diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..818d649bf21cdef29b21f885c8f770f9baa1714e --- /dev/null +++ b/.gitattributes @@ -0,0 +1,31 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/check_leaderboard_can_update.yaml b/.github/workflows/check_leaderboard_can_update.yaml deleted file mode 100644 index 501677f8d567c3d1a075c30fb65c625003681815..0000000000000000000000000000000000000000 --- a/.github/workflows/check_leaderboard_can_update.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# This workflow will install Python dependencies, run tests and lint with a single version of Python -# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions - -name: check_leaderboard_can_update - -on: - pull_request: - branches: [ main ] - -jobs: - build: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - with: - fetch-depth: 0 - lfs: true - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: 3.9 - - name: Install requirements - run: | - pip install -r requirements.txt - - name: Run leaderboard updating code - run: | - python refresh.py diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml deleted file mode 100644 index 6c44926ff6ab963641de468f1e7d065efb052992..0000000000000000000000000000000000000000 --- a/.github/workflows/main.yml +++ /dev/null @@ -1,19 +0,0 @@ -name: Sync to Hugging Face hub -on: - push: - branches: [main] - # to run this workflow manually from the Actions tab - workflow_dispatch: - -jobs: - sync-to-hub: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - with: - fetch-depth: 0 - lfs: true - - name: Push to hub - env: - HF_TOKEN: ${{ secrets.HF_TOKEN }} - run: git push https://mteb:$HF_TOKEN@huggingface.co/spaces/mteb/leaderboard main diff --git a/.github/workflows/update_leaderboard.yml b/.github/workflows/update_leaderboard.yml deleted file mode 100644 index bac6d41f7c704073994db268a301cc481ca39c26..0000000000000000000000000000000000000000 --- a/.github/workflows/update_leaderboard.yml +++ /dev/null @@ -1,45 +0,0 @@ -# This workflow will install Python dependencies, run tests and lint with a single version of Python -# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions - -name: update_leaderboard_daily - -on: - schedule: - - cron: '30 2 * * *' - push: - branches: [ main ] - workflow_dispatch: # Allows us to trigger the workflow manually via the GitHub UI - -jobs: - update_leaderboard: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - with: - fetch-depth: 0 - lfs: true - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: 3.9 - - name: Install requirements - run: | - pip install -r requirements.txt - - name: Run leaderboard updating code - run: | - python refresh.py - - name: Commit updates - uses: stefanzweifel/git-auto-commit-action@v4 - with: - commit_message: Automated Leaderboard Update - file_pattern: '*.json boards_data/* all_data_tasks/*' - # Needed so that it can override the PR settings - commit_user_name: Orion Weller - commit_user_email: wellerorion@gmail.com - commit_author: Orion Weller - - name: Push to hub - env: - HF_TOKEN: ${{ secrets.HF_TOKEN }} - run: git push https://mteb:$HF_TOKEN@huggingface.co/spaces/mteb/leaderboard main diff --git a/.gitignore b/.gitignore index 470aca5314b4706dc53c5849fbb13ccf5820d9c2..7e99e367f8443d86e5e8825b9fda39dfbb39630d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1 @@ -*.pyc -model_infos.json -space -.venv -results -mteb +*.pyc \ No newline at end of file diff --git a/EXTERNAL_MODEL_RESULTS.json b/EXTERNAL_MODEL_RESULTS.json index 10c57b4fa45929a7c76146dfd59c989886b94153..55b4e074ccc51012c178a6760601b55568b8d0c3 100644 --- a/EXTERNAL_MODEL_RESULTS.json +++ b/EXTERNAL_MODEL_RESULTS.json @@ -1,25145 +1 @@ -{ - "Alibaba-NLP__gte-Qwen1.5-7B-instruct": { - "BitextMining": { - "f1": [] - }, - "Classification": { - "accuracy": [ - { - "Model": "gte-Qwen1.5-7B-instruct", - "AmazonCounterfactualClassification (en)": 83.16, - "AmazonPolarityClassification": 96.7, - "AmazonReviewsClassification (en)": 62.17, - "AmazonReviewsClassification (zh)": 52.95, - "Banking77Classification": 81.68, - "EmotionClassification": 54.53, - "IFlyTek": 53.77, - "ImdbClassification": 95.58, - "JDReview": 88.2, - "MTOPDomainClassification (en)": 95.75, - "MTOPIntentClassification (en)": 84.26, - "MassiveIntentClassification (zh-CN)": 76.25, - "MassiveIntentClassification (en)": 78.47, - 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37.22, - "HeadlineClassification": 75.23, - "InappropriatenessClassification": 57.34, - "KinopoiskClassification": 49.91, - "MassiveIntentClassification (ru)": 53.02, - "MassiveScenarioClassification (ru)": 56.79, - "RuReviewsClassification": 50.74, - "RuSciBenchGRNTIClassification": 48.03, - "RuSciBenchOECDClassification": 36.13 - } - ] - }, - "Clustering": { - "v_measure": [ - { - "Model": "rubert-base-cased", - "GeoreviewClusteringP2P": 28.77, - "MLSUMClusteringP2P (ru)": 41.42, - "MLSUMClusteringS2S (ru)": 40.52, - "RuSciBenchGRNTIClusteringP2P": 28.29, - "RuSciBenchOECDClusteringP2P": 26.67 - } - ] - }, - "PairClassification": { - "max_ap": [ - { - "Model": "rubert-base-cased", - "OpusparcusPC (ru)": 81.65, - "TERRa": 52.12 - }, - { - "Model": "rubert-base-cased", - "OpusparcusPC (ru)": 81.65, - "TERRa": 53.17 - } - ] - }, - "Reranking": { - "map": [ - { - "Model": "rubert-base-cased", - "MIRACLReranking (ru)": 13.27 - }, - { - "Model": "rubert-base-cased", - "RuBQReranking": 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"DKHateClassification": 56.78, - "DanishPoliticalCommentsClassification": 34.03, - "LccSentimentClassification": 52.27, - "MTOPDomainClassification (fr)": 43.83, - "MTOPIntentClassification (fr)": 19.38, - "MasakhaNEWSClassification (fra)": 60.5, - "MassiveIntentClassification (da)": 41.06, - "MassiveIntentClassification (nb)": 40.46, - "MassiveIntentClassification (sv)": 45.12, - "MassiveIntentClassification (fr)": 13.58, - "MassiveScenarioClassification (da)": 43.91, - "MassiveScenarioClassification (nb)": 44.83, - "MassiveScenarioClassification (sv)": 47.35, - "MassiveScenarioClassification (fr)": 23.21, - "NoRecClassification": 46.28, - "NordicLangClassification": 79.39, - "NorwegianParliament": 56.75, - "ScalaDaClassification": 57.3, - "ScalaNbClassification": 58.33 - } - ] - }, - "Clustering": { - "v_measure": [ - { - "Model": "xlm-roberta-base", - "AlloProfClusteringP2P": 52.24, - "AlloProfClusteringS2S": 20.37, - "HALClusteringS2S": 8.68, - "MLSUMClusteringP2P": 40.44, - 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"xlm-roberta-base", - "SummEvalFr": 29.14 - } - ] - }, - "MultilabelClassification": { - "accuracy": [] - }, - "InstructionRetrieval": { - "p-MRR": [] - } - }, - "FacebookAI__xlm-roberta-large": { - "BitextMining": { - "f1": [] - }, - "Classification": { - "accuracy": [ - { - "Model": "xlm-roberta-large", - "AmazonReviewsClassification (fr)": 26.62, - "MTOPDomainClassification (fr)": 36.77, - "MTOPIntentClassification (fr)": 15.37, - "MasakhaNEWSClassification (fra)": 65.76, - "MassiveIntentClassification (fr)": 15.82, - "MassiveScenarioClassification (fr)": 23.92 - } - ] - }, - "Clustering": { - "v_measure": [ - { - "Model": "xlm-roberta-large", - "AlloProfClusteringP2P": 56.54, - "AlloProfClusteringS2S": 21.18, - "BlurbsClusteringP2P": 29.84, - "BlurbsClusteringS2S": 7.29, - "HALClusteringS2S": 5.94, - "MLSUMClusteringP2P": 42.67, - "MLSUMClusteringS2S": 18.5, - "MasakhaNEWSClusteringP2P (fra)": 34.02, - "MasakhaNEWSClusteringS2S (fra)": 21.52, - "TenKGnadClusteringP2P": 32.46, - "TenKGnadClusteringS2S": 6.16 - } - ] - }, - "PairClassification": { - "max_ap": [ - { - "Model": "xlm-roberta-large", - "OpusparcusPC (fr)": 83.73, - "PawsXPairClassification (fr)": 53.38 - }, - { - "Model": "xlm-roberta-large", - "OpusparcusPC (fr)": 83.79, - "PawsXPairClassification (fr)": 53.44 - } - ] - }, - "Reranking": { - "map": [ - { - "Model": "xlm-roberta-large", - "AlloprofReranking": 28.62, - "SyntecReranking": 49.4 - } - ] - }, - "Retrieval": { - "ndcg_at_10": [ - { - "Model": "xlm-roberta-large", - "AlloprofRetrieval": 0.52, - "BSARDRetrieval": 0.0, - "MintakaRetrieval (fr)": 0.9, - "SyntecRetrieval": 6.6, - "XPQARetrieval (fr)": 12.7 - } - ] - }, - "STS": { - "cosine_spearman": [ - { - "Model": "xlm-roberta-large", - "SICKFr": 50.01, - "STS22 (fr)": 55.49, - "STSBenchmarkMultilingualSTS (fr)": 42.32 - } - ] - }, - "Summarization": { - "cosine_spearman": [ - { - "Model": "xlm-roberta-large", - "SummEvalFr": 28.89 - } - ] - }, - "MultilabelClassification": { - "accuracy": 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"bert-base-10lang-cased", - "OpusparcusPC (fr)": 87.78, - "PawsXPairClassification (fr)": 53.4 - } - ] - }, - "Reranking": { - "map": [ - { - "Model": "bert-base-10lang-cased", - "AlloprofReranking": 36.21, - "SyntecReranking": 53.25 - } - ] - }, - "Retrieval": { - "ndcg_at_10": [ - { - "Model": "bert-base-10lang-cased", - "AlloprofRetrieval": 1.6, - "BSARDRetrieval": 0.0, - "MintakaRetrieval (fr)": 3.55, - "SyntecRetrieval": 18.95, - "XPQARetrieval (fr)": 18.39 - } - ] - }, - "STS": { - "cosine_spearman": [ - { - "Model": "bert-base-10lang-cased", - "SICKFr": 58.76, - "STS22 (fr)": 40.31, - "STSBenchmarkMultilingualSTS (fr)": 52.25 - } - ] - }, - "Summarization": { - "cosine_spearman": [ - { - "Model": "bert-base-10lang-cased", - "SummEvalFr": 29.06 - } - ] - }, - "MultilabelClassification": { - "accuracy": [] - }, - "InstructionRetrieval": { - "p-MRR": [] - } - }, - "Geotrend__bert-base-15lang-cased": { - "BitextMining": { - "f1": [] - }, - "Classification": { - "accuracy": [ - { - "Model": "bert-base-15lang-cased", - "AmazonReviewsClassification (fr)": 29.35, - "MTOPDomainClassification (fr)": 63.7, - "MTOPIntentClassification (fr)": 37.85, - "MasakhaNEWSClassification (fra)": 63.89, - "MassiveIntentClassification (fr)": 37.28, - "MassiveScenarioClassification (fr)": 44.47 - } - ] - }, - "Clustering": { - "v_measure": [ - { - "Model": "bert-base-15lang-cased", - "AlloProfClusteringP2P": 53.16, - "AlloProfClusteringS2S": 43.43, - "HALClusteringS2S": 20.26, - "MLSUMClusteringP2P": 41.22, - "MLSUMClusteringS2S": 31.88, - "MasakhaNEWSClusteringP2P (fra)": 24.23, - "MasakhaNEWSClusteringS2S (fra)": 24.46 - } - ] - }, - "PairClassification": { - "max_ap": [ - { - "Model": "bert-base-15lang-cased", - "OpusparcusPC (fr)": 86.78, - "PawsXPairClassification (fr)": 53.38 - }, - { - "Model": "bert-base-15lang-cased", - "OpusparcusPC (fr)": 87.73, - "PawsXPairClassification (fr)": 53.38 - } - ] - }, - "Reranking": { - "map": [ - { - "Model": "bert-base-15lang-cased", - "AlloprofReranking": 36.21, - "SyntecReranking": 53.25 - } - ] - }, - "Retrieval": { - "ndcg_at_10": [ - { - "Model": "bert-base-15lang-cased", - "AlloprofRetrieval": 1.61, - "BSARDRetrieval": 0.0, - "MintakaRetrieval (fr)": 3.55, - "SyntecRetrieval": 18.95, - "XPQARetrieval (fr)": 18.35 - } - ] - }, - "STS": { - "cosine_spearman": [ - { - "Model": "bert-base-15lang-cased", - "SICKFr": 58.77, - "STS22 (fr)": 40.4, - "STSBenchmarkMultilingualSTS (fr)": 52.25 - } - ] - }, - "Summarization": { - "cosine_spearman": [ - { - "Model": "bert-base-15lang-cased", - "SummEvalFr": 29.13 - } - ] - }, - "MultilabelClassification": { - "accuracy": [] - }, - "InstructionRetrieval": { - "p-MRR": [] - } - }, - "Geotrend__bert-base-25lang-cased": { - "BitextMining": { - "f1": [] - }, - "Classification": { - "accuracy": [ - { - "Model": "bert-base-25lang-cased", - "AmazonReviewsClassification (fr)": 29.39, - "MTOPDomainClassification (fr)": 63.63, - "MTOPIntentClassification (fr)": 37.86, - "MasakhaNEWSClassification (fra)": 63.91, - "MassiveIntentClassification (fr)": 37.3, - "MassiveScenarioClassification (fr)": 44.47 - } - ] - }, - "Clustering": { - "v_measure": [ - { - "Model": "bert-base-25lang-cased", - "AlloProfClusteringP2P": 53.49, - "AlloProfClusteringS2S": 43.1, - "HALClusteringS2S": 19.78, - "MLSUMClusteringP2P": 40.73, - "MLSUMClusteringS2S": 31.94, - "MasakhaNEWSClusteringP2P (fra)": 24.23, - "MasakhaNEWSClusteringS2S (fra)": 24.46 - } - ] - }, - "PairClassification": { - "max_ap": [ - { - "Model": "bert-base-25lang-cased", - "OpusparcusPC (fr)": 86.79, - "PawsXPairClassification (fr)": 53.39 - }, - { - "Model": "bert-base-25lang-cased", - "OpusparcusPC (fr)": 87.78, - "PawsXPairClassification (fr)": 53.4 - } - ] - }, - "Reranking": { - "map": [ - { - "Model": "bert-base-25lang-cased", - "AlloprofReranking": 36.25, - "SyntecReranking": 53.25 - } - ] - }, - "Retrieval": { - "ndcg_at_10": [ - { - "Model": "bert-base-25lang-cased", - "AlloprofRetrieval": 1.6, - 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"StackOverflowDupQuestions": 40.85}]}, "Retrieval": {"ndcg_at_10": [{"Model": "text-similarity-ada-001", "ArguAna": 39.65, "CQADupstackAndroidRetrieval": 17.62, "CQADupstackEnglishRetrieval": 12.07, "CQADupstackGamingRetrieval": 19.59, "CQADupstackGisRetrieval": 8.01, "CQADupstackMathematicaRetrieval": 4.7, "CQADupstackPhysicsRetrieval": 10.95, "CQADupstackProgrammersRetrieval": 9.41, "CQADupstackRetrieval": 10.17, "CQADupstackStatsRetrieval": 7.15, "CQADupstackTexRetrieval": 4.21, "CQADupstackUnixRetrieval": 9.79, "CQADupstackWebmastersRetrieval": 12.02, "CQADupstackWordpressRetrieval": 6.52, "ClimateFEVER": 2.83, "DBPedia": 3.48, "FEVER": 4.45, "FiQA2018": 7.54, "HotpotQA": 12.6, "MSMARCO": 10.53, "NFCorpus": 20.59, "NQ": 2.02, "QuoraRetrieval": 82.18, "SCIDOCS": 6.28, "SciFact": 45.46, "TRECCOVID": 24.56, "Touche2020": 3.1}]}, "STS": {"cos_sim_spearman": [{"Model": "text-similarity-ada-001", "BIOSSES": 78.04, "SICK-R": 77.48, "STS12": 72.3, "STS13": 81.49, "STS14": 74.74, "STS15": 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"CQADupstackGamingRetrieval": 15.74, "CQADupstackPhysicsRetrieval": 8.23, "CQADupstackWebmastersRetrieval": 8.76, "FiQA2018": 5.14, "NFCorpus": 19.96, "QuoraRetrieval": 83.11, "SciFact": 46.68, "TRECCOVID": 7.61}]}, "STS": {"cos_sim_spearman": [{"Model": "text-similarity-curie-001", "BIOSSES": 77.46, "SICK-R": 77.26, "STSBenchmark": 83.02}]}, "Summarization": {"cos_sim_spearman": [{"Model": "text-similarity-curie-001"}]}}, "text-similarity-davinci-001": {"BitextMining": {"f1": [{"Model": "text-similarity-davinci-001"}]}, "Clustering": {"v_measure": [{"Model": "text-similarity-davinci-001", "RedditClustering": 31.78, "StackExchangeClustering": 36.86, "TwentyNewsgroupsClustering": 29.33}]}, "Classification": {"accuracy": [{"Model": "text-similarity-davinci-001"}]}, "PairClassification": {"cos_sim_ap": [{"Model": "text-similarity-davinci-001", "SprintDuplicateQuestions": 69.52, "TwitterSemEval2015": 74.42, "TwitterURLCorpus": 83.75}]}, "Reranking": {"map": [{"Model": 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"OpusparcusPC (fr)": 100.0, "PawsX (fr)": 53.39}]}, "Reranking": {"map": [{"Model": "bert-base-25lang-cased", "AlloprofReranking": 36.25, "SyntecReranking": 53.25}]}, "Retrieval": {"ndcg_at_10": [{"Model": "bert-base-25lang-cased", "AlloprofRetrieval": 1.6, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 3.55, "SyntecRetrieval": 18.95, "XPQARetrieval (fr)": 18.46}]}, "STS": {"cos_sim_spearman": [{"Model": "bert-base-25lang-cased", "SICKFr": 58.76, "STS22 (fr)": 38.77, "STSBenchmarkMultilingualSTS (fr)": 52.25}]}, "Summarization": {"cos_sim_spearman": [{"Model": "bert-base-25lang-cased", "SummEvalFr": 28.84}]}}, "bert-base-multilingual-cased": {"BitextMining": {"f1": [{"Model": "bert-base-multilingual-cased"}]}, "Clustering": {"v_measure": [{"Model": "bert-base-multilingual-cased", "AlloProfClusteringP2P": 51.5, "AlloProfClusteringS2S": 43.06, "HALClusteringS2S": 20.81, "MLSUMClusteringP2P": 40.9, "MLSUMClusteringS2S": 31.8, "MasakhaNEWSClusteringP2P (fra)": 24.23, "MasakhaNEWSClusteringS2S (fra)": 24.46}]}, "Classification": {"accuracy": [{"Model": "bert-base-multilingual-cased", "AmazonReviewsClassification (fr)": 29.39, "MTOPDomainClassification (fr)": 63.61, "MTOPIntentClassification (fr)": 37.84, "MasakhaNEWSClassification (fra)": 64.0, "MassiveIntentClassification (fr)": 37.3, "MassiveScenarioClassification (fr)": 44.47}]}, "PairClassification": {"cos_sim_ap": [{"Model": "bert-base-multilingual-cased", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 53.39}]}, "Reranking": {"map": [{"Model": "bert-base-multilingual-cased", "AlloprofReranking": 36.23, "SyntecReranking": 53.25}]}, "Retrieval": {"ndcg_at_10": [{"Model": "bert-base-multilingual-cased", "AlloprofRetrieval": 1.63, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 3.55, "SyntecRetrieval": 18.95, "XPQARetrieval (fr)": 18.49}]}, "STS": {"cos_sim_spearman": [{"Model": "bert-base-multilingual-cased", "SICKFr": 58.75, "STS22 (fr)": 39.05, "STSBenchmarkMultilingualSTS (fr)": 52.25}]}, "Summarization": {"cos_sim_spearman": [{"Model": "bert-base-multilingual-cased", "SummEvalFr": 28.81}]}}, "bert-base-multilingual-uncased": {"BitextMining": {"f1": [{"Model": "bert-base-multilingual-uncased"}]}, "Clustering": {"v_measure": [{"Model": "bert-base-multilingual-uncased", "AlloProfClusteringP2P": 60.66, "AlloProfClusteringS2S": 35.05, "HALClusteringS2S": 20.9, "MLSUMClusteringP2P": 43.5, "MLSUMClusteringS2S": 30.99, "MasakhaNEWSClusteringP2P (fra)": 49.71, "MasakhaNEWSClusteringS2S (fra)": 42.23}]}, "Classification": {"accuracy": [{"Model": "bert-base-multilingual-uncased", "AmazonReviewsClassification (fr)": 29.02, "MTOPDomainClassification (fr)": 64.49, "MTOPIntentClassification (fr)": 39.4, "MasakhaNEWSClassification (fra)": 75.69, "MassiveIntentClassification (fr)": 38.01, "MassiveScenarioClassification (fr)": 43.63}]}, "PairClassification": {"cos_sim_ap": [{"Model": "bert-base-multilingual-uncased", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 53.22}]}, "Reranking": {"map": [{"Model": "bert-base-multilingual-uncased", "AlloprofReranking": 38.85, "SyntecReranking": 66.4}]}, "Retrieval": {"ndcg_at_10": [{"Model": "bert-base-multilingual-uncased", "AlloprofRetrieval": 5.51, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 2.87, "SyntecRetrieval": 34.95, "XPQARetrieval (fr)": 26.12}]}, "STS": {"cos_sim_spearman": [{"Model": "bert-base-multilingual-uncased", "SICKFr": 58.26, "STS22 (fr)": 56.47, "STSBenchmarkMultilingualSTS (fr)": 54.97}]}, "Summarization": {"cos_sim_spearman": [{"Model": "bert-base-multilingual-uncased", "SummEvalFr": 30.72}]}}, "flaubert_base_cased": {"BitextMining": {"f1": [{"Model": "flaubert_base_cased"}]}, "Clustering": {"v_measure": [{"Model": "flaubert_base_cased", "AlloProfClusteringP2P": 