREv1) | ['eng', 'fra', 'spa'] | Retrieval | s2p | [Medical, Academic, Written] | None | None |
 | [CanadaTaxCourtOutcomesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
 | [CataloniaTweetClassification](https://aclanthology.org/2020.lrec-1.171/) | ['cat', 'spa'] | Classification | s2s | [Social, Government, Written] | None | None |
 | [ClimateFEVER](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p |  | None | None |
 | [ClimateFEVERHardNegatives](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p |  | None | None |
-| [CmedqaRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | ['cmn'] | Retrieval | s2p |  | None | None |
+| [CmedqaRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | ['cmn'] | Retrieval | s2p | [Medical, Written] | None | None |
 | [Cmnli](https://huggingface.co/datasets/clue/viewer/cmnli) | ['cmn'] | PairClassification | s2s |  | None | None |
 | [CodeEditSearchRetrieval](https://huggingface.co/datasets/cassanof/CodeEditSearch/viewer) (Niklas Muennighoff, 2023) | ['c', 'c++', 'go', 'java', 'javascript', 'php', 'python', 'ruby', 'rust', 'scala', 'shell', 'swift', 'typescript'] | Retrieval | p2p | [Programming, Written] | {'train': 26000} | {'train': {'number_of_characters': 935841, 'num_samples': 26000, 'num_queries': 13000, 'num_documents': 13000, 'min_document_length': 18, 'average_document_length': 70.99, 'max_document_length': 2532, 'unique_documents': 13000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 13000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 13000, 'hf_subset_descriptive_stats': {'python': {'number_of_characters': 70519, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 21, 'average_document_length': 69.52, 'max_document_length': 1811, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'javascript': {'number_of_characters': 57880, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 18, 'average_document_length': 56.88, 'max_document_length': 601, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'typescript': {'number_of_characters': 61092, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 60.09, 'max_document_length': 659, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'go': {'number_of_characters': 71797, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 70.8, 'max_document_length': 1529, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'ruby': {'number_of_characters': 67900, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 20, 'average_document_length': 66.9, 'max_document_length': 751, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'java': {'number_of_characters': 63984, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 23, 'average_document_length': 62.98, 'max_document_length': 807, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'php': {'number_of_characters': 62927, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 21, 'average_document_length': 61.93, 'max_document_length': 766, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'c': {'number_of_characters': 98588, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 20, 'average_document_length': 97.59, 'max_document_length': 1672, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'c++': {'number_of_characters': 115480, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 22, 'average_document_length': 114.48, 'max_document_length': 1856, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'rust': {'number_of_characters': 68503, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 67.5, 'max_document_length': 2532, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'swift': {'number_of_characters': 58279, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 57.28, 'max_document_length': 727, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'scala': {'number_of_characters': 65833, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 22, 'average_document_length': 64.83, 'max_document_length': 685, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'shell': {'number_of_characters': 73059, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 18, 'average_document_length': 72.06, 'max_document_length': 813, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}}}} |
 | [CodeFeedbackMT](https://arxiv.org/abs/2402.14658) (Tianyu Zheng, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 79660} | {'test': {'number_of_characters': 156266302, 'num_samples': 79660, 'num_queries': 13277, 'num_documents': 66383, 'min_document_length': 127, 'average_document_length': 885.13, 'max_document_length': 32432, 'unique_documents': 66383, 'min_query_length': 2, 'average_query_length': 7344.18, 'max_query_length': 9403, 'unique_queries': 13277, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 13277}} |
@@ -348,7 +348,7 @@ The following tables give you an overview of the tasks within MTEB.
 | [MultilingualSentiment](https://github.com/tyqiangz/multilingual-sentiment-datasets) | ['cmn'] | Classification | s2s |  | None | None |
 | [MultilingualSentimentClassification](https://huggingface.co/datasets/mteb/multilingual-sentiment-classification) | ['ara', 'bam', 'bul', 'cmn', 'cym', 'deu', 'dza', 'ell', 'eng', 'eus', 'fas', 'fin', 'heb', 'hrv', 'ind', 'jpn', 'kor', 'mlt', 'nor', 'pol', 'rus', 'slk', 'spa', 'tha', 'tur', 'uig', 'urd', 'vie', 'zho'] | Classification | s2s | [Reviews, Written] | None | None |
 | [MyanmarNews](https://huggingface.co/datasets/myanmar_news) (A. H. Khine, 2017) | ['mya'] | Classification | p2p | [News, Written] | None | None |
-| [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p |  | {'test': 3956} | {'test': {'number_of_characters': 1612.55, 'num_samples': 3956, 'num_queries': 323, 'num_documents': 3633, 'average_document_length': 0.44, 'average_query_length': 0.07, 'average_relevant_docs_per_query': 38.19}} |
+| [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | [Medical, Academic, Written] | {'test': 3956} | {'test': {'number_of_characters': 1612.55, 'num_samples': 3956, 'num_queries': 323, 'num_documents': 3633, 'average_document_length': 0.44, 'average_query_length': 0.07, 'average_relevant_docs_per_query': 38.19}} |
 | [NFCorpus-PL](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p |  | None | None |
 | [NLPJournalAbsIntroRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | None | None |
 | [NLPJournalTitleAbsRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | None | None |
@@ -360,6 +360,20 @@ The following tables give you an overview of the tasks within MTEB.
 | [NTREXBitextMining](https://huggingface.co/datasets/davidstap/NTREX) | ['afr', 'amh', 'arb', 'aze', 'bak', 'bel', 'bem', 'ben', 'bod', 'bos', 'bul', 'cat', 'ces', 'ckb', 'cym', 'dan', 'deu', 'div', 'dzo', 'ell', 'eng', 'eus', 'ewe', 'fao', 'fas', 'fij', 'fil', 'fin', 'fra', 'fuc', 'gle', 'glg', 'guj', 'hau', 'heb', 'hin', 'hmn', 'hrv', 'hun', 'hye', 'ibo', 'ind', 'isl', 'ita', 'jpn', 'kan', 'kat', 'kaz', 'khm', 'kin', 'kir', 'kmr', 'kor', 'lao', 'lav', 'lit', 'ltz', 'mal', 'mar', 'mey', 'mkd', 'mlg', 'mlt', 'mon', 'mri', 'msa', 'mya', 'nde', 'nep', 'nld', 'nno', 'nob', 'nso', 'nya', 'orm', 'pan', 'pol', 'por', 'prs', 'pus', 'ron', 'rus', 'shi', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'spa', 'sqi', 'srp', 'ssw', 'swa', 'swe', 'tah', 'tam', 'tat', 'tel', 'tgk', 'tha', 'tir', 'ton', 'tsn', 'tuk', 'tur', 'uig', 'ukr', 'urd', 'uzb', 'ven', 'vie', 'wol', 'xho', 'yor', 'yue', 'zho', 'zul'] | BitextMining | s2s | [News, Written] | {'test': 3826252} | {'test': {'num_samples': 3826252, 'number_of_characters': 988355274, 'unique_pairs': 3820263, 'min_sentence1_length': 1, 'average_sentence1_length': 129.15, 'max_sentence1_length': 773, 'unique_sentence1': 241259, 'min_sentence2_length': 1, 'average_sentence2_length': 129.15, 'max_sentence2_length': 773, 'unique_sentence2': 241259, 'hf_subset_descriptive_stats': {'afr_Latn-dan_Latn': {'num_samples': 1997, 'number_of_characters': 520490, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 126.26, 'max_sentence2_length': 522, 'unique_sentence2': 1995}, 'afr_Latn-deu_Latn': {'num_samples': 1997, 'number_of_characters': 564002, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 148.05, 'max_sentence2_length': 508, 'unique_sentence2': 1996}, 'afr_Latn-eng_Latn': {'num_samples': 1997, 'number_of_characters': 516072, 'unique_pairs': 1997, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 124.05, 'max_sentence2_length': 437, 'unique_sentence2': 1997}, 'afr_Latn-fao_Latn': {'num_samples': 1997, 'number_of_characters': 526155, 'unique_pairs': 1997, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 129.1, 'max_sentence2_length': 433, 'unique_sentence2': 1997}, 'afr_Latn-isl_Latn': {'num_samples': 1997, 'number_of_characters': 530560, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 131.3, 'max_sentence2_length': 399, 'unique_sentence2': 1996}, 'afr_Latn-ltz_Latn': {'num_samples': 1997, 'number_of_characters': 549109, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 140.59, 'max_sentence2_length': 543, 'unique_sentence2': 1996}, 'afr_Latn-nld_Latn': {'num_samples': 1997, 'number_of_characters': 560267, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 146.18, 'max_sentence2_length': 539, 'unique_sentence2': 1996}, 'afr_Latn-nno_Latn': {'num_samples': 1997, 'number_of_characters': 516709, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 124.37, 'max_sentence2_length': 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1997, 'number_of_characters': 519307, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 130.98, 'max_sentence2_length': 394, 'unique_sentence2': 1996}, 'zul_Latn-hun_Latn': {'num_samples': 1997, 'number_of_characters': 536108, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 139.4, 'max_sentence2_length': 508, 'unique_sentence2': 1997}, 'zul_Latn-ibo_Latn': {'num_samples': 1997, 'number_of_characters': 503833, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 123.24, 'max_sentence2_length': 469, 'unique_sentence2': 1997}, 'zul_Latn-ind_Latn': {'num_samples': 1997, 'number_of_characters': 544704, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 143.7, 'max_sentence2_length': 486, 'unique_sentence2': 1997}, 'zul_Latn-jpn_Jpan': {'num_samples': 1997, 'number_of_characters': 369358, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 4, 'average_sentence2_length': 55.9, 'max_sentence2_length': 189, 'unique_sentence2': 1994}, 'zul_Latn-kor_Hang': {'num_samples': 1997, 'number_of_characters': 391137, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 66.8, 'max_sentence2_length': 217, 'unique_sentence2': 1995}, 'zul_Latn-lit_Latn': {'num_samples': 1997, 'number_of_characters': 517129, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 129.89, 'max_sentence2_length': 446, 'unique_sentence2': 1995}, 'zul_Latn-nld_Latn': {'num_samples': 1997, 'number_of_characters': 549647, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 146.18, 'max_sentence2_length': 539, 'unique_sentence2': 1996}, 'zul_Latn-nso_Latn': {'num_samples': 1997, 'number_of_characters': 549231, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 145.97, 'max_sentence2_length': 487, 'unique_sentence2': 1996}, 'zul_Latn-orm_Ethi': {'num_samples': 1997, 'number_of_characters': 495163, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 118.89, 'max_sentence2_length': 466, 'unique_sentence2': 1984}, 'zul_Latn-pol_Latn': {'num_samples': 1997, 'number_of_characters': 535598, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 139.14, 'max_sentence2_length': 468, 'unique_sentence2': 1996}, 'zul_Latn-por_Latn': {'num_samples': 1997, 'number_of_characters': 534947, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 138.82, 'max_sentence2_length': 497, 'unique_sentence2': 1996}, 'zul_Latn-rus_Cyrl': {'num_samples': 1997, 'number_of_characters': 532625, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 137.65, 'max_sentence2_length': 419, 'unique_sentence2': 1996}, 'zul_Latn-som_Latn': {'num_samples': 1997, 'number_of_characters': 549024, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 145.86, 'max_sentence2_length': 455, 'unique_sentence2': 1997}, 'zul_Latn-spa_Latn': {'num_samples': 1997, 'number_of_characters': 545932, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 1, 'average_sentence2_length': 144.32, 'max_sentence2_length': 504, 'unique_sentence2': 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'unique_sentence2': 1996}, 'zul_Latn-tam_Taml': {'num_samples': 1997, 'number_of_characters': 567693, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 11, 'average_sentence2_length': 155.21, 'max_sentence2_length': 581, 'unique_sentence2': 1997}, 'zul_Latn-tir_Ethi': {'num_samples': 1997, 'number_of_characters': 422970, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 82.74, 'max_sentence2_length': 272, 'unique_sentence2': 1996}, 'zul_Latn-tsn_Latn': {'num_samples': 1997, 'number_of_characters': 592015, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 167.39, 'max_sentence2_length': 556, 'unique_sentence2': 1997}, 'zul_Latn-tur_Latn': {'num_samples': 1997, 'number_of_characters': 523345, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 133.01, 'max_sentence2_length': 504, 'unique_sentence2': 1997}, 'zul_Latn-vie_Latn': {'num_samples': 1997, 'number_of_characters': 528853, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 135.76, 'max_sentence2_length': 437, 'unique_sentence2': 1996}, 'zul_Latn-wol_Latn': {'num_samples': 1997, 'number_of_characters': 497535, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 120.08, 'max_sentence2_length': 405, 'unique_sentence2': 1990}, 'zul_Latn-xho_Latn': {'num_samples': 1997, 'number_of_characters': 525822, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 134.25, 'max_sentence2_length': 492, 'unique_sentence2': 1997}, 'zul_Latn-yor_Latn': {'num_samples': 1997, 'number_of_characters': 573820, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 158.28, 'max_sentence2_length': 582, 'unique_sentence2': 1996}, 'zul_Latn-zho_Hant': {'num_samples': 1997, 'number_of_characters': 349210, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 3, 'average_sentence2_length': 45.81, 'max_sentence2_length': 200, 'unique_sentence2': 1996}}}} |
 | [NYSJudicialEthicsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
 | [NaijaSenti](https://github.com/hausanlp/NaijaSenti) | ['hau', 'ibo', 'pcm', 'yor'] | Classification | s2s | [Social, Written] | None | None |
+| [NamaaMrTydiReranking](https://huggingface.co/NAMAA-Space) (Muennighoff et al., 2022) | ['ara'] | Reranking | s2s | [Encyclopaedic, Written] | None | None |
+| [NanoArguAnaRetrieval](http://argumentation.bplaced.net/arguana/data) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | [Medical, Written] | None | None |
+| [NanoClimateFeverRetrieval](https://arxiv.org/abs/2012.00614) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p | [Non-fiction, Academic, News] | None | None |
+| [NanoDBPediaRetrieval](https://huggingface.co/datasets/zeta-alpha-ai/NanoDBPedia) (Lehmann et al., 2015) | ['eng'] | Retrieval | s2p | [Encyclopaedic] | None | None |
+| [NanoFEVERRetrieval](https://fever.ai/) | ['eng'] | Retrieval | s2p | [Academic, Encyclopaedic] | None | None |
+| [NanoFiQA2018Retrieval](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['eng'] | Retrieval | s2p | [Academic, Social] | None | None |
+| [NanoHotpotQARetrieval](https://hotpotqa.github.io/) | ['eng'] | Retrieval | s2p | [Web, Written] | None | None |
+| [NanoMSMARCORetrieval](https://microsoft.github.io/msmarco/) (Tri Nguyen and                Mir Rosenberg and                Xia Song and                Jianfeng Gao and                Saurabh Tiwary and                Rangan Majumder and                Li Deng, 2016) | ['eng'] | Retrieval | s2p | [Web] | None | None |
+| [NanoNFCorpusRetrieval](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | [Medical, Academic, Written] | None | None |
+| [NanoNQRetrieval](https://ai.google.com/research/NaturalQuestions) (Tom Kwiatkowski, 2019) | ['eng'] | Retrieval | s2p | [Academic, Web] | None | None |
+| [NanoQuoraRetrieval](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (DataCanary et al., 2017) | ['eng'] | Retrieval | s2s | [Social] | None | None |
+| [NanoSCIDOCSRetrieval](https://allenai.org/data/scidocs) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | [Academic, Written, Non-fiction] | None | None |
+| [NanoSciFactRetrieval](https://github.com/allenai/scifact) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | [Academic, Medical, Written] | None | None |
+| [NanoTouche2020Retrieval](https://webis.de/events/touche-20/shared-task-1.html) | ['eng'] | Retrieval | s2p | [Academic] | None | None |
 | [NarrativeQARetrieval](https://metatext.io/datasets/narrativeqa) (Tomáš Kočiský, 2017) | ['eng'] | Retrieval | s2p |  | None | None |
 | [NepaliNewsClassification](https://github.com/goru001/nlp-for-nepali) | ['nep'] | Classification | s2s | [News, Written] | None | None |
 | [NeuCLIR2022Retrieval](https://neuclir.github.io/) (Lawrie et al., 2023) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | None |
@@ -459,7 +473,7 @@ The following tables give you an overview of the tasks within MTEB.
 | [SICK-BR-PC](https://linux.ime.usp.br/~thalen/SICK_PT.pdf) | ['por'] | PairClassification | s2s | [Web, Written] | None | None |
 | [SICK-BR-STS](https://linux.ime.usp.br/~thalen/SICK_PT.pdf) | ['por'] | STS | s2s | [Web, Written] | None | None |
 | [SICK-E-PL](https://aclanthology.org/2020.lrec-1.207) | ['pol'] | PairClassification | s2s |  | None | None |
-| [SICK-R](https://aclanthology.org/2020.lrec-1.207) | ['eng'] | STS | s2s |  | None | None |
+| [SICK-R](https://aclanthology.org/L14-1314/) | ['eng'] | STS | s2s | [Web, Written] | None | None |
 | [SICK-R-PL](https://aclanthology.org/2020.lrec-1.207) | ['pol'] | STS | s2s | [Web, Written] | None | None |
 | [SICKFr](https://huggingface.co/datasets/Lajavaness/SICK-fr) | ['fra'] | STS | s2s |  | None | None |
 | [SIQA](https://leaderboard.allenai.org/socialiqa/submissions/get-started) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
@@ -483,8 +497,8 @@ The following tables give you an overview of the tasks within MTEB.
 | [SanskritShlokasClassification](https://github.com/goru001/nlp-for-sanskrit) | ['san'] | Classification | s2s | [Religious, Written] | None | None |
 | [ScalaClassification](https://aclanthology.org/2023.nodalida-1.20/) | ['dan', 'nno', 'nob', 'swe'] | Classification | s2s | [Fiction, News, Non-fiction, Blog, Spoken, Web, Written] | None | None |
 | [SciDocsRR](https://allenai.org/data/scidocs) | ['eng'] | Reranking | s2s | [Academic, Non-fiction, Written] | None | None |
-| [SciFact](https://github.com/allenai/scifact) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p |  | None | None |
-| [SciFact-PL](https://github.com/allenai/scifact) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p |  | None | None |
+| [SciFact](https://github.com/allenai/scifact) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | [Academic, Medical, Written] | None | None |
+| [SciFact-PL](https://github.com/allenai/scifact) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Academic, Medical, Written] | None | None |
 | [SemRel24STS](https://huggingface.co/datasets/SemRel/SemRel2024) (Nedjma Ousidhoum, 2024) | ['afr', 'amh', 'arb', 'arq', 'ary', 'eng', 'hau', 'hin', 'ind', 'kin', 'mar', 'tel'] | STS | s2s | [Spoken, Written] | None | None |
 | [SensitiveTopicsClassification](https://aclanthology.org/2021.bsnlp-1.4) | ['rus'] | MultilabelClassification | s2s | [Web, Social, Written] | None | None |
 | [SentimentAnalysisHindi](https://huggingface.co/datasets/OdiaGenAI/sentiment_analysis_hindi) (Shantipriya Parida, 2023) | ['hin'] | Classification | s2s | [Reviews, Written] | None | None |
@@ -524,8 +538,8 @@ The following tables give you an overview of the tasks within MTEB.
 | [T2Retrieval](https://arxiv.org/abs/2304.03679) (Xiaohui Xie, 2023) | ['cmn'] | Retrieval | s2p |  | None | None |
 | [TERRa](https://arxiv.org/pdf/2010.15925) (Shavrina et al., 2020) | ['rus'] | PairClassification | s2s | [News, Web, Written] | None | None |
 | [TNews](https://www.cluebenchmarks.com/introduce.html) | ['cmn'] | Classification | s2s |  | None | None |
-| [TRECCOVID](https://ir.nist.gov/covidSubmit/index.html) (Kirk Roberts, 2021) | ['eng'] | Retrieval | s2p |  | None | None |
-| [TRECCOVID-PL](https://ir.nist.gov/covidSubmit/index.html) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Academic, Non-fiction, Written] | None | None |
+| [TRECCOVID](https://ir.nist.gov/covidSubmit/index.html) (Kirk Roberts, 2021) | ['eng'] | Retrieval | s2p | [Medical, Academic, Written] | None | None |
+| [TRECCOVID-PL](https://ir.nist.gov/covidSubmit/index.html) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Academic, Medical, Non-fiction, Written] | None | None |
 | [TV2Nordretrieval](https://huggingface.co/datasets/alexandrainst/nordjylland-news-summarization) | ['dan'] | Retrieval | p2p | [News, Non-fiction, Written] | None | None |
 | [TamilNewsClassification](https://github.com/vanangamudi/tamil-news-classification) (Anoop Kunchukuttan, 2020) | ['tam'] | Classification | s2s | [News, Written] | None | None |
 | [Tatoeba](https://github.com/facebookresearch/LASER/tree/main/data/tatoeba/v1) (Tatoeba community, 2021) | ['afr', 'amh', 'ang', 'ara', 'arq', 'arz', 'ast', 'awa', 'aze', 'bel', 'ben', 'ber', 'bos', 'bre', 'bul', 'cat', 'cbk', 'ceb', 'ces', 'cha', 'cmn', 'cor', 'csb', 'cym', 'dan', 'deu', 'dsb', 'dtp', 'ell', 'eng', 'epo', 'est', 'eus', 'fao', 'fin', 'fra', 'fry', 'gla', 'gle', 'glg', 'gsw', 'heb', 'hin', 'hrv', 'hsb', 'hun', 'hye', 'ido', 'ile', 'ina', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kat', 'kaz', 'khm', 'kor', 'kur', 'kzj', 'lat', 'lfn', 'lit', 'lvs', 'mal', 'mar', 'max', 'mhr', 'mkd', 'mon', 'nds', 'nld', 'nno', 'nob', 'nov', 'oci', 'orv', 'pam', 'pes', 'pms', 'pol', 'por', 'ron', 'rus', 'slk', 'slv', 'spa', 'sqi', 'srp', 'swe', 'swg', 'swh', 'tam', 'tat', 'tel', 'tgl', 'tha', 'tuk', 'tur', 'tzl', 'uig', 'ukr', 'urd', 'uzb', 'vie', 'war', 'wuu', 'xho', 'yid', 'yue', 'zsm'] | BitextMining | s2s | [Written] | None | None |
@@ -608,1060 +622,1060 @@ The following tables give you an overview of the tasks within MTEB.
 <details>
 
