More training data annotations

mteb/models/bge_models.py

@@ -656,40 +656,6 @@
     training_datasets=bge_m3_training_data,
 )
 
-bge_multilingual_gemma2 = ModelMeta(
-    loader=partial(  # type: ignore
-        sentence_transformers_loader,
-        model_name="BAAI/bge-multilingual-gemma2",
-        revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
-    ),
-    name="BAAI/bge-multilingual-gemma2",
-    languages=[
-        "eng_Latn",
-        "zho_Hans",
-        "kor_Hang",
-        "kor_Latn",
-        "fra_Latn",
-        "jpn_Jpan",
-        "jpn_Latn",
-    ],  # This list is incomlete. Their description says "and more".
-    # I'm also unsure about the scripts.
-    open_weights=True,
-    revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
-    release_date="2024-07-25",  # initial commit of hf model.
-    n_parameters=9.24 * 1e9,
-    memory_usage_mb=35254,
-    embed_dim=3584,  # from old C-MTEB leaderboard
-    license="gemma",
-    max_tokens=8192,  # from old C-MTEB leaderboard
-    reference="https://huggingface.co/BAAI/bge-multilingual-gemma2",
-    similarity_fn_name="cosine",
-    framework=["Sentence Transformers", "PyTorch"],
-    use_instructions=False,
-    public_training_code=None,
-    public_training_data=None,
-    training_datasets=None,  # not disclosed
-)
-
 # Contents of cfli/bge-full-data
 bge_full_data = {
     # source: https://arxiv.org/pdf/2409.15700
@@ -746,6 +712,46 @@
     "STSBenchmark": ["train"],
 }
 
+
+bge_multilingual_gemma2 = ModelMeta(
+    loader=partial(  # type: ignore
+        sentence_transformers_loader,
+        model_name="BAAI/bge-multilingual-gemma2",
+        revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
+    ),
+    name="BAAI/bge-multilingual-gemma2",
+    languages=[
+        "eng_Latn",
+        "zho_Hans",
+        "kor_Hang",
+        "kor_Latn",
+        "fra_Latn",
+        "jpn_Jpan",
+        "jpn_Latn",
+    ],  # This list is incomlete. Their description says "and more".
+    # I'm also unsure about the scripts.
+    open_weights=True,
+    revision="992e13d8984fde2c31ef8a3cb2c038aeec513b8a",
+    release_date="2024-07-25",  # initial commit of hf model.
+    n_parameters=9.24 * 1e9,
+    memory_usage_mb=35254,
+    embed_dim=3584,  # from old C-MTEB leaderboard
+    license="gemma",
+    max_tokens=8192,  # from old C-MTEB leaderboard
+    reference="https://huggingface.co/BAAI/bge-multilingual-gemma2",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=False,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets={
+        **bge_full_data,
+        **bge_m3_training_data,
+        "MIRACLReranking": ["train"],
+        "MrTidyRetrieval": ["train"],
+    },
+)
+
 bge_en_icl = ModelMeta(
     loader=partial(
         sentence_transformers_loader,

mteb/models/misc_models.py

@@ -50,7 +50,75 @@
     reference="https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2",
     similarity_fn_name="cosine",
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={
+        # Source: https://huggingface.co/datasets/Gameselo/monolingual-wideNLI
+        # https://huggingface.co/Gameselo/STS-multilingual-mpnet-base-v2/discussions/2
+        # SNLI,
+        # MNLI,
+        # QNLI,
+        # WNLI,
+        # SciTail
+        # Vitamin C
+        # Trains on all of MTEB
+        "AlphaNLI": ["train"],
+        "RTE3": ["train"],
+        "AmazonPolarityClassification": ["train"],
+        "AmazonReviewsClassification": ["train"],
+        "ArguAna": ["train"],
+        "ArxivClusteringP2P": ["train"],
+        "ArxivClusteringS2S": ["train"],
+        "AskUbuntuDupQuestions": ["train"],
+        "BIOSSES": ["train"],
+        "Banking77Classification": ["train"],
+        "BiorxivClusteringP2P": ["train"],
+        "BiorxivClusteringS2S": ["train"],
+        "CQADupstackRetrieval": ["train"],
+        "ClimateFEVER": ["train"],
+        "DBPedia": ["train"],
+        "EmotionClassification": ["train"],
+        "FEVER": ["train"],
+        "FiQA2018": ["train"],
+        "HotpotQA": ["train"],
+        "ImdbClassification": ["train"],
+        "MTOPDomainClassification": ["train"],
+        "MTOPIntentClassification": ["train"],
+        "MassiveIntentClassification": ["train"],
+        "MassiveScenarioClassification": ["train"],
+        "MedrxivClusteringP2P": ["train"],
+        "MedrxivClusteringS2S": ["train"],
+        "MindSmallReranking": ["train"],
+        "NFCorpus": ["train"],
+        "NQ": ["train"],
+        "QuoraRetrieval": ["train"],
+        "RedditClustering": ["train"],
+        "RedditClusteringP2P": ["train"],
+        "SCIDOCS": ["train"],
+        "SICK-R": ["train"],
+        "STS12": ["train"],
+        "STS13": ["train"],
+        "STS14": ["train"],
+        "STS15": ["train"],
+        "STS16": ["train"],
+        "STSBenchmark": ["train"],
+        "SciDocsRR": ["train"],
+        "SciFact": ["train"],
+        "SprintDuplicateQuestions": ["train"],
+        "StackExchangeClustering": ["train"],
+        "StackExchangeClusteringP2P": ["train"],
+        "StackOverflowDupQuestions": ["train"],
+        "SummEval": ["train"],
+        "TRECCOVID": ["train"],
+        "Touche2020": ["train"],
+        "ToxicConversationsClassification": ["train"],
+        "TweetSentimentExtractionClassification": ["train"],
+        "TwentyNewsgroupsClustering": ["train"],
+        "TwitterSemEval2015": ["train"],
+        "TwitterURLCorpus": ["train"],
+        "MSMARCO": ["train"],
+        "AmazonCounterfactualClassification": ["train"],
+        "STS17": ["train"],
+        "STS22": ["train"],
+    },
     adapted_from="sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
     superseded_by=None,
 )

mteb/models/voyage_models.py

@@ -262,7 +262,7 @@ def _batched_encode(
     similarity_fn_name="cosine",
     framework=["API"],
     use_instructions=True,
-    training_datasets=None,  # Not known
+    training_datasets=VOYAGE_TRAINING_DATA,  # src: private communication with Voyage
     public_training_code=None,
     public_training_data=None,
 )

mteb/models/voyage_v.py

@@ -259,5 +259,5 @@ def get_fused_embeddings(
     public_training_data=None,
     reference="https://huggingface.co/voyageai/voyage-multimodal-3",
     use_instructions=None,
-    training_datasets=None,
+    training_datasets={},  # No overlap with MTEB according to Voyage, could overlap with MIEB, didn't ask
 )