52.86, "AlloProfClusteringS2S": 14.46, "HALClusteringS2S": 3.85, "MLSUMClusteringP2P": 39.06, "MLSUMClusteringS2S": 17.13, "MasakhaNEWSClusteringP2P (fra)": 41.61, "MasakhaNEWSClusteringS2S (fra)": 21.26}]}, "Classification": {"accuracy": [{"Model": "flaubert_base_cased", "AmazonReviewsClassification (fr)": 24.9, "MTOPDomainClassification (fr)": 25.55, "MTOPIntentClassification (fr)": 9.49, "MasakhaNEWSClassification (fra)": 71.14, "MassiveIntentClassification (fr)": 6.98, "MassiveScenarioClassification (fr)": 11.41}]}, "PairClassification": {"cos_sim_ap": [{"Model": "flaubert_base_cased", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 51.89}]}, "Reranking": {"map": [{"Model": "flaubert_base_cased", "AlloprofReranking": 34.81, "SyntecReranking": 55.88}]}, "Retrieval": {"ndcg_at_10": [{"Model": "flaubert_base_cased", "AlloprofRetrieval": 1.63, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 0.58, "SyntecRetrieval": 20.56, "XPQARetrieval (fr)": 6.59}]}, "STS": {"cos_sim_spearman": [{"Model": "flaubert_base_cased", "SICKFr": 53.86, "STS22 (fr)": 65.37, "STSBenchmarkMultilingualSTS (fr)": 37.14}]}, "Summarization": {"cos_sim_spearman": [{"Model": "flaubert_base_cased", "SummEvalFr": 31.26}]}}, "flaubert_base_uncased": {"BitextMining": {"f1": [{"Model": "flaubert_base_uncased"}]}, "Clustering": {"v_measure": [{"Model": "flaubert_base_uncased", "AlloProfClusteringP2P": 43.2, "AlloProfClusteringS2S": 12.94, "HALClusteringS2S": 1.8, "MLSUMClusteringP2P": 33.22, "MLSUMClusteringS2S": 14.9, "MasakhaNEWSClusteringP2P (fra)": 28.49, "MasakhaNEWSClusteringS2S (fra)": 22.58}]}, "Classification": {"accuracy": [{"Model": "flaubert_base_uncased", "AmazonReviewsClassification (fr)": 23.52, "MTOPDomainClassification (fr)": 27.74, "MTOPIntentClassification (fr)": 8.61, "MasakhaNEWSClassification (fra)": 62.61, "MassiveIntentClassification (fr)": 6.24, "MassiveScenarioClassification (fr)": 10.98}]}, "PairClassification": {"cos_sim_ap": [{"Model": "flaubert_base_uncased", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 52.78}]}, "Reranking": {"map": [{"Model": "flaubert_base_uncased", "AlloprofReranking": 34.55, "SyntecReranking": 57.18}]}, "Retrieval": {"ndcg_at_10": [{"Model": "flaubert_base_uncased", "AlloprofRetrieval": 1.72, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 0.51, "SyntecRetrieval": 22.33, "XPQARetrieval (fr)": 9.09}]}, "STS": {"cos_sim_spearman": [{"Model": "flaubert_base_uncased", "SICKFr": 41.9, "STS22 (fr)": 55.15, "STSBenchmarkMultilingualSTS (fr)": 33.41}]}, "Summarization": {"cos_sim_spearman": [{"Model": "flaubert_base_uncased", "SummEvalFr": 29.43}]}}, "flaubert_large_cased": {"BitextMining": {"f1": [{"Model": "flaubert_large_cased"}]}, "Clustering": {"v_measure": [{"Model": "flaubert_large_cased", "AlloProfClusteringP2P": 40.85, "AlloProfClusteringS2S": 21.76, "HALClusteringS2S": 5.26, "MLSUMClusteringP2P": 38.09, "MLSUMClusteringS2S": 18.71, "MasakhaNEWSClusteringP2P (fra)": 26.43, "MasakhaNEWSClusteringS2S (fra)": 24.68}]}, "Classification": {"accuracy": [{"Model": "flaubert_large_cased", "AmazonReviewsClassification (fr)": 22.45, "MTOPDomainClassification (fr)": 24.27, "MTOPIntentClassification (fr)": 9.79, "MasakhaNEWSClassification (fra)": 55.64, "MassiveIntentClassification (fr)": 16.41, "MassiveScenarioClassification (fr)": 22.72}]}, "PairClassification": {"cos_sim_ap": [{"Model": "flaubert_large_cased", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 54.14}]}, "Reranking": {"map": [{"Model": "flaubert_large_cased", "AlloprofReranking": 26.29, "SyntecReranking": 42.8}]}, "Retrieval": {"ndcg_at_10": [{"Model": "flaubert_large_cased", "AlloprofRetrieval": 0.58, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 0.26, "SyntecRetrieval": 1.58, "XPQARetrieval (fr)": 3.69}]}, "STS": {"cos_sim_spearman": [{"Model": "flaubert_large_cased", "SICKFr": 34.6, "STS22 (fr)": 48.52, "STSBenchmarkMultilingualSTS (fr)": 15.66}]}, "Summarization": {"cos_sim_spearman": [{"Model": "flaubert_large_cased", "SummEvalFr": 29.25}]}}, "multi-qa-MiniLM-L6-cos-v1": {"BitextMining": {"f1": [{"Model": "multi-qa-MiniLM-L6-cos-v1"}]}, "Clustering": {"v_measure": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "AlloProfClusteringP2P": 49.13, "AlloProfClusteringS2S": 26.16, "HALClusteringS2S": 12.49, "MLSUMClusteringP2P": 35.15, "MLSUMClusteringS2S": 25.95, "MasakhaNEWSClusteringP2P (fra)": 53.73, "MasakhaNEWSClusteringS2S (fra)": 27.27}]}, "Classification": {"accuracy": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "AmazonReviewsClassification (fr)": 27.05, "MTOPDomainClassification (fr)": 72.97, "MTOPIntentClassification (fr)": 37.18, "MasakhaNEWSClassification (fra)": 75.62, "MassiveIntentClassification (fr)": 42.64, "MassiveScenarioClassification (fr)": 49.92}]}, "PairClassification": {"cos_sim_ap": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 57.36}]}, "Reranking": {"map": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "AlloprofReranking": 40.28, "SyntecReranking": 65.08}]}, "Retrieval": {"ndcg_at_10": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "AlloprofRetrieval": 30.23, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 16.31, "SyntecRetrieval": 58.07, "XPQARetrieval (fr)": 48.83}]}, "STS": {"cos_sim_spearman": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "SICKFr": 62.11, "STS22 (fr)": 74.62, "STSBenchmarkMultilingualSTS (fr)": 63.85}]}, "Summarization": {"cos_sim_spearman": [{"Model": "multi-qa-MiniLM-L6-cos-v1", "SummEvalFr": 27.59}]}}, "sentence-camembert-base": {"BitextMining": {"f1": [{"Model": "sentence-camembert-base"}]}, "Clustering": {"v_measure": [{"Model": "sentence-camembert-base", "AlloProfClusteringP2P": 59.09, "AlloProfClusteringS2S": 38.92, "HALClusteringS2S": 20.22, "MLSUMClusteringP2P": 35.98, "MLSUMClusteringS2S": 27.05, "MasakhaNEWSClusteringP2P (fra)": 36.03, "MasakhaNEWSClusteringS2S (fra)": 30.77}]}, "Classification": {"accuracy": [{"Model": "sentence-camembert-base", "AmazonReviewsClassification (fr)": 36.03, "MTOPDomainClassification (fr)": 77.1, "MTOPIntentClassification (fr)": 43.44, "MasakhaNEWSClassification (fra)": 70.36, "MassiveIntentClassification (fr)": 51.59, "MassiveScenarioClassification (fr)": 61.28}]}, "PairClassification": {"cos_sim_ap": [{"Model": "sentence-camembert-base", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 57.44}]}, "Reranking": {"map": [{"Model": "sentence-camembert-base", "AlloprofReranking": 48.68, "SyntecReranking": 79.75}]}, "Retrieval": {"ndcg_at_10": [{"Model": "sentence-camembert-base", "AlloprofRetrieval": 21.94, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 13.36, "SyntecRetrieval": 68.62, "XPQARetrieval (fr)": 57.92}]}, "STS": {"cos_sim_spearman": [{"Model": "sentence-camembert-base", "SICKFr": 74.18, "STS22 (fr)": 77.54, "STSBenchmarkMultilingualSTS (fr)": 81.64}]}, "Summarization": {"cos_sim_spearman": [{"Model": "sentence-camembert-base", "SummEvalFr": 28.77}]}}, "sentence-camembert-large": {"BitextMining": {"f1": [{"Model": "sentence-camembert-large"}]}, "Clustering": {"v_measure": [{"Model": "sentence-camembert-large", "AlloProfClusteringP2P": 62.69, "AlloProfClusteringS2S": 42.06, "HALClusteringS2S": 23.9, "MLSUMClusteringP2P": 42.04, "MLSUMClusteringS2S": 32.29, "MasakhaNEWSClusteringP2P (fra)": 54.51, "MasakhaNEWSClusteringS2S (fra)": 44.73}]}, "Classification": {"accuracy": [{"Model": "sentence-camembert-large", "AmazonReviewsClassification (fr)": 37.97, "MTOPDomainClassification (fr)": 85.74, "MTOPIntentClassification (fr)": 58.62, "MasakhaNEWSClassification (fra)": 80.62, "MassiveIntentClassification (fr)": 62.65, "MassiveScenarioClassification (fr)": 69.29}]}, "PairClassification": {"cos_sim_ap": [{"Model": "sentence-camembert-large", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 59.59}]}, "Reranking": {"map": [{"Model": "sentence-camembert-large", "AlloprofReranking": 57.62, "SyntecReranking": 88.15}]}, "Retrieval": {"ndcg_at_10": [{"Model": "sentence-camembert-large", "AlloprofRetrieval": 31.62, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 21.87, "SyntecRetrieval": 81.11, "XPQARetrieval (fr)": 65.62}]}, "STS": {"cos_sim_spearman": [{"Model": "sentence-camembert-large", "SICKFr": 77.7, "STS22 (fr)": 81.73, "STSBenchmarkMultilingualSTS (fr)": 85.79}]}, "Summarization": {"cos_sim_spearman": [{"Model": "sentence-camembert-large", "SummEvalFr": 30.88}]}}, "sentence-croissant-llm-base": {"BitextMining": {"f1": [{"Model": "sentence-croissant-llm-base"}]}, "Clustering": {"v_measure": [{"Model": "sentence-croissant-llm-base", "AlloProfClusteringP2P": 64.12, "AlloProfClusteringS2S": 32.52, "HALClusteringS2S": 23.4, "MLSUMClusteringP2P": 42.94, "MLSUMClusteringS2S": 33.91, "MasakhaNEWSClusteringP2P (fra)": 53.94, "MasakhaNEWSClusteringS2S (fra)": 41.05}]}, "Classification": {"accuracy": [{"Model": "sentence-croissant-llm-base", "AmazonReviewsClassification (fr)": 34.79, "MTOPDomainClassification (fr)": 85.52, "MTOPIntentClassification (fr)": 63.12, "MasakhaNEWSClassification (fra)": 79.29, "MassiveIntentClassification (fr)": 59.41, "MassiveScenarioClassification (fr)": 