 <!-- TASK LANG TABLE START -->
-| Language | BitextMining | Classification | Clustering | InstructionRetrieval | MultilabelClassification | PairClassification | Reranking | Retrieval | STS | Speed | Summarization |
-|---|------|------|------|------|------|------|------|------|------|------|---|
-| aai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aau | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aaz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| abs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| abt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| abx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aby | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ace | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acm | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| acq | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| adz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aeb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| afr | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| agd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aia | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aii | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ajp | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aka | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ake | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| alp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| alq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| als | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| aly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ame | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amh | 3 | 6 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| amk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ang | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| anh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| anp | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| anv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aoi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aoj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aom | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apc | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ape | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ara | 2 | 12 | 0 | 0 | 0 | 2 | 1 | 9 | 2 | 0 | 0 |
-| arb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
-| are | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arq | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
-| ars | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ary | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| arz | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| asm | 5 | 3 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| aso | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ast | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ata | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| atb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| atd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| atg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| att | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| auc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| auy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| avt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awa | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ayr | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| azb | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aze | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| azg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| azj | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| azz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bak | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bam | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ban | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bba | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bbc | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bbr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bdd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bef | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bel | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bem | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ben | 7 | 9 | 2 | 0 | 0 | 1 | 2 | 6 | 1 | 0 | 0 |
-| beo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ber | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| beu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bew | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bgc | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bgs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bgt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhb | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhd | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bho | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
-| bhp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| big | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjj | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjn | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bkd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bki | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bkq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bkx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| blw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| blz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bnp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bns | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| boa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bod | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| boj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bos | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| box | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| boy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bpr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bqc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bqp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bra | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bre | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| brx | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bsj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bsn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bsp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bug | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| buk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bul | 3 | 4 | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 |
-| bus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bvd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bvr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bxh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| byr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| byx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bzd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bzh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| caa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| caf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| car | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cat | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| cav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cax | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbk | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ceb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| cek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ces | 4 | 5 | 2 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 |
-| cgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cha | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chv | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cjk | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cjo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cjv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ckb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| cle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| clu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cme | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cmn | 4 | 10 | 4 | 0 | 0 | 3 | 4 | 10 | 9 | 0 | 0 |
-| cmo | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cni | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cnl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cnt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| code | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 0 |
-| cof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| con | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cor | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cot | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| crh | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| crn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| crx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| csb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cso | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| csy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cta | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cth | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ctp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ctu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cuk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cwe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cya | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cym | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| daa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dan | 5 | 9 | 2 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 |
-| ded | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| deu | 6 | 14 | 7 | 0 | 1 | 6 | 2 | 18 | 4 | 0 | 0 |
-| dgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dgr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dgz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dik | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| div | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dji | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| djk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| djr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dob | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| doi | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dov | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dsb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dtp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dwr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dww | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dwy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dyu | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dza | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dzo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ebk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eko | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ell | 3 | 6 | 1 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 0 |
-| emi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| emp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eng | 16 | 143 | 16 | 3 | 1 | 8 | 8 | 91 | 13 | 2 | 1 |
-| enq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| epo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ese | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| esk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| est | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
-| etr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eus | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ewe | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| faa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fao | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
-| far | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fas | 1 | 4 | 0 | 0 | 0 | 1 | 2 | 9 | 0 | 0 | 0 |
-| ffm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fij | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fil | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fin | 3 | 5 | 1 | 0 | 1 | 1 | 2 | 5 | 1 | 0 | 0 |
-| fon | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| for | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fra | 7 | 13 | 8 | 0 | 1 | 5 | 3 | 14 | 4 | 0 | 1 |
-| fry | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fur | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuv | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| gah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gaw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gaz | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| gbm | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gdn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gdr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| geb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gfk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ghs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gla | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gle | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| glg | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| glk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| glv | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gmv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gng | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gnw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gom | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| grc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| grn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| gsw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| guh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| guj | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| gul | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gum | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gun | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| guo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gym | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gyr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hat | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| hau | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| haw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hbo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| heb | 4 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| heg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hin | 9 | 12 | 2 | 0 | 0 | 1 | 2 | 10 | 2 | 0 | 0 |
-| hix | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hla | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hlt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hmn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hne | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hns | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hot | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hrv | 4 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
-| hsb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hun | 5 | 3 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
-| hus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| huu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| huv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hye | 3 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
-| ian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ibo | 3 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ido | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ign | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ikk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ikw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ile | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ilo | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| imo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ina | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| inb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ind | 6 | 7 | 1 | 0 | 0 | 1 | 1 | 4 | 1 | 0 | 0 |
-| ino | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| iou | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ipi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| isl | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| isn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ita | 5 | 9 | 1 | 0 | 1 | 2 | 1 | 5 | 3 | 0 | 0 |
-| iws | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ixl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jae | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jav | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| jic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jiv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jni | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jpn | 5 | 8 | 3 | 0 | 0 | 1 | 3 | 13 | 2 | 0 | 0 |
-| jvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kab | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kac | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kam | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kan | 6 | 7 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| kaq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kas | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kat | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
-| kaz | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbp | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kdc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kdl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kea | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ken | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kew | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kfg | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kfy | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kgf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kgk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kgp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| khk | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| khm | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| khs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| khz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kik | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kin | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| kir | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kiw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kiz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kje | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kjs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kkc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kkl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| klt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| klv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmr | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kms | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knc | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kne | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kon | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kor | 4 | 8 | 1 | 0 | 1 | 2 | 1 | 9 | 3 | 0 | 0 |
-| kos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kql | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| krc | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ksd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ksj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ksr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ktm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kud | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kur | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kvg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kze | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lao | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lat | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lav | 1 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lbk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lcm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| leu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lex | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lfn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lgl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lij | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lim | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lin | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lit | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
-| llg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lmo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ltg | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ltz | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lua | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lug | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| luo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lus | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lvs | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lww | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mad | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mag | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mai | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mak | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mal | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
-| mam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mar | 7 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 |
-| mau | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| max | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mdy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| med | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mee | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| meq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| met | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| meu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mgh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mgw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mhl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mhr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mib | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mie | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mig | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mih | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mil | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| min | 3 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mio | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mir | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| miz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mjc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mkd | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| mkj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mkl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mkn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mks | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlt | 2 | 2 | 2 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
-| mmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mmx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mni | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mon | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mos | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mox | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mph | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mqb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mqj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mri | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| msa | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mti | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mui | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mup | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| muy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mva | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mya | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| myk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| myu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| myw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| myy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mzz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| naf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nas | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nbl | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nbq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ncj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ncl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ncu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ndg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ndj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nds | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nep | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nfa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ngp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ngu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nho | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nii | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nij | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nin | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nko | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nld | 6 | 6 | 1 | 0 | 1 | 0 | 1 | 2 | 2 | 0 | 0 |
-| nlg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nno | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nnq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| noa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nob | 4 | 7 | 5 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
-| noe | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nor | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
-| not | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nou | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nov | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| npi | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| npl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nqo | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nsn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nso | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| nss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ntj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ntp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ntu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nus | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nuy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nvm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nya | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| nys | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nyu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| obo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| oci | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| okv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| omw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ong | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ons | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ood | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| opm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ori | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| orm | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| orv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ory | 5 | 4 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| ote | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| otm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| otn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| otq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ots | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pag | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pan | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| pao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pap | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pbt | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| pcm | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pes | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| pib | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pio | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pir | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| piu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pjt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pls | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| plt | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| plu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pma | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pms | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| poe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| poh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| poi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pol | 4 | 11 | 4 | 0 | 1 | 4 | 0 | 18 | 4 | 0 | 0 |
-| pon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| por | 4 | 9 | 1 | 0 | 2 | 2 | 1 | 5 | 3 | 0 | 0 |
-| poy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ppo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| prf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| prs | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ptp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ptu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pus | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pwg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qul | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quy | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qve | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qwh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qxh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qxn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qxo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| raj | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| reg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rej | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rgu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rkb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rmc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rmy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rom | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ron | 5 | 6 | 1 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 0 |
-| roo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| row | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rro | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ruf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rug | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| run | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rus | 5 | 13 | 6 | 0 | 2 | 4 | 2 | 16 | 4 | 0 | 0 |
-| rwo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sag | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sah | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| san | 5 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
-| sat | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sbe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sbk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| scn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sco | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| seh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sgb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sgz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| shi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| shj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| shn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| shp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sin | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| sja | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| slk | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 |
-| sll | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| slv | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
-| smk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| smo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sna | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| snc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| snd | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| snn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| snp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| snx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sny | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| som | 3 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| soq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sot | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| soy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spa | 4 | 13 | 4 | 0 | 1 | 2 | 2 | 12 | 4 | 0 | 0 |
-| spl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sqi | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srd | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srn | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srp | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
-| srq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ssd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ssg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ssw | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ssx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| stp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sua | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sun | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| sus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| suz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| svk | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| swa | 1 | 7 | 2 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 |
-| swe | 4 | 8 | 3 | 0 | 1 | 1 | 1 | 4 | 0 | 0 | 0 |
-| swg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| swh | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| swp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sxb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| szl | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| taj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tam | 7 | 7 | 2 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 |
-| taq | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tat | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| taw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tcs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tcz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tdt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tee | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tel | 7 | 7 | 2 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 |
-| ter | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tet | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tew | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tfr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tgk | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tgl | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tgo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tgp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tha | 4 | 8 | 1 | 0 | 0 | 1 | 1 | 6 | 0 | 0 | 0 |
-| tif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tir | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tiw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tiy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tke | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tku | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tlf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tmd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| toc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tod | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| toj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ton | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| too | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| top | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpi | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| trc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tsn | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tso | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tsw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ttc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tte | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuk | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tum | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tur | 4 | 7 | 1 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 |
-| tvk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| twi | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| txq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| txu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tyv | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzm | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ubr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ubu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| udu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uig | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ukr | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| uli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ulk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| umb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| upv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ura | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urd | 7 | 8 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| uri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| usa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| usp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uvh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uvl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uzb | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uzn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| vec | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ven | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| vid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| vie | 5 | 6 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 |
-| viv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| vmy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| waj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wal | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| war | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| wat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wbp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wed | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wiu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wiv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wln | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wmt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wmw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wnc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wnu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wol | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| wos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wrk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wro | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wrs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wsk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wuu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wuv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xed | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xho | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| xla | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xsi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xtd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xtm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yaa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yal | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yaq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yby | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ycn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ydd | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yka | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yml | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yor | 4 | 5 | 3 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 |
-| yrb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yre | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yue | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yuj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yuw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yva | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zaa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zaj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zar | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zas | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zaw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zga | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zho | 2 | 2 | 1 | 0 | 0 | 1 | 1 | 13 | 0 | 0 | 0 |
-| zia | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ziw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zlm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zsm | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| zsr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ztq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zty | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zul | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| zyp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| Total | 1394 | 795 | 304 | 3 | 28 | 67 | 50 | 457 | 85 | 2 | 2 |
+| ISO Code | Language | Family | BitextMining | Classification | Clustering | InstructionRetrieval | MultilabelClassification | PairClassification | Reranking | Retrieval | STS | Speed | Summarization | Sum |
+|---|------|------|------|------|------|------|------|------|------|------|------|---|
+| aai | Arifama-Miniafia | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aak | Ankave | Angan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aau | Abau | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aaz | Amarasi | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abs | Ambonese Malay | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abt | Ambulas | Ndu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abx | Inabaknon | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aby | Aneme Wake | Yareban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ace | Achinese | Austronesian | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| acf | Saint Lucian Creole French | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| acm | Mesopotamian Arabic | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| acq | Ta'izzi-Adeni Arabic | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| acr | Achi | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| acu | Achuar-Shiwiar | Chicham | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| adz | Adzera | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aeb | Tunisian Arabic | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| aer | Eastern Arrernte | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aey | Amele | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| afr | Afrikaans | Indo-European | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 10 |
+| agd | Agarabi | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agg | Angor | Senagi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agm | Angaataha | Angan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agn | Agutaynen | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agr | Aguaruna | Chicham | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agt | Central Cagayan Agta | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agu | Aguacateco | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aia | Arosi | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aii | Assyrian Neo-Aramaic | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ajp | South Levantine Arabic | Unclassified | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| aka | Akan | Atlantic-Congo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ake | Akawaio | Cariban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| alp | Alune | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| alq | Algonquin | Algic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| als | Tosk Albanian | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| aly | Alyawarr | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ame | Yanesha' | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amf | Hamer-Banna | South Omotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amh | Amharic | Afro-Asiatic | 3 | 6 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 14 |
+| amk | Ambai | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amm | Ama (Papua New Guinea) | Left May | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amn | Amanab | Border | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amo | Amo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amp | Alamblak | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amr | Amarakaeri | Harakmbut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amu | Guerrero Amuzgo | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amx | Anmatyerre | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ang | Old English (ca. 450-1100) | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anh | Nend | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anp | Angika | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anv | Denya | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aoi | Anindilyakwa | Gunwinyguan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aoj | Mufian | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aom | Ömie | Koiarian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aon | Bumbita Arapesh | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apb | Sa'a | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apc | Levantine Arabic | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| ape | Bukiyip | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apn | Apinayé | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apr | Arop-Lokep | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apu | Apurinã | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apw | Western Apache | Athabaskan-Eyak-Tlingit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apz | Safeyoka | Angan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ara | Arabic | Unclassified | 2 | 12 | 0 | 0 | 0 | 2 | 2 | 9 | 2 | 0 | 0 | 29 |
+| arb | Standard Arabic | Afro-Asiatic | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 8 |
+| are | Western Arrarnta | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arl | Arabela | Zaparoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arn | Mapudungun | Araucanian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arp | Arapaho | Algic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arq | Algerian Arabic | Afro-Asiatic | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
+| ars | Najdi Arabic | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| ary | Moroccan Arabic | Afro-Asiatic | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
+| arz | Egyptian Arabic | Afro-Asiatic | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| asm | Assamese | Indo-European | 5 | 3 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 14 |
+| aso | Dano | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ast | Asturian | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ata | Pele-Ata | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atb | Zaiwa | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atd | Ata Manobo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atg | Ivbie North-Okpela-Arhe | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| att | Pamplona Atta | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| auc | Waorani | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aui | Anuki | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| auy | Awiyaana | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| avt | Au | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awa | Awadhi | Indo-European | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| awb | Awa (Papua New Guinea) | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awk | Awabakal | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awx | Awara | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ayr | Central Aymara | Aymaran | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| azb | South Azerbaijani | Turkic | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| aze | Azerbaijani | Unclassified | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| azg | San Pedro Amuzgos Amuzgo | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| azj | North Azerbaijani | Turkic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| azz | Highland Puebla Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bak | Bashkir | Turkic | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bam | Bambara | Mande | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| ban | Balinese | Austronesian | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bao | Waimaha | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bba | Baatonum | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bbb | Barai | Koiarian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bbc | Batak Toba | Austronesian | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bbr | Girawa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bch | Bariai | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bco | Kaluli | Bosavi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bdd | Bunama | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bea | Beaver | Athabaskan-Eyak-Tlingit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bef | Benabena | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bel | Belarusian | Indo-European | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bem | Bemba (Zambia) | Atlantic-Congo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ben | Bengali | Indo-European | 7 | 9 | 2 | 0 | 0 | 1 | 2 | 6 | 1 | 0 | 0 | 28 |
+| beo | Beami | Bosavi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ber | Berber (Other) | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| beu | Blagar | Timor-Alor-Pantar | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bew | Betawi | Austronesian | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| bgc | Haryanvi | Indo-European | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| bgs | Tagabawa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bgt | Bughotu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhb | Bhili | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhd | Bhadrawahi | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhg | Binandere | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhl | Bimin | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bho | Bhojpuri | Indo-European | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bhp | Bima | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| big | Biangai | Kunimaipan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjj | Kanauji | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjk | Barok | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjn | Banjar | Austronesian | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bjp | Fanamaket | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjr | Binumarien | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjv | Bedjond | Central Sudanic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjz | Baruga | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkd | Binukid | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bki | Baki | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkq | Bakairí | Cariban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkx | Baikeno | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| blw | Balangao | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| blz | Balantak | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmh | Kein | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmk | Ghayavi | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmr | Muinane | Boran | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmu | Somba-Siawari | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bnp | Bola | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bns | Bundeli | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| boa | Bora | Boran | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bod | Tibetan | Sino-Tibetan | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| boj | Anjam | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bon | Bine | Eastern Trans-Fly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bos | Bosnian | Indo-European | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| box | Buamu | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| boy | Bodo (Central African Republic) | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bpr | Koronadal Blaan | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bps | Sarangani Blaan | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bqc | Boko (Benin) | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bqp | Busa | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bra | Braj | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bre | Breton | Indo-European | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| brx | Bodo (India) | Sino-Tibetan | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bsj | Bangwinji | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bsn | Barasana-Eduria | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bsp | Baga Sitemu | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bss | Akoose | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bug | Buginese | Austronesian | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| buk | Bugawac | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bul | Bulgarian | Indo-European | 3 | 4 | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 13 |
+| bus | Bokobaru | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bvd | Baeggu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bvr | Burarra | Maningrida | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bxh | Buhutu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| byr | Baruya | Angan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| byx | Qaqet | Baining | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzd | Bribri | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzh | Mapos Buang | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzj | Belize Kriol English | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| caa | Chortí | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cab | Garifuna | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cac | Chuj | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| caf | Southern Carrier | Athabaskan-Eyak-Tlingit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cak | Kaqchikel | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cao | Chácobo | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cap | Chipaya | Uru-Chipaya | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| car | Galibi Carib | Cariban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cat | Catalan | Indo-European | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| cav | Cavineña | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cax | Chiquitano | Chiquitano | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbc | Carapana | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbi | Chachi | Barbacoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbk | Chavacano | Indo-European | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cbr | Cashibo-Cacataibo | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbs | Cashinahua | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbt | Chayahuita | Cahuapanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbu | Candoshi-Shapra | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbv | Cacua | Kakua-Nukak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cco | Comaltepec Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ceb | Cebuano | Austronesian | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| cek | Eastern Khumi Chin | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ces | Czech | Indo-European | 4 | 5 | 2 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 16 |
+| cgc | Kagayanen | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cha | Chamorro | Austronesian | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| chd | Highland Oaxaca Chontal | Tequistlatecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chf | Tabasco Chontal | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chk | Chuukese | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chq | Quiotepec Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chv | Chuvash | Turkic | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chz | Ozumacín Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cjk | Chokwe | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| cjo | Ashéninka Pajonal | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cjv | Chuave | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ckb | Central Kurdish | Indo-European | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| cle | Lealao Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| clu | Caluyanun | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cme | Cerma | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cmn | Mandarin Chinese | Sino-Tibetan | 4 | 10 | 4 | 0 | 0 | 3 | 4 | 10 | 9 | 0 | 0 | 44 |
+| cmo | Central Mnong | Austroasiatic | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cni | Asháninka | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cnl | Lalana Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cnt | Tepetotutla Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| code | unknown | Unclassified | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 0 | 37 |
+| cof | Colorado | Barbacoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| con | Cofán | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cop | Coptic | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cor | Cornish | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cot | Caquinte | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpa | Palantla Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpb | Ucayali-Yurúa Ashéninka | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpc | Ajyíninka Apurucayali | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpu | Pichis Ashéninka | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpy | South Ucayali Ashéninka | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| crh | Crimean Tatar | Turkic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| crn | El Nayar Cora | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| crx | Carrier | Athabaskan-Eyak-Tlingit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| csb | Kashubian | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cso | Sochiapam Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| csy | Siyin Chin | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cta | Tataltepec Chatino | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cth | Thaiphum Chin | Bookkeeping | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ctp | Western Highland Chatino | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ctu | Chol | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cub | Cubeo | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cuc | Usila Chinantec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cui | Cuiba | Guahiboan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cuk | San Blas Kuna | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cut | Teutila Cuicatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cux | Tepeuxila Cuicatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cwe | Kwere | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cya | Nopala Chatino | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cym | Welsh | Indo-European | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| daa | Dangaléat | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dad | Marik | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dah | Gwahatike | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dan | Danish | Indo-European | 5 | 9 | 2 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 | 23 |
+| ded | Dedua | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| deu | German | Indo-European | 6 | 14 | 7 | 0 | 1 | 6 | 2 | 18 | 4 | 0 | 0 | 58 |
+| dgc | Casiguran Dumagat Agta | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dgr | Dogrib | Athabaskan-Eyak-Tlingit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dgz | Daga | Dagan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dhg | Dhangu-Djangu | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dif | Dieri | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dik | Southwestern Dinka | Nilotic | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| div | Dhivehi | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dji | Djinang | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| djk | Eastern Maroon Creole | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| djr | Djambarrpuyngu | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dob | Dobu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| doi | Dogri (macrolanguage) | Unclassified | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| dop | Lukpa | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dov | Dombe | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dsb | Lower Sorbian | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dtp | Kadazan Dusun | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dwr | Dawro | Ta-Ne-Omotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dww | Dawawa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dwy | Dhuwaya | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dyu | Dyula | Mande | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| dza | Tunzu | Atlantic-Congo | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dzo | Dzongkha | Sino-Tibetan | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ebk | Eastern Bontok | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eko | Koti | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ell | Modern Greek (1453-) | Indo-European | 3 | 6 | 1 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 0 | 16 |
+| emi | Mussau-Emira | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| emp | Northern Emberá | Chocoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eng | English | Indo-European | 16 | 143 | 16 | 3 | 1 | 8 | 8 | 105 | 13 | 2 | 1 | 316 |
+| enq | Enga | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| epo | Esperanto | Artificial Language | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| eri | Ogea | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ese | Ese Ejja | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| esk | Northwest Alaska Inupiatun | Eskimo-Aleut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| est | Estonian | Uralic | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 8 |
+| etr | Edolo | Bosavi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eus | Basque | Unclassified | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| ewe | Ewe | Atlantic-Congo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| faa | Fasu | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fai | Faiwol | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fao | Faroese | Indo-European | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
+| far | Fataleka | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fas | Persian | Indo-European | 1 | 4 | 0 | 0 | 0 | 1 | 2 | 9 | 0 | 0 | 0 | 17 |
+| ffm | Maasina Fulfulde | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fij | Fijian | Austronesian | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| fil | Filipino | Austronesian | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| fin | Finnish | Uralic | 3 | 5 | 1 | 0 | 1 | 1 | 2 | 5 | 1 | 0 | 0 | 19 |
+| fon | Fon | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| for | Fore | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fra | French | Indo-European | 7 | 13 | 8 | 0 | 1 | 5 | 3 | 15 | 4 | 0 | 1 | 57 |
+| fry | Western Frisian | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuc | Pulaar | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fue | Borgu Fulfulde | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuf | Pular | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuh | Western Niger Fulfulde | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fur | Friulian | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| fuv | Nigerian Fulfulde | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| gah | Alekano | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gai | Borei | Ramu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gam | Kandawo | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gaw | Nobonob | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gaz | West Central Oromo | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| gbm | Garhwali | Indo-European | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| gdn | Umanakaina | Dagan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gdr | Wipi | Eastern Trans-Fly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| geb | Kire | Ramu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gfk | Patpatar | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ghs | Guhu-Samane | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gla | Scottish Gaelic | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| gle | Irish | Indo-European | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| glg | Galician | Indo-European | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| glk | Gilaki | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| glv | Manx | Indo-European | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gmv | Gamo | Ta-Ne-Omotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gng | Ngangam | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gnn | Gumatj | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gnw | Western Bolivian Guaraní | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gof | Gofa | Ta-Ne-Omotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gom | Goan Konkani | Indo-European | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| grc | Ancient Greek (to 1453) | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| grn | Guarani | Unclassified | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| gsw | Swiss German | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gub | Guajajára | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guh | Guahibo | Guahiboan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gui | Eastern Bolivian Guaraní | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guj | Gujarati | Indo-European | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 18 |
+| gul | Sea Island Creole English | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gum | Guambiano | Barbacoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gun | Mbyá Guaraní | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guo | Guayabero | Guahiboan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gup | Gunwinggu | Gunwinyguan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gux | Gourmanchéma | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvc | Guanano | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvf | Golin | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvn | Kuku-Yalanji | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvs | Gumawana | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gwi | Gwichʼin | Athabaskan-Eyak-Tlingit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gym | Ngäbere | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gyr | Guarayu | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hat | Haitian | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| hau | Hausa | Afro-Asiatic | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 14 |
+| haw | Hawaiian | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hbo | Ancient Hebrew | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hch | Huichol | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| heb | Hebrew | Afro-Asiatic | 4 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 11 |
+| heg | Helong | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hin | Hindi | Indo-European | 9 | 12 | 2 | 0 | 0 | 1 | 2 | 10 | 2 | 0 | 0 | 38 |
+| hix | Hixkaryána | Cariban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hla | Halia | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hlt | Matu Chin | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hmn | Hmong | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hmo | Hiri Motu | Pidgin | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hne | Chhattisgarhi | Indo-European | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| hns | Caribbean Hindustani | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hop | Hopi | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hot | Hote | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hrv | Croatian | Indo-European | 4 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 10 |
+| hsb | Upper Sorbian | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hto | Minica Huitoto | Huitotoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hub | Huambisa | Chicham | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hui | Huli | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hun | Hungarian | Uralic | 5 | 3 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 12 |
+| hus | Huastec | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| huu | Murui Huitoto | Huitotoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| huv | San Mateo Del Mar Huave | Huavean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hvn | Sabu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hye | Armenian | Indo-European | 3 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 9 |
+| ian | Iatmul | Ndu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ibo | Igbo | Atlantic-Congo | 3 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 12 |
+| ido | Ido | Artificial Language | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ign | Ignaciano | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ikk | Ika | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ikw | Ikwere | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ile | Interlingue | Artificial Language | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ilo | Iloko | Austronesian | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| imo | Imbongu | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ina | Interlingua (International Auxiliary Language Association) | Artificial Language | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| inb | Inga | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ind | Indonesian | Austronesian | 6 | 7 | 1 | 0 | 0 | 1 | 1 | 4 | 1 | 0 | 0 | 21 |
+| ino | Inoke-Yate | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| iou | Tuma-Irumu | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ipi | Ipili | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| isl | Icelandic | Indo-European | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| isn | Isanzu | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ita | Italian | Indo-European | 5 | 9 | 1 | 0 | 1 | 2 | 1 | 5 | 3 | 0 | 0 | 27 |
+| iws | Sepik Iwam | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ixl | Ixil | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jac | Popti' | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jae | Yabem | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jao | Yanyuwa | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jav | Javanese | Austronesian | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 13 |
+| jic | Tol | Jicaquean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jid | Bu (Kaduna State) | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jiv | Shuar | Chicham | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jni | Janji | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jpn | Japanese | Japonic | 5 | 8 | 3 | 0 | 0 | 1 | 3 | 13 | 2 | 0 | 0 | 35 |
+| jvn | Caribbean Javanese | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kab | Kabyle | Afro-Asiatic | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kac | Kachin | Sino-Tibetan | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| kam | Kamba (Kenya) | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kan | Kannada | Dravidian | 6 | 7 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 19 |
+| kaq | Capanahua | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kas | Kashmiri | Indo-European | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| kat | Georgian | Kartvelian | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 10 |
+| kaz | Kazakh | Turkic | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| kbc | Kadiwéu | Guaicuruan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbh | Camsá | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbm | Iwal | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbp | Kabiyè | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kbq | Kamano | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kdc | Kutu | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kde | Makonde | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kdl | Tsikimba | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kea | Kabuverdianu | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| kek | Kekchí | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ken | Kenyang | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kew | West Kewa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kfg | Kudiya | Dravidian | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kfy | Kumaoni | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgf | Kube | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgk | Kaiwá | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgp | Kaingang | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| khk | Halh Mongolian | Mongolic-Khitan | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| khm | Khmer | Austroasiatic | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| khs | Kasua | Bosavi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| khz | Keapara | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kik | Kikuyu | Atlantic-Congo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kin | Kinyarwanda | Atlantic-Congo | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
+| kir | Kirghiz | Turkic | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| kiw | Northeast Kiwai | Kiwaian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kiz | Kisi | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kje | Kisar | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kjs | East Kewa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kkc | Odoodee | East Strickland | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kkl | Kosarek Yale | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| klt | Nukna | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| klv | Maskelynes | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmb | Kimbundu | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kmg | Kâte | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmh | Kalam | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmk | Limos Kalinga | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmo | Kwoma | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmr | Northern Kurdish | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kms | Kamasau | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmu | Kanite | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knc | Central Kanuri | Saharan | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kne | Kankanaey | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knf | Mankanya | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knj | Western Kanjobal | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knv | Tabo | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kon | Kongo | Unclassified | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kor | Korean | Koreanic | 4 | 8 | 1 | 0 | 1 | 2 | 1 | 9 | 3 | 0 | 0 | 29 |
+| kos | Kosraean | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpf | Komba | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpg | Kapingamarangi | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpj | Karajá | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpr | Korafe-Yegha | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpw | Kobon | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpx | Mountain Koiali | Koiarian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqa | Mum | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqc | Doromu-Koki | Manubaran | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqf | Kakabai | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kql | Kyenele | Yuat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqw | Kandas | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| krc | Karachay-Balkar | Turkic | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksd | Kuanua | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksj | Uare | Kwalean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksr | Borong | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ktm | Kurti | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kto | Kuot | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kud | 'Auhelawa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kue | Kuman (Papua New Guinea) | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kup | Kunimaipa | Kunimaipan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kur | Kurdish | Unclassified | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kvg | Kuni-Boazi | Anim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kvn | Border Kuna | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwd | Kwaio | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwf | Kwara'ae | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwi | Awa-Cuaiquer | Barbacoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwj | Kwanga | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyc | Kyaka | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyf | Kouya | Kru | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyg | Keyagana | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyq | Kenga | Central Sudanic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyz | Kayabí | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kze | Kosena | Bookkeeping | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kzj | Coastal Kadazan | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lac | Lacandon | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lao | Lao | Tai-Kadai | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| lat | Latin | Indo-European | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| lav | Latvian | Indo-European | 1 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| lbb | Label | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lbk | Central Bontok | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lcm | Tungag | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| leu | Kara (Papua New Guinea) | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lex | Luang | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lfn | Lingua Franca Nova | Artificial Language | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lgl | Wala | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lid | Nyindrou | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lif | Limbu | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lij | Ligurian | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lim | Limburgan | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lin | Lingala | Atlantic-Congo | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| lit | Lithuanian | Indo-European | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| llg | Lole | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lmo | Lombard | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ltg | Latgalian | Unclassified | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ltz | Luxembourgish | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| lua | Luba-Lulua | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lug | Ganda | Atlantic-Congo | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| luo | Luo (Kenya and Tanzania) | Nilotic | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| lus | Lushai | Sino-Tibetan | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lvs | Standard Latvian | Unclassified | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| lww | Lewo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maa | San Jerónimo Tecóatl Mazatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mad | Madurese | Austronesian | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| mag | Magahi | Indo-European | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| mai | Maithili | Indo-European | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| maj | Jalapa De Díaz Mazatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mak | Makasar | Austronesian | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mal | Malayalam | Dravidian | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 19 |
+| mam | Mam | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maq | Chiquihuitlán Mazatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mar | Marathi | Indo-European | 7 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 20 |
+| mau | Huautla Mazatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mav | Sateré-Mawé | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| max | North Moluccan Malay | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maz | Central Mazahua | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbb | Western Bukidnon Manobo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbc | Macushi | Cariban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbh | Mangseng | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbj | Nadëb | Naduhup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbl | Maxakalí | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbs | Sarangani Manobo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbt | Matigsalug Manobo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mca | Maca | Mataguayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcb | Machiguenga | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcd | Sharanahua | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcf | Matsés | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mco | Coatlán Mixe | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcp | Makaa | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcq | Ese | Koiarian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcr | Menya | Angan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mdy | Male (Ethiopia) | Ta-Ne-Omotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| med | Melpa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mee | Mengen | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mek | Mekeo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| meq | Merey | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| met | Mato | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| meu | Motu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mey | Hassaniyya | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgc | Morokodo | Central Sudanic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgh | Makhuwa-Meetto | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgw | Matumbi | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mhl | Mauwake | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mhr | Eastern Mari | Uralic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mib | Atatláhuca Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mic | Mi'kmaq | Algic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mie | Ocotepec Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mig | San Miguel El Grande Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mih | Chayuco Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mil | Peñoles Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| min | Minangkabau | Austronesian | 3 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| mio | Pinotepa Nacional Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mir | Isthmus Mixe | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mit | Southern Puebla Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| miz | Coatzospan Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mjc | San Juan Colorado Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkd | Macedonian | Indo-European | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| mkj | Mokilese | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkl | Mokole | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkn | Kupang Malay | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mks | Silacayoapan Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mle | Manambu | Ndu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlg | Malagasy | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlh | Mape | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlp | Bargam | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlt | Maltese | Afro-Asiatic | 2 | 2 | 2 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| mmo | Mangga Buang | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mmx | Madak | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mna | Mbula | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mni | Manipuri | Sino-Tibetan | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| mon | Mongolian | Unclassified | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| mop | Mopán Maya | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mos | Mossi | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mox | Molima | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mph | Maung | Iwaidjan Proper | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpj | Martu Wangka | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpm | Yosondúa Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpp | Migabac | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mps | Dadibi | Teberan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpt | Mian | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpx | Misima-Panaeati | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mqb | Mbuko | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mqj | Mamasa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mri | Maori | Austronesian | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| msa | Malay (macrolanguage) | Unclassified | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| msb | Masbatenyo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msc | Sankaran Maninka | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msk | Mansaka | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msm | Agusan Manobo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msy | Aruamu | Ramu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mti | Maiwa (Papua New Guinea) | Dagan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mto | Totontepec Mixe | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mui | Musi | Austronesian | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mup | Malvi | Indo-European | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| mux | Bo-Ung | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| muy | Muyang | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mva | Manam | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mvn | Minaveha | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwc | Are | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwe | Mwera (Chimwera) | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwf | Murrinh-Patha | Southern Daly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwp | Kala Lagaw Ya | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwr | Marwari | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxb | Tezoatlán Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxp | Tlahuitoltepec Mixe | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxq | Juquila Mixe | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxt | Jamiltepec Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mya | Burmese | Sino-Tibetan | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| myk | Mamara Senoufo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myu | Mundurukú | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myw | Muyuw | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myy | Macuna | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mzz | Maiadomu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nab | Southern Nambikuára | Nambiquaran | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| naf | Nabak | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nak | Nakanai | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nas | Naasioi | South Bougainville | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nbl | South Ndebele | Unclassified | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nbq | Nggem | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nca | Iyo | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nch | Central Huasteca Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncj | Northern Puebla Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncl | Michoacán Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncu | Chumburung | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nde | North Ndebele | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ndg | Ndengereko | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ndj | Ndamba | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nds | Low German | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nep | Nepali (macrolanguage) | Unclassified | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| nfa | Dhao | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ngp | Ngulu | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ngu | Guerrero Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhe | Eastern Huasteca Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhg | Tetelcingo Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhi | Zacatlán-Ahuacatlán-Tepetzintla Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nho | Takuu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhr | Naro | Khoe-Kwadi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhu | Noone | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhw | Western Huasteca Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhy | Northern Oaxaca Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nif | Nek | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nii | Nii | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nij | Ngaju | Austronesian | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| nin | Ninzo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nko | Nkonya | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nld | Dutch | Indo-European | 6 | 6 | 1 | 0 | 1 | 0 | 1 | 2 | 2 | 0 | 0 | 19 |
+| nlg | Gela | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nna | Nyangumarta | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nno | Norwegian Nynorsk | Unclassified | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| nnq | Ngindo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| noa | Woun Meu | Chocoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nob | Norwegian Bokmål | Unclassified | 4 | 7 | 5 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 19 |
+| noe | Nimadi | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nop | Numanggang | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nor | Norwegian | Indo-European | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 3 |
+| not | Nomatsiguenga | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nou | Ewage-Notu | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nov | Novial | Artificial Language | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| npi | Nepali (individual language) | Indo-European | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| npl | Southeastern Puebla Nahuatl | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nqo | N'Ko | Artificial Language | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| nsn | Nehan | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nso | Pedi | Atlantic-Congo | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| nss | Nali | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntj | Ngaanyatjarra | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntp | Northern Tepehuan | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntu | Natügu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nus | Nuer | Nilotic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| nuy | Nunggubuyu | Gunwinyguan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nvm | Namiae | Koiarian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nwi | Southwest Tanna | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nya | Nyanja | Atlantic-Congo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| nys | Nyungar | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nyu | Nyungwe | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| obo | Obo Manobo | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| oci | Occitan (post 1500) | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| okv | Orokaiva | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| omw | South Tairora | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ong | Olo | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ons | Ono | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ood | Tohono O'odham | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| opm | Oksapmin | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ori | Oriya (macrolanguage) | Unclassified | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| orm | Oromo | Unclassified | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| orv | Old Russian | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ory | Odia | Indo-European | 5 | 4 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 15 |
+| ote | Mezquital Otomi | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otm | Eastern Highland Otomi | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otn | Tenango Otomi | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otq | Querétaro Otomi | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ots | Estado de México Otomi | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pab | Parecís | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pad | Paumarí | Arawan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pag | Pangasinan | Austronesian | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| pah | Tenharim | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pam | Pampanga | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pan | Panjabi | Indo-European | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 18 |
+| pao | Northern Paiute | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pap | Papiamento | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| pbt | Southern Pashto | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| pcm | Nigerian Pidgin | Indo-European | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| pes | Iranian Persian | Indo-European | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| pib | Yine | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pio | Piapoco | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pir | Piratapuyo | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| piu | Pintupi-Luritja | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pjt | Pitjantjatjara | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pls | San Marcos Tlacoyalco Popoloca | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| plt | Plateau Malagasy | Austronesian | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| plu | Palikúr | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pma | Paama | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pms | Piemontese | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poe | San Juan Atzingo Popoloca | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poh | Poqomchi' | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poi | Highland Popoluca | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pol | Polish | Indo-European | 4 | 11 | 4 | 0 | 1 | 4 | 0 | 18 | 4 | 0 | 0 | 46 |
+| pon | Pohnpeian | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| por | Portuguese | Indo-European | 4 | 9 | 1 | 0 | 2 | 2 | 1 | 5 | 3 | 0 | 0 | 27 |
+| poy | Pogolo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ppo | Folopa | Teberan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| prf | Paranan | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pri | Paicî | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| prs | Dari | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ptp | Patep | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ptu | Bambam | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pus | Pushto | Unclassified | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| pwg | Gapapaiwa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qub | Huallaga Huánuco Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quc | K'iche' | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quf | Lambayeque Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quh | South Bolivian Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qul | North Bolivian Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qup | Southern Pastaza Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quy | Ayacucho Quechua | Quechuan | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| qvc | Cajamarca Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qve | Eastern Apurímac Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvh | Huamalíes-Dos de Mayo Huánuco Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvm | Margos-Yarowilca-Lauricocha Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvn | North Junín Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvs | San Martín Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvw | Huaylla Wanca Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvz | Northern Pastaza Quichua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qwh | Huaylas Ancash Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxh | Panao Huánuco Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxn | Northern Conchucos Ancash Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxo | Southern Conchucos Ancash Quechua | Quechuan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rai | Ramoaaina | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| raj | Rajasthani | Unclassified | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| reg | Kara (Tanzania) | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rej | Rejang | Austronesian | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| rgu | Ringgou | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rkb | Rikbaktsa | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rmc | Carpathian Romani | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rmy | Vlax Romani | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rom | Romany | Unclassified | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| ron | Romanian | Indo-European | 5 | 6 | 1 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 0 | 18 |
+| roo | Rotokas | North Bougainville | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rop | Kriol | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| row | Dela-Oenale | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rro | Waima | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ruf | Luguru | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rug | Roviana | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| run | Rundi | Atlantic-Congo | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| rus | Russian | Indo-European | 5 | 13 | 6 | 0 | 2 | 4 | 2 | 16 | 4 | 0 | 0 | 52 |
+| rwo | Rawa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sab | Buglere | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sag | Sango | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sah | Yakut | Turkic | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| san | Sanskrit | Indo-European | 5 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
+| sat | Santali | Austroasiatic | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| sbe | Saliba | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sbk | Safwa | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sbs | Subiya | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| scn | Sicilian | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sco | Scots | Indo-European | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| seh | Sena | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sey | Secoya | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sgb | Mag-antsi Ayta | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sgz | Sursurunga | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shi | Tachelhit | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shj | Shatt | Dajuic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shn | Shan | Tai-Kadai | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| shp | Shipibo-Conibo | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sim | Mende (Papua New Guinea) | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sin | Sinhala | Indo-European | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| sja | Epena | Chocoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| slk | Slovak | Indo-European | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 12 |
+| sll | Salt-Yui | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| slv | Slovenian | Indo-European | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 10 |
+| smk | Bolinao | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| smo | Samoan | Austronesian | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| sna | Shona | Atlantic-Congo | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| snc | Sinaugoro | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snd | Sindhi | Indo-European | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| snn | Siona | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snp | Siane | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snx | Sam | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sny | Saniyo-Hiyewe | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| som | Somali | Afro-Asiatic | 3 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| soq | Kanasi | Dagan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sot | Southern Sotho | Atlantic-Congo | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| soy | Miyobe | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spa | Spanish | Indo-European | 4 | 13 | 4 | 0 | 1 | 2 | 2 | 13 | 4 | 0 | 0 | 43 |
+| spl | Selepet | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spm | Akukem | Ramu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spp | Supyire Senoufo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sps | Saposa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spy | Sabaot | Nilotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sqi | Albanian | Unclassified | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| srd | Sardinian | Unclassified | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sri | Siriano | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| srm | Saramaccan | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| srn | Sranan Tongo | Indo-European | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| srp | Serbian | Indo-European | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 9 |
+| srq | Sirionó | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssd | Siroi | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssg | Seimat | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssw | Swati | Atlantic-Congo | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| ssx | Samberigi | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| stp | Southeastern Tepehuan | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sua | Sulka | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sue | Suena | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sun | Sundanese | Austronesian | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| sus | Susu | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| suz | Sunwar | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| svk | Slovakian Sign Language | Sign Language | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| swa | Swahili (macrolanguage) | Atlantic-Congo | 1 | 7 | 2 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 15 |
+| swe | Swedish | Indo-European | 4 | 8 | 3 | 0 | 1 | 1 | 1 | 4 | 0 | 0 | 0 | 22 |
+| swg | Swabian | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| swh | Swahili (individual language) | Atlantic-Congo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| swp | Suau | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sxb | Suba | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| szl | Silesian | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tac | Lowland Tarahumara | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tah | Tahitian | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| taj | Eastern Tamang | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tam | Tamil | Dravidian | 7 | 7 | 2 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 21 |
+| taq | Tamasheq | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tat | Tatar | Turkic | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| tav | Tatuyo | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| taw | Tai | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbc | Takia | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbf | Mandara | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbg | North Tairora | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbo | Tawala | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbz | Ditammari | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tca | Ticuna | Ticuna-Yuri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tcs | Torres Strait Creole | Indo-European | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tcz | Thado Chin | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tdt | Tetun Dili | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tee | Huehuetla Tepehua | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tel | Telugu | Dravidian | 7 | 7 | 2 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 | 24 |
+| ter | Tereno | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tet | Tetum | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tew | Tewa (USA) | Kiowa-Tanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tfr | Teribe | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tgk | Tajik | Indo-European | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| tgl | Tagalog | Austronesian | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| tgo | Sudest | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tgp | Tangoa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tha | Thai | Tai-Kadai | 4 | 8 | 1 | 0 | 0 | 1 | 1 | 6 | 0 | 0 | 0 | 21 |
+| tif | Tifal | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tim | Timbe | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tir | Tigrinya | Afro-Asiatic | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| tiw | Tiwi | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tiy | Tiruray | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tke | Takwane | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tku | Upper Necaxa Totonac | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tlf | Telefol | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tmd | Haruai | Piawi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tna | Tacana | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnc | Tanimuca-Retuarã | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnk | Kwamera | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnn | North Tanna | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnp | Whitesands | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| toc | Coyutla Totonac | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tod | Toma | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tof | Gizrra | Eastern Trans-Fly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| toj | Tojolabal | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ton | Tonga (Tonga Islands) | Austronesian | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| too | Xicotepec De Juárez Totonac | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| top | Papantla Totonac | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tos | Highland Totonac | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpa | Taupota | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpi | Tok Pisin | Indo-European | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| tpt | Tlachichilco Tepehua | Totonacan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpz | Tinputz | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| trc | Copala Triqui | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tsn | Tswana | Atlantic-Congo | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| tso | Tsonga | Atlantic-Congo | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| tsw | Tsishingini | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ttc | Tektiteko | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tte | Bwanabwana | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuc | Mutu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tue | Tuyuca | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuf | Central Tunebo | Chibchan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuk | Turkmen | Turkic | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| tum | Tumbuka | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tuo | Tucano | Tucanoan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tur | Turkish | Turkic | 4 | 7 | 1 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 | 19 |
+| tvk | Southeast Ambrym | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| twi | Twi | Unclassified | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| txq | Tii | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| txu | Kayapó | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tyv | Tuvinian | Turkic | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzj | Tz'utujil | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzl | Talossan | Artificial Language | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzm | Central Atlas Tamazight | Afro-Asiatic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tzo | Tzotzil | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ubr | Ubir | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ubu | Umbu-Ungu | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| udu | Uduk | Koman | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uig | Uighur | Turkic | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| ukr | Ukrainian | Indo-European | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| uli | Ulithian | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ulk | Meriam Mir | Eastern Trans-Fly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| umb | Umbundu | Atlantic-Congo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| upv | Uripiv-Wala-Rano-Atchin | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ura | Urarina | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urb | Urubú-Kaapor | Tupian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urd | Urdu | Indo-European | 7 | 8 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 19 |
+| uri | Urim | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urt | Urat | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urw | Sop | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| usa | Usarufa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| usp | Uspanteco | Mayan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uvh | Uri | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uvl | Lote | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uzb | Uzbek | Unclassified | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| uzn | Northern Uzbek | Turkic | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| vec | Venetian | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ven | Venda | Atlantic-Congo | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| vid | Vidunda | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| vie | Vietnamese | Austroasiatic | 5 | 6 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 18 |
+| viv | Iduna | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| vmy | Ayautla Mazatec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| waj | Waffa | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wal | Wolaytta | Ta-Ne-Omotic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wap | Wapishana | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| war | Waray (Philippines) | Austronesian | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| wat | Kaninuwa | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wbi | Vwanji | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wbp | Warlpiri | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wed | Wedau | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wer | Weri | Kunimaipan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wim | Wik-Mungkan | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wiu | Wiru | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wiv | Vitu | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wln | Walloon | Indo-European | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wmt | Walmajarri | Pama-Nyungan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wmw | Mwani | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wnc | Wantoat | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wnu | Usan | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wol | Wolof | Atlantic-Congo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| wos | Hanga Hundi | Ndu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wrk | Garrwa | Garrwan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wro | Worrorra | Worrorran | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wrs | Waris | Border | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wsk | Waskia | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wuu | Wu Chinese | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wuv | Wuvulu-Aua | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xav | Xavánte | Nuclear-Macro-Je | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xbi | Kombio | Nuclear Torricelli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xed | Hdi | Afro-Asiatic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xho | Xhosa | Atlantic-Congo | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 10 |
+| xla | Kamula | Kamula-Elevala | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xnn | Northern Kankanay | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xon | Konkomba | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xsi | Sio | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xtd | Diuxi-Tilantongo Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xtm | Magdalena Peñasco Mixtec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yaa | Yaminahua | Pano-Tacanan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yad | Yagua | Peba-Yagua | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yal | Yalunka | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yap | Yapese | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yaq | Yaqui | Uto-Aztecan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yby | Yaweyuha | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ycn | Yucuna | Arawakan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ydd | Eastern Yiddish | Indo-European | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| yid | Yiddish | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yka | Yakan | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yle | Yele | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yml | Iamalele | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yon | Yongkom | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yor | Yoruba | Atlantic-Congo | 4 | 5 | 3 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 16 |
+| yrb | Yareba | Yareban | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yre | Yaouré | Mande | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yss | Yessan-Mayo | Sepik | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yue | Yue Chinese | Sino-Tibetan | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| yuj | Karkar-Yuri | Pauwasi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yut | Yopno | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yuw | Yau (Morobe Province) | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yva | Yawa | Yawa-Saweru | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaa | Sierra de Juárez Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zab | Western Tlacolula Valley Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zac | Ocotlán Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zad | Cajonos Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zai | Isthmus Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaj | Zaramo | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zam | Miahuatlán Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zao | Ozolotepec Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zap | Zapotec | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zar | Rincón Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zas | Santo Domingo Albarradas Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zat | Tabaa Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zav | Yatzachi Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaw | Mitla Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zca | Coatecas Altas Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zga | Kinga | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zho | Chinese | Unclassified | 2 | 2 | 1 | 0 | 0 | 1 | 1 | 13 | 0 | 0 | 0 | 20 |
+| zia | Zia | Nuclear Trans New Guinea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ziw | Zigula | Atlantic-Congo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zlm | Malay (individual language) | Austronesian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zos | Francisco León Zoque | Mixe-Zoque | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpc | Choapan Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpl | Lachixío Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpm | Mixtepec Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpo | Amatlán Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpq | Zoogocho Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpu | Yalálag Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpv | Chichicapan Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpz | Texmelucan Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zsm | Standard Malay | Austronesian | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| zsr | Southern Rincon Zapotec | Unclassified | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ztq | Quioquitani-Quierí Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zty | Yatee Zapotec | Otomanguean | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zul | Zulu | Atlantic-Congo | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| zyp | Zyphe Chin | Sino-Tibetan | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| Total | None | None | None | 1394 | 795 | 304 | 3 | 28 | 67 | 51 | 473 | 85 | 2 | 2 |
 <!-- TASK LANG TABLE END -->
 