65.29}]}, "PairClassification": {"cos_sim_ap": [{"Model": "sentence-croissant-llm-base", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 63.13}]}, "Reranking": {"map": [{"Model": "sentence-croissant-llm-base", "AlloprofReranking": 53.0, "SyntecReranking": 82.9}]}, "Retrieval": {"ndcg_at_10": [{"Model": "sentence-croissant-llm-base", "AlloprofRetrieval": 29.97, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 21.31, "SyntecRetrieval": 74.2, "XPQARetrieval (fr)": 58.57}]}, "STS": {"cos_sim_spearman": [{"Model": "sentence-croissant-llm-base", "SICKFr": 69.6, "STS22 (fr)": 78.77, "STSBenchmarkMultilingualSTS (fr)": 79.23}]}, "Summarization": {"cos_sim_spearman": [{"Model": "sentence-croissant-llm-base", "SummEvalFr": 29.04}]}}, "silver-retriever-base-v1": {"BitextMining": {"f1": [{"Model": "silver-retriever-base-v1"}]}, "Clustering": {"v_measure": [{"Model": "silver-retriever-base-v1", "8TagsClustering": 31.49}]}, "Classification": {"accuracy": [{"Model": "silver-retriever-base-v1", "AllegroReviews": 33.35, "CBD": 68.51, "MassiveIntentClassification (pl)": 66.63, "MassiveScenarioClassification (pl)": 69.97, "PAC": 66.26, "PolEmo2.0-IN": 63.52, "PolEmo2.0-OUT": 44.7}]}, "PairClassification": {"cos_sim_ap": [{"Model": "silver-retriever-base-v1", "CDSC-E": 67.35, "PPC": 85.33, "PSC": 98.46, "SICK-E-PL": 58.19}]}, "Reranking": {"map": [{"Model": "silver-retriever-base-v1"}]}, "Retrieval": {"ndcg_at_10": [{"Model": "silver-retriever-base-v1", "ArguAna-PL": 44.12, "DBPedia-PL": 26.32, "FiQA-PL": 24.95, "HotpotQA-PL": 45.13, "MSMARCO-PL": 25.47, "NFCorpus-PL": 28.55, "NQ-PL": 37.9, "Quora-PL": 77.98, "SCIDOCS-PL": 10.9, "SciFact-PL": 54.44, "TRECCOVID-PL": 46.98}]}, "STS": {"cos_sim_spearman": [{"Model": "silver-retriever-base-v1", "CDSC-R": 89.09, "SICK-R-PL": 67.26, "STS22 (pl)": 38.69}]}, "Summarization": {"cos_sim_spearman": [{"Model": "silver-retriever-base-v1"}]}}, "text2vec-base-multilingual": {"BitextMining": {"f1": [{"Model": "text2vec-base-multilingual"}]}, "Clustering": {"v_measure": [{"Model": "text2vec-base-multilingual", "AlloProfClusteringP2P": 49.11, "AlloProfClusteringS2S": 32.72, "HALClusteringS2S": 16.19, "MLSUMClusteringP2P": 36.19, "MLSUMClusteringS2S": 30.39, "MasakhaNEWSClusteringP2P (fra)": 38.51, "MasakhaNEWSClusteringS2S (fra)": 32.51}]}, "Classification": {"accuracy": [{"Model": "text2vec-base-multilingual", "AmazonReviewsClassification (fr)": 34.25, "MTOPDomainClassification (fr)": 71.83, "MTOPIntentClassification (fr)": 44.53, "MasakhaNEWSClassification (fra)": 73.84, "MassiveIntentClassification (fr)": 51.93, "MassiveScenarioClassification (fr)": 58.31}]}, "PairClassification": {"cos_sim_ap": [{"Model": "text2vec-base-multilingual", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 65.57}]}, "Reranking": {"map": [{"Model": "text2vec-base-multilingual", "AlloprofReranking": 51.48, "SyntecReranking": 70.28}]}, "Retrieval": {"ndcg_at_10": [{"Model": "text2vec-base-multilingual", "AlloprofRetrieval": 18.9, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 14.81, "SyntecRetrieval": 49.69, "XPQARetrieval (fr)": 40.4}]}, "STS": {"cos_sim_spearman": [{"Model": "text2vec-base-multilingual", "SICKFr": 77.25, "STS22 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{"ndcg_at_10": [{"Model": "universal-sentence-encoder-multilingual-3", "AlloprofRetrieval": 35.27, "BSARDRetrieval": 0.0, "MintakaRetrieval (fr)": 26.12, "SyntecRetrieval": 69.82, "XPQARetrieval (fr)": 59.59}]}, "STS": {"cos_sim_spearman": [{"Model": "universal-sentence-encoder-multilingual-3", "SICKFr": 71.37, "STS22 (fr)": 77.91, "STSBenchmarkMultilingualSTS (fr)": 75.48}]}, "Summarization": {"cos_sim_spearman": [{"Model": "universal-sentence-encoder-multilingual-3", "SummEvalFr": 28.21}]}}, "universal-sentence-encoder-multilingual-large-3": {"BitextMining": {"f1": [{"Model": "universal-sentence-encoder-multilingual-large-3"}]}, "Clustering": {"v_measure": [{"Model": "universal-sentence-encoder-multilingual-large-3", "AlloProfClusteringP2P": 54.21, "AlloProfClusteringS2S": 37.95, "HALClusteringS2S": 18.94, "MLSUMClusteringP2P": 41.02, "MLSUMClusteringS2S": 37.97, "MasakhaNEWSClusteringP2P (fra)": 24.09, "MasakhaNEWSClusteringS2S (fra)": 40.24}]}, "Classification": {"accuracy": 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82.65}]}, "Retrieval": {"ndcg_at_10": [{"Model": "voyage-2", "AlloprofRetrieval": 45.5, "BSARDRetrieval": 0.15, "MintakaRetrieval (fr)": 15.51, "SyntecRetrieval": 75.83, "XPQARetrieval (fr)": 67.07}]}, "STS": {"cos_sim_spearman": [{"Model": "voyage-2", "SICKFr": 68.51, "STS22 (fr)": 70.51, "STSBenchmarkMultilingualSTS (fr)": 76.43}]}, "Summarization": {"cos_sim_spearman": [{"Model": "voyage-2", "SummEvalFr": 30.88}]}}, "voyage-code-2": {"BitextMining": {"f1": [{"Model": "voyage-code-2"}]}, "Clustering": {"v_measure": [{"Model": "voyage-code-2", "AlloProfClusteringP2P": 61.63, "AlloProfClusteringS2S": 50.67, "HALClusteringS2S": 27.44, "MLSUMClusteringP2P": 45.23, "MLSUMClusteringS2S": 41.48, "MasakhaNEWSClusteringP2P (fra)": 56.59, "MasakhaNEWSClusteringS2S (fra)": 35.18}]}, "Classification": {"accuracy": [{"Model": "voyage-code-2", "AmazonReviewsClassification (fr)": 42.15, "MTOPDomainClassification (fr)": 87.68, "MTOPIntentClassification (fr)": 59.44, "MasakhaNEWSClassification 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{"map": [{"Model": "facebook-dpr-ctx_encoder-multiset-base", "AskUbuntuDupQuestions": 47.79, "MindSmallReranking": 28.92, "SciDocsRR": 58.96, "StackOverflowDupQuestions": 33.47}]}, "Retrieval": {"ndcg_at_10": [{"Model": "facebook-dpr-ctx_encoder-multiset-base", "ArguAna": 17.82, "CQADupstackAndroidRetrieval": 12.9, "CQADupstackEnglishRetrieval": 14.2, "CQADupstackGamingRetrieval": 13.02, "CQADupstackGisRetrieval": 9.67, "CQADupstackMathematicaRetrieval": 6.68, "CQADupstackPhysicsRetrieval": 17.08, "CQADupstackProgrammersRetrieval": 9.99, "CQADupstackRetrieval": 10.67, "CQADupstackStatsRetrieval": 10.23, "CQADupstackTexRetrieval": 7.95, "CQADupstackUnixRetrieval": 9.98, "CQADupstackWebmastersRetrieval": 10.03, "CQADupstackWordpressRetrieval": 6.33, "ClimateFEVER": 12.88, "DBPedia": 13.4, "FEVER": 35.78, "FiQA2018": 4.76, "HotpotQA": 22.86, "MSMARCO": 6.62, "NFCorpus": 15.65, "NQ": 13.02, "QuoraRetrieval": 61.42, "SCIDOCS": 6.36, "SciFact": 27.29, "TRECCOVID": 16.46, "Touche2020": 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"google-gecko.text-embedding-preview-0409", "AmazonCounterfactualClassification (en)": 75.34, "AmazonPolarityClassification": 97.34, "AmazonReviewsClassification (en)": 51.17, "Banking77Classification": 88.62, "EmotionClassification": 52.51, "ImdbClassification": 95.65, "MTOPDomainClassification (en)": 98.35, "MTOPIntentClassification (en)": 83.43, "MassiveIntentClassification (en)": 80.22, "MassiveScenarioClassification (en)": 87.19, "ToxicConversationsClassification": 89.67, "TweetSentimentExtractionClassification": 74.52}]}, "PairClassification": {"cos_sim_ap": [{"Model": "google-gecko.text-embedding-preview-0409", "SprintDuplicateQuestions": 96.26, "TwitterSemEval2015": 79.04, "TwitterURLCorpus": 87.53}]}, "Reranking": {"map": [{"Model": "google-gecko.text-embedding-preview-0409", "AskUbuntuDupQuestions": 64.4, "MindSmallReranking": 33.07, "SciDocsRR": 83.59, "StackOverflowDupQuestions": 54.56}]}, "Retrieval": {"ndcg_at_10": [{"Model": "google-gecko.text-embedding-preview-0409", "ArguAna": 62.18, "CQADupstackAndroidRetrieval": 58.31, "CQADupstackEnglishRetrieval": 54.83, "CQADupstackGamingRetrieval": 67.79, "CQADupstackGisRetrieval": 46.01, "CQADupstackMathematicaRetrieval": 37.22, "CQADupstackPhysicsRetrieval": 52.9, "CQADupstackProgrammersRetrieval": 50.82, "CQADupstackRetrieval": 48.89, "CQADupstackStatsRetrieval": 41.24, "CQADupstackTexRetrieval": 35.92, "CQADupstackUnixRetrieval": 50.57, "CQADupstackWebmastersRetrieval": 50.51, "CQADupstackWordpressRetrieval": 40.54, "ClimateFEVER": 33.21, "DBPedia": 47.12, "FEVER": 86.96, "FiQA2018": 59.24, "HotpotQA": 71.33, "MSMARCO": 32.58, "NFCorpus": 40.33, "NQ": 61.28, "QuoraRetrieval": 88.18, "SCIDOCS": 20.34, "SciFact": 75.42, "TRECCOVID": 82.62, "Touche2020": 25.86}]}, "STS": {"cos_sim_spearman": [{"Model": "google-gecko.text-embedding-preview-0409", "BIOSSES": 89.46, "SICK-R": 81.93, "STS12": 77.59, "STS13": 90.36, "STS14": 85.25, "STS15": 89.66, "STS16": 87.34, "STS17 (en-en)": 92.06, "STS22 (en)": 68.02, "STSBenchmark": 88.99}]}, "Summarization": {"cos_sim_spearman": [{"Model": "google-gecko.text-embedding-preview-0409", "SummEval": 32.63}]}}, "google-gecko-256.text-embedding-preview-0409": {"BitextMining": {"f1": [{"Model": "google-gecko-256.text-embedding-preview-0409"}]}, "Clustering": {"v_measure": [{"Model": "google-gecko-256.text-embedding-preview-0409", "ArxivClusteringP2P": 44.12, "ArxivClusteringS2S": 36.54, "BiorxivClusteringP2P": 36.28, "BiorxivClusteringS2S": 33.09, "MedrxivClusteringP2P": 32.08, "MedrxivClusteringS2S": 30.84, "RedditClustering": 62.24, "RedditClusteringP2P": 63.7, "StackExchangeClustering": 70.19, "StackExchangeClusteringP2P": 36.1, "TwentyNewsgroupsClustering": 50.6}]}, "Classification": {"accuracy": [{"Model": "google-gecko-256.text-embedding-preview-0409", "AmazonCounterfactualClassification (en)": 70.93, "AmazonPolarityClassification": 97.34, "AmazonReviewsClassification (en)": 48.47, "Banking77Classification": 86.01, "EmotionClassification": 51.53, "ImdbClassification": 95.7, "MTOPDomainClassification (en)": 98.02, "MTOPIntentClassification (en)": 77.82, "MassiveIntentClassification (en)": 75.67, "MassiveScenarioClassification (en)": 85.16, "ToxicConversationsClassification": 88.33, "TweetSentimentExtractionClassification": 72.97}]}, "PairClassification": {"cos_sim_ap": [{"Model": "google-gecko-256.text-embedding-preview-0409", "SprintDuplicateQuestions": 96.49, "TwitterSemEval2015": 78.23, "TwitterURLCorpus": 87.04}]}, "Reranking": {"map": [{"Model": "google-gecko-256.text-embedding-preview-0409", "AskUbuntuDupQuestions": 63.84, "MindSmallReranking": 31.89, "SciDocsRR": 81.62, "StackOverflowDupQuestions": 53.76}]}, "Retrieval": {"ndcg_at_10": [{"Model": "google-gecko-256.text-embedding-preview-0409", "ArguAna": 56.27, "CQADupstackAndroidRetrieval": 55.76, "CQADupstackEnglishRetrieval": 51.43, "CQADupstackGamingRetrieval": 63.4, "CQADupstackGisRetrieval": 42.93, "CQADupstackMathematicaRetrieval": 32.72, "CQADupstackPhysicsRetrieval": 49.09, "CQADupstackProgrammersRetrieval": 47.53, "CQADupstackRetrieval": 45.41, "CQADupstackStatsRetrieval": 37.68, "CQADupstackTexRetrieval": 32.77, "CQADupstackUnixRetrieval": 47.0, "CQADupstackWebmastersRetrieval": 47.8, "CQADupstackWordpressRetrieval": 36.83, "ClimateFEVER": 29.35, "DBPedia": 41.91, "FEVER": 82.61, "FiQA2018": 55.54, "HotpotQA": 64.65, "MSMARCO": 31.12, "NFCorpus": 37.81, "NQ": 57.37, "QuoraRetrieval": 87.89, "SCIDOCS": 18.21, "SciFact": 70.86, "TRECCOVID": 80.13, "Touche2020": 27.4}]}, "STS": {"cos_sim_spearman": [{"Model": "google-gecko-256.text-embedding-preview-0409", "BIOSSES": 89.42, "SICK-R": 81.67, "STS12": 78.02, "STS13": 90.1, "STS14": 85.44, "STS15": 89.64, "STS16": 87.24, "STS17 (en-en)": 90.46, "STS22 (en)": 67.99, "STSBenchmark": 89.33}]}, "Summarization": {"cos_sim_spearman": [{"Model": "google-gecko-256.text-embedding-preview-0409", "SummEval": 32.36}]}}, "Cohere-embed-english-v3.0": {"BitextMining": {"f1": [{"Model": "Cohere-embed-english-v3.0"}]}, "Clustering": {"v_measure": [{"Model": "Cohere-embed-english-v3.0"}]}, "Classification": {"accuracy": [{"Model": "Cohere-embed-english-v3.0"}]}, "PairClassification": {"cos_sim_ap": [{"Model": "Cohere-embed-english-v3.0"}]}, "Reranking": {"map": [{"Model": "Cohere-embed-english-v3.0"}]}, "Retrieval": {"ndcg_at_10": [{"Model": "Cohere-embed-english-v3.0", "AILACasedocs": 31.54, "AILAStatutes": 27.15, "GerDaLIRSmall": 6.05, "LeCaRDv2": 21.02, "LegalBenchConsumerContractsQA": 77.12, "LegalBenchCorporateLobbying": 93.68, "LegalQuAD": 26.08, "LegalSummarization": 61.7}]}, "STS": {"cos_sim_spearman": [{"Model": "Cohere-embed-english-v3.0"}]}, "Summarization": {"cos_sim_spearman": [{"Model": "Cohere-embed-english-v3.0"}]}}, "bge-large-en-v1.5": {"BitextMining": {"f1": [{"Model": "bge-large-en-v1.5"}]}, "Clustering": {"v_measure": [{"Model": "bge-large-en-v1.5"}]}, "Classification": {"accuracy": [{"Model": "bge-large-en-v1.5"}]}, "PairClassification": {"cos_sim_ap": [{"Model": "bge-large-en-v1.5"}]}, "Reranking": {"map": [{"Model": "bge-large-en-v1.5"}]}, "Retrieval": {"ndcg_at_10": [{"Model": "bge-large-en-v1.5", "AILACasedocs": 25.15, "AILAStatutes": 20.74, "GerDaLIRSmall": 3.96, "LeCaRDv2": 22.68, "LegalBenchConsumerContractsQA": 73.52, "LegalBenchCorporateLobbying": 91.51, "LegalQuAD": 16.22, "LegalSummarization": 59.99}]}, "STS": {"cos_sim_spearman": [{"Model": "bge-large-en-v1.5"}]}, "Summarization": {"cos_sim_spearman": [{"Model": "bge-large-en-v1.5"}]}}, "e5-mistral-7b-instruct": {"BitextMining": {"f1": [{"Model": "e5-mistral-7b-instruct"}]}, "Clustering": {"v_measure": [{"Model": "e5-mistral-7b-instruct", "AlloProfClusteringP2P": 61.06, "AlloProfClusteringS2S": 28.12, "HALClusteringS2S": 19.69, "MLSUMClusteringP2P": 45.59, "MLSUMClusteringS2S": 32.0, "MasakhaNEWSClusteringP2P (fra)": 52.47, "MasakhaNEWSClusteringS2S (fra)": 49.2}]}, "Classification": {"accuracy": [{"Model": "e5-mistral-7b-instruct", "AmazonReviewsClassification (fr)": 36.71, "MTOPDomainClassification (fr)": 74.8, "MTOPIntentClassification (fr)": 53.97, "MasakhaNEWSClassification (fra)": 80.59, "MassiveIntentClassification (fr)": 46.39, "MassiveScenarioClassification (fr)": 53.86}]}, "PairClassification": {"cos_sim_ap": [{"Model": "e5-mistral-7b-instruct", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 63.65}]}, "Reranking": {"map": [{"Model": "e5-mistral-7b-instruct", "AlloprofReranking": 47.36, "SyntecReranking": 77.05}]}, "Retrieval": {"ndcg_at_10": [{"Model": "e5-mistral-7b-instruct", "AILACasedocs": 38.76, "AILAStatutes": 38.07, "AlloprofRetrieval": 16.46, "BSARDRetrieval": 0.0, "GerDaLIRSmall": 37.18, "LeCaRDv2": 68.56, "LegalBenchConsumerContractsQA": 75.46, "LegalBenchCorporateLobbying": 94.01, "LegalQuAD": 59.64, "LegalSummarization": 66.51, "MintakaRetrieval (fr)": 3.57, "SyntecRetrieval": 55.9, "XPQARetrieval (fr)": 41.29}]}, "STS": {"cos_sim_spearman": [{"Model": "e5-mistral-7b-instruct", "SICKFr": 64.39, "STS22 (fr)": 69.82, "STSBenchmarkMultilingualSTS (fr)": 61.87}]}, "Summarization": {"cos_sim_spearman": [{"Model": "e5-mistral-7b-instruct", "SummEvalFr": 32.22}]}}, "mistral-embed": {"BitextMining": {"f1": [{"Model": "mistral-embed"}]}, "Clustering": {"v_measure": [{"Model": "mistral-embed", "AlloProfClusteringP2P": 62.01, "AlloProfClusteringS2S": 49.2, "HALClusteringS2S": 26.17, "MLSUMClusteringP2P": 45.28, "MLSUMClusteringS2S": 42.74, "MasakhaNEWSClusteringP2P (fra)": 48.13, "MasakhaNEWSClusteringS2S (fra)": 39.62}]}, "Classification": {"accuracy": [{"Model": "mistral-embed", "AmazonReviewsClassification (fr)": 41.59, "MTOPDomainClassification (fr)": 90.05, "MTOPIntentClassification (fr)": 66.09, "MasakhaNEWSClassification (fra)": 81.4, "MassiveIntentClassification (fr)": 62.83, "MassiveScenarioClassification (fr)": 69.71}]}, "PairClassification": {"cos_sim_ap": [{"Model": "mistral-embed", "OpusparcusPC (fr)": 100.0, "PawsX (fr)": 62.02}]}, "Reranking": {"map": [{"Model": "mistral-embed", "AlloprofReranking": 72.36, "SyntecReranking": 88.57}]}, "Retrieval": {"ndcg_at_10": [{"Model": "mistral-embed", "AILACasedocs": 38.2, "AILAStatutes": 44.81, "AlloprofRetrieval": 56.84, "BSARDRetrieval": 2.48, "GerDaLIRSmall": 17.85, "LeCaRDv2": 61.12, "LegalBenchConsumerContractsQA": 80.8, "LegalBenchCorporateLobbying": 94.11, "LegalQuAD": 47.17, "LegalSummarization": 67.39, "MintakaRetrieval (fr)": 21.73, "SyntecRetrieval": 78.77, "XPQARetrieval (fr)": 74.24}]}, "STS": {"cos_sim_spearman": [{"Model": "mistral-embed", "SICKFr": 76.21, "STS22 (fr)": 82.74, "STSBenchmarkMultilingualSTS (fr)": 79.72}]}, "Summarization": {"cos_sim_spearman": [{"Model": "mistral-embed", "SummEvalFr": 31.47}]}}, "text-embedding-3-large": {"BitextMining": {"f1": [{"Model": "text-embedding-3-large"}]}, "Clustering": {"v_measure": [{"Model": "text-embedding-3-large", "ArxivClusteringP2P": 49.01, "ArxivClusteringS2S": 44.45, "BiorxivClusteringP2P": 38.03, "BiorxivClusteringS2S": 36.53, "MedrxivClusteringP2P": 32.7, "MedrxivClusteringS2S": 31.27, "RedditClustering": 67.84, "RedditClusteringP2P": 67.96, "StackExchangeClustering": 76.26, "StackExchangeClusteringP2P": 36.88, "TwentyNewsgroupsClustering": 58.14}]}, "Classification": {"accuracy": [{"Model": "text-embedding-3-large", "AmazonCounterfactualClassification (en)": 78.93, "AmazonPolarityClassification": 92.85, "AmazonReviewsClassification (en)": 48.7, "Banking77Classification": 85.69, "EmotionClassification": 51.58, "ImdbClassification": 87.67, "MTOPDomainClassification (en)": 95.36, "MTOPIntentClassification (en)": 75.07, "MassiveIntentClassification (en)": 74.64, "MassiveScenarioClassification (en)": 79.79, "ToxicConversationsClassification": 72.92, "TweetSentimentExtractionClassification": 62.22}]}, "PairClassification": {"cos_sim_ap": [{"Model": "text-embedding-3-large", "SprintDuplicateQuestions": 92.25, "TwitterSemEval2015": 77.13, "TwitterURLCorpus": 87.78}]}, "Reranking": {"map": [{"Model": "text-embedding-3-large", "AskUbuntuDupQuestions": 65.03, "MindSmallReranking": 29.86, "SciDocsRR": 86.66, "StackOverflowDupQuestions": 55.08}]}, "Retrieval": {"ndcg_at_10": [{"Model": "text-embedding-3-large", "AILACasedocs": 39.0, "AILAStatutes": 41.31, "ArguAna": 58.05, "CQADupstackAndroidRetrieval": 57.28, "CQADupstackEnglishRetrieval": 55.77, "CQADupstackGamingRetrieval": 65.4, "CQADupstackGisRetrieval": 45.63, "CQADupstackMathematicaRetrieval": 36.99, "CQADupstackPhysicsRetrieval": 52.16, "CQADupstackProgrammersRetrieval": 49.09, "CQADupstackRetrieval": 47.54, "CQADupstackStatsRetrieval": 40.67, "CQADupstackTexRetrieval": 34.56, "CQADupstackUnixRetrieval": 50.02, "CQADupstackWebmastersRetrieval": 45.28, "CQADupstackWordpressRetrieval": 37.57, "ClimateFEVER": 30.27, "DBPedia": 44.76, "FEVER": 87.94, "FiQA2018": 55.0, "GerDaLIRSmall": 32.77, "HotpotQA": 71.58, "LeCaRDv2": 57.2, "LegalBenchConsumerContractsQA": 79.39, "LegalBenchCorporateLobbying": 95.09, "LegalQuAD": 57.47, "LegalSummarization": 71.55, "MSMARCO": 40.24, "NFCorpus": 