 </details>
diff --git a/mteb/abstasks/AbsTask.py b/mteb/abstasks/AbsTask.py
index 127b251b7e..cbdcac6372 100644
--- a/mteb/abstasks/AbsTask.py
+++ b/mteb/abstasks/AbsTask.py
@@ -109,17 +109,18 @@ def evaluate(
         self,
         model: Encoder,
         split: str = "test",
+        subsets_to_run: list[HFSubset] | None = None,
         *,
         encode_kwargs: dict[str, Any] = {},
         **kwargs: Any,
     ) -> dict[HFSubset, ScoresDict]:
         """Evaluates a Sentence Embedding Model on the task.
-        Returns a dict (that can be serialized to json).
 
         Args:
             model: Sentence embedding method. Implements a encode(sentences) method, that encodes sentences and returns a numpy matrix with the
                 sentence embeddings
             split: Which datasplit to be used.
+            subsets_to_run: List of HFSubsets to evaluate. If None, all subsets are evaluated.
             encode_kwargs: Additional keyword arguments that are passed to the model's `encode` method.
             kwargs: Additional keyword arguments that are passed to the _evaluate_subset method.
         """
@@ -131,6 +132,9 @@ def evaluate(
         scores = {}
         hf_subsets = list(self.dataset.keys()) if self.is_multilingual else ["default"]
 
+        if subsets_to_run is not None:
+            hf_subsets = [s for s in hf_subsets if s in subsets_to_run]
+
         for hf_subset in hf_subsets:
             logger.info(
                 f"\nTask: {self.metadata_dict['name']}, split: {split}, subset: {hf_subset}. Running..."
diff --git a/mteb/abstasks/AbsTaskBitextMining.py b/mteb/abstasks/AbsTaskBitextMining.py
index 4be4ec1562..635f0a67b1 100644
--- a/mteb/abstasks/AbsTaskBitextMining.py
+++ b/mteb/abstasks/AbsTaskBitextMining.py
@@ -67,7 +67,8 @@ def __init__(self, **kwargs):
     def evaluate(
         self,
         model: Encoder,
-        split: str,
+        split: str = "test",
+        subsets_to_run: list[HFSubset] | None = None,
         *,
         encode_kwargs: dict[str, Any] = {},
         **kwargs,
@@ -77,6 +78,10 @@ def evaluate(
 
         hf_subsets = list(self.dataset) if self.is_multilingual else ["default"]
 
+        # If subsets_to_run is specified, filter the hf_subsets accordingly
+        if subsets_to_run is not None:
+            hf_subsets = [s for s in hf_subsets if s in subsets_to_run]
+
         scores = {}
         if self.parallel_subsets:
             scores = self._evaluate_subset(
diff --git a/mteb/abstasks/AbsTaskClassification.py b/mteb/abstasks/AbsTaskClassification.py
index 5e48dfab49..42de8dd273 100644
--- a/mteb/abstasks/AbsTaskClassification.py
+++ b/mteb/abstasks/AbsTaskClassification.py
@@ -95,6 +95,7 @@ def evaluate(
         model,
         eval_split: str = "test",
         train_split: str = "train",
+        subsets_to_run: list[HFSubset] | None = None,
         *,
         encode_kwargs: dict[str, Any] = {},
         **kwargs,
@@ -104,6 +105,8 @@ def evaluate(
 
         scores = {}
         hf_subsets = list(self.dataset) if self.is_multilingual else ["default"]
+        if subsets_to_run is not None:
+            hf_subsets = [s for s in hf_subsets if s in subsets_to_run]
 
         for hf_subset in hf_subsets:
             logger.info(
diff --git a/mteb/abstasks/AbsTaskMultilabelClassification.py b/mteb/abstasks/AbsTaskMultilabelClassification.py
index 1c3cba33e5..5172b06d60 100644
--- a/mteb/abstasks/AbsTaskMultilabelClassification.py
+++ b/mteb/abstasks/AbsTaskMultilabelClassification.py
@@ -121,6 +121,7 @@ def evaluate(
         model: Encoder,
         eval_split: str = "test",
         train_split: str = "train",
+        subsets_to_run: list[HFSubset] | None = None,
         *,
         encode_kwargs: dict[str, Any] = {},
         **kwargs: Any,
@@ -130,6 +131,9 @@ def evaluate(
 
         scores = {}
         hf_subsets = list(self.dataset) if self.is_multilingual else ["default"]
+        # If subsets_to_run is specified, filter the hf_subsets accordingly
+        if subsets_to_run is not None:
+            hf_subsets = [s for s in hf_subsets if s in subsets_to_run]
 
         for hf_subset in hf_subsets:
             logger.info(
diff --git a/mteb/abstasks/AbsTaskReranking.py b/mteb/abstasks/AbsTaskReranking.py
index 2ae1f5c359..e8ea495366 100644
--- a/mteb/abstasks/AbsTaskReranking.py
+++ b/mteb/abstasks/AbsTaskReranking.py
@@ -26,6 +26,7 @@
     "MMarcoReranking",
     "CMedQAv1-reranking",
     "CMedQAv2-reranking",
+    "NamaaMrTydiReranking",
 ]
 
 
diff --git a/mteb/abstasks/AbsTaskRetrieval.py b/mteb/abstasks/AbsTaskRetrieval.py
index b8f255b356..b709c046f2 100644
--- a/mteb/abstasks/AbsTaskRetrieval.py
+++ b/mteb/abstasks/AbsTaskRetrieval.py
@@ -237,6 +237,7 @@ def evaluate(
         self,
         model,
         split: str = "test",
+        subsets_to_run: list[HFSubset] | None = None,
         *,
         encode_kwargs: dict[str, Any] = {},
         **kwargs,
@@ -250,6 +251,8 @@ def evaluate(
 
         scores = {}
         hf_subsets = list(self.hf_subsets) if self.is_multilingual else ["default"]
+        if subsets_to_run is not None:
+            hf_subsets = [s for s in hf_subsets if s in subsets_to_run]
 
         for hf_subset in hf_subsets:
             logger.info(f"Subset: {hf_subset}")
diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py
index 8ff56acb4e..0954c2de26 100644
--- a/mteb/benchmarks/benchmarks.py
+++ b/mteb/benchmarks/benchmarks.py
@@ -979,3 +979,27 @@ def load_results(
   year={2024}
 }""",
 )
+
+NANOBEIR = Benchmark(
+    name="NanoBEIR",
+    tasks=get_tasks(
+        tasks=[
+            "NanoArguAnaRetrieval",
+            "NanoClimateFeverRetrieval",
+            "NanoDBPediaRetrieval",
+            "NanoFEVERRetrieval",
+            "NanoFiQA2018Retrieval",
+            "NanoHotpotQARetrieval",
+            "NanoMSMARCORetrieval",
+            "NanoNFCorpusRetrieval",
+            "NanoNQRetrieval",
+            "NanoQuoraRetrieval",
+            "NanoSCIDOCSRetrieval",
+            "NanoSciFactRetrieval",
+            "NanoTouche2020Retrieval",
+        ],
+    ),
+    description="A benchmark to evaluate with subsets of BEIR datasets to use less computational power",
+    reference="https://huggingface.co/collections/zeta-alpha-ai/nanobeir-66e1a0af21dfd93e620cd9f6",
+    citation=None,
+)
diff --git a/mteb/descriptive_stats/Reranking/NamaaMrTydiReranking.json b/mteb/descriptive_stats/Reranking/NamaaMrTydiReranking.json
new file mode 100644
index 0000000000..74da38e4f0
--- /dev/null
+++ b/mteb/descriptive_stats/Reranking/NamaaMrTydiReranking.json
@@ -0,0 +1,31 @@
+{
+    "test": {
+        "num_samples": 5504,
+        "number_of_characters": 1293166,
+        "num_documents": 4586,
+        "min_document_length": 0,
+        "average_document_length": 275.8353685128652,
+        "max_document_length": 4158,
+        "unique_documents": 4586,
+        "num_queries": 918,
+        "min_query_length": 13,
+        "average_query_length": 30.702614379084967,
+        "max_query_length": 93,
+        "unique_queries": 918,
+        "none_queries": 0,
+        "num_relevant_docs": 4586,
+        "min_relevant_docs_per_query": 2,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 6,
+        "unique_relevant_docs": 4586,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": 918,
+        "min_top_ranked_per_query": 2,
+        "average_top_ranked_per_query": 4.995642701525054,
+        "max_top_ranked_per_query": 6
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoArguAnaRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoArguAnaRetrieval.json
new file mode 100644
index 0000000000..51d1be4353
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoArguAnaRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 3685,
+        "number_of_characters": 3737951,
+        "num_documents": 3635,
+        "min_document_length": 70,
+        "average_document_length": 1011.7914718019257,
+        "max_document_length": 6673,
+        "unique_documents": 3635,
+        "num_queries": 50,
+        "min_query_length": 504,
+        "average_query_length": 1201.78,
+        "max_query_length": 2164,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoClimateFeverRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoClimateFeverRetrieval.json
new file mode 100644
index 0000000000..5a408ec517
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoClimateFeverRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 3458,
+        "number_of_characters": 5525784,
+        "num_documents": 3408,
+        "min_document_length": 33,
+        "average_document_length": 1619.531690140845,
+        "max_document_length": 6619,
+        "unique_documents": 3408,
+        "num_queries": 50,
+        "min_query_length": 38,
+        "average_query_length": 128.4,
+        "max_query_length": 265,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 38,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoDBPediaRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoDBPediaRetrieval.json
new file mode 100644
index 0000000000..cd6f035639
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoDBPediaRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 6095,
+        "number_of_characters": 2034629,
+        "num_documents": 6045,
+        "min_document_length": 1,
+        "average_document_length": 336.30669975186106,
+        "max_document_length": 1390,
+        "unique_documents": 6045,
+        "num_queries": 50,
+        "min_query_length": 8,
+        "average_query_length": 33.1,
+        "max_query_length": 63,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoFEVERRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoFEVERRetrieval.json
new file mode 100644
index 0000000000..eb7f3d6e95
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoFEVERRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 5046,
+        "number_of_characters": 6140916,
+        "num_documents": 4996,
+        "min_document_length": 25,
+        "average_document_length": 1228.7119695756605,
+        "max_document_length": 8491,
+        "unique_documents": 4996,
+        "num_queries": 50,
+        "min_query_length": 17,
+        "average_query_length": 45.42,
+        "max_query_length": 83,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoFiQA2018Retrieval.json b/mteb/descriptive_stats/Retrieval/NanoFiQA2018Retrieval.json
new file mode 100644
index 0000000000..92c11900a9
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoFiQA2018Retrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 4648,
+        "number_of_characters": 4139437,
+        "num_documents": 4598,
+        "min_document_length": 0,
+        "average_document_length": 899.6326663766855,
+        "max_document_length": 10506,
+        "unique_documents": 4598,
+        "num_queries": 50,
+        "min_query_length": 18,
+        "average_query_length": 58.52,
+        "max_query_length": 97,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoHotpotQARetrieval.json b/mteb/descriptive_stats/Retrieval/NanoHotpotQARetrieval.json
new file mode 100644
index 0000000000..ec35252f78
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoHotpotQARetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 5140,
+        "number_of_characters": 1784059,
+        "num_documents": 5090,
+        "min_document_length": 24,
+        "average_document_length": 349.6349705304519,
+        "max_document_length": 2079,
+        "unique_documents": 5090,
+        "num_queries": 50,
+        "min_query_length": 37,
+        "average_query_length": 88.34,
+        "max_query_length": 184,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoMSMARCORetrieval.json b/mteb/descriptive_stats/Retrieval/NanoMSMARCORetrieval.json
new file mode 100644
index 0000000000..2deebc7918
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoMSMARCORetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 5093,
+        "number_of_characters": 1666607,
+        "num_documents": 5043,
+        "min_document_length": 32,
+        "average_document_length": 330.159825500694,
+        "max_document_length": 990,
+        "unique_documents": 5043,
+        "num_queries": 50,
+        "min_query_length": 13,
+        "average_query_length": 32.22,
+        "max_query_length": 101,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoNFCorpusRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoNFCorpusRetrieval.json
new file mode 100644
index 0000000000..b6e2e3fb1a
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoNFCorpusRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 3003,
+        "number_of_characters": 4468144,
+        "num_documents": 2953,
+        "min_document_length": 90,
+        "average_document_length": 1512.7301049779885,
+        "max_document_length": 9939,
+        "unique_documents": 2953,
+        "num_queries": 50,
+        "min_query_length": 4,
+        "average_query_length": 21.04,
+        "max_query_length": 53,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 42,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoNQRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoNQRetrieval.json
new file mode 100644
index 0000000000..254a0ac40f
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoNQRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 5085,
+        "number_of_characters": 2648727,
+        "num_documents": 5035,
+        "min_document_length": 1,
+        "average_document_length": 525.5958291956306,
+        "max_document_length": 6138,
+        "unique_documents": 5035,
+        "num_queries": 50,
+        "min_query_length": 32,
+        "average_query_length": 47.04,
+        "max_query_length": 83,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoQuoraRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoQuoraRetrieval.json
new file mode 100644
index 0000000000..540b0fd3aa
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoQuoraRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 5096,
+        "number_of_characters": 278960,
+        "num_documents": 5046,
+        "min_document_length": 2,
+        "average_document_length": 54.808164883075705,
+        "max_document_length": 332,
+        "unique_documents": 5046,
+        "num_queries": 50,
+        "min_query_length": 19,
+        "average_query_length": 47.96,
+        "max_query_length": 139,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoSCIDOCSRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoSCIDOCSRetrieval.json
new file mode 100644
index 0000000000..78e927e208
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoSCIDOCSRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 2260,
+        "number_of_characters": 2044730,
+        "num_documents": 2210,
+        "min_document_length": 0,
+        "average_document_length": 923.5705882352942,
+        "max_document_length": 10000,
+        "unique_documents": 2210,
+        "num_queries": 50,
+        "min_query_length": 38,
+        "average_query_length": 72.78,
+        "max_query_length": 143,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoSciFactRetrieval.json b/mteb/descriptive_stats/Retrieval/NanoSciFactRetrieval.json
new file mode 100644
index 0000000000..00e8cb4be1
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoSciFactRetrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 2969,
+        "number_of_characters": 4182563,
+        "num_documents": 2919,
+        "min_document_length": 260,
+        "average_document_length": 1431.2343268242548,
+        "max_document_length": 10000,
+        "unique_documents": 2919,
+        "num_queries": 50,
+        "min_query_length": 37,
+        "average_query_length": 95.8,
+        "max_query_length": 200,
+        "unique_queries": 50,
+        "none_queries": 0,
+        "num_relevant_docs": 50,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 50,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NanoTouche2020Retrieval.json b/mteb/descriptive_stats/Retrieval/NanoTouche2020Retrieval.json
new file mode 100644
index 0000000000..de076dae57
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NanoTouche2020Retrieval.json
@@ -0,0 +1,31 @@
+{
+    "train": {
+        "num_samples": 5794,
+        "number_of_characters": 12311190,
+        "num_documents": 5745,
+        "min_document_length": 3,
+        "average_document_length": 2142.56953872933,
+        "max_document_length": 37100,
+        "unique_documents": 5745,
+        "num_queries": 49,
+        "min_query_length": 16,
+        "average_query_length": 43.42857142857143,
+        "max_query_length": 83,
+        "unique_queries": 49,
+        "none_queries": 0,
+        "num_relevant_docs": 49,
+        "min_relevant_docs_per_query": 1,
+        "average_relevant_docs_per_query": 1.0,
+        "max_relevant_docs_per_query": 1,
+        "unique_relevant_docs": 49,
+        "num_instructions": null,
+        "min_instruction_length": null,
+        "average_instruction_length": null,
+        "max_instruction_length": null,
+        "unique_instructions": null,
+        "num_top_ranked": null,
+        "min_top_ranked_per_query": null,
+        "average_top_ranked_per_query": null,
+        "max_top_ranked_per_query": null
+    }
+}
\ No newline at end of file
diff --git a/mteb/evaluation/MTEB.py b/mteb/evaluation/MTEB.py
index 6ca449e1c5..4b1e06e077 100644
--- a/mteb/evaluation/MTEB.py
+++ b/mteb/evaluation/MTEB.py
@@ -299,8 +299,8 @@ def load_tasks_data(self):
     def _run_eval(
         task: AbsTask,
         model: Encoder,
-        split,
-        output_folder,
+        split: str,
+        subsets_to_run: list[str] | None = None,
         *,
         encode_kwargs: dict[str, Any],
         **kwargs: Any,
@@ -309,7 +309,7 @@ def _run_eval(
         results = task.evaluate(
             model,
             split,
-            output_folder=output_folder,
+            subsets_to_run=subsets_to_run,
             encode_kwargs=encode_kwargs,
             **kwargs,
         )
@@ -385,7 +385,8 @@ def run(
         model: SentenceTransformer | Encoder,
         verbosity: int = 1,
         output_folder: str | None = "results",
-        eval_splits=None,
+        eval_splits: list[str] | None = None,
+        eval_subsets: list[str] | None = None,
         overwrite_results: bool = False,
         raise_error: bool = True,
         co2_tracker: bool = True,
@@ -404,6 +405,7 @@ def run(
             output_folder: Folder where the results will be saved. Default to 'results'. Where it will save the results in the format:
                 `{output_folder}/{model_name}/{model_revision}/{task_name}.json`.
             eval_splits: List of splits to evaluate on. If None, the splits are taken from the task metadata.
+            eval_subsets: List of subsets to evaluate on. If None, the subsets are taken from the task metadata.
             overwrite_results: Whether to overwrite existing results.
             raise_error: Whether to raise an error if an exception occurs during evaluation.
             co2_tracker: Whether to enable or disable CO2 emissions tracker using codecarbon.
@@ -438,6 +440,10 @@ def run(
         if isinstance(model, (SentenceTransformer, CrossEncoder)):
             model = SentenceTransformerWrapper(model)
 
+        ## Disable co2_tracker for API models
+        if "API" in meta.framework:
+            co2_tracker = False
+
         if output_path:
             self._save_model_metadata(meta, output_path)
 
@@ -461,9 +467,17 @@ def run(
                 f"\n\n********************** Evaluating {task.metadata.name} **********************"
             )
 
+            task_eval_splits = (
+                eval_splits if eval_splits is not None else task.eval_splits
+            )
+            task_subsets = list(task.metadata.hf_subsets_to_langscripts.keys())
+
+            existing_results = None
+            save_path = None
+
             if output_path:
+                kwargs["output_folder"] = output_folder  # needed for retrieval tasks
                 save_path = output_path / f"{task.metadata.name}{task.save_suffix}.json"
-                existing_results = None
                 if save_path.exists():
                     existing_results = TaskResult.from_disk(save_path)
 
@@ -475,38 +489,53 @@ def run(
                         del self.tasks[0]  # empty memory
                         continue
 
-                task_eval_splits = (
-                    eval_splits if eval_splits is not None else task.eval_splits
-                )
-                missing_splits = self._get_missing_splits(
-                    existing_results, task_eval_splits
-                )
-
-                if not missing_splits and existing_results:
-                    evaluation_results.append(existing_results)
+            # Unified call to get missing splits and subsets
+            missing_evaluations = self._get_missing_evaluations(
+                existing_results,
+                task_eval_splits,
+                task_subsets,
+                eval_subsets,
+            )
 
-                    # no splits are evaluated.
-                    self.last_evaluated_splits[task.metadata.name] = []
-                    del self.tasks[0]
-                    continue
+            # Determine final splits to run
+            final_splits_to_run = []
+            # We need to run any split that is fully missing or has missing subsets
+            for sp, info in missing_evaluations.items():
+                if info["whole_split_missing"] or info["missing_subsets"]:
+                    final_splits_to_run.append(sp)
 
-                if missing_splits:
+            # If no splits need to be run and results exist, skip
+            if not final_splits_to_run:
+                if existing_results is not None:
+                    evaluation_results.append(existing_results)
+                else:
                     logger.info(
-                        f"Running evaluation for missing splits: {missing_splits}"
+                        f"No splits to evaluate for {task.metadata.name}. Skipping evaluation."
                     )
+                self.last_evaluated_splits[task.metadata.name] = []
+                del self.tasks[0]
+                continue
 
             try:
                 task.check_if_dataset_is_superseded()
                 task.load_data(eval_splits=task_eval_splits, **kwargs)
 
-                # run evaluation
                 task_results = {}
                 evaluation_time = 0
                 kg_co2_emissions: int | None = 0 if co2_tracker else None
 
                 self.last_evaluated_splits[task.metadata.name] = []
 
-                for split in missing_splits:
+                for split in final_splits_to_run:
+                    info = missing_evaluations[split]
+
+                    # Determine subsets to run for this split
+                    # If the whole split is missing, run all required subsets
+                    # If only some subsets are missing, run only those
+                    subsets_to_run = info["missing_subsets"]
+                    if info["whole_split_missing"] and task_subsets is None:
+                        subsets_to_run = ["default"]
+
                     if co2_tracker:
                         logger.warning(
                             "Evaluating multiple MTEB runs simultaniously will produce incorrect CO₂ results"
@@ -521,8 +550,8 @@ def run(
                                 task,
                                 model,
                                 split,
-                                output_folder,
                                 encode_kwargs=encode_kwargs,
+                                subsets_to_run=subsets_to_run,
                                 **kwargs,
                             )
 
@@ -535,12 +564,11 @@ def run(
                             model,
                             split,
                             output_folder,
+                            subsets_to_run=subsets_to_run,
                             encode_kwargs=encode_kwargs,
                             **kwargs,
                         )
 
-                    self.last_evaluated_splits[task.metadata.name].append(split)
-
                     logger.info(
                         f"Evaluation for {task.metadata_dict['name']} on {split} took {tock - tick:.2f} seconds"
                     )
@@ -548,8 +576,11 @@ def run(
 
                     task_results[split] = results
                     if verbosity >= 1:
-                        logger.info(f"Scores: {results}")
+                        logger.info(f"Scores: {task_results[split]}")
+
+                    self.last_evaluated_splits[task.metadata.name].append(split)
 
+                # Create new TaskResult
                 new_results = TaskResult.from_task_results(
                     task,
                     task_results,
@@ -557,6 +588,9 @@ def run(
                     kg_co2_emissions=kg_co2_emissions,
                 )
 
+                # Merge with existing if needed
+                if output_path and save_path.exists():
+                    existing_results = TaskResult.from_disk(save_path)
                 if existing_results:
                     merged_results = self._merge_results(existing_results, new_results)
                 else:
@@ -642,3 +676,56 @@ def get_last_evaluated_splits(self):
         return deepcopy(
             {task: list(splits) for task, splits in self.last_evaluated_splits.items()}
         )
+
+    @staticmethod
+    def _get_missing_evaluations(
+        existing_results: TaskResult | None,
+        task_eval_splits: list[str],
+        task_eval_langs: list[str],
+        eval_subsets: list[str] | None,
+    ) -> dict[str, dict[str, Any]]:
+        """Return a dictionary for each split, indicating if the whole split is missing and which subsets are missing."""
+        missing_evaluations = {
+            split: {"whole_split_missing": False, "missing_subsets": []}
+            for split in task_eval_splits
+        }
+
+        # Determine subsets to consider if multilingual
+        if eval_subsets is None:
+            # If no eval_langs specified, consider all subsets
+            subsets_to_consider = task_eval_langs
+        else:
+            subsets_to_consider = [
+                subset for subset in task_eval_langs if subset in eval_subsets
+            ]
+
+        # If no existing results, all splits and subsets are missing
+        if existing_results is None:
+            for split in task_eval_splits:
+                missing_evaluations[split]["whole_split_missing"] = True
+                missing_evaluations[split]["missing_subsets"] = list(
+                    subsets_to_consider
+                )
+            return missing_evaluations
+
+        # If we have existing results, check which splits and subsets are missing
+        for split in task_eval_splits:
+            if split not in existing_results.scores:
+                # Whole split missing
+                missing_evaluations[split]["whole_split_missing"] = True
+                missing_evaluations[split]["missing_subsets"] = list(
+                    subsets_to_consider
+                )
+            else:
+                # Some subsets may be missing
+                existing_subsets = {
+                    score_dict["hf_subset"]
+                    for score_dict in existing_results.scores[split]
+                }
+                missing_subsets = [
+                    s for s in subsets_to_consider if s not in existing_subsets
+                ]
+                if missing_subsets:
+                    missing_evaluations[split]["missing_subsets"] = missing_subsets
+
+        return missing_evaluations
diff --git a/mteb/evaluation/evaluators/BitextMiningEvaluator.py b/mteb/evaluation/evaluators/BitextMiningEvaluator.py
index 4fa7022ed6..4b068653da 100644
--- a/mteb/evaluation/evaluators/BitextMiningEvaluator.py
+++ b/mteb/evaluation/evaluators/BitextMiningEvaluator.py
@@ -62,7 +62,7 @@ def compute_metrics(self, model: Encoder, encode_kwargs: dict[str, Any] = {}):
             tqdm.tqdm(self.pairs, desc="Matching sentences")
         ):
             scores[f"{key1}-{key2}"] = self._compute_metrics(
-                embeddings[key1], embeddings[key2]
+                embeddings[key1], embeddings[key2], model
             )
 
         # in case of default pair unnest the dict
@@ -76,10 +76,13 @@ def _compute_metrics(
         self,
         embeddings1,
         embeddings2,
+        model: Encoder,
     ):
         # Find nearest neighbors
         logger.info("Finding nearest neighbors...")
-        nearest_neighbors = self._similarity_search(embeddings1, embeddings2, top_k=1)
+        nearest_neighbors = self._similarity_search(
+            embeddings1, embeddings2, model, top_k=1
+        )
 
         # Compute errors
         logger.info("Computing metrics...")
@@ -106,10 +109,10 @@ def _similarity_search(
         self,
         query_embeddings,
         corpus_embeddings,
+        model: Encoder,
         query_chunk_size: int = 100,
         corpus_chunk_size: int = 500000,
         top_k: int = 10,
-        score_function=cos_sim,
     ):
         """This function performs a cosine similarity search between a list of query embeddings  and a list of corpus embeddings.
         It can be used for Information Retrieval / Semantic Search for corpora up to about 1 Million entries.
@@ -117,10 +120,10 @@ def _similarity_search(
         Args:
             query_embeddings: A 2 dimensional tensor with the query embeddings.
             corpus_embeddings: A 2 dimensional tensor with the corpus embeddings.
+            model: The model used to encode the queries and corpus. This is used to check if the embeddings are on the same device and to encode the queries and corpus if they are not already tensors.
             query_chunk_size: Process 100 queries simultaneously. Increasing that value increases the speed, but requires more memory.
             corpus_chunk_size: Scans the corpus 100k entries at a time. Increasing that value increases the speed, but requires more memory.
             top_k: Retrieve top k matching entries.
-            score_function: Function for computing scores. By default, cosine similarity.
 
         Returns:
             Returns a list with one entry for each query. Each entry is a list of dictionaries with the keys 'corpus_id' and 'score', sorted by decreasing cosine similarity scores.
@@ -142,7 +145,7 @@ def _similarity_search(
             # Iterate over chunks of the corpus
             for corpus_start_idx in range(0, len(corpus_embeddings), corpus_chunk_size):
                 # Compute cosine similarities
-                cos_scores = score_function(
+                similarity_scores = cos_sim(
                     query_embeddings[
                         query_start_idx : query_start_idx + query_chunk_size
                     ],
@@ -151,10 +154,20 @@ def _similarity_search(
                     ],
                 )
 
+                if hasattr(model, "similarity"):
+                    similarity_scores = model.similarity(
+                        query_embeddings[
+                            query_start_idx : query_start_idx + query_chunk_size
+                        ],
+                        corpus_embeddings[
+                            corpus_start_idx : corpus_start_idx + corpus_chunk_size
+                        ],
+                    )
+
                 # Get top-k scores
                 cos_scores_top_k_values, cos_scores_top_k_idx = torch.topk(
-                    cos_scores,
-                    min(top_k, len(cos_scores[0])),
+                    similarity_scores,
+                    min(top_k, len(similarity_scores[0])),
                     dim=1,
                     largest=True,
                     sorted=False,
@@ -162,7 +175,7 @@ def _similarity_search(
                 cos_scores_top_k_values = cos_scores_top_k_values.cpu().tolist()
                 cos_scores_top_k_idx = cos_scores_top_k_idx.cpu().tolist()
 
-                for query_itr in range(len(cos_scores)):
+                for query_itr in range(len(similarity_scores)):
                     for sub_corpus_id, score in zip(
                         cos_scores_top_k_idx[query_itr],
                         cos_scores_top_k_values[query_itr],
diff --git a/mteb/evaluation/evaluators/RetrievalEvaluator.py b/mteb/evaluation/evaluators/RetrievalEvaluator.py
index 3dca66b0fa..be2f5af1f0 100644
--- a/mteb/evaluation/evaluators/RetrievalEvaluator.py
+++ b/mteb/evaluation/evaluators/RetrievalEvaluator.py
@@ -3,13 +3,12 @@
 import logging
 from typing import Any
 
-from mteb.evaluation.evaluators.model_classes import (
+from .Evaluator import Evaluator
+from .model_classes import (
     DenseRetrievalExactSearch,
     DRESModel,
     is_cross_encoder_compatible,
 )
-
-from .Evaluator import Evaluator
 from .utils import (
     add_task_specific_scores,
     calculate_retrieval_scores,
@@ -30,7 +29,6 @@ def __init__(
         retriever,
         task_name: str | None = None,
         k_values: list[int] = [1, 3, 5, 10, 20, 100, 1000],
-        score_function: str = "cos_sim",
         encode_kwargs: dict[str, Any] = {},
         **kwargs,
     ):
@@ -52,14 +50,6 @@ def __init__(
         self.top_k = (
             max(k_values) if "top_k" not in kwargs else kwargs["top_k"]
         )  # can lower it if reranking
-        self.score_function = (
-            retriever.mteb_model_meta.similarity_fn_name
-            if (
-                hasattr(retriever, "mteb_model_meta")
-                and retriever.mteb_model_meta.similarity_fn_name
-            )
-            else score_function
-        )
         self.task_name = task_name
 
     def __call__(
@@ -90,7 +80,6 @@ def __call__(
                 corpus,
                 queries,
                 self.top_k,
-                self.score_function,
                 task_name=self.task_name,  # type: ignore
                 instructions=instructions,
                 **kwargs,
@@ -100,7 +89,6 @@ def __call__(
                 corpus,
                 queries,
                 self.top_k,
-                self.score_function,
                 instructions=instructions,
                 request_qid=qid,
                 task_name=self.task_name,
@@ -120,6 +108,7 @@ def evaluate(
         dict[str, float],
         dict[str, float],
         dict[str, float],
+        dict[str, float],
     ]:
         if ignore_identical_ids:
             logger.debug(
diff --git a/mteb/evaluation/evaluators/model_classes.py b/mteb/evaluation/evaluators/model_classes.py
index 146d529dc9..7e66f22e65 100644
--- a/mteb/evaluation/evaluators/model_classes.py
+++ b/mteb/evaluation/evaluators/model_classes.py
@@ -7,14 +7,15 @@
 from pathlib import Path
 from typing import Any
 
+import numpy as np
 import torch
 import tqdm
-from sentence_transformers import CrossEncoder, SentenceTransformer
+from sentence_transformers import SentenceTransformer
 
 from mteb.encoder_interface import Encoder, PromptType
 from mteb.model_meta import ModelMeta
 
-from .utils import convert_conv_history_to_query, cos_sim, dot_score, download
+from .utils import convert_conv_history_to_query, cos_sim, download
 
 logger = logging.getLogger(__name__)
 
@@ -53,32 +54,24 @@ def __init__(
     ):
         # Model is class that provides encode_corpus() and encode_queries()
         self.model = model
-        self.encode_kwargs = encode_kwargs
+        self.encode_kwargs = encode_kwargs.copy()
 
-        if "batch_size" not in encode_kwargs:
-            encode_kwargs["batch_size"] = 128
         if "show_progress_bar" not in encode_kwargs:
-            encode_kwargs["show_progress_bar"] = True
-
-        self.score_functions = {"cos_sim": cos_sim, "dot": dot_score, "cosine": cos_sim}
-        self.score_function_desc = {
-            "cos_sim": "Cosine Similarity",
-            "cosine": "Cosine Similarity",
-            "dot": "Dot Product",
-        }
+            self.encode_kwargs["show_progress_bar"] = True
+
         self.corpus_chunk_size = corpus_chunk_size
         if isinstance(previous_results, Path):
             self.previous_results = str(previous_results)
         else:
             self.previous_results = previous_results
-        self.batch_size = encode_kwargs.get("batch_size")
-        self.show_progress_bar = encode_kwargs.get("show_progress_bar")
+        self.batch_size = self.encode_kwargs.get("batch_size", 32)
+        self.show_progress_bar = self.encode_kwargs.get("show_progress_bar")
         self.results = {}
 
         if self.previous_results is not None:
             self.previous_results = self.load_results_file()
 
-        if isinstance(self.model, CrossEncoder):
+        if hasattr(self.model, "predict"):
             # load the predict instance from the CrossEncoder
             # custom functions can be used by extending the DenseRetrievalExactSearch class
             self.predict = self.model.predict
@@ -88,7 +81,6 @@ def search(
         corpus: dict[str, dict[str, str]],
         queries: dict[str, str],
         top_k: int,
-        score_function: str,
         task_name: str,
         instructions: dict[str, str] | None = None,
         request_qid: str | None = None,
@@ -102,7 +94,6 @@ def search(
             corpus: Dictionary mapping corpus IDs to document dictionaries
             queries: Dictionary mapping query IDs to query strings
             top_k: Number of top results to return
-            score_function: Scoring function to use ('cos_sim' or 'dot')
             task_name: Name of the task
             instructions: Optional instructions to append to queries
             request_qid: Optional request query ID
@@ -110,11 +101,6 @@ def search(
             top_ranked: Optional dict mapping query IDs to lists of pre-ranked corpus IDs
             **kwargs: Additional keyword arguments passed to the underlying model
         """
-        if score_function not in self.score_functions:
-            raise ValueError(
-                f"score function: {score_function} must be either (cos_sim) for cosine similarity or (dot) for dot product"
-            )
-
         logger.info("Encoding Queries.")
         query_ids = list(queries.keys())
         self.results = {qid: {} for qid in query_ids}
@@ -159,10 +145,6 @@ def search(
         # Map back to original order but reuse embeddings
         query_embeddings = unique_query_embeddings[query_idx_mapping]
 
-        logger.info(
-            f"Scoring Function: {self.score_function_desc[score_function]} ({score_function})"
-        )
-
         if top_ranked is not None:
             logger.info("Performing reranking on pre-ranked documents...")
             result_heaps = self._rerank_documents(
@@ -171,7 +153,6 @@ def search(
                 corpus=corpus,
                 top_ranked=top_ranked,
                 top_k=top_k,
-                score_function=score_function,
                 task_name=task_name,
                 request_qid=request_qid,
                 return_sorted=return_sorted,
@@ -183,7 +164,6 @@ def search(
                 query_embeddings=query_embeddings,
                 corpus=corpus,
                 top_k=top_k,
-                score_function=score_function,
                 task_name=task_name,
                 request_qid=request_qid,
                 return_sorted=return_sorted,
@@ -198,11 +178,10 @@ def search(
     def _rerank_documents(
         self,
         query_ids: list[str],
-        query_embeddings: torch.Tensor,
+        query_embeddings: np.ndarray,
         corpus: dict[str, dict[str, str]],
         top_ranked: dict[str, list[str]],
         top_k: int,
-        score_function: str,
         task_name: str,
         request_qid: str | None = None,
         return_sorted: bool = False,
@@ -259,8 +238,12 @@ def _rerank_documents(
             # Ensure query embedding is on the correct device and has correct shape
             query_embedding = query_embeddings[query_idx].unsqueeze(0)
 
+            score_function = (
+                self.model.similarity if hasattr(self.model, "similarity") else cos_sim
+            )
+
             with torch.inference_mode():
-                scores = self.score_functions[score_function](
+                scores = score_function(
                     query_embedding,
                     query_doc_embeddings,
                 )
@@ -305,7 +288,6 @@ def _full_corpus_search(
         query_embeddings: torch.Tensor,
         corpus: dict[str, dict[str, str]],
         top_k: int,
-        score_function: str,
         task_name: str,
         request_qid: str | None = None,
         return_sorted: bool = False,
@@ -338,17 +320,20 @@ def _full_corpus_search(
             logging.info("Computing Similarities...")
             query_embeddings = torch.as_tensor(query_embeddings).to(device)
             sub_corpus_embeddings = torch.as_tensor(sub_corpus_embeddings).to(device)
+
+            score_function = (
+                self.model.similarity if hasattr(self.model, "similarity") else cos_sim
+            )
+
             with torch.inference_mode():
-                cos_scores = self.score_functions[score_function](
-                    query_embeddings, sub_corpus_embeddings
-                )
+                scores = score_function(query_embeddings, sub_corpus_embeddings)
 
             # get top-k values
             cos_scores_top_k_values, cos_scores_top_k_idx = torch.topk(
-                cos_scores,
+                scores,
                 min(
                     top_k + 1,
-                    len(cos_scores[1]) if len(cos_scores) > 1 else len(cos_scores[-1]),
+                    len(scores[1]) if len(scores) > 1 else len(scores[-1]),
                 ),
                 dim=1,
                 largest=True,
@@ -411,7 +396,7 @@ def search_cross_encoder(
         for qid in queries.keys():
             if self.previous_results is None:
                 # try to use all of them
-                logging.logging(
+                logging.info(
                     f"previous_results is None. Using all the documents to rerank: {len(corpus)}"
                 )
                 q_results = {doc_id: 0.0 for doc_id in corpus.keys()}
@@ -461,7 +446,7 @@ def search_cross_encoder(
                 len(queries_in_pair) == len(corpus_in_pair) == len(instructions_in_pair)
             )
 