42.07, "NQ": 61.27, "QuoraRetrieval": 89.05, "SCIDOCS": 23.11, "SciFact": 77.77, "TRECCOVID": 79.56, "Touche2020": 23.35}]}, "STS": {"cos_sim_spearman": [{"Model": "text-embedding-3-large", "BIOSSES": 84.68, "SICK-R": 79.0, "STS12": 72.84, "STS13": 86.1, "STS14": 81.15, "STS15": 88.49, "STS16": 85.08, "STS17 (en-en)": 90.22, "STS22 (en)": 66.14, "STSBenchmark": 83.56}]}, "Summarization": {"cos_sim_spearman": [{"Model": "text-embedding-3-large", "SummEval": 29.92}]}}, "voyage-law-2": {"BitextMining": {"f1": [{"Model": "voyage-law-2"}]}, "Clustering": {"v_measure": [{"Model": "voyage-law-2"}]}, "Classification": {"accuracy": [{"Model": "voyage-law-2"}]}, "PairClassification": {"cos_sim_ap": [{"Model": "voyage-law-2"}]}, "Reranking": {"map": [{"Model": "voyage-law-2"}]}, "Retrieval": {"ndcg_at_10": [{"Model": "voyage-law-2", "AILACasedocs": 44.56, "AILAStatutes": 45.51, "GerDaLIRSmall": 44.91, "LeCaRDv2": 72.75, "LegalBenchConsumerContractsQA": 83.27, "LegalBenchCorporateLobbying": 95.66, "LegalQuAD": 67.47, "LegalSummarization": 68.96}]}, "STS": {"cos_sim_spearman": [{"Model": "voyage-law-2"}]}, "Summarization": {"cos_sim_spearman": [{"Model": "voyage-law-2"}]}}} \ No newline at end of file diff --git a/README.md b/README.md index 6791872629847398b8dea5204ffeadca5ab983ed..872a038c0d3e4ee4bd54197df01f2c5b0e423e35 100644 --- a/README.md +++ b/README.md @@ -12,30 +12,3 @@ tags: startup_duration_timeout: 1h fullWidth: true --- - -## The MTEB Leaderboard repository - -This repository contains the code for pushing and updating the MTEB leaderboard daily. - -| Relevant Links | Decription | -|------------------------------------------|------------------------------| -| [mteb](https://github.com/embeddings-benchmark/mteb) | The implementation of the benchmark. Here you e.g. find the code to run your model on the benchmark. | -| [leaderboard](https://huggingface.co/spaces/mteb/leaderboard) | The leaderboard itself, here you can view results of model run on MTEB. | -| [results](https://github.com/embeddings-benchmark/results) | The results of MTEB is stored here. Though you can publish them to the leaderboard [adding](https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md) the result to your model card. | - -## Developer setup - -To setup the repository: - -```bash -git clone https://github.com/embeddings-benchmark/leaderboard.git -cd leaderboard -# install requirements -pip install -r requirements.txt -# fetch new results -# python refresh.py -# if you'd like to add results to previously cached models, you may have to remove these models in `EXTERNAL_MODEL_RESULTS.json` -# you can also directly delete `EXTERNAL_MODEL_RESULTS.json` and it will recreate it (but be much slower) -# run the leaderboard -python app.py -``` diff --git a/all_data_tasks/0/default.jsonl b/all_data_tasks/0/default.jsonl deleted file mode 100644 index be1cb8813f4f1479856a99fb99dd9e91c97ecae1..0000000000000000000000000000000000000000 --- a/all_data_tasks/0/default.jsonl +++ /dev/null @@ -1,331 +0,0 @@ -{"index":249,"Rank":1,"Model":"NV-Embed-v2<\/a>","Model Size (Million Parameters)":7851,"Memory Usage (GB, fp32)":29.25,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":90.37,"AmazonCounterfactualClassification (en)":94.28,"AmazonPolarityClassification":97.74,"AmazonReviewsClassification (en)":63.96,"Banking77Classification":92.42,"EmotionClassification":93.38,"ImdbClassification":97.14,"MassiveIntentClassification (en)":86.1,"MassiveScenarioClassification (en)":92.17,"MTOPDomainClassification (en)":99.25,"MTOPIntentClassification (en)":94.37,"ToxicConversationsClassification":92.74,"TweetSentimentExtractionClassification":80.87} -{"index":112,"Rank":2,"Model":"SFR-Embedding-2_R<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":89.05,"AmazonCounterfactualClassification (en)":92.72,"AmazonPolarityClassification":97.31,"AmazonReviewsClassification (en)":61.04,"Banking77Classification":90.02,"EmotionClassification":93.37,"ImdbClassification":96.8,"MassiveIntentClassification (en)":85.97,"MassiveScenarioClassification (en)":90.61,"MTOPDomainClassification (en)":98.58,"MTOPIntentClassification (en)":91.3,"ToxicConversationsClassification":91.14,"TweetSentimentExtractionClassification":79.7} -{"index":24,"Rank":3,"Model":"bge-en-icl<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":88.95,"AmazonCounterfactualClassification (en)":93.15,"AmazonPolarityClassification":96.98,"AmazonReviewsClassification (en)":61.46,"Banking77Classification":91.49,"EmotionClassification":93.36,"ImdbClassification":96.91,"MassiveIntentClassification (en)":82.93,"MassiveScenarioClassification (en)":85.6,"MTOPDomainClassification (en)":98.42,"MTOPIntentClassification (en)":94.0,"ToxicConversationsClassification":93.17,"TweetSentimentExtractionClassification":79.93} -{"index":27,"Rank":4,"Model":"bge-multilingual-gemma2<\/a>","Model Size (Million Parameters)":9242,"Memory Usage (GB, fp32)":34.43,"Embedding Dimensions":3584,"Max Tokens":8192,"Average":88.08,"AmazonCounterfactualClassification (en)":89.48,"AmazonPolarityClassification":96.9,"AmazonReviewsClassification (en)":61.6,"Banking77Classification":92.53,"EmotionClassification":92.97,"ImdbClassification":96.66,"MassiveIntentClassification (en)":82.05,"MassiveScenarioClassification (en)":84.4,"MTOPDomainClassification (en)":98.61,"MTOPIntentClassification (en)":95.51,"ToxicConversationsClassification":87.34,"TweetSentimentExtractionClassification":78.86} -{"index":165,"Rank":5,"Model":"stella_en_1.5B_v5<\/a>","Model Size (Million Parameters)":1543,"Memory Usage (GB, fp32)":5.75,"Embedding Dimensions":8192,"Max Tokens":131072,"Average":87.63,"AmazonCounterfactualClassification (en)":92.87,"AmazonPolarityClassification":97.16,"AmazonReviewsClassification (en)":59.36,"Banking77Classification":89.79,"EmotionClassification":84.29,"ImdbClassification":96.66,"MassiveIntentClassification (en)":85.83,"MassiveScenarioClassification (en)":90.2,"MTOPDomainClassification (en)":99.01,"MTOPIntentClassification (en)":92.78,"ToxicConversationsClassification":88.76,"TweetSentimentExtractionClassification":74.84} -{"index":248,"Rank":6,"Model":"NV-Embed-v1<\/a>","Model Size (Million Parameters)":7851,"Memory Usage (GB, fp32)":29.25,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":87.35,"AmazonCounterfactualClassification (en)":95.12,"AmazonPolarityClassification":97.14,"AmazonReviewsClassification (en)":55.47,"Banking77Classification":90.34,"EmotionClassification":91.7,"ImdbClassification":97.06,"MassiveIntentClassification (en)":80.07,"MassiveScenarioClassification (en)":81.74,"MTOPDomainClassification (en)":96.51,"MTOPIntentClassification (en)":89.77,"ToxicConversationsClassification":92.6,"TweetSentimentExtractionClassification":80.64} -{"index":166,"Rank":7,"Model":"stella_en_400M_v5<\/a>","Model Size (Million Parameters)":435,"Memory Usage (GB, fp32)":1.62,"Embedding Dimensions":8192,"Max Tokens":8192,"Average":86.67,"AmazonCounterfactualClassification (en)":92.36,"AmazonPolarityClassification":97.19,"AmazonReviewsClassification (en)":59.53,"Banking77Classification":89.3,"EmotionClassification":78.77,"ImdbClassification":96.49,"MassiveIntentClassification (en)":85.17,"MassiveScenarioClassification (en)":89.62,"MTOPDomainClassification (en)":98.83,"MTOPIntentClassification (en)":92.3,"ToxicConversationsClassification":86.94,"TweetSentimentExtractionClassification":73.58} -{"index":302,"Rank":8,"Model":"gte-Qwen2-7B-instruct-GGUF<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":"","Max Tokens":"","Average":86.58,"AmazonCounterfactualClassification (en)":91.31,"AmazonPolarityClassification":97.5,"AmazonReviewsClassification 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-{"index":371,"Rank":374,"Model":"Zeta-Alpha-E5-Mistral<\/a>","Model Size (Million Parameters)":7111,"Memory Usage (GB, fp32)":26.49,"Embedding Dimensions":4096,"Max Tokens":32768,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":372,"Rank":375,"Model":"bi-cse<\/a>","Model Size (Million Parameters)":560,"Memory Usage (GB, fp32)":2.09,"Embedding Dimensions":1024,"Max Tokens":514,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":374,"Rank":377,"Model":"text-similarity-ada-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1024,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":375,"Rank":378,"Model":"text-similarity-babbage-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":2048,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":376,"Rank":379,"Model":"text-similarity-curie-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":4096,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":377,"Rank":380,"Model":"text-similarity-davinci-001<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":12288,"Max Tokens":2046,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":379,"Rank":381,"Model":"text-embedding-3-large-256<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":256,"Max Tokens":8191,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":380,"Rank":382,"Model":"text-embedding-3-large<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":3072,"Max