-            if isinstance(self.model.model, CrossEncoder):
+            if hasattr(self.model, "predict"):
                 # can't take instructions, so add them here
                 queries_in_pair = [
                     f"{q} {i}".strip()
@@ -527,7 +512,6 @@ def encode_corpus(
         self,
         corpus: list[dict[str, str]],
         task_name: str,
-        batch_size: int,
         prompt_type: PromptType = PromptType.passage,
         **kwargs,
     ):
@@ -536,7 +520,6 @@ def encode_corpus(
             sentences,
             task_name=task_name,
             prompt_type=prompt_type,
-            batch_size=batch_size,
             **kwargs,
         )
         return corpus_embeddings
diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py
index a6dd1c7325..7cc658d0cb 100644
--- a/mteb/leaderboard/app.py
+++ b/mteb/leaderboard/app.py
@@ -1,10 +1,13 @@
 from __future__ import annotations
 
 import json
+import tempfile
 from collections import defaultdict
 from pathlib import Path
+from urllib.parse import urlencode
 
 import gradio as gr
+import pandas as pd
 from gradio_rangeslider import RangeSlider
 
 import mteb
@@ -24,6 +27,30 @@ def load_results():
             return mteb.BenchmarkResults.from_validated(**json.load(cache_file))
 
 
+def produce_benchmark_link(benchmark_name: str, request: gr.Request) -> str:
+    """Produces a URL for the selected benchmark."""
+    params = urlencode(
+        {
+            "benchmark_name": benchmark_name,
+        }
+    )
+    base_url = request.request.base_url
+    url = f"{base_url}?{params}"
+    md = f"```\n{url}\n```"
+    return md
+
+
+def set_benchmark_on_load(request: gr.Request):
+    query_params = request.query_params
+    return query_params.get("benchmark_name", "MTEB(Multilingual, beta)")
+
+
+def download_table(table: pd.DataFrame) -> Path:
+    file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    table.to_csv(file)
+    return file.name
+
+
 def update_citation(benchmark_name: str) -> str:
     benchmark = mteb.get_benchmark(benchmark_name)
     if str(benchmark.citation) != "None":
@@ -66,6 +93,7 @@ def update_task_info(task_names: str) -> gr.DataFrame:
         properties=["name", "type", "languages", "domains", "reference", "main_score"]
     )
     df["languages"] = df["languages"].map(format_list)
+    df = df.sort_values("name")
     df["domains"] = df["domains"].map(format_list)
     df["name"] = "[" + df["name"] + "](" + df["reference"] + ")"
     df = df.rename(
@@ -217,24 +245,50 @@ def update_task_info(task_names: str) -> gr.DataFrame:
                 inputs=[benchmark_select, lang_select, type_select, domain_select],
             )
             citation = gr.Markdown(update_citation, inputs=[benchmark_select])
+            with gr.Accordion("Share this benchmark:", open=False):
+                gr.Markdown(produce_benchmark_link, inputs=[benchmark_select])
         with gr.Column():
-            with gr.Tab("Performance-Size Plot"):
+            with gr.Tab("Performance per Model Size"):
                 plot = gr.Plot(performance_size_plot, inputs=[summary_table])
                 gr.Markdown(
                     "*We only display models that have been run on all tasks in the benchmark*"
                 )
-            with gr.Tab("Top 5 Radar Chart"):
+            with gr.Tab("Performance per Task Type (Radar Chart)"):
                 radar_plot = gr.Plot(radar_chart, inputs=[summary_table])
                 gr.Markdown(
                     "*We only display models that have been run on all task types in the benchmark*"
                 )
     with gr.Tab("Summary"):
+        with gr.Accordion(
+            "What do aggregate measures (Rank(Borda), Mean(Task), etc.) mean?",
+            open=False,
+        ):
+            gr.Markdown(
+                """
+    **Rank(borda)** is computed based on the [borda count](https://en.wikipedia.org/wiki/Borda_count), where each task is treated as a preference voter, which gives votes on the models in accordance with their relative performance on the task. The best model obtains the highest number of votes. The model with the highest number of votes across tasks obtains the highest rank. The Borda rank tends to prefer models that perform well broadly across tasks. However, given that it is a rank it can be unclear if the two models perform similarly.
+
+    **Mean(Task)**: This is a naïve average computed across all the tasks within the benchmark. This score is simple to understand and is continuous as opposed to the Borda rank. However, the mean can overvalue tasks with higher variance in its scores. 
+
+    **Mean(TaskType)**: This is a weighted average across different task categories, such as classification or retrieval. It is computed by first computing the average by task category and then computing the average on each category. Similar to the Mean(Task) this measure is continuous and tends to overvalue tasks with higher variance. This score also prefers models that perform well across all task categories.
+            """
+            )
         summary_table.render()
+        download_summary = gr.DownloadButton("Download Table")
+        download_summary.click(
+            download_table, inputs=[summary_table], outputs=[download_summary]
+        )
     with gr.Tab("Performance per task"):
         per_task_table.render()
+        download_per_task = gr.DownloadButton("Download Table")
+        download_per_task.click(
+            download_table, inputs=[per_task_table], outputs=[download_per_task]
+        )
     with gr.Tab("Task information"):
         task_info_table = gr.DataFrame(update_task_info, inputs=[task_select])
 
+    # This sets the benchmark from the URL query parameters
+    demo.load(set_benchmark_on_load, inputs=[], outputs=[benchmark_select])
+
     @gr.on(inputs=[scores, searchbar], outputs=[summary_table, per_task_table])
     def update_tables(scores, search_query: str):
         summary, per_task = scores_to_tables(scores, search_query)
diff --git a/mteb/leaderboard/figures.py b/mteb/leaderboard/figures.py
index 9f3e73f7a4..35f91dd363 100644
--- a/mteb/leaderboard/figures.py
+++ b/mteb/leaderboard/figures.py
@@ -6,6 +6,28 @@
 import plotly.graph_objects as go
 
 
+def text_plot(text: str):
+    """Returns empty scatter plot with text added, this can be great for error messages."""
+    return px.scatter(template="plotly_white").add_annotation(
+        text=text, showarrow=False, font=dict(size=20)
+    )
+
+
+def failsafe_plot(fun):
+    """Decorator that turns the function producing a figure failsafe.
+    This is necessary, because once a Callback encounters an exception it
+    becomes useless in Gradio.
+    """
+
+    def wrapper(*args, **kwargs):
+        try:
+            return fun(*args, **kwargs)
+        except Exception:
+            return text_plot("Couldn't produce plot.")
+
+    return wrapper
+
+
 def parse_n_params(text: str) -> int:
     if text.endswith("M"):
         return float(text[:-1]) * 1e6
@@ -37,6 +59,48 @@ def parse_float(value) -> float:
 ]
 
 
+def add_size_guide(fig: go.Figure):
+    xpos = [5 * 1e9] * 4
+    ypos = [7.8, 8.5, 9, 10]
+    sizes = [256, 1024, 2048, 4096]
+    fig.add_trace(
+        go.Scatter(
+            showlegend=False,
+            opacity=0.3,
+            mode="markers",
+            marker=dict(
+                size=np.sqrt(sizes),
+                color="rgba(0,0,0,0)",
+                line=dict(color="black", width=2),
+            ),
+            x=xpos,
+            y=ypos,
+        )
+    )
+    fig.add_annotation(
+        text="<b>Embedding Size:</b>",
+        font=dict(size=16),
+        x=np.log10(1.5e9),
+        y=10,
+        showarrow=False,
+        opacity=0.3,
+    )
+    for x, y, size in zip(xpos, np.linspace(7.5, 14, 4), sizes):
+        fig.add_annotation(
+            text=f"<b>{size}</b>",
+            font=dict(size=12),
+            x=np.log10(x),
+            y=y,
+            showarrow=True,
+            ay=0,
+            ax=50,
+            opacity=0.3,
+            arrowwidth=2,
+        )
+    return fig
+
+
+@failsafe_plot
 def performance_size_plot(df: pd.DataFrame) -> go.Figure:
     df = df.copy()
     df["Number of Parameters"] = df["Number of Parameters"].map(parse_n_params)
@@ -50,6 +114,7 @@ def performance_size_plot(df: pd.DataFrame) -> go.Figure:
     if not len(df.index):
         return go.Figure()
     min_score, max_score = df["Mean (Task)"].min(), df["Mean (Task)"].max()
+    df["sqrt(dim)"] = np.sqrt(df["Embedding Dimensions"])
     fig = px.scatter(
         df,
         x="Number of Parameters",
@@ -57,7 +122,7 @@ def performance_size_plot(df: pd.DataFrame) -> go.Figure:
         log_x=True,
         template="plotly_white",
         text="model_text",
-        size="Embedding Dimensions",
+        size="sqrt(dim)",
         color="Log(Tokens)",
         range_color=[2, 5],
         range_x=[8 * 1e6, 11 * 1e9],
@@ -69,10 +134,21 @@ def performance_size_plot(df: pd.DataFrame) -> go.Figure:
             "Mean (Task)": True,
             "Rank (Borda)": True,
             "Log(Tokens)": False,
+            "sqrt(dim)": False,
             "model_text": False,
         },
         hover_name="Model",
     )
+    # Note: it's important that this comes before setting the size mode
+    fig = add_size_guide(fig)
+    fig.update_traces(
+        marker=dict(
+            sizemode="diameter",
+            sizeref=1.5,
+            sizemin=0,
+        )
+    )
+    fig.add_annotation(x=1e9, y=10, text="Model size:")
     fig.update_layout(
         coloraxis_colorbar=dict(  # noqa
             title="Max Tokens",
@@ -124,14 +200,15 @@ def performance_size_plot(df: pd.DataFrame) -> go.Figure:
     "#3CBBB1",
 ]
 fill_colors = [
-    "rgba(238,66,102,0.2)",
-    "rgba(0,166,237,0.2)",
-    "rgba(236,167,44,0.2)",
-    "rgba(180,35,24,0.2)",
-    "rgba(60,187,177,0.2)",
+    "rgba(238,66,102,0.05)",
+    "rgba(0,166,237,0.05)",
+    "rgba(236,167,44,0.05)",
+    "rgba(180,35,24,0.05)",
+    "rgba(60,187,177,0.05)",
 ]
 
 
+@failsafe_plot
 def radar_chart(df: pd.DataFrame) -> go.Figure:
     df = df.copy()
     df["Model"] = df["Model"].map(parse_model_name)
@@ -139,6 +216,10 @@ def radar_chart(df: pd.DataFrame) -> go.Figure:
     task_type_columns = [
         column for column in df.columns if "".join(column.split()) in task_types
     ]
+    if len(task_type_columns) <= 1:
+        raise ValueError(
+            "Couldn't produce radar chart, the benchmark only contains one task category."
+        )
     df = df[["Model", *task_type_columns]].set_index("Model")
     df = df.replace("", np.nan)
     df = df.dropna()
@@ -156,7 +237,7 @@ def radar_chart(df: pd.DataFrame) -> go.Figure:
                 mode="lines",
                 line=dict(width=2, color=line_colors[i]),
                 fill="toself",
-                fillcolor=fill_colors[i],
+                fillcolor="rgba(0,0,0,0)",
             )
         )
     fig.update_layout(
diff --git a/mteb/model_meta.py b/mteb/model_meta.py
index 52b2a8872d..27bc1ffd41 100644
--- a/mteb/model_meta.py
+++ b/mteb/model_meta.py
@@ -26,8 +26,10 @@
     "API",
     "Tevatron",
     "NumPy",
+    "PyLate",
+    "ColBERT",
 ]
-DISTANCE_METRICS = Literal["cosine", "dot"]
+DISTANCE_METRICS = Literal["cosine", "max_sim", "dot"]
 
 
 def sentence_transformers_loader(
diff --git a/mteb/models/arctic_models.py b/mteb/models/arctic_models.py
index ce1db29bbd..6240962b27 100644
--- a/mteb/models/arctic_models.py
+++ b/mteb/models/arctic_models.py
@@ -4,6 +4,84 @@
 
 from mteb.model_meta import ModelMeta, sentence_transformers_loader
 
+LANGUAGES_V2_0 = [
+    "afr_Latn",
+    "ara_Arab",
+    "aze_Latn",
+    "bel_Cyrl",
+    "bul_Cyrl",
+    "ben_Beng",
+    "cat_Latn",
+    "ceb_Latn",
+    "ces_Latn",
+    "cym_Latn",
+    "dan_Latn",
+    "deu_Latn",
+    "ell_Grek",
+    "eng_Latn",
+    "spa_Latn",
+    "est_Latn",
+    "eus_Latn",
+    "fas_Arab",
+    "fin_Latn",
+    "fra_Latn",
+    "glg_Latn",
+    "guj_Gujr",
+    "heb_Hebr",
+    "hin_Deva",
+    "hrv_Latn",
+    "hat_Latn",
+    "hun_Latn",
+    "hye_Armn",
+    "ind_Latn",
+    "isl_Latn",
+    "ita_Latn",
+    "jpn_Jpan",
+    "jav_Latn",
+    "kat_Geor",
+    "kaz_Cyrl",
+    "khm_Khmr",
+    "kan_Knda",
+    "kor_Hang",
+    "kir_Cyrl",
+    "lao_Laoo",
+    "lit_Latn",
+    "lav_Latn",
+    "mkd_Cyrl",
+    "mal_Mlym",
+    "mon_Cyrl",
+    "mar_Deva",
+    "msa_Latn",
+    "mya_Mymr",
+    "nep_Deva",
+    "nld_Latn",
+    "pan_Guru",
+    "pol_Latn",
+    "por_Latn",
+    "que_Latn",
+    "ron_Latn",
+    "rus_Cyrl",
+    "sin_Sinh",
+    "slk_Latn",
+    "slv_Latn",
+    "som_Latn",
+    "sqi_Latn",
+    "srp_Cyrl",
+    "swe_Latn",
+    "swa_Latn",
+    "tam_Taml",
+    "tel_Telu",
+    "tha_Thai",
+    "tgl_Latn",
+    "tur_Latn",
+    "ukr_Cyrl",
+    "urd_Arab",
+    "vie_Latn",
+    "yor_Latn",
+    "zho_Hans",
+]
+
+
 arctic_m_v1_5 = ModelMeta(
     loader=partial(
         sentence_transformers_loader,
@@ -60,7 +138,7 @@
     license="apache-2.0",
     reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-xs",
     similarity_fn_name="cosine",
-    use_instructions=False,
+    use_instructions=True,
     adapted_from="sentence-transformers/all-MiniLM-L6-v2",
     superseded_by=None,
 )
@@ -85,7 +163,7 @@
     license="apache-2.0",
     reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-s",
     similarity_fn_name="cosine",
-    use_instructions=False,
+    use_instructions=True,
     adapted_from="intfloat/e5-small-unsupervised",
     superseded_by=None,
 )
@@ -110,7 +188,7 @@
     license="apache-2.0",
     reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-m",
     similarity_fn_name="cosine",
-    use_instructions=False,
+    use_instructions=True,
     adapted_from="intfloat/e5-base-unsupervised",
     superseded_by="Snowflake/snowflake-arctic-embed-m-v1.5",
 )
@@ -127,19 +205,18 @@
     languages=["eng_Latn"],
     open_weights=True,
     framework=["Sentence Transformers", "PyTorch"],
-    n_parameters=109_000_000,
+    n_parameters=137_000_000,
     memory_usage=None,
     max_tokens=2048,
     embed_dim=768,
     license="apache-2.0",
     reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long",
     similarity_fn_name="cosine",
-    use_instructions=False,
+    use_instructions=True,
     adapted_from="nomic-ai/nomic-embed-text-v1-unsupervised",
-    superseded_by=None,
+    superseded_by="Snowflake/snowflake-arctic-embed-m-v2.0",
 )
 
-
 arctic_embed_l = ModelMeta(
     loader=partial(
         sentence_transformers_loader,
@@ -152,14 +229,89 @@
     languages=["eng_Latn"],
     open_weights=True,
     framework=["Sentence Transformers", "PyTorch"],
-    n_parameters=109_000_000,
+    n_parameters=335_000_000,
     memory_usage=None,
     max_tokens=512,
-    embed_dim=768,
+    embed_dim=1024,
     license="apache-2.0",
     reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-l",
     similarity_fn_name="cosine",
-    use_instructions=False,
+    use_instructions=True,
     adapted_from="intfloat/e5-base-unsupervised",
+    superseded_by="Snowflake/snowflake-arctic-embed-l-v2.0",
+)
+
+arctic_embed_m_v1_5 = ModelMeta(
+    loader=partial(
+        sentence_transformers_loader,
+        model_name="Snowflake/snowflake-arctic-embed-m-v1.5",
+        revision="97eab2e17fcb7ccb8bb94d6e547898fa1a6a0f47",
+        model_prompts={
+            "query": "Represent this sentence for searching relevant passages: "
+        },
+    ),
+    name="Snowflake/snowflake-arctic-embed-m-v1.5",
+    revision="97eab2e17fcb7ccb8bb94d6e547898fa1a6a0f47",
+    release_date="2024-07-08",  # initial commit of hf model.
+    languages=["eng_Latn"],
+    open_weights=True,
+    framework=["Sentence Transformers", "PyTorch"],
+    n_parameters=109_000_000,
+    memory_usage=None,
+    max_tokens=512,
+    embed_dim=768,
+    license="apache-2.0",
+    reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5",
+    similarity_fn_name="cosine",
+    use_instructions=True,
+    adapted_from=None,
+    superseded_by="Snowflake/snowflake-arctic-embed-m-v2.0",
+)
+
+arctic_embed_m_v2_0 = ModelMeta(
+    loader=partial(
+        sentence_transformers_loader,
+        model_name="Snowflake/snowflake-arctic-embed-m-v2.0",
+        revision="f2a7d59d80dfda5b1d14f096f3ce88bb6bf9ebdc",
+    ),
+    name="Snowflake/snowflake-arctic-embed-m-v2.0",
+    revision="f2a7d59d80dfda5b1d14f096f3ce88bb6bf9ebdc",
+    release_date="2024-12-04",  # initial commit of hf model.
+    languages=LANGUAGES_V2_0,
+    open_weights=True,
+    framework=["Sentence Transformers", "PyTorch"],
+    n_parameters=305_000_000,
+    memory_usage=None,
+    max_tokens=8192,
+    embed_dim=768,
+    license="apache-2.0",
+    reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0",
+    similarity_fn_name="cosine",
+    use_instructions=True,
+    adapted_from="Alibaba-NLP/gte-multilingual-base",
+    superseded_by=None,
+)
+
+arctic_embed_l_v2_0 = ModelMeta(
+    loader=partial(
+        sentence_transformers_loader,
+        model_name="Snowflake/snowflake-arctic-embed-l-v2.0",
+        revision="edc2df7b6c25794b340229ca082e7c78782e6374",
+    ),
+    name="Snowflake/snowflake-arctic-embed-l-v2.0",
+    revision="edc2df7b6c25794b340229ca082e7c78782e6374",
+    release_date="2024-12-04",  # initial commit of hf model.
+    languages=LANGUAGES_V2_0,
+    open_weights=True,
+    framework=["Sentence Transformers", "PyTorch"],
+    n_parameters=568_000_000,
+    memory_usage=None,
+    max_tokens=8192,
+    embed_dim=1024,
+    license="apache-2.0",
+    reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0",
+    similarity_fn_name="cosine",
+    use_instructions=True,
+    adapted_from="BAAI/bge-m3-retromae",
     superseded_by=None,
 )
diff --git a/mteb/models/bge_models.py b/mteb/models/bge_models.py
index 0fb3c0242e..d136ccd834 100644
--- a/mteb/models/bge_models.py
+++ b/mteb/models/bge_models.py
@@ -15,7 +15,7 @@
 }"""
 
 bge_small_en_v1_5 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="BAAI/bge-small-en-v1.5",
         revision="5c38ec7c405ec4b44b94cc5a9bb96e735b38267a",
@@ -34,12 +34,12 @@
     reference="https://huggingface.co/BAAI/bge-small-en-v1.5",
     similarity_fn_name="cosine",
     framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
+    use_instructions=True,
     citation=BGE_15_CITATION,
 )
 
 bge_base_en_v1_5 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="BAAI/bge-base-en-v1.5",
         revision="a5beb1e3e68b9ab74eb54cfd186867f64f240e1a",
@@ -58,12 +58,12 @@
     reference="https://huggingface.co/BAAI/bge-base-en-v1.5",
     similarity_fn_name="cosine",
     framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
+    use_instructions=True,
     citation=BGE_15_CITATION,
 )
 
 bge_large_en_v1_5 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="BAAI/bge-large-en-v1.5",
         revision="d4aa6901d3a41ba39fb536a557fa166f842b0e09",
@@ -82,6 +82,6 @@
     reference="https://huggingface.co/BAAI/bge-large-en-v1.5",
     similarity_fn_name="cosine",
     framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
+    use_instructions=True,
     citation=BGE_15_CITATION,
 )
diff --git a/mteb/models/cohere_models.py b/mteb/models/cohere_models.py
index 3f07a0d23b..43a797342d 100644
--- a/mteb/models/cohere_models.py
+++ b/mteb/models/cohere_models.py
@@ -216,7 +216,7 @@ def encode(
 }
 
 cohere_mult_3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         CohereTextEmbeddingModel,
         model_name="embed-multilingual-v3.0",
         model_prompts=model_prompts,
@@ -234,11 +234,11 @@ def encode(
     license=None,
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 cohere_eng_3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         CohereTextEmbeddingModel,
         model_name="embed-english-v3.0",
         model_prompts=model_prompts,
@@ -256,7 +256,7 @@ def encode(
     license=None,
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 cohere_mult_light_3 = ModelMeta(
@@ -278,7 +278,7 @@ def encode(
     license=None,
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 cohere_eng_light_3 = ModelMeta(
@@ -300,5 +300,5 @@ def encode(
     license=None,
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
diff --git a/mteb/models/colbert_models.py b/mteb/models/colbert_models.py
new file mode 100644
index 0000000000..8753791bff
--- /dev/null
+++ b/mteb/models/colbert_models.py
@@ -0,0 +1,218 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from functools import partial
+from typing import Any
+
+import numpy as np
+import torch
+
+from mteb.encoder_interface import PromptType
+from mteb.model_meta import ModelMeta
+
+from .wrapper import Wrapper
+
+logger = logging.getLogger(__name__)
+
+
+class ColBERTWrapper(Wrapper):
+    def __init__(
+        self,
+        model_name: str,
+        revision: str | None = None,
+        model_prompts: dict[str, str] | None = None,
+        **kwargs,
+    ) -> None:
+        """Wrapper for ColBERT models.
+
+        Args:
+            model_name: The ColBERT model to load from HuggingFace Hub.
+            revision: The revision of the model to use.
+            model_prompts: A dictionary mapping task names to prompt names.
+                First priority is given to the composed prompt of task name + prompt type (query or passage), then to the specific task prompt,
+                then to the composed prompt of task type + prompt type, then to the specific task type prompt,
+                and finally to the specific prompt type.
+            **kwargs: Additional arguments to pass to the model.
+        """
+        try:
+            from pylate import models as colbert_model
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError(
+                "To use the ColBERT models `pylate` is required. Please install it with `pip install mteb[pylate]`."
+            ) from e
+
+        self.model_name = model_name
+        self.model = colbert_model.ColBERT(self.model_name, revision=revision, **kwargs)
+        if (
+            model_prompts is None
+            and hasattr(self.model, "prompts")
+            and len(self.model.prompts) > 0
+        ):
+            try:
+                model_prompts = self.validate_task_to_prompt_name(self.model.prompts)
+            except ValueError:
+                model_prompts = None
+        elif model_prompts is not None and hasattr(self.model, "prompts"):
+            logger.info(f"Model prompts will be overwritten with {model_prompts}")
+            self.model.prompts = model_prompts
+        self.model_prompts = self.validate_task_to_prompt_name(model_prompts)
+
+    def encode(
+        self,
+        sentences: Sequence[str],
+        *,
+        task_name: str,
+        prompt_type: PromptType | None = None,
+        **kwargs: Any,
+    ) -> np.ndarray:
+        """Encodes the given sentences using the encoder.
+
+        Args:
+            sentences: The sentences to encode.
+            task_name: The name of the task. Pylate uses this to
+                determine which prompt to use from a specified dictionary.
+            prompt_type: The name type of prompt. (query or passage)
+            **kwargs: Additional arguments to pass to the encoder.
+
+            The order of priorities for prompt selection are:
+                1. Composed prompt of task name + prompt type (query or passage)
+                2. Specific task prompt
+                3. Composed prompt of task type + prompt type (query or passage)
+                4. Specific task type prompt
+                5. Specific prompt type (query or passage)
+
+        Returns:
+            The encoded sentences as a numpy array.
+        """
+        prompt_name = None
+        if self.model_prompts is not None:
+            prompt_name = self.get_prompt_name(
+                self.model_prompts, task_name, prompt_type
+            )
+        if prompt_name:
+            logger.info(
+                f"Using prompt_name={prompt_name} for task={task_name} prompt_type={prompt_type}"
+            )
+        else:
+            logger.info(
+                f"No model prompts found for task={task_name} prompt_type={prompt_type}"
+            )
+        logger.info(f"Encoding {len(sentences)} sentences.")
+
+        pred = self.model.encode(
+            sentences,
+            prompt_name=prompt_name,
+            is_query=True if prompt_type == PromptType.query else False,
+            **kwargs,
+        )
+
+        # encode returns a list of tensors shaped (x, token_dim) where x is the number of tokens in the sentence
+        # we need to pad these tensors to the same length
+        # Tensors have varying lengths; therefore, they need to be padded with zeros to ensure uniformity before being combined
+        # output shape will be (batch_size, len(max(tokens)), embedding_token_dim)
+        pred = torch.nn.utils.rnn.pad_sequence(pred, batch_first=True, padding_value=0)
+
+        return pred.cpu().numpy()
+
+    def similarity(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
+        """Computes the max-similarity max_sim(a[i], b[j]) for all i and j.
+            Works with a Tensor of the shape (batch_size, num_tokens, token_dim)
+
+        Return:
+            Matrix with res[i][j]  = max_sim(a[i], b[j])
+        """  # noqa: D402
+        if not isinstance(a, torch.Tensor):
+            a = torch.tensor(a, dtype=torch.float32)
+
+        if not isinstance(b, torch.Tensor):
+            b = torch.tensor(b, dtype=torch.float32)
+
+        if len(a.shape) == 2:
+            a = a.unsqueeze(0)
+
+        if len(b.shape) == 2:
+            b = b.unsqueeze(0)
+
+        scores = torch.einsum(
+            "ash,bth->abst",
+            a,
+            b,
+        )
+
+        return scores.max(axis=-1).values.sum(axis=-1)
+
+
+colbert_v2 = ModelMeta(
+    loader=partial(
+        ColBERTWrapper,
+        model_name="colbert-ir/colbertv2.0",
+    ),
+    name="colbert-ir/colbertv2.0",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="c1e84128e85ef755c096a95bdb06b47793b13acf",
+    public_training_code=True,
+    release_date="2024-09-21",
+    n_parameters=110 * 1e6,
+    max_tokens=180,  # Reduced for Benchmarking - see ColBERT paper
+    embed_dim=None,  # Bag of Embeddings (128) for each token
+    license="mit",
+    similarity_fn_name="max_sim",
+    framework=["PyLate", "ColBERT"],
+    reference="https://huggingface.co/colbert-ir/colbertv2.0",
+    use_instructions=False,
+    adapted_from=None,
+    superseded_by=None,
+)
+
+
+jina_colbert_v2 = ModelMeta(
+    loader=partial(
+        ColBERTWrapper,
+        model_name="jinaai/jina-colbert-v2",
+        query_prefix="[QueryMarker]",
+        document_prefix="[DocumentMarker]",
+        attend_to_expansion_tokens=True,
+        trust_remote_code=True,
+    ),
+    name="jinaai/jina-colbert-v2",
+    languages=[  # list of languages the model has been evaluated on
+        "ara-Arab",  # Arabic
+        "ben-Beng",  # Bengali
+        "deu-Latn",  # German
+        "spa-Latn",  # Spanish
+        "eng-Latn",  # English
+        "fas-Arab",  # Persian
+        "fin-Latn",  # Finnish
+        "fra-Latn",  # French
+        "hin-Deva",  # Hindi
+        "ind-Latn",  # Indonesian
+        "jpn-Jpan",  # Japanese
+        "kor-Kore",  # Korean
+        "rus-Cyrl",  # Russian
+        "swa-Latn",  # Swahili
+        "tel-Telu",  # Telugu
+        "tha-Thai",  # Thai
+        "yor-Latn",  # Yoruba
+        "zho-Hans",  # Chinese (Simplified)
+        "nld-Latn",  # Dutch
+        "ita-Latn",  # Italian
+        "por-Latn",  # Portuguese
+        "vie-Latn",  # Vietnamese
+    ],
+    open_weights=True,
+    revision="4cf816e5e2b03167b132a3c847a9ecd48ba708e1",
+    public_training_code=False,
+    release_date="2024-08-16",
+    n_parameters=559 * 1e6,
+    max_tokens=8192,
+    embed_dim=None,  # Bag of Embeddings (128) for each token
+    license="cc-by-nc-4.0",
+    similarity_fn_name="max_sim",
+    framework=["PyLate", "ColBERT"],
+    reference="https://huggingface.co/jinaai/jina-colbert-v2",
+    use_instructions=False,
+    adapted_from=None,
+    superseded_by=None,
+)
diff --git a/mteb/models/e5_instruct.py b/mteb/models/e5_instruct.py
index 8441ba5978..312b7c671a 100644
--- a/mteb/models/e5_instruct.py
+++ b/mteb/models/e5_instruct.py
@@ -12,15 +12,14 @@
 MISTRAL_LANGUAGES = ["eng_Latn", "fra_Latn", "deu_Latn", "ita_Latn", "spa_Latn"]
 
 
-def e5_instruction(instruction: str) -> str:
-    return f"Instruct: {instruction}\nQuery: "
+E5_INSTRUCTION = "Instruct: {instruction}\nQuery: "
 
 
 e5_instruct = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="intfloat/multilingual-e5-large-instruct",
-        instruction_template=e5_instruction,
+        instruction_template=E5_INSTRUCTION,
         attn="cccc",
         pooling_method="mean",
         mode="embedding",
@@ -50,10 +49,10 @@ def e5_instruction(instruction: str) -> str:
 )
 
 e5_mistral = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="intfloat/e5-mistral-7b-instruct",
-        instruction_template=e5_instruction,
+        instruction_template=E5_INSTRUCTION,
         attn="cccc",
         pooling_method="lasttoken",
         mode="embedding",
diff --git a/mteb/models/e5_models.py b/mteb/models/e5_models.py
index 3bce039f02..83f6dec08d 100644
--- a/mteb/models/e5_models.py
+++ b/mteb/models/e5_models.py
@@ -132,7 +132,7 @@
 }
 
 e5_mult_small = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/multilingual-e5-small",
         revision="fd1525a9fd15316a2d503bf26ab031a61d056e98",
@@ -156,7 +156,7 @@
 )
 
 e5_mult_base = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/multilingual-e5-base",
         model_prompts=model_prompts,
@@ -179,7 +179,7 @@
 )
 
 e5_mult_large = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/multilingual-e5-large",
         revision="ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb",
@@ -203,7 +203,7 @@
 )
 
 e5_eng_small_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-small-v2",
         model_prompts=model_prompts,
@@ -226,7 +226,7 @@
 )
 
 e5_eng_small = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-small",
         revision="e272f3049e853b47cb5ca3952268c6662abda68f",
@@ -250,7 +250,7 @@
 )
 
 e5_eng_base_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-base-v2",
         revision="1c644c92ad3ba1efdad3f1451a637716616a20e8",
@@ -276,7 +276,7 @@
 )
 
 e5_eng_large_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-large-v2",
         revision="b322e09026e4ea05f42beadf4d661fb4e101d311",
diff --git a/mteb/models/gritlm_models.py b/mteb/models/gritlm_models.py
index 596169b9b4..384f5c71cf 100644
--- a/mteb/models/gritlm_models.py
+++ b/mteb/models/gritlm_models.py
@@ -29,7 +29,7 @@ def gritlm_instruction(instruction: str = "") -> str:
 
 
 gritlm7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="GritLM/GritLM-7B",
         instruction_template=gritlm_instruction,
@@ -53,7 +53,7 @@ def gritlm_instruction(instruction: str = "") -> str:
     citation=GRITLM_CITATION,
 )
 gritlm8x7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="GritLM/GritLM-8x7B",
         instruction_template=gritlm_instruction,
diff --git a/mteb/models/gte_models.py b/mteb/models/gte_models.py
index 5c7043dd8d..7a6396ba9e 100644
--- a/mteb/models/gte_models.py
+++ b/mteb/models/gte_models.py
@@ -3,8 +3,12 @@
 from functools import partial
 
 from mteb.model_meta import ModelMeta
+from mteb.models.instruct_wrapper import instruct_wrapper
+
+
+def instruction_template(instruction: str) -> str:
+    return f"Instruct: {instruction}\nQuery: " if instruction else ""
 
-from .instruct_wrapper import instruct_wrapper
 
 GTE_CITATION = """
 @article{li2023towards,
@@ -16,10 +20,10 @@
 """
 
 gte_Qwen2_7B_instruct = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="Alibaba-NLP/gte-Qwen2-7B-instruct",
-        instruction_template="Instruct: {instruction}\nQuery: ",
+        instruction_template=instruction_template,
         attn="cccc",
         pooling_method="lasttoken",
         mode="embedding",
@@ -43,3 +47,59 @@
     use_instructions=True,
     citation=GTE_CITATION,
 )
+
+
+gte_Qwen1_5_7B_instruct = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Alibaba-NLP/gte-Qwen1.5-7B-instruct",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype="auto",
+        normalized=True,
+    ),
+    name="Alibaba-NLP/gte-Qwen1.5-7B-instruct",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="07d27e5226328010336563bc1b564a5e3436a298",
+    release_date="2024-04-20",  # initial commit of hf model.
+    n_parameters=7_720_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="apache-2.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
+
+
+gte_Qwen2_1_5B_instruct = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype="auto",
+        normalized=True,
+    ),
+    name="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="c6c1b92f4a3e1b92b326ad29dd3c8433457df8dd",
+    release_date="2024-07-29",  # initial commit of hf model.
+    n_parameters=1_780_000_000,
+    memory_usage=None,
+    embed_dim=8960,
+    license="apache-2.0",
+    max_tokens=131072,
+    reference="https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
diff --git a/mteb/models/ibm_granite_models.py b/mteb/models/ibm_granite_models.py
new file mode 100644
index 0000000000..c2443de233
--- /dev/null
+++ b/mteb/models/ibm_granite_models.py
@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+from functools import partial
+
+from mteb.model_meta import ModelMeta, sentence_transformers_loader
+
+GRANITE_LANGUAGES = [
+    "ara_Latn",
+    "ces_Latn",
+    "deu_Latn",
+    "eng_Latn",
+    "spa_Latn",
+    "fra_Latn",
+    "ita_Latn",
+    "jpn_Latn",
+    "kor_Latn",
+    "nld_Latn",
+    "por_Latn",
+    "zho_Hant",
+    "zho_Hans",
+]
+
+
+granite_107m_multilingual = ModelMeta(
+    loader=partial(  # type: ignore
+        sentence_transformers_loader,
+        model_name="ibm-granite/granite-embedding-107m-multilingual",
+        revision="47db56afe692f731540413c67dd818ff492277e7",
+    ),
+    name="ibm-granite/granite-embedding-107m-multilingual",
+    languages=GRANITE_LANGUAGES,
+    open_weights=True,
+    revision="47db56afe692f731540413c67dd818ff492277e7",
+    release_date="2024-12-18",
+    n_parameters=107_000_000,
+    memory_usage=None,
+    embed_dim=384,
+    license="apache-2.0",
+    max_tokens=512,
+    reference="https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    adapted_from=None,
+    superseded_by=None,
+)
+
+granite_278m_multilingual = ModelMeta(
+    loader=partial(  # type: ignore
+        sentence_transformers_loader,
+        model_name="ibm-granite/granite-embedding-278m-multilingual",
+        revision="84e3546b88b0cb69f8078608a1df558020bcbf1f",
+    ),
+    name="ibm-granite/granite-embedding-278m-multilingual",
+    languages=GRANITE_LANGUAGES,
+    open_weights=True,
+    revision="84e3546b88b0cb69f8078608a1df558020bcbf1f",
+    release_date="2024-12-18",
+    n_parameters=278_000_000,
+    memory_usage=None,
+    embed_dim=768,
+    license="apache-2.0",
+    max_tokens=512,
+    reference="https://huggingface.co/ibm-granite/granite-embedding-278m-multilingual",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    adapted_from=None,
+    superseded_by=None,
+)
+
+granite_30m_english = ModelMeta(
+    loader=partial(  # type: ignore
+        sentence_transformers_loader,
+        model_name="ibm-granite/granite-embedding-30m-english",
+        revision="eddbb57470f896b5f8e2bfcb823d8f0e2d2024a5",
+    ),
+    name="ibm-granite/granite-embedding-30m-english",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="eddbb57470f896b5f8e2bfcb823d8f0e2d2024a5",
+    release_date="2024-12-18",
+    n_parameters=30_000_000,
+    memory_usage=None,
+    embed_dim=384,
+    license="apache-2.0",
+    max_tokens=512,
+    reference="https://huggingface.co/ibm-granite/granite-embedding-30m-english",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    adapted_from=None,
+    superseded_by=None,
+)
+
+granite_125m_english = ModelMeta(
+    loader=partial(  # type: ignore
+        sentence_transformers_loader,
+        model_name="ibm-granite/granite-embedding-125m-english",
+        revision="e48d3a5b47eaa18e3fe07d4676e187fd80f32730",
+    ),
+    name="ibm-granite/granite-embedding-125m-english",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="e48d3a5b47eaa18e3fe07d4676e187fd80f32730",
+    release_date="2024-12-18",
+    n_parameters=125_000_000,
+    memory_usage=None,
+    embed_dim=768,
+    license="apache-2.0",
+    max_tokens=512,
+    reference="https://huggingface.co/ibm-granite/granite-embedding-125m-english",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    adapted_from=None,
+    superseded_by=None,
+)
diff --git a/mteb/models/instruct_wrapper.py b/mteb/models/instruct_wrapper.py
index 30c173c779..d6b5cc0388 100644
--- a/mteb/models/instruct_wrapper.py
+++ b/mteb/models/instruct_wrapper.py
@@ -47,6 +47,11 @@ def __init__(
                     "No instruction template provided. Instructions will be used as-is."
                 )
 
+            if "gte-Qwen" in model_name_or_path:
+                logger.warning(
+                    "Instructions are used in both query and docs, which may cause performance discrepancies from the original implementation."
+                )
+
             self.instruction_template = instruction_template
             super().__init__(model_name_or_path=model_name_or_path, mode=mode, **kwargs)
 
diff --git a/mteb/models/jina_models.py b/mteb/models/jina_models.py
index a9bc680585..192ad4cc5c 100644
--- a/mteb/models/jina_models.py
+++ b/mteb/models/jina_models.py
@@ -191,7 +191,7 @@ def encode(
 
 
 jina_embeddings_v3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         JinaWrapper,
         model="jinaai/jina-embeddings-v3",
         revision="215a6e121fa0183376388ac6b1ae230326bfeaed",
@@ -220,7 +220,7 @@ def encode(
     license="cc-by-nc-4.0",
     similarity_fn_name="cosine",
     framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
+    use_instructions=True,
     reference="https://huggingface.co/jinaai/jina-embeddings-v3",
     citation="""
     @misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
diff --git a/mteb/models/linq_models.py b/mteb/models/linq_models.py
new file mode 100644
index 0000000000..48e86ac8d5
--- /dev/null
+++ b/mteb/models/linq_models.py
@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from functools import partial
+
+import torch
+
+from mteb.model_meta import ModelMeta
+from mteb.models.instruct_wrapper import instruct_wrapper
+
+
+def instruction_template(instruction: str) -> str:
+    return f"Instruct: {instruction}\nQuery: " if instruction else ""
+
+
+Linq_Embed_Mistral = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Linq-AI-Research/Linq-Embed-Mistral",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype=torch.bfloat16,
+        normalized=True,
+    ),
+    name="Linq-AI-Research/Linq-Embed-Mistral",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="0c1a0b0589177079acc552433cad51d7c9132379",
+    release_date="2024-05-29",  # initial commit of hf model.
+    n_parameters=7_110_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="cc-by-nc-4.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
diff --git a/mteb/models/mxbai_models.py b/mteb/models/mxbai_models.py
index 7df0247e7f..5507bab8c0 100644
--- a/mteb/models/mxbai_models.py
+++ b/mteb/models/mxbai_models.py
@@ -5,7 +5,7 @@
 from mteb.model_meta import ModelMeta, sentence_transformers_loader
 
 mxbai_embed_large_v1 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="mixedbread-ai/mxbai-embed-large-v1",
         revision="990580e27d329c7408b3741ecff85876e128e203",
diff --git a/mteb/models/nvidia_models.py b/mteb/models/nvidia_models.py
new file mode 100644
index 0000000000..0c0170de6e
--- /dev/null
+++ b/mteb/models/nvidia_models.py
@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from functools import partial
+from typing import Any
+
+import numpy as np
+import torch
+from sentence_transformers import CrossEncoder, SentenceTransformer
+
+from mteb.encoder_interface import PromptType
+from mteb.model_meta import ModelMeta
+from mteb.models.sentence_transformer_wrapper import SentenceTransformerWrapper
+
+logger = logging.getLogger(__name__)
+
+
+def instruction_template(instruction: str) -> str:
+    return f"Instruct: {instruction}\nQuery: " if instruction else ""
+
+
+class NvEmbedWrapper(SentenceTransformerWrapper):
+    def __init__(
+        self,
+        model: str | SentenceTransformer | CrossEncoder,
+        revision: str | None = None,
+        model_prompts: dict[str, str] | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(model, revision, model_prompts, **kwargs)
+        self.model.max_seq_length = 32768
+        self.model.tokenizer.padding_side = "right"
+        logger.warning(
+            "Instructions are used in both query and docs, which may cause performance discrepancies from the original implementation."
+        )
+
+    def encode(
+        self,
+        sentences: Sequence[str],
+        *,
+        task_name: str,
+        prompt_type: PromptType | None = None,
+        **kwargs: Any,
+    ) -> np.ndarray:
+        # Add eos token to each input example
+        sentences = [example + self.model.tokenizer.eos_token for example in sentences]
+
+        instruction = ""
+        if prompt_type == PromptType.query:
+            instruction = self.get_instruction(task_name, prompt_type)
+
+        prompt = instruction_template(instruction)
+
+        if prompt:
+            logger.info(f"Using {prompt=} for task={task_name} {prompt_type=}")
+        else:
+            logger.info(f"No model prompts found for task={task_name} {prompt_type=}")
+
+        logger.info(f"Encoding {len(sentences)} sentences.")
+
+        embeddings = self.model.encode(
+            sentences,
+            prompt=prompt,
+            normalize_embeddings=True,
+            **kwargs,
+        )
+        if isinstance(embeddings, torch.Tensor):
+            embeddings = embeddings.cpu().detach().float().numpy()
+        return embeddings
+
+
+NV_embed_v2 = ModelMeta(
+    loader=partial(  # type: ignore
+        NvEmbedWrapper,
+        model="nvidia/NV-Embed-v2",
+        trust_remote_code=True,
+    ),
+    name="nvidia/NV-Embed-v2",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="7604d305b621f14095a1aa23d351674c2859553a",
+    release_date="2024-09-09",  # initial commit of hf model.
+    n_parameters=7_850_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="cc-by-nc-4.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/nvidia/NV-Embed-v2",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
+
+NV_embed_v1 = ModelMeta(
+    loader=partial(  # type: ignore
+        NvEmbedWrapper,
+        model="nvidia/NV-Embed-v1",
+        trust_remote_code=True,
+    ),
+    name="nvidia/NV-Embed-v1",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="570834afd5fef5bf3a3c2311a2b6e0a66f6f4f2c",
+    release_date="2024-09-13",  # initial commit of hf model.
+    n_parameters=7_850_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="cc-by-nc-4.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/nvidia/NV-Embed-v1",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
diff --git a/mteb/models/overview.py b/mteb/models/overview.py
index f1b90f6c05..5e6cd0184c 100644
--- a/mteb/models/overview.py
+++ b/mteb/models/overview.py
@@ -15,17 +15,21 @@
     bge_models,
     bm25,
     cohere_models,
+    colbert_models,
     e5_instruct,
     e5_models,
     google_models,
     gritlm_models,
     gte_models,
+    ibm_granite_models,
     jina_models,
+    linq_models,
     llm2vec_models,
     misc_models,
     model2vec_models,
     mxbai_models,
     nomic_models,
+    nvidia_models,
     openai_models,
     promptriever_models,
     repllama_models,
@@ -46,29 +50,33 @@
     bge_models,
     bm25,
     cohere_models,
+    colbert_models,
     e5_instruct,
     e5_models,
     google_models,
+    google_models,
     gritlm_models,
     gte_models,
+    ibm_granite_models,
+    jina_models,
+    linq_models,
     llm2vec_models,
     mxbai_models,
     model2vec_models,
     misc_models,
     nomic_models,
+    nvidia_models,
     openai_models,
+    promptriever_models,
+    repllama_models,
+    rerankers_custom,
+    rerankers_monot5_based,
     ru_sentence_models,
     salesforce_models,
     sentence_transformers_models,
-    voyage_models,
-    google_models,
-    repllama_models,
-    promptriever_models,
-    jina_models,
-    uae_models,
     stella_models,
-    rerankers_monot5_based,
-    rerankers_custom,
+    uae_models,
+    voyage_models,
 ]
 MODEL_REGISTRY = {}
 
diff --git a/mteb/models/rerankers_custom.py b/mteb/models/rerankers_custom.py
index 4555888be0..d9ab7a061e 100644
--- a/mteb/models/rerankers_custom.py
+++ b/mteb/models/rerankers_custom.py
@@ -175,7 +175,7 @@ def loader_inner(**kwargs: Any) -> Encoder:
 
 
 monobert_large = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoBERTReranker,
         model_name_or_path="castorini/monobert-large-msmarco",
@@ -190,7 +190,7 @@ def loader_inner(**kwargs: Any) -> Encoder:
 
 # languages unclear: https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual/discussions/28
 jina_reranker_multilingual = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=JinaReranker,
         model_name_or_path="jinaai/jina-reranker-v2-base-multilingual",
@@ -204,7 +204,7 @@ def loader_inner(**kwargs: Any) -> Encoder:
 )
 
 bge_reranker_v2_m3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=BGEReranker,
         model_name_or_path="BAAI/bge-reranker-v2-m3",
diff --git a/mteb/models/rerankers_monot5_based.py b/mteb/models/rerankers_monot5_based.py
index d40c3409ed..d95639e041 100644
--- a/mteb/models/rerankers_monot5_based.py
+++ b/mteb/models/rerankers_monot5_based.py
@@ -94,8 +94,10 @@ def get_prediction_tokens(
                 token_true_id = tokenizer.get_vocab()[token_true]
                 return token_false_id, token_true_id
             else:
-                raise Exception(f"We don't know the indexes for the non-relevant/relevant tokens for\
-                        the checkpoint {model_name_or_path} and you did not provide any.")
+                raise Exception(
+                    f"We don't know the indexes for the non-relevant/relevant tokens for\
+                        the checkpoint {model_name_or_path} and you did not provide any."
+                )
         else:
             token_false_id = tokenizer.get_vocab()[token_false]
             token_true_id = tokenizer.get_vocab()[token_true]
@@ -276,7 +278,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 
 
 monot5_small = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoT5Reranker,
         model_name_or_path="castorini/monot5-small-msmarco-10k",
@@ -299,7 +301,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 monot5_base = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoT5Reranker,
         model_name_or_path="castorini/monot5-base-msmarco-10k",
@@ -322,7 +324,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 monot5_large = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoT5Reranker,
         model_name_or_path="castorini/monot5-large-msmarco-10k",
@@ -345,7 +347,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 monot5_3b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoT5Reranker,
         model_name_or_path="castorini/monot5-3b-msmarco-10k",
@@ -368,7 +370,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 flant5_base = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=FLANT5Reranker,
         model_name_or_path="google/flan-t5-base",
@@ -393,7 +395,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 flant5_large = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=FLANT5Reranker,
         model_name_or_path="google/flan-t5-large",
@@ -418,7 +420,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 flant5_xl = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=FLANT5Reranker,
         model_name_or_path="google/flan-t5-xl",
@@ -443,7 +445,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 flant5_xxl = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=FLANT5Reranker,
         model_name_or_path="google/flan-t5-xxl",
@@ -469,7 +471,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 
 
 llama2_7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=LlamaReranker,
         model_name_or_path="meta-llama/Llama-2-7b-hf",
@@ -492,7 +494,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 llama2_7b_chat = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=LlamaReranker,
         model_name_or_path="meta-llama/Llama-2-7b-chat-hf",
@@ -515,7 +517,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 mistral_7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MistralReranker,
         model_name_or_path="mistralai/Mistral-7B-Instruct-v0.2",
@@ -538,7 +540,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 followir_7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=FollowIRReranker,
         model_name_or_path="jhu-clsp/FollowIR-7B",
@@ -667,7 +669,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 ]
 
 mt5_base_mmarco_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoT5Reranker,
         model_name_or_path="unicamp-dl/mt5-base-mmarco-v2",
@@ -690,7 +692,7 @@ def get_prediction_tokens(self, *args, **kwargs):
 )
 
 mt5_13b_mmarco_100k = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         _loader,
         wrapper=MonoT5Reranker,
         model_name_or_path="unicamp-dl/mt5-13b-mmarco-100k",
diff --git a/mteb/models/ru_sentence_models.py b/mteb/models/ru_sentence_models.py
index 027b7c4840..a25353faf5 100644
--- a/mteb/models/ru_sentence_models.py
+++ b/mteb/models/ru_sentence_models.py
@@ -75,11 +75,11 @@
 )
 
 user_base_ru = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="deepvk/USER-base",
         revision="436a489a2087d61aa670b3496a9915f84e46c861",
-        prompts={"query": "query: ", "passage": "passage: "},
+        model_prompts={"query": "query: ", "passage": "passage: "},
     ),
     name="deepvk/USER-base",
     languages=["rus_Cyrl"],
@@ -94,7 +94,7 @@
     reference="https://huggingface.co/ai-forever/sbert_large_mt_nlu_ru",
     similarity_fn_name="cosine",
     framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
+    use_instructions=True,
     citation="""@misc{deepvk2024user,
         title={USER: Universal Sentence Encoder for Russian},
         author={Malashenko, Boris and  Zemerov, Anton and Spirin, Egor},
@@ -245,7 +245,7 @@
 
 
 rosberta_ru_en = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="ai-forever/ru-en-RoSBERTa",
         revision="89fb1651989adbb1cfcfdedafd7d102951ad0555",
@@ -261,6 +261,7 @@
     open_weights=True,
     revision="89fb1651989adbb1cfcfdedafd7d102951ad0555",
     release_date="2024-07-29",
+    use_instructions=True,
     citation="""@misc{snegirev2024russianfocusedembeddersexplorationrumteb,
       title={The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design}, 
       author={Artem Snegirev and Maria Tikhonova and Anna Maksimova and Alena Fenogenova and Alexander Abramov},
diff --git a/mteb/models/salesforce_models.py b/mteb/models/salesforce_models.py
index 3a2ab12670..ab63b30eca 100644
--- a/mteb/models/salesforce_models.py
+++ b/mteb/models/salesforce_models.py
@@ -2,26 +2,23 @@
 
 from functools import partial
 
-import torch
-
 from mteb.model_meta import ModelMeta
-
-from .instruct_wrapper import instruct_wrapper
+from mteb.models.instruct_wrapper import instruct_wrapper
 
 
-def sfr_instruction(instruction: str) -> str:
-    return f"Instruct: {instruction}\nQuery: "
+def instruction_template(instruction: str) -> str:
+    return f"Instruct: {instruction}\nQuery: " if instruction else ""
 
 
 SFR_Embedding_2_R = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="Salesforce/SFR-Embedding-2_R",
-        instruction_template=sfr_instruction,
+        instruction_template=instruction_template,
         attn="cccc",
         pooling_method="lasttoken",
         mode="embedding",
-        torch_dtype=torch.bfloat16,
+        torch_dtype="auto",
         # The ST script does not normalize while the HF one does so unclear what to do
         # https://huggingface.co/Salesforce/SFR-Embedding-2_R
         normalized=True,
@@ -48,3 +45,31 @@ def sfr_instruction(instruction: str) -> str:
     }
     """,
 )
+
+
+SFR_Embedding_Mistral = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Salesforce/SFR-Embedding-Mistral",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype="auto",
+        normalized=True,
+    ),
+    name="Salesforce/SFR-Embedding-Mistral",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="938c560d1c236aa563b2dbdf084f28ab28bccb11",
+    release_date="2024-01-24",  # initial commit of hf model.
+    n_parameters=7_110_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="cc-by-nc-4.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/Salesforce/SFR-Embedding-Mistral",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
diff --git a/mteb/models/sentence_transformer_wrapper.py b/mteb/models/sentence_transformer_wrapper.py
index 13d39e4031..763fa7e154 100644
--- a/mteb/models/sentence_transformer_wrapper.py
+++ b/mteb/models/sentence_transformer_wrapper.py
@@ -56,6 +56,9 @@ def __init__(
         if isinstance(self.model, CrossEncoder):
             self.predict = self._predict
 
+        if hasattr(self.model, "similarity"):
+            self.similarity = self.model.similarity
+
     def encode(
         self,
         sentences: Sequence[str],
diff --git a/mteb/models/stella_models.py b/mteb/models/stella_models.py
index 8fc19fd06d..153ee6aa99 100644
--- a/mteb/models/stella_models.py
+++ b/mteb/models/stella_models.py
@@ -7,7 +7,7 @@
 
 stella_en_400M = ModelMeta(
     # https://huggingface.co/dunzhang/stella_en_400M_v5/discussions/21#671a6205ac1e2416090f2bf4
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="dunzhang/stella_en_400M_v5",
         attn="cccc",
@@ -31,7 +31,7 @@
 )
 
 stella_en_1_5b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="dunzhang/stella_en_1.5B_v5",
         attn="cccc",
diff --git a/mteb/models/uae_models.py b/mteb/models/uae_models.py
index 33f2cb03ac..cb83d57c77 100644
--- a/mteb/models/uae_models.py
+++ b/mteb/models/uae_models.py
@@ -52,7 +52,7 @@ def encode(
 
 
 uae_large_v1 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         UAEWrapper,
         model="WhereIsAI/UAE-Large-V1",
         revision="369c368f70f16a613f19f5598d4f12d9f44235d4",
@@ -74,7 +74,7 @@ def encode(
     similarity_fn_name="cosine",
     framework=["Sentence Transformers", "PyTorch"],
     reference="https://huggingface.co/WhereIsAI/UAE-Large-V1",
-    use_instructions=False,
+    use_instructions=True,
     citation="""
     @article{li2023angle,
       title={AnglE-optimized Text Embeddings},
diff --git a/mteb/models/voyage_models.py b/mteb/models/voyage_models.py
index 9f42808b37..0e6ef71d94 100644
--- a/mteb/models/voyage_models.py
+++ b/mteb/models/voyage_models.py
@@ -145,7 +145,7 @@ def _batched_encode(
     revision="1",
     release_date="2024-05-05",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-large-2-instruct",
         model_prompts=model_prompts,
@@ -167,7 +167,7 @@ def _batched_encode(
     revision="1",
     release_date="2024-05-30",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-finance-2",
         model_prompts=model_prompts,
@@ -181,7 +181,7 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2024/06/03/domain-specific-embeddings-finance-edition-voyage-finance-2/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 voyage_law_2 = ModelMeta(
@@ -189,7 +189,7 @@ def _batched_encode(
     revision="1",
     release_date="2024-04-15",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-law-2",
         model_prompts=model_prompts,
@@ -203,7 +203,7 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2024/04/15/domain-specific-embeddings-and-retrieval-legal-edition-voyage-law-2/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 voyage_code_2 = ModelMeta(
@@ -211,7 +211,7 @@ def _batched_encode(
     revision="1",
     release_date="2024-01-23",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-code-2",
         model_prompts=model_prompts,
@@ -225,7 +225,7 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2024/01/23/voyage-code-2-elevate-your-code-retrieval/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 voyage_large_2 = ModelMeta(
@@ -233,7 +233,7 @@ def _batched_encode(
     revision="1",
     release_date="2023-10-29",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-large-2",
         model_prompts=model_prompts,
@@ -247,7 +247,7 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2023/10/29/voyage-embeddings/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 voyage_2 = ModelMeta(
@@ -255,7 +255,7 @@ def _batched_encode(
     revision="1",
     release_date="2023-10-29",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-2",
         model_prompts=model_prompts,
@@ -269,14 +269,14 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2023/10/29/voyage-embeddings/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 voyage_multilingual_2 = ModelMeta(
     name="voyageai/voyage-multilingual-2",
     revision="1",
     release_date="2024-06-10",
     languages=None,  # supported languages not specified
-    loader=partial(
+    loader=partial(  # type: ignore
         VoyageWrapper,
         model_name="voyage-multilingual-2",
         model_prompts=model_prompts,
@@ -290,7 +290,7 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2024/06/10/voyage-multilingual-2-multilingual-embedding-model/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 voyage_3 = ModelMeta(
@@ -312,7 +312,7 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2024/09/18/voyage-3/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
 
 voyage_3_lite = ModelMeta(
@@ -334,5 +334,5 @@ def _batched_encode(
     reference="https://blog.voyageai.com/2024/09/18/voyage-3/",
     similarity_fn_name="cosine",
     framework=["API"],
-    use_instructions=False,
+    use_instructions=True,
 )
diff --git a/mteb/tasks/Classification/multilingual/HinDialectClassification.py b/mteb/tasks/Classification/multilingual/HinDialectClassification.py
index 6565d4b71a..c9d6b36669 100644
--- a/mteb/tasks/Classification/multilingual/HinDialectClassification.py
+++ b/mteb/tasks/Classification/multilingual/HinDialectClassification.py
@@ -3,29 +3,29 @@
 from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
 from mteb.abstasks.TaskMetadata import TaskMetadata
 
-_LANGUAGES = {
-    "pan": ["pan-Guru"],
-    "bgc": ["bgc-Deva"],
-    "mag": ["mag-Deva"],
-    "bns": ["bns-Deva"],
-    "kfq": ["kfg-Deva"],
-    "noe": ["noe-Deva"],
-    "bhb": ["bhb-Deva"],
-    "bho": ["bho-Deva"],
-    "gbm": ["gbm-Deva"],
-    "mup": ["mup-Deva"],
-    "anp": ["anp-Deva"],
-    "hne": ["hne-Deva"],
-    "bra": ["bra-Deva"],
-    "raj": ["raj-Deva"],
-    "awa": ["awa-Deva"],
-    "guj": ["guj-Gujr"],
-    "ben": ["ben-Beng"],
-    "bhd": ["bhd-Deva"],
-    "kfy": ["kfy-Deva"],
-    "mar": ["mar-Deva"],
-    "bjj": ["bjj-Deva"],
-}
+_LANGUAGES = [
+    "pan-Guru",
+    "bgc-Deva",
+    "mag-Deva",
+    "bns-Deva",
+    "kfg-Deva",
+    "noe-Deva",
+    "bhb-Deva",
+    "bho-Deva",
+    "gbm-Deva",
+    "mup-Deva",
+    "anp-Deva",
+    "hne-Deva",
+    "bra-Deva",
+    "raj-Deva",
+    "awa-Deva",
+    "guj-Gujr",
+    "ben-Beng",
+    "bhd-Deva",
+    "kfy-Deva",
+    "mar-Deva",
+    "bjj-Deva",
+]
 
 
 class HinDialectClassification(AbsTaskClassification):
diff --git a/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py b/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py
index 4cef2c0604..217d300ec0 100644
--- a/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py
+++ b/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py
@@ -3,19 +3,19 @@
 from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
 from mteb.abstasks.TaskMetadata import TaskMetadata
 
-_LANGUAGES = {
-    "afr": ["afr-Latn"],
-    "eng": ["eng-Latn"],
-    "nbl": ["nbl-Latn"],
-    "nso": ["nso-Latn"],
-    "sot": ["sot-Latn"],
-    "ssw": ["ssw-Latn"],
-    "tsn": ["tsn-Latn"],
-    "tso": ["tso-Latn"],
-    "ven": ["ven-Latn"],
-    "xho": ["xho-Latn"],
-    "zul": ["zul-Latn"],
-}
+_LANGUAGES = [
+    "afr-Latn",
+    "eng-Latn",
+    "nbl-Latn",
+    "nso-Latn",
+    "sot-Latn",
+    "ssw-Latn",
+    "tsn-Latn",
+    "tso-Latn",
+    "ven-Latn",
+    "xho-Latn",
+    "zul-Latn",
+]
 
 
 class SouthAfricanLangClassification(AbsTaskClassification):
diff --git a/mteb/tasks/Reranking/__init__.py b/mteb/tasks/Reranking/__init__.py
index 497e2751b8..1e34adfc44 100644
--- a/mteb/tasks/Reranking/__init__.py
+++ b/mteb/tasks/Reranking/__init__.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from .ara import NamaaMrTydiReranking
 from .eng import (
     AskUbuntuDupQuestions,
     MindSmallReranking,
@@ -19,6 +20,7 @@
     "CMedQAv2",
     "MMarcoReranking",
     "T2Reranking",
+    "NamaaMrTydiReranking",
     "AskUbuntuDupQuestions",
     "WebLINXCandidatesReranking",
     "StackOverflowDupQuestions",
diff --git a/mteb/tasks/Reranking/ara/NamaaMrTydiReranking.py b/mteb/tasks/Reranking/ara/NamaaMrTydiReranking.py
new file mode 100644
index 0000000000..4a9d755747
--- /dev/null
+++ b/mteb/tasks/Reranking/ara/NamaaMrTydiReranking.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+from ....abstasks.AbsTaskReranking import AbsTaskReranking
+
+
+class NamaaMrTydiReranking(AbsTaskReranking):
+    metadata = TaskMetadata(
+        name="NamaaMrTydiReranking",
+        description="Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations. This dataset adapts the arabic test split for Reranking evaluation purposes by the addition of multiple (Hard) Negatives to each query and positive",
+        reference="https://huggingface.co/NAMAA-Space",
+        dataset={
+            "path": "NAMAA-Space/mteb-eval-mrtydi",
+            "revision": "502637220a7ad0ecc5c39ff5518d7508d2624af8",
+        },
+        type="Reranking",
+        category="s2s",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["ara-Arab"],
+        main_score="map",
+        date=("2023-11-01", "2024-05-15"),
+        domains=["Encyclopaedic", "Written"],
+        task_subtypes=[],
+        license="cc-by-sa-3.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@article{muennighoff2022mteb,
+  doi = {10.48550/ARXIV.2210.07316},
+  url = {https://arxiv.org/abs/2210.07316},
+  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+  title = {MTEB: Massive Text Embedding Benchmark},
+  publisher = {arXiv},
+  journal={arXiv preprint arXiv:2210.07316},  
+  year = {2022}
+}""",
+    )
diff --git a/mteb/tasks/Reranking/ara/__init__.py b/mteb/tasks/Reranking/ara/__init__.py
new file mode 100644
index 0000000000..8f56ceada7
--- /dev/null
+++ b/mteb/tasks/Reranking/ara/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from .NamaaMrTydiReranking import NamaaMrTydiReranking
+
+__all__ = ["NamaaMrTydiReranking"]
diff --git a/mteb/tasks/Retrieval/__init__.py b/mteb/tasks/Retrieval/__init__.py
index 859e8d3a49..7118699ac8 100644
--- a/mteb/tasks/Retrieval/__init__.py
+++ b/mteb/tasks/Retrieval/__init__.py
@@ -77,6 +77,19 @@
     MLQuestionsRetrieval,
     MSMARCOHardNegatives,
     MSMARCOv2,
+    NanoArguAnaRetrieval,
+    NanoClimateFeverRetrieval,
+    NanoDBPediaRetrieval,
+    NanoFEVERRetrieval,
+    NanoFiQA2018Retrieval,
+    NanoHotpotQARetrieval,
+    NanoMSMARCORetrieval,
+    NanoNFCorpusRetrieval,
+    NanoNQRetrieval,
+    NanoQuoraRetrieval,
+    NanoSCIDOCSRetrieval,
+    NanoSciFactRetrieval,
+    NanoTouche2020Retrieval,
     NarrativeQARetrieval,
     NFCorpus,
     NQHardNegatives,
@@ -246,6 +259,7 @@
     "LEMBPasskeyRetrieval",
     "CQADupstackAndroidRetrieval",
     "TempReasonL2Context",
+    "NanoDBPediaRetrieval",
     "ARCChallenge",
     "LegalBenchCorporateLobbying",
     "SCIDOCS",
@@ -260,22 +274,29 @@
     "ClimateFEVERHardNegatives",
     "CQADupstackWordpressRetrieval",
     "CQADupstackEnglishRetrieval",
+    "NanoTouche2020Retrieval",
     "CQADupstackStatsRetrieval",
     "MLQuestionsRetrieval",
     "TempReasonL2Fact",
+    "NanoSciFactRetrieval",
     "CQADupstackGamingRetrieval",
     "CQADupstackWebmastersRetrieval",
+    "NanoFiQA2018Retrieval",
     "CQADupstackUnixRetrieval",
     "TempReasonL3Pure",
     "CQADupstackPhysicsRetrieval",
     "FiQA2018",
     "LitSearchRetrieval",
+    "NanoFEVERRetrieval",
+    "NanoMSMARCORetrieval",
     "FeedbackQARetrieval",
     "HagridRetrieval",
+    "NanoNFCorpusRetrieval",
     "FaithDialRetrieval",
     "SciFact",
     "CQADupstackMathematicaRetrieval",
     "RARbMath",
+    "NanoNQRetrieval",
     "HellaSwag",
     "PIQA",
     "SpartQA",
@@ -283,13 +304,18 @@
     "TempReasonL1",
     "HotpotQA",
     "HotpotQAHardNegatives",
+    "NanoClimateFeverRetrieval",
+    "NanoQuoraRetrieval",
+    "NanoArguAnaRetrieval",
     "LegalBenchConsumerContractsQA",
+    "NanoHotpotQARetrieval",
     "ArguAna",
     "LEMBWikimQARetrieval",
     "TempReasonL3Fact",
     "FEVER",
     "FEVERHardNegatives",
     "CQADupstackGisRetrieval",
+    "NanoSCIDOCSRetrieval",
     "AILACasedocs",
     "NFCorpus",
     "LEMBSummScreenFDRetrieval",
diff --git a/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py b/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py
new file mode 100644
index 0000000000..2230368b94
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoArguAnaRetrieval.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoArguAnaRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoArguAnaRetrieval",
+        description="NanoArguAna is a smaller subset of ArguAna, a dataset for argument retrieval in debate contexts.",
+        reference="http://argumentation.bplaced.net/arguana/data",
+        dataset={
+            "path": "zeta-alpha-ai/NanoArguAna",
+            "revision": "8f4a982d470a32c45817738b9d29042ca55d75ad",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2020-01-01", "2020-12-31"],
+        domains=["Medical", "Written"],
+        task_subtypes=["Discourse coherence"],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{boteva2016,
+  author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan},
+  title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},
+  journal = {Proceedings of the 38th European Conference on Information Retrieval},
+  journal-abbrev = {ECIR},
+  year = {2016},
+  city = {Padova},
+  country = {Italy},
+  url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf}
+}""",
+        prompt={"query": "Given a claim, find documents that refute the claim"},
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoArguAna",
+            "corpus",
+            revision="8f4a982d470a32c45817738b9d29042ca55d75ad",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoArguAna",
+            "queries",
+            revision="8f4a982d470a32c45817738b9d29042ca55d75ad",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoArguAna",
+            "qrels",
+            revision="8f4a982d470a32c45817738b9d29042ca55d75ad",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py b/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py
new file mode 100644
index 0000000000..0185a454d3
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoClimateFeverRetrieval.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoClimateFeverRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoClimateFeverRetrieval",
+        description="NanoClimateFever is a small version of the BEIR dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change.",
+        reference="https://arxiv.org/abs/2012.00614",
+        dataset={
+            "path": "zeta-alpha-ai/NanoClimateFEVER",
+            "revision": "96741bfa30b9f56db8c9eb7d08e775ed6474f206",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2020-01-01", "2020-12-31"],
+        domains=["Non-fiction", "Academic", "News"],
+        task_subtypes=["Claim verification"],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@misc{diggelmann2021climatefever,
+      title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims}, 
+      author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold},
+      year={2021},
+      eprint={2012.00614},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}""",
+        prompt={
+            "query": "Given a claim about climate change, retrieve documents that support or refute the claim"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoClimateFEVER",
+            "corpus",
+            revision="96741bfa30b9f56db8c9eb7d08e775ed6474f206",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoClimateFEVER",
+            "queries",
+            revision="96741bfa30b9f56db8c9eb7d08e775ed6474f206",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoClimateFEVER",
+            "qrels",
+            revision="96741bfa30b9f56db8c9eb7d08e775ed6474f206",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py b/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py
new file mode 100644
index 0000000000..caa638743c
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoDBPediaRetrieval.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoDBPediaRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoDBPediaRetrieval",
+        description="NanoDBPediaRetrieval is a small version of the standard test collection for entity search over the DBpedia knowledge base.",
+        reference="https://huggingface.co/datasets/zeta-alpha-ai/NanoDBPedia",
+        dataset={
+            "path": "zeta-alpha-ai/NanoDBPedia",
+            "revision": "438f1c25129f05db6238699b5afdc9c6b58d2096",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2015-01-01", "2015-12-31"],
+        domains=["Encyclopaedic"],
+        task_subtypes=["Topic classification"],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        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}}""",
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoDBPedia",
+            "corpus",
+            revision="438f1c25129f05db6238699b5afdc9c6b58d2096",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoDBPedia",
+            "queries",
+            revision="438f1c25129f05db6238699b5afdc9c6b58d2096",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoDBPedia",
+            "qrels",
+            revision="438f1c25129f05db6238699b5afdc9c6b58d2096",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py b/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py
new file mode 100644
index 0000000000..6bdd0ab4cf
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoFEVERRetrieval.py
@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoFEVERRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoFEVERRetrieval",
+        description="NanoFEVER is a smaller version of "
+        + "FEVER (Fact Extraction and VERification), which consists of 185,445 claims generated by altering sentences"
+        + " extracted from Wikipedia and subsequently verified without knowledge of the sentence they were"
+        + " derived from.",
+        reference="https://fever.ai/",
+        dataset={
+            "path": "zeta-alpha-ai/NanoFEVER",
+            "revision": "a8bfdf1bf15181167a7e22e69cf8754bdea9b4c8",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2018-01-01", "2018-12-31"],
+        domains=["Academic", "Encyclopaedic"],
+        task_subtypes=["Claim verification"],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{thorne-etal-2018-fever,
+    title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",
+    author = "Thorne, James  and
+      Vlachos, Andreas  and
+      Christodoulopoulos, Christos  and
+      Mittal, Arpit",
+    editor = "Walker, Marilyn  and
+      Ji, Heng  and
+      Stent, Amanda",
+    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
+    month = jun,
+    year = "2018",
+    address = "New Orleans, Louisiana",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N18-1074",
+    doi = "10.18653/v1/N18-1074",
+    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.",
+}""",
+        prompt={
+            "query": "Given a claim, retrieve documents that support or refute the claim"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoFEVER",
+            "corpus",
+            revision="a8bfdf1bf15181167a7e22e69cf8754bdea9b4c8",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoFEVER",
+            "queries",
+            revision="a8bfdf1bf15181167a7e22e69cf8754bdea9b4c8",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoFEVER",
+            "qrels",
+            revision="a8bfdf1bf15181167a7e22e69cf8754bdea9b4c8",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py b/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py
new file mode 100644
index 0000000000..1a3467c1d7
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoFiQA2018Retrieval.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoFiQA2018Retrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoFiQA2018Retrieval",
+        description="NanoFiQA2018 is a smaller subset of the Financial Opinion Mining and Question Answering dataset.",
+        reference="https://sites.google.com/view/fiqa/",
+        dataset={
+            "path": "zeta-alpha-ai/NanoFiQA2018",
+            "revision": "4163ba032953d5044a7a6244261413f609c14342",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2018-01-01", "2018-12-31"],
+        domains=["Academic", "Social"],
+        task_subtypes=["Sentiment/Hate speech"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}""",
+        prompt={
+            "query": "Given a financial question, retrieve user replies that best answer the question"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoFiQA2018",
+            "corpus",
+            revision="4163ba032953d5044a7a6244261413f609c14342",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoFiQA2018",
+            "queries",
+            revision="4163ba032953d5044a7a6244261413f609c14342",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoFiQA2018",
+            "qrels",
+            revision="4163ba032953d5044a7a6244261413f609c14342",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py b/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py
new file mode 100644
index 0000000000..4389aeafa8
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoHotpotQARetrieval.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoHotpotQARetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoHotpotQARetrieval",
+        description="NanoHotpotQARetrieval is a smaller subset of the "
+        + "HotpotQA dataset, which is a question answering dataset featuring natural, multi-hop questions, with strong"
+        + " supervision for supporting facts to enable more explainable question answering systems.",
+        reference="https://hotpotqa.github.io/",
+        dataset={
+            "path": "zeta-alpha-ai/NanoHotpotQA",
+            "revision": "d79c0cdda980aba54842756770928035e1b61a51",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2018-01-01", "2018-12-31"],
+        domains=["Web", "Written"],
+        task_subtypes=["Question answering"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{yang-etal-2018-hotpotqa,
+    title = "{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering",
+    author = "Yang, Zhilin  and
+      Qi, Peng  and
+      Zhang, Saizheng  and
+      Bengio, Yoshua  and
+      Cohen, William  and
+      Salakhutdinov, Ruslan  and
+      Manning, Christopher D.",
+    editor = "Riloff, Ellen  and
+      Chiang, David  and
+      Hockenmaier, Julia  and
+      Tsujii, Jun{'}ichi",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D18-1259",
+    doi = "10.18653/v1/D18-1259",
+    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.",
+}""",
+        prompt={
+            "query": "Given a multi-hop question, retrieve documents that can help answer the question"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoHotpotQA",
+            "corpus",
+            revision="d79c0cdda980aba54842756770928035e1b61a51",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoHotpotQA",
+            "queries",
+            revision="d79c0cdda980aba54842756770928035e1b61a51",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoHotpotQA",
+            "qrels",
+            revision="d79c0cdda980aba54842756770928035e1b61a51",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py b/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py
new file mode 100644
index 0000000000..8a2f51e7fd
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoMSMARCORetrieval.py
@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoMSMARCORetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoMSMARCORetrieval",
+        description="NanoMSMARCORetrieval is a smaller subset of MS MARCO, a collection of datasets focused on deep learning in search.",
+        reference="https://microsoft.github.io/msmarco/",
+        dataset={
+            "path": "zeta-alpha-ai/NanoMSMARCO",
+            "revision": "7b8ff22f2771dc65ac5b439f222eb19a1f56abda",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2016-01-01", "2016-12-31"],
+        domains=["Web"],
+        task_subtypes=["Question answering"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@article{DBLP:journals/corr/NguyenRSGTMD16,
+  author    = {Tri Nguyen and
+               Mir Rosenberg and
+               Xia Song and
+               Jianfeng Gao and
+               Saurabh Tiwary and
+               Rangan Majumder and
+               Li Deng},
+  title     = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
+  journal   = {CoRR},
+  volume    = {abs/1611.09268},
+  year      = {2016},
+  url       = {http://arxiv.org/abs/1611.09268},
+  archivePrefix = {arXiv},
+  eprint    = {1611.09268},
+  timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+}""",
+        prompt={
+            "query": "Given a web search query, retrieve relevant passages that answer the query"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoMSMARCO",
+            "corpus",
+            revision="7b8ff22f2771dc65ac5b439f222eb19a1f56abda",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoMSMARCO",
+            "queries",
+            revision="7b8ff22f2771dc65ac5b439f222eb19a1f56abda",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoMSMARCO",
+            "qrels",
+            revision="7b8ff22f2771dc65ac5b439f222eb19a1f56abda",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py b/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py
new file mode 100644
index 0000000000..0f6ac8533a
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoNFCorpusRetrieval.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoNFCorpusRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoNFCorpusRetrieval",
+        description="NanoNFCorpus is a smaller subset of NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval.",
+        reference="https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/",
+        dataset={
+            "path": "zeta-alpha-ai/NanoNFCorpus",
+            "revision": "dd542a7efb9ad2136b9e00768b60fca9038f8156",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2016-01-01", "2016-12-31"],
+        domains=["Medical", "Academic", "Written"],
+        task_subtypes=["Question answering"],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{boteva2016,
+  author = {Boteva, Vera and Gholipour, Demian and Sokolov, Artem and Riezler, Stefan},
+  title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},
+  journal = {Proceedings of the 38th European Conference on Information Retrieval},
+  journal-abbrev = {ECIR},
+  year = {2016},
+  city = {Padova},
+  country = {Italy},
+  url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf}
+}""",
+        prompt={
+            "query": "Given a question, retrieve relevant documents that best answer the question"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoNFCorpus",
+            "corpus",
+            revision="dd542a7efb9ad2136b9e00768b60fca9038f8156",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoNFCorpus",
+            "queries",
+            revision="dd542a7efb9ad2136b9e00768b60fca9038f8156",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoNFCorpus",
+            "qrels",
+            revision="dd542a7efb9ad2136b9e00768b60fca9038f8156",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py b/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py
new file mode 100644
index 0000000000..5aa831f799
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoNQRetrieval.py
@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoNQRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoNQRetrieval",
+        description="NanoNQ is a smaller subset of a dataset which contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question.",
+        reference="https://ai.google.com/research/NaturalQuestions",
+        dataset={
+            "path": "zeta-alpha-ai/NanoNQ",
+            "revision": "77540146379abf95df8326a3c5bb9eb21c7146c3",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2019-01-01", "2019-12-31"],
+        domains=["Academic", "Web"],
+        task_subtypes=["Question answering"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@article{47761,title	= {Natural Questions: a Benchmark for Question Answering Research},
+        author	= {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh 
+        and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee 
+        and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le 
+        and Slav Petrov},year	= {2019},journal	= {Transactions of the Association of Computational 
+        Linguistics}}""",
+        prompt={
+            "query": "Given a question, retrieve Wikipedia passages that answer the question"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoNQ",
+            "corpus",
+            revision="77540146379abf95df8326a3c5bb9eb21c7146c3",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoNQ",
+            "queries",
+            revision="77540146379abf95df8326a3c5bb9eb21c7146c3",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoNQ",
+            "qrels",
+            revision="77540146379abf95df8326a3c5bb9eb21c7146c3",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py b/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py
new file mode 100644
index 0000000000..1391d12b93
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoQuoraRetrieval.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoQuoraRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoQuoraRetrieval",
+        description="NanoQuoraRetrieval is a smaller subset of the "
+        + "QuoraRetrieval dataset, which is based on questions that are marked as duplicates on the Quora platform. Given a"
+        + " question, find other (duplicate) questions.",
+        reference="https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs",
+        dataset={
+            "path": "zeta-alpha-ai/NanoQuoraRetrieval",
+            "revision": "2ab2d73e6c862026282808b913a34f4136928545",
+        },
+        type="Retrieval",
+        category="s2s",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2017-01-01", "2017-12-31"],
+        domains=["Social"],
+        task_subtypes=["Duplicate Detection"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@misc{quora-question-pairs,
+    author = {DataCanary, hilfialkaff, Lili Jiang, Meg Risdal, Nikhil Dandekar, tomtung},
+    title = {Quora Question Pairs},
+    publisher = {Kaggle},
+    year = {2017},
+    url = {https://kaggle.com/competitions/quora-question-pairs}
+}""",
+        prompt={
+            "query": "Given a question, retrieve questions that are semantically equivalent to the given question"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoQuoraRetrieval",
+            "corpus",
+            revision="2ab2d73e6c862026282808b913a34f4136928545",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoQuoraRetrieval",
+            "queries",
+            revision="2ab2d73e6c862026282808b913a34f4136928545",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoQuoraRetrieval",
+            "qrels",
+            revision="2ab2d73e6c862026282808b913a34f4136928545",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py b/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py
new file mode 100644
index 0000000000..2d27e1a2dc
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoSCIDOCSRetrieval.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoSCIDOCSRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoSCIDOCSRetrieval",
+        description="NanoFiQA2018 is a smaller subset of "
+        + "SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation"
+        + " prediction, to document classification and recommendation.",
+        reference="https://allenai.org/data/scidocs",
+        dataset={
+            "path": "zeta-alpha-ai/NanoSCIDOCS",
+            "revision": "484eb90549fc3f0b9c42b3551e80ceb999515537",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2020-01-01", "2020-12-31"],
+        domains=["Academic", "Written", "Non-fiction"],
+        task_subtypes=[],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{specter2020cohan,
+  title={SPECTER: Document-level Representation Learning using Citation-informed Transformers},
+  author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
+  booktitle={ACL},
+  year={2020}
+}""",
+        prompt={
+            "query": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoSCIDOCS",
+            "corpus",
+            revision="484eb90549fc3f0b9c42b3551e80ceb999515537",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoSCIDOCS",
+            "queries",
+            revision="484eb90549fc3f0b9c42b3551e80ceb999515537",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoSCIDOCS",
+            "qrels",
+            revision="484eb90549fc3f0b9c42b3551e80ceb999515537",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py b/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py
new file mode 100644
index 0000000000..aff949d319
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoSciFactRetrieval.py
@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoSciFactRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoSciFactRetrieval",
+        description="NanoSciFact is a smaller subset of SciFact, which verifies scientific claims using evidence from the research literature containing scientific paper abstracts.",
+        reference="https://github.com/allenai/scifact",
+        dataset={
+            "path": "zeta-alpha-ai/NanoSciFact",
+            "revision": "309f1d1ae3ae2e092444a8a0c25bed59b82318bc",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=["2018-01-01", "2018-12-31"],
+        domains=["Academic", "Medical", "Written"],
+        task_subtypes=["Claim verification"],
+        license="cc-by-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@inproceedings{specter2020cohan,
+  title={SPECTER: Document-level Representation Learning using Citation-informed Transformers},
+  author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
+  booktitle={ACL},
+  year={2020}
+}""",
+        prompt={
+            "query": "Given a scientific claim, retrieve documents that support or refute the claim"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoSciFact",
+            "corpus",
+            revision="309f1d1ae3ae2e092444a8a0c25bed59b82318bc",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoSciFact",
+            "queries",
+            revision="309f1d1ae3ae2e092444a8a0c25bed59b82318bc",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoSciFact",
+            "qrels",
+            revision="309f1d1ae3ae2e092444a8a0c25bed59b82318bc",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py b/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py
new file mode 100644
index 0000000000..656b5494a0
--- /dev/null
+++ b/mteb/tasks/Retrieval/eng/NanoTouche2020Retrieval.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class NanoTouche2020Retrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="NanoTouche2020Retrieval",
+        description="NanoTouche2020 is a smaller subset of Touché Task 1: Argument Retrieval for Controversial Questions.",
+        reference="https://webis.de/events/touche-20/shared-task-1.html",
+        dataset={
+            "path": "zeta-alpha-ai/NanoTouche2020",
+            "revision": "0d2f26ed8c5ad309f95c7f9499c70a40e140fccd",
+        },
+        type="Retrieval",
+        category="s2p",
+        modalities=["text"],
+        eval_splits=["train"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_10",
+        date=("2020-09-23", "2020-09-23"),
+        domains=["Academic"],
+        task_subtypes=["Question answering"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="found",
+        bibtex_citation="""@dataset{potthast_2022_6862281,
+  author       = {Potthast, Martin and
+                  Gienapp, Lukas and
+                  Wachsmuth, Henning and
+                  Hagen, Matthias and
+                  Fröbe, Maik and
+                  Bondarenko, Alexander and
+                  Ajjour, Yamen and
+                  Stein, Benno},
+  title        = {{Touché20-Argument-Retrieval-for-Controversial- 
+                   Questions}},
+  month        = jul,
+  year         = 2022,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.6862281},
+  url          = {https://doi.org/10.5281/zenodo.6862281}
+}""",
+        prompt={
+            "query": "Given a question, retrieve detailed and persuasive arguments that answer the question"
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus = load_dataset(
+            "zeta-alpha-ai/NanoTouche2020",
+            "corpus",
+            revision="0d2f26ed8c5ad309f95c7f9499c70a40e140fccd",
+        )
+        self.queries = load_dataset(
+            "zeta-alpha-ai/NanoTouche2020",
+            "queries",
+            revision="0d2f26ed8c5ad309f95c7f9499c70a40e140fccd",
+        )
+        self.relevant_docs = load_dataset(
+            "zeta-alpha-ai/NanoTouche2020",
+            "qrels",
+            revision="0d2f26ed8c5ad309f95c7f9499c70a40e140fccd",
+        )
+
+        self.corpus = {
+            split: {
+                sample["_id"]: {"_id": sample["_id"], "text": sample["text"]}
+                for sample in self.corpus[split]
+            }
+            for split in self.corpus
+        }
+
+        self.queries = {
+            split: {sample["_id"]: sample["text"] for sample in self.queries[split]}
+            for split in self.queries
+        }
+
+        self.relevant_docs = {
+            split: {
+                sample["query-id"]: {sample["corpus-id"]: 1}
+                for sample in self.relevant_docs[split]
+            }
+            for split in self.relevant_docs
+        }
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/eng/__init__.py b/mteb/tasks/Retrieval/eng/__init__.py
index 47e2498709..9f1717a89f 100644
--- a/mteb/tasks/Retrieval/eng/__init__.py
+++ b/mteb/tasks/Retrieval/eng/__init__.py
@@ -41,6 +41,19 @@
 from .MLQuestions import MLQuestionsRetrieval
 from .MSMARCORetrieval import MSMARCO, MSMARCOHardNegatives
 from .MSMARCOv2Retrieval import MSMARCOv2
+from .NanoArguAnaRetrieval import NanoArguAnaRetrieval
+from .NanoClimateFeverRetrieval import NanoClimateFeverRetrieval
+from .NanoDBPediaRetrieval import NanoDBPediaRetrieval
+from .NanoFEVERRetrieval import NanoFEVERRetrieval
+from .NanoFiQA2018Retrieval import NanoFiQA2018Retrieval
+from .NanoHotpotQARetrieval import NanoHotpotQARetrieval
+from .NanoMSMARCORetrieval import NanoMSMARCORetrieval
+from .NanoNFCorpusRetrieval import NanoNFCorpusRetrieval
+from .NanoNQRetrieval import NanoNQRetrieval
+from .NanoQuoraRetrieval import NanoQuoraRetrieval
+from .NanoSCIDOCSRetrieval import NanoSCIDOCSRetrieval
+from .NanoSciFactRetrieval import NanoSciFactRetrieval
+from .NanoTouche2020Retrieval import NanoTouche2020Retrieval
 from .NarrativeQARetrieval import NarrativeQARetrieval
 from .NFCorpusRetrieval import NFCorpus
 from .NQRetrieval import NQ, NQHardNegatives
@@ -90,6 +103,7 @@
     "LEMBPasskeyRetrieval",
     "CQADupstackAndroidRetrieval",
     "TempReasonL2Context",
+    "NanoDBPediaRetrieval",
     "ARCChallenge",
     "LegalBenchCorporateLobbying",
     "SCIDOCS",
@@ -104,22 +118,29 @@
     "ClimateFEVERHardNegatives",
     "CQADupstackWordpressRetrieval",
     "CQADupstackEnglishRetrieval",
+    "NanoTouche2020Retrieval",
     "CQADupstackStatsRetrieval",
     "MLQuestionsRetrieval",
     "TempReasonL2Fact",
+    "NanoSciFactRetrieval",
     "CQADupstackGamingRetrieval",
     "CQADupstackWebmastersRetrieval",
+    "NanoFiQA2018Retrieval",
     "CQADupstackUnixRetrieval",
     "TempReasonL3Pure",
     "CQADupstackPhysicsRetrieval",
     "FiQA2018",
     "LitSearchRetrieval",
+    "NanoFEVERRetrieval",
+    "NanoMSMARCORetrieval",
     "FeedbackQARetrieval",
     "HagridRetrieval",
+    "NanoNFCorpusRetrieval",
     "FaithDialRetrieval",
     "SciFact",
     "CQADupstackMathematicaRetrieval",
     "RARbMath",
+    "NanoNQRetrieval",
     "HellaSwag",
     "PIQA",
     "SpartQA",
@@ -127,13 +148,18 @@
     "TempReasonL1",
     "HotpotQA",
     "HotpotQAHardNegatives",
+    "NanoClimateFeverRetrieval",
+    "NanoQuoraRetrieval",
+    "NanoArguAnaRetrieval",
     "LegalBenchConsumerContractsQA",
+    "NanoHotpotQARetrieval",
     "ArguAna",
     "LEMBWikimQARetrieval",
     "TempReasonL3Fact",
     "FEVER",
     "FEVERHardNegatives",
     "CQADupstackGisRetrieval",
+    "NanoSCIDOCSRetrieval",
     "AILACasedocs",
     "NFCorpus",
     "LEMBSummScreenFDRetrieval",
diff --git a/mteb/tasks/__init__.py b/mteb/tasks/__init__.py
index 6e09541e10..745d4066ae 100644
--- a/mteb/tasks/__init__.py
+++ b/mteb/tasks/__init__.py
@@ -426,6 +426,7 @@
     MindSmallReranking,
     MIRACLReranking,
     MMarcoReranking,
+    NamaaMrTydiReranking,
     NevIR,
     RuBQReranking,
     SciDocsReranking,
@@ -547,6 +548,19 @@
     MSMARCOPLHardNegatives,
     MSMARCOv2,
     MultiLongDocRetrieval,
+    NanoArguAnaRetrieval,
+    NanoClimateFeverRetrieval,
+    NanoDBPediaRetrieval,
+    NanoFEVERRetrieval,
+    NanoFiQA2018Retrieval,
+    NanoHotpotQARetrieval,
+    NanoMSMARCORetrieval,
+    NanoNFCorpusRetrieval,
+    NanoNQRetrieval,
+    NanoQuoraRetrieval,
+    NanoSCIDOCSRetrieval,
+    NanoSciFactRetrieval,
+    NanoTouche2020Retrieval,
     NarrativeQARetrieval,
     NeuCLIR2022Retrieval,
     NeuCLIR2022RetrievalHardNegatives,
@@ -1140,6 +1154,7 @@
     "LEMBPasskeyRetrieval",
     "CQADupstackAndroidRetrieval",
     "TempReasonL2Context",
+    "NanoDBPediaRetrieval",
     "ARCChallenge",
     "LegalBenchCorporateLobbying",
     "SCIDOCS",
@@ -1154,22 +1169,29 @@
     "ClimateFEVERHardNegatives",
     "CQADupstackWordpressRetrieval",
     "CQADupstackEnglishRetrieval",
+    "NanoTouche2020Retrieval",
     "CQADupstackStatsRetrieval",
     "MLQuestionsRetrieval",
     "TempReasonL2Fact",
+    "NanoSciFactRetrieval",
     "CQADupstackGamingRetrieval",
     "CQADupstackWebmastersRetrieval",
+    "NanoFiQA2018Retrieval",
     "CQADupstackUnixRetrieval",
     "TempReasonL3Pure",
     "CQADupstackPhysicsRetrieval",
     "FiQA2018",
     "LitSearchRetrieval",
+    "NanoFEVERRetrieval",
+    "NanoMSMARCORetrieval",
     "FeedbackQARetrieval",
     "HagridRetrieval",
+    "NanoNFCorpusRetrieval",
     "FaithDialRetrieval",
     "SciFact",
     "CQADupstackMathematicaRetrieval",
     "RARbMath",
+    "NanoNQRetrieval",
     "HellaSwag",
     "PIQA",
     "SpartQA",
@@ -1177,13 +1199,18 @@
     "TempReasonL1",
     "HotpotQA",
     "HotpotQAHardNegatives",
+    "NanoClimateFeverRetrieval",
+    "NanoQuoraRetrieval",
+    "NanoArguAnaRetrieval",
     "LegalBenchConsumerContractsQA",
+    "NanoHotpotQARetrieval",
     "ArguAna",
     "LEMBWikimQARetrieval",
     "TempReasonL3Fact",
     "FEVER",
     "FEVERHardNegatives",
     "CQADupstackGisRetrieval",
+    "NanoSCIDOCSRetrieval",
     "AILACasedocs",
     "NFCorpus",
     "LEMBSummScreenFDRetrieval",
@@ -1280,6 +1307,7 @@
     "CMedQAv2",
     "MMarcoReranking",
     "T2Reranking",
+    "NamaaMrTydiReranking",
     "AskUbuntuDupQuestions",
     "WebLINXCandidatesReranking",
     "StackOverflowDupQuestions",
diff --git a/pyproject.toml b/pyproject.toml
index 4ab7f1f88e..58d1b4d3e4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "mteb"
-version = "1.23.2"
+version = "1.25.3"
 description = "Massive Text Embedding Benchmark"
 readme = "README.md"
 authors = [
@@ -70,6 +70,7 @@ jina = ["einops>=0.8.0"]
 flash_attention = ["flash-attn>=2.6.3"]
 openai = ["openai>=1.41.0", "tiktoken>=0.8.0"]
 model2vec = ["model2vec>=0.3.0"]
+pylate = ["pylate>=1.1.4"]
 
 
 [tool.coverage.report]
diff --git a/scripts/generate_imports.py b/scripts/generate_imports.py
index 469d894441..ae331de9df 100644
--- a/scripts/generate_imports.py
+++ b/scripts/generate_imports.py
@@ -7,8 +7,7 @@
 import types
 from pathlib import Path
 
-# Adjust this import to the correct location of AbsTask.
-from mteb.tasks import AbsTask
+from mteb.abstasks import AbsTask
 
 BASE_DIR = Path("../mteb/tasks")
 
diff --git a/tests/test_TaskMetadata.py b/tests/test_TaskMetadata.py
index f3e6b48260..701abab729 100644
--- a/tests/test_TaskMetadata.py
+++ b/tests/test_TaskMetadata.py
@@ -523,3 +523,15 @@ def test_empty_descriptive_stat_in_new_datasets(task: AbsTask):
         task.metadata.descriptive_stats is not None
     ), f"Dataset {task.metadata.name} should have descriptive stats. You can add metadata to your task by running `YorTask().calculate_metadata_metrics()`"
     assert task.metadata.n_samples is not None
+
+
+@pytest.mark.parametrize("task", get_tasks())
+def test_eval_langs_correctly_specified(task: AbsTask):
+    if task.is_multilingual:
+        assert isinstance(
+            task.metadata.eval_langs, dict
+        ), f"{task.metadata.name} should have eval_langs as a dict"
+    else:
+        assert isinstance(
+            task.metadata.eval_langs, list
+        ), f"{task.metadata.name} should have eval_langs as a list"
diff --git a/tests/test_benchmark/mock_tasks.py b/tests/test_benchmark/mock_tasks.py
index 7a096828e5..142b4b42ad 100644
--- a/tests/test_benchmark/mock_tasks.py
+++ b/tests/test_benchmark/mock_tasks.py
@@ -1447,6 +1447,95 @@ def load_data(self, **kwargs):
 
 class MockMultilingualRetrievalTask(AbsTaskRetrieval, MultilingualTask):
     expected_stats = {
+        "val": {
+            "num_samples": 8,
+            "number_of_characters": 224,
+            "num_documents": 4,
+            "min_document_length": 27,
+            "average_document_length": 30.0,
+            "max_document_length": 33,
+            "unique_documents": 4,
+            "num_queries": 4,
+            "min_query_length": 23,
+            "average_query_length": 26.0,
+            "max_query_length": 29,
+            "unique_queries": 4,
+            "none_queries": 0,
+            "num_relevant_docs": 8,
+            "min_relevant_docs_per_query": 2,
+            "average_relevant_docs_per_query": 1.0,
+            "max_relevant_docs_per_query": 2,
+            "unique_relevant_docs": 4,
+            "num_instructions": None,
+            "min_instruction_length": None,
+            "average_instruction_length": None,
+            "max_instruction_length": None,
+            "unique_instructions": None,
+            "num_top_ranked": None,
+            "min_top_ranked_per_query": None,
+            "average_top_ranked_per_query": None,
+            "max_top_ranked_per_query": None,
+            "hf_subset_descriptive_stats": {
+                "eng": {
+                    "num_samples": 4,
+                    "number_of_characters": 112,
+                    "num_documents": 2,
+                    "min_document_length": 27,
+                    "average_document_length": 30.0,
+                    "max_document_length": 33,
+                    "unique_documents": 2,
+                    "num_queries": 2,
+                    "min_query_length": 23,
+                    "average_query_length": 26.0,
+                    "max_query_length": 29,
+                    "unique_queries": 2,
+                    "none_queries": 0,
+                    "num_relevant_docs": 4,
+                    "min_relevant_docs_per_query": 2,
+                    "average_relevant_docs_per_query": 1.0,
+                    "max_relevant_docs_per_query": 2,
+                    "unique_relevant_docs": 2,
+                    "num_instructions": None,
+                    "min_instruction_length": None,
+                    "average_instruction_length": None,
+                    "max_instruction_length": None,
+                    "unique_instructions": None,
+                    "num_top_ranked": None,
+                    "min_top_ranked_per_query": None,
+                    "average_top_ranked_per_query": None,
+                    "max_top_ranked_per_query": None,
+                },
+                "fra": {
+                    "num_samples": 4,
+                    "number_of_characters": 112,
+                    "num_documents": 2,
+                    "min_document_length": 27,
+                    "average_document_length": 30.0,
+                    "max_document_length": 33,
+                    "unique_documents": 2,
+                    "num_queries": 2,
+                    "min_query_length": 23,
+                    "average_query_length": 26.0,
+                    "max_query_length": 29,
+                    "unique_queries": 2,
+                    "none_queries": 0,
+                    "num_relevant_docs": 4,
+                    "min_relevant_docs_per_query": 2,
+                    "average_relevant_docs_per_query": 1.0,
+                    "max_relevant_docs_per_query": 2,
+                    "unique_relevant_docs": 2,
+                    "num_instructions": None,
+                    "min_instruction_length": None,
+                    "average_instruction_length": None,
+                    "max_instruction_length": None,
+                    "unique_instructions": None,
+                    "num_top_ranked": None,
+                    "min_top_ranked_per_query": None,
+                    "average_top_ranked_per_query": None,
+                    "max_top_ranked_per_query": None,
+                },
+            },
+        },
         "test": {
             "num_samples": 8,
             "number_of_characters": 224,
@@ -1535,14 +1624,14 @@ class MockMultilingualRetrievalTask(AbsTaskRetrieval, MultilingualTask):
                     "max_top_ranked_per_query": None,
                 },
             },
-        }
+        },
     }
 
     metadata = TaskMetadata(
         type="Retrieval",
         name="MockMultilingualRetrievalTask",
         main_score="ndcg_at_10",
-        **general_args,  # type: ignore
+        **dict(general_args | {"eval_splits": ["val", "test"]}),  # type: ignore
     )
     metadata.eval_langs = multilingual_eval_langs
 
@@ -1551,14 +1640,22 @@ def load_data(self, **kwargs):
             "test": {
                 "q1": "This is a test sentence",
                 "q2": "This is another test sentence",
-            }
+            },
+            "val": {
+                "q1": "This is a test sentence",
+                "q2": "This is another test sentence",
+            },
         }
         self.queries = {"eng": queries, "fra": queries}
         corpus = {
             "test": {
                 "d1": "This is a positive sentence",
                 "d2": "This is another positive sentence",
-            }
+            },
+            "val": {
+                "d1": "This is a positive sentence",
+                "d2": "This is another positive sentence",
+            },
         }
         self.corpus = {"eng": corpus, "fra": corpus}
 
@@ -1567,6 +1664,10 @@ def load_data(self, **kwargs):
                 "q1": {"d1": 1, "d2": 0},
                 "q2": {"d1": 0, "d2": 1},
             },
+            "val": {
+                "q1": {"d1": 1, "d2": 0},
+                "q2": {"d1": 0, "d2": 1},
+            },
         }
         self.relevant_docs = {
             "eng": relevant_docs,
diff --git a/tests/test_evaluation/test_split_evaluation.py b/tests/test_evaluation/test_split_evaluation.py
index a2ca249747..c79f25ac42 100644
--- a/tests/test_evaluation/test_split_evaluation.py
+++ b/tests/test_evaluation/test_split_evaluation.py
@@ -7,6 +7,7 @@
     MockSentenceTransformer,
 )
 from tests.test_benchmark.mock_tasks import (
+    MockMultilingualRetrievalTask,
     MockRetrievalTask,
 )
 
@@ -21,6 +22,11 @@ def tasks():
     return [MockRetrievalTask()]
 
 
+@pytest.fixture
+def multilingual_tasks():
+    return [MockMultilingualRetrievalTask()]
+
+
 def test_all_splits_evaluated(model, tasks, tmp_path):
     evaluation = MTEB(tasks=tasks)
     results = evaluation.run(
@@ -34,6 +40,7 @@ def test_all_splits_evaluated(model, tasks, tmp_path):
     last_evaluated_splits = evaluation.get_last_evaluated_splits()
     assert set(last_evaluated_splits["MockRetrievalTask"]) == {"val", "test"}
     assert len(last_evaluated_splits["MockRetrievalTask"]) == 2
+    assert results[0].scores.keys() == {"val", "test"}
 
 
 def test_one_missing_split(model, tasks, tmp_path):
@@ -49,6 +56,7 @@ def test_one_missing_split(model, tasks, tmp_path):
     last_evaluated_splits = evaluation.get_last_evaluated_splits()
     assert set(last_evaluated_splits["MockRetrievalTask"]) == {"val"}
     assert len(last_evaluated_splits["MockRetrievalTask"]) == 1
+    assert results[0].scores.keys() == {"val"}
 
     results2 = evaluation.run(
         model,
@@ -62,11 +70,12 @@ def test_one_missing_split(model, tasks, tmp_path):
     last_evaluated_splits = evaluation.get_last_evaluated_splits()
     assert set(last_evaluated_splits["MockRetrievalTask"]) == {"test"}
     assert len(last_evaluated_splits["MockRetrievalTask"]) == 1
+    assert results2[0].scores.keys() == {"test", "val"}
 
 
 def test_no_missing_splits(model, tasks, tmp_path):
     evaluation = MTEB(tasks=tasks)
-    _ = evaluation.run(
+    results = evaluation.run(
         model,
         eval_splits=["val", "test"],
         output_folder=str(tmp_path / "testcase3"),
@@ -76,9 +85,10 @@ def test_no_missing_splits(model, tasks, tmp_path):
     last_evaluated_splits = evaluation.get_last_evaluated_splits()
     assert "MockRetrievalTask" in last_evaluated_splits
     assert len(last_evaluated_splits["MockRetrievalTask"]) == 2
+    assert results[0].scores.keys() == {"test", "val"}
 
     evaluation = MTEB(tasks=tasks)
-    _ = evaluation.run(
+    results = evaluation.run(
         model,
         eval_splits=["val", "test"],
         output_folder=str(tmp_path / "testcase3"),
@@ -89,3 +99,209 @@ def test_no_missing_splits(model, tasks, tmp_path):
     last_evaluated_splits = evaluation.get_last_evaluated_splits()
     assert "MockRetrievalTask" in last_evaluated_splits
     assert len(last_evaluated_splits["MockRetrievalTask"]) == 0
+    assert results[0].scores.keys() == {"test", "val"}
+
+
+def test_all_languages_evaluated(model, multilingual_tasks, tmp_path):
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "all_lang_evaluated"),
+        verbosity=2,
+        eval_subsets=None,
+    )
+    assert "MockMultilingualRetrievalTask" == results[0].task_name
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert "MockMultilingualRetrievalTask" in last_evaluated_splits
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert last_evaluated_splits["MockMultilingualRetrievalTask"] == ["test"]
+    assert results[0].scores.keys() == {"test"}
+    assert len(results[0].scores["test"]) == 2
+
+
+def test_missing_language(model, multilingual_tasks, tmp_path):
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "missing_lang_test"),
+        verbosity=2,
+        eval_subsets=["eng"],
+    )
+
+    assert "MockMultilingualRetrievalTask" == results[0].task_name
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert "MockMultilingualRetrievalTask" in last_evaluated_splits
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert last_evaluated_splits["MockMultilingualRetrievalTask"] == ["test"]
+    assert results[0].scores.keys() == {"test"}
+    assert results[0].languages == ["eng"]
+
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "missing_lang_test"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+        overwrite_results=True,
+    )
+
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert last_evaluated_splits["MockMultilingualRetrievalTask"] == ["test"]
+    assert sorted(results[0].languages) == ["eng", "fra"]
+    assert results[0].scores.keys() == {"test"}
+    assert len(results[0].scores["test"]) == 2
+
+
+def test_no_missing_languages(model, multilingual_tasks, tmp_path):
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "no_missing_lang_test"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+    )
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert "MockMultilingualRetrievalTask" in last_evaluated_splits
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert results[0].scores.keys() == {"test"}
+    assert len(results[0].scores["test"]) == 2
+    assert sorted(results[0].languages) == ["eng", "fra"]
+
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "no_missing_lang_test"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+        overwrite_results=True,
+    )
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert "MockMultilingualRetrievalTask" in last_evaluated_splits
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 0
+    assert results[0].scores.keys() == {"test"}
+    assert len(results[0].scores["test"]) == 2
+    assert sorted(results[0].languages) == ["eng", "fra"]
+
+
+def test_partial_languages(model, multilingual_tasks, tmp_path):
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "partial_lang_test"),
+        verbosity=2,
+        eval_subsets=["fra"],
+    )
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert last_evaluated_splits["MockMultilingualRetrievalTask"] == ["test"]
+    assert results[0].scores.keys() == {"test"}
+    assert len(results[0].scores["test"]) == 1
+    assert results[0].languages == ["fra"]
+
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "partial_lang_test"),
+        verbosity=2,
+        eval_subsets=["fra", "eng"],
+        overwrite_results=True,
+    )
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert last_evaluated_splits["MockMultilingualRetrievalTask"] == ["test"]
+    assert results[0].scores.keys() == {"test"}
+    assert len(results[0].scores["test"]) == 2
+    assert sorted(results[0].languages) == ["eng", "fra"]
+
+
+def test_multilingual_one_missing_split_no_missing_lang(
+    model, multilingual_tasks, tmp_path
+):
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["val"],
+        output_folder=str(tmp_path / "partial_langs_partial_splits"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+    )
+
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert len(last_evaluated_splits["MockMultilingualRetrievalTask"]) == 1
+    assert set(last_evaluated_splits["MockMultilingualRetrievalTask"]) == {"val"}
+    assert sorted(results[0].languages) == ["eng", "fra"]
+    assert results[0].scores.keys() == {"val"}
+    assert len(results[0].scores["val"]) == 2
+
+    results = evaluation.run(
+        model,
+        eval_splits=["val", "test"],
+        output_folder=str(tmp_path / "partial_langs_partial_splits"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+        overwrite_results=True,
+    )
+
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert set(last_evaluated_splits["MockMultilingualRetrievalTask"]) == {"test"}
+    assert sorted(results[0].languages) == ["eng", "fra"]
+    assert results[0].scores.keys() == {"test", "val"}
+    assert len(results[0].scores["test"]) == 2
+    assert len(results[0].scores["val"]) == 2
+
+
+def test_multilingual_one_missing_lang_in_one_split(
+    model, multilingual_tasks, tmp_path
+):
+    evaluation = MTEB(tasks=multilingual_tasks)
+    results = evaluation.run(
+        model,
+        eval_splits=["val"],
+        output_folder=str(tmp_path / "one_lang_one_split"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+    )
+
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert set(last_evaluated_splits["MockMultilingualRetrievalTask"]) == {"val"}
+    assert sorted(results[0].languages) == ["eng", "fra"]
+    assert results[0].scores.keys() == {"val"}
+    assert len(results[0].scores["val"]) == 2
+
+    results = evaluation.run(
+        model,
+        eval_splits=["val", "test"],
+        output_folder=str(tmp_path / "one_lang_one_split"),
+        verbosity=2,
+        eval_subsets=["eng"],
+        overwrite_results=True,
+    )
+
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert set(last_evaluated_splits["MockMultilingualRetrievalTask"]) == {"test"}
+    assert sorted(results[0].languages) == ["eng", "fra"]
+    assert results[0].scores.keys() == {"test", "val"}
+    assert len(results[0].scores["test"]) == 1
+    assert len(results[0].scores["val"]) == 2
+
+    results = evaluation.run(
+        model,
+        eval_splits=["test"],
+        output_folder=str(tmp_path / "one_lang_one_split"),
+        verbosity=2,
+        eval_subsets=["eng", "fra"],
+        overwrite_results=True,
+    )
+
+    last_evaluated_splits = evaluation.get_last_evaluated_splits()
+    assert set(last_evaluated_splits["MockMultilingualRetrievalTask"]) == {"test"}
+    assert sorted(results[0].languages) == ["eng", "fra"]
+    # output merged result with previous results
+    assert results[0].scores.keys() == {"test", "val"}
+    assert len(results[0].scores["test"]) == 2