Tokens":8191,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":381,"Rank":383,"Model":"text-embedding-3-small<\/a>","Model Size (Million Parameters)":"","Memory Usage (GB, fp32)":"","Embedding Dimensions":1536,"Max Tokens":8191,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} -{"index":383,"Rank":384,"Model":"elser-v2<\/a>","Model Size (Million Parameters)":110,"Memory Usage (GB, fp32)":0.41,"Embedding Dimensions":30522,"Max Tokens":512,"Average":"","CLSClusteringP2P":"","CLSClusteringS2S":"","ThuNewsClusteringP2P":"","ThuNewsClusteringS2S":""} diff --git a/app.py b/app.py index b6d912991ea6655433b3a2562311a795825081aa..cb4dd814c96a8f662a9005b49f0b119ed98d0adc 100644 --- a/app.py +++ b/app.py @@ -1,40 +1,1263 @@ -from functools import reduce +from functools import partial, reduce +import json +import os import re +from datasets import load_dataset import gradio as gr +from huggingface_hub import HfApi, hf_hub_download +from huggingface_hub.repocard import metadata_load import pandas as pd +from tqdm.autonotebook import tqdm -from envs import REPO_ID -from refresh import BOARDS_CONFIG, TASKS, TASKS_CONFIG, TASK_DESCRIPTIONS, PRETTY_NAMES, load_results, make_clickable_model -from refresh import PROPRIETARY_MODELS, SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS, CROSS_ENCODERS, BI_ENCODERS, INSTRUCT_MODELS, NOINSTRUCT_MODELS, EXTERNAL_MODEL_TO_LINK +from utils.model_size import get_model_parameters_memory +TASKS = [ + "BitextMining", + "Classification", + "Clustering", + "PairClassification", + "Reranking", + "Retrieval", + "STS", + "Summarization", +] + +TASK_LIST_BITEXT_MINING = ['BUCC (de-en)', 'BUCC (fr-en)', 'BUCC (ru-en)', 'BUCC (zh-en)', 'Tatoeba (afr-eng)', 'Tatoeba (amh-eng)', 'Tatoeba (ang-eng)', 'Tatoeba (ara-eng)', 'Tatoeba (arq-eng)', 'Tatoeba (arz-eng)', 'Tatoeba (ast-eng)', 'Tatoeba (awa-eng)', 'Tatoeba (aze-eng)', 'Tatoeba (bel-eng)', 'Tatoeba (ben-eng)', 'Tatoeba (ber-eng)', 'Tatoeba (bos-eng)', 'Tatoeba (bre-eng)', 'Tatoeba (bul-eng)', 'Tatoeba (cat-eng)', 'Tatoeba (cbk-eng)', 'Tatoeba (ceb-eng)', 'Tatoeba (ces-eng)', 'Tatoeba (cha-eng)', 'Tatoeba (cmn-eng)', 'Tatoeba (cor-eng)', 'Tatoeba (csb-eng)', 'Tatoeba (cym-eng)', 'Tatoeba (dan-eng)', 'Tatoeba (deu-eng)', 'Tatoeba (dsb-eng)', 'Tatoeba (dtp-eng)', 'Tatoeba (ell-eng)', 'Tatoeba (epo-eng)', 'Tatoeba (est-eng)', 'Tatoeba (eus-eng)', 'Tatoeba (fao-eng)', 'Tatoeba (fin-eng)', 'Tatoeba (fra-eng)', 'Tatoeba (fry-eng)', 'Tatoeba (gla-eng)', 'Tatoeba (gle-eng)', 'Tatoeba (glg-eng)', 'Tatoeba (gsw-eng)', 'Tatoeba (heb-eng)', 'Tatoeba (hin-eng)', 'Tatoeba (hrv-eng)', 'Tatoeba (hsb-eng)', 'Tatoeba (hun-eng)', 'Tatoeba (hye-eng)', 'Tatoeba (ido-eng)', 'Tatoeba (ile-eng)', 'Tatoeba (ina-eng)', 'Tatoeba (ind-eng)', 'Tatoeba (isl-eng)', 'Tatoeba (ita-eng)', 'Tatoeba (jav-eng)', 'Tatoeba (jpn-eng)', 'Tatoeba (kab-eng)', 'Tatoeba (kat-eng)', 'Tatoeba (kaz-eng)', 'Tatoeba (khm-eng)', 'Tatoeba (kor-eng)', 'Tatoeba (kur-eng)', 'Tatoeba (kzj-eng)', 'Tatoeba (lat-eng)', 'Tatoeba (lfn-eng)', 'Tatoeba (lit-eng)', 'Tatoeba (lvs-eng)', 'Tatoeba (mal-eng)', 'Tatoeba (mar-eng)', 'Tatoeba (max-eng)', 'Tatoeba (mhr-eng)', 'Tatoeba (mkd-eng)', 'Tatoeba (mon-eng)', 'Tatoeba (nds-eng)', 'Tatoeba (nld-eng)', 'Tatoeba (nno-eng)', 'Tatoeba (nob-eng)', 'Tatoeba (nov-eng)', 'Tatoeba (oci-eng)', 'Tatoeba (orv-eng)', 'Tatoeba (pam-eng)', 'Tatoeba (pes-eng)', 'Tatoeba (pms-eng)', 'Tatoeba (pol-eng)', 'Tatoeba (por-eng)', 'Tatoeba (ron-eng)', 'Tatoeba (rus-eng)', 'Tatoeba (slk-eng)', 'Tatoeba (slv-eng)', 'Tatoeba (spa-eng)', 'Tatoeba (sqi-eng)', 'Tatoeba (srp-eng)', 'Tatoeba (swe-eng)', 'Tatoeba (swg-eng)', 'Tatoeba (swh-eng)', 'Tatoeba (tam-eng)', 'Tatoeba (tat-eng)', 'Tatoeba (tel-eng)', 'Tatoeba (tgl-eng)', 'Tatoeba (tha-eng)', 'Tatoeba (tuk-eng)', 'Tatoeba (tur-eng)', 'Tatoeba (tzl-eng)', 'Tatoeba (uig-eng)', 'Tatoeba (ukr-eng)', 'Tatoeba (urd-eng)', 'Tatoeba (uzb-eng)', 'Tatoeba (vie-eng)', 'Tatoeba (war-eng)', 'Tatoeba (wuu-eng)', 'Tatoeba (xho-eng)', 'Tatoeba (yid-eng)', 'Tatoeba (yue-eng)', 'Tatoeba (zsm-eng)'] +TASK_LIST_BITEXT_MINING_DA = ["BornholmBitextMining"] + +TASK_LIST_CLASSIFICATION = [ + "AmazonCounterfactualClassification (en)", + "AmazonPolarityClassification", + "AmazonReviewsClassification (en)", + "Banking77Classification", + "EmotionClassification", + "ImdbClassification", + "MassiveIntentClassification (en)", + "MassiveScenarioClassification (en)", + "MTOPDomainClassification (en)", + "MTOPIntentClassification (en)", + "ToxicConversationsClassification", + "TweetSentimentExtractionClassification", +] + +TASK_LIST_CLASSIFICATION_DA = [ + "AngryTweetsClassification", + "DanishPoliticalCommentsClassification", + "DKHateClassification", + "LccSentimentClassification", + "MassiveIntentClassification (da)", + "MassiveScenarioClassification (da)", + "NordicLangClassification", + "ScalaDaClassification", +] + +TASK_LIST_CLASSIFICATION_FR = [ + "AmazonReviewsClassification (fr)", + "MasakhaNEWSClassification (fra)", + "MassiveIntentClassification (fr)", + "MassiveScenarioClassification (fr)", + "MTOPDomainClassification (fr)", + "MTOPIntentClassification (fr)", +] + +TASK_LIST_CLASSIFICATION_NB = [ + "NoRecClassification", + "NordicLangClassification", + "NorwegianParliament", + "MassiveIntentClassification (nb)", + "MassiveScenarioClassification (nb)", + "ScalaNbClassification", +] + +TASK_LIST_CLASSIFICATION_PL = [ + "AllegroReviews", + "CBD", + "MassiveIntentClassification (pl)", + "MassiveScenarioClassification (pl)", + "PAC", + "PolEmo2.0-IN", + "PolEmo2.0-OUT", +] + +TASK_LIST_CLASSIFICATION_SV = [ + "DalajClassification", + "MassiveIntentClassification (sv)", + "MassiveScenarioClassification (sv)", + "NordicLangClassification", + "ScalaSvClassification", + "SweRecClassification", +] + +TASK_LIST_CLASSIFICATION_ZH = [ + "AmazonReviewsClassification (zh)", + "IFlyTek", + "JDReview", + "MassiveIntentClassification (zh-CN)", + "MassiveScenarioClassification (zh-CN)", + "MultilingualSentiment", + "OnlineShopping", + "TNews", + "Waimai", +] + +TASK_LIST_CLASSIFICATION_OTHER = ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)'] + +TASK_LIST_CLUSTERING = [ + "ArxivClusteringP2P", + "ArxivClusteringS2S", + "BiorxivClusteringP2P", + "BiorxivClusteringS2S", + "MedrxivClusteringP2P", + "MedrxivClusteringS2S", + "RedditClustering", + "RedditClusteringP2P", + "StackExchangeClustering", + "StackExchangeClusteringP2P", + "TwentyNewsgroupsClustering", +] + + +TASK_LIST_CLUSTERING_DE = [ + "BlurbsClusteringP2P", + "BlurbsClusteringS2S", + "TenKGnadClusteringP2P", + "TenKGnadClusteringS2S", +] + +TASK_LIST_CLUSTERING_FR = [ + "AlloProfClusteringP2P", + "AlloProfClusteringS2S", + "HALClusteringS2S", + "MLSUMClusteringP2P", + "MLSUMClusteringS2S", + "MasakhaNEWSClusteringP2P (fra)", + "MasakhaNEWSClusteringS2S (fra)", +] + +TASK_LIST_CLUSTERING_PL = [ + "8TagsClustering", +] + +TASK_LIST_CLUSTERING_ZH = [ + "CLSClusteringP2P", + "CLSClusteringS2S", + "ThuNewsClusteringP2P", + "ThuNewsClusteringS2S", +] + +TASK_LIST_PAIR_CLASSIFICATION = [ + "SprintDuplicateQuestions", + "TwitterSemEval2015", + "TwitterURLCorpus", +] + +TASK_LIST_PAIR_CLASSIFICATION_FR = [ + "OpusparcusPC (fr)", + "PawsX (fr)", +] + +TASK_LIST_PAIR_CLASSIFICATION_PL = [ + "CDSC-E", + "PPC", + "PSC", + "SICK-E-PL", +] + +TASK_LIST_PAIR_CLASSIFICATION_ZH = [ + "Cmnli", + "Ocnli", +] + +TASK_LIST_RERANKING = [ + "AskUbuntuDupQuestions", + "MindSmallReranking", + "SciDocsRR", + "StackOverflowDupQuestions", +] + +TASK_LIST_RERANKING_FR = [ + "AlloprofReranking", + "SyntecReranking", +] + +TASK_LIST_RERANKING_ZH = [ + "CMedQAv1", + "CMedQAv2", + "MMarcoReranking", + "T2Reranking", +] + +TASK_LIST_RETRIEVAL = [ + "ArguAna", + "ClimateFEVER", + "CQADupstackRetrieval", + "DBPedia", + "FEVER", + "FiQA2018", + "HotpotQA", + "MSMARCO", + "NFCorpus", + "NQ", + "QuoraRetrieval", + "SCIDOCS", + "SciFact", + "Touche2020", + "TRECCOVID", +] + +TASK_LIST_RETRIEVAL_FR = [ + "AlloprofRetrieval", + "BSARDRetrieval", + "MintakaRetrieval (fr)", +# "MultiLongDocRetrieval", + "SyntecRetrieval", + "XPQARetrieval (fr)", +] + +TASK_LIST_RETRIEVAL_LAW = [ + "AILACasedocs", + "AILAStatutes", + "GerDaLIRSmall", + "LeCaRDv2", + "LegalBenchConsumerContractsQA", + "LegalBenchCorporateLobbying", + "LegalQuAD", + "LegalSummarization", +] + +TASK_LIST_RETRIEVAL_PL = [ + "ArguAna-PL", + "DBPedia-PL", + "FiQA-PL", + "HotpotQA-PL", + "MSMARCO-PL", + "NFCorpus-PL", + "NQ-PL", + "Quora-PL", + "SCIDOCS-PL", + "SciFact-PL", + "TRECCOVID-PL", +] + +TASK_LIST_RETRIEVAL_ZH = [ + "CmedqaRetrieval", + "CovidRetrieval", + "DuRetrieval", + "EcomRetrieval", + "MedicalRetrieval", + "MMarcoRetrieval", + "T2Retrieval", + "VideoRetrieval", +] + +TASK_LIST_RETRIEVAL_NORM = TASK_LIST_RETRIEVAL + [ + "CQADupstackAndroidRetrieval", + "CQADupstackEnglishRetrieval", + "CQADupstackGamingRetrieval", + "CQADupstackGisRetrieval", + "CQADupstackMathematicaRetrieval", + "CQADupstackPhysicsRetrieval", + "CQADupstackProgrammersRetrieval", + "CQADupstackStatsRetrieval", + "CQADupstackTexRetrieval", + "CQADupstackUnixRetrieval", + "CQADupstackWebmastersRetrieval", + "CQADupstackWordpressRetrieval" +] + +TASK_LIST_STS = [ + "BIOSSES", + "SICK-R", + "STS12", + "STS13", + "STS14", + "STS15", + "STS16", + "STS17 (en-en)", + "STS22 (en)", + "STSBenchmark", +] + +TASK_LIST_STS_FR = [ + "STS22 (fr)", + "STSBenchmarkMultilingualSTS (fr)", + "SICKFr", +] + +TASK_LIST_STS_PL = [ + "CDSC-R", + "SICK-R-PL", + "STS22 (pl)", +] + +TASK_LIST_STS_ZH = [ + "AFQMC", + "ATEC", + "BQ", + "LCQMC", + "PAWSX", + "QBQTC", + "STS22 (zh)", + "STSB", +] + +TASK_LIST_STS_OTHER = ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark",] + +TASK_LIST_SUMMARIZATION = ["SummEval",] + +TASK_LIST_SUMMARIZATION_FR = ["SummEvalFr"] + +TASK_LIST_EN = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION +TASK_LIST_FR = TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLUSTERING_FR + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_RERANKING_FR + TASK_LIST_RETRIEVAL_FR + TASK_LIST_STS_FR + TASK_LIST_SUMMARIZATION_FR +TASK_LIST_PL = TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL +TASK_LIST_ZH = TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH + +TASK_TO_METRIC = { + "BitextMining": "f1", + "Clustering": "v_measure", + "Classification": "accuracy", + "PairClassification": "cos_sim_ap", + "Reranking": "map", + "Retrieval": "ndcg_at_10", + "STS": "cos_sim_spearman", + "Summarization": "cos_sim_spearman", +} + +def make_clickable_model(model_name, link=None): + if link is None: + link = "https://huggingface.co/" + model_name + # Remove user from model name + return ( + f'{model_name.split("/")[-1]}' + ) + +# Models without metadata, thus we cannot fetch their results naturally +EXTERNAL_MODELS = [ + "Baichuan-text-embedding", + "Cohere-embed-english-v3.0", + "Cohere-embed-multilingual-v3.0", + "Cohere-embed-multilingual-light-v3.0", + "DanskBERT", + "LASER2", + "LaBSE", + "OpenSearch-text-hybrid", + "all-MiniLM-L12-v2", + "all-MiniLM-L6-v2", + "all-mpnet-base-v2", + "allenai-specter", + "bert-base-10lang-cased", + "bert-base-15lang-cased", + "bert-base-25lang-cased", + "bert-base-multilingual-cased", + "bert-base-multilingual-uncased", + "bert-base-swedish-cased", + "bert-base-uncased", + "bge-base-zh-v1.5", + "bge-large-en-v1.5", + "bge-large-zh-v1.5", + "bge-large-zh-noinstruct", + "bge-small-zh-v1.5", + "contriever-base-msmarco", + "cross-en-de-roberta-sentence-transformer", + "dfm-encoder-large-v1", + "dfm-sentence-encoder-large-1", + "distiluse-base-multilingual-cased-v2", + "e5-base", + "e5-large", + "e5-mistral-7b-instruct", + "e5-small", + "electra-small-nordic", + "electra-small-swedish-cased-discriminator", + "flaubert_base_cased", + "flaubert_base_uncased", + "flaubert_large_cased", + "gbert-base", + "gbert-large", + "gelectra-base", + "gelectra-large", + "glove.6B.300d", + "google-gecko.text-embedding-preview-0409", + "google-gecko-256.text-embedding-preview-0409", + "gottbert-base", + "gtr-t5-base", + "gtr-t5-large", + "gtr-t5-xl", + "gtr-t5-xxl", + "herbert-base-retrieval-v2", + "komninos", + "luotuo-bert-medium", + "m3e-base", + "m3e-large", + "mistral-embed", + "msmarco-bert-co-condensor", + "multi-qa-MiniLM-L6-cos-v1", + "multilingual-e5-base", + "multilingual-e5-large", + "multilingual-e5-small", + "nb-bert-base", + "nb-bert-large", + "nomic-embed-text-v1.5-64", + "nomic-embed-text-v1.5-128", + "nomic-embed-text-v1.5-256", + "nomic-embed-text-v1.5-512", + "norbert3-base", + "norbert3-large", + "paraphrase-multilingual-MiniLM-L12-v2", + "paraphrase-multilingual-mpnet-base-v2", + "sentence-bert-swedish-cased", + "sentence-camembert-base", + "sentence-camembert-large", + "sentence-croissant-llm-base", + "sentence-t5-base", + "sentence-t5-large", + "sentence-t5-xl", + "sentence-t5-xxl", + "silver-retriever-base-v1", + "sup-simcse-bert-base-uncased", + "st-polish-paraphrase-from-distilroberta", + "st-polish-paraphrase-from-mpnet", + "text2vec-base-chinese", + "text2vec-base-multilingual", + "text2vec-large-chinese", + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-3-large-256", + "text-embedding-ada-002", + "text-similarity-ada-001", + "text-similarity-babbage-001", + "text-similarity-curie-001", + "text-similarity-davinci-001", + "text-search-ada-doc-001", + "text-search-ada-001", + "text-search-babbage-001", + "text-search-curie-001", + "text-search-davinci-001", + "titan-embed-text-v1", + "udever-bloom-1b1", + "udever-bloom-560m", + "universal-sentence-encoder-multilingual-3", + "universal-sentence-encoder-multilingual-large-3", + "unsup-simcse-bert-base-uncased", + "use-cmlm-multilingual", + "voyage-2", + "voyage-code-2", + "voyage-law-2", + "voyage-lite-01-instruct", + "voyage-lite-02-instruct", + "xlm-roberta-base", + "xlm-roberta-large", +] + +EXTERNAL_MODEL_TO_LINK = { + "Cohere-embed-english-v3.0": "https://huggingface.co/Cohere/Cohere-embed-english-v3.0", + "Cohere-embed-multilingual-v3.0": "https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0", + "Cohere-embed-multilingual-light-v3.0": "https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0", + "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter", + "allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter", + "all-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2", + "all-MiniLM-L6-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2", + "all-mpnet-base-v2": "https://huggingface.co/sentence-transformers/all-mpnet-base-v2", + "Baichuan-text-embedding": "https://platform.baichuan-ai.com/docs/text-Embedding", + "bert-base-10lang-cased": "https://huggingface.co/Geotrend/bert-base-10lang-cased", + "bert-base-15lang-cased": "https://huggingface.co/Geotrend/bert-base-15lang-cased", + "bert-base-25lang-cased": "https://huggingface.co/Geotrend/bert-base-25lang-cased", + "bert-base-multilingual-cased": "https://huggingface.co/google-bert/bert-base-multilingual-cased", + "bert-base-multilingual-uncased": "https://huggingface.co/google-bert/bert-base-multilingual-uncased", + "bert-base-swedish-cased": "https://huggingface.co/KB/bert-base-swedish-cased", + "bert-base-uncased": "https://huggingface.co/bert-base-uncased", + "bge-base-zh-v1.5": "https://huggingface.co/BAAI/bge-base-zh-v1.5", + "bge-large-en-v1.5": "https://huggingface.co/BAAI/bge-large-en-v1.5", + "bge-large-zh-v1.5": "https://huggingface.co/BAAI/bge-large-zh-v1.5", + "bge-large-zh-noinstruct": "https://huggingface.co/BAAI/bge-large-zh-noinstruct", + "bge-small-zh-v1.5": "https://huggingface.co/BAAI/bge-small-zh-v1.5", + "camembert-base": "https://huggingface.co/almanach/camembert-base", + "camembert-large": "https://huggingface.co/almanach/camembert-large", + "contriever-base-msmarco": "https://huggingface.co/nthakur/contriever-base-msmarco", + "cross-en-de-roberta-sentence-transformer": "https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer", + "DanskBERT": "https://huggingface.co/vesteinn/DanskBERT", + "distilbert-base-25lang-cased": "https://huggingface.co/Geotrend/distilbert-base-25lang-cased", + "distilbert-base-en-fr-cased": "https://huggingface.co/Geotrend/distilbert-base-en-fr-cased", + "distilbert-base-en-fr-es-pt-it-cased": "https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased", + "distilbert-base-fr-cased": "https://huggingface.co/Geotrend/distilbert-base-fr-cased", + "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased", + "distiluse-base-multilingual-cased-v2": "https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2", + "dfm-encoder-large-v1": "https://huggingface.co/chcaa/dfm-encoder-large-v1", + "dfm-sentence-encoder-large-1": "https://huggingface.co/chcaa/dfm-encoder-large-v1", + "e5-base": "https://huggingface.co/intfloat/e5-base", + "e5-large": "https://huggingface.co/intfloat/e5-large", + "e5-mistral-7b-instruct": "https://huggingface.co/intfloat/e5-mistral-7b-instruct", + "e5-small": "https://huggingface.co/intfloat/e5-small", + "electra-small-nordic": "https://huggingface.co/jonfd/electra-small-nordic", + "electra-small-swedish-cased-discriminator": "https://huggingface.co/KBLab/electra-small-swedish-cased-discriminator", + "flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased", + "flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased", + "flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased", + "gbert-base": "https://huggingface.co/deepset/gbert-base", + "gbert-large": "https://huggingface.co/deepset/gbert-large", + "gelectra-base": "https://huggingface.co/deepset/gelectra-base", + "gelectra-large": "https://huggingface.co/deepset/gelectra-large", + "glove.6B.300d": "https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d", + "google-gecko.text-embedding-preview-0409": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models", + "google-gecko-256.text-embedding-preview-0409": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models", + "gottbert-base": "https://huggingface.co/uklfr/gottbert-base", + "gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base", + "gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large", + "gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl", + "gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl", + "herbert-base-retrieval-v2": "https://huggingface.co/ipipan/herbert-base-retrieval-v2", + "komninos": "https://huggingface.co/sentence-transformers/average_word_embeddings_komninos", + "luotuo-bert-medium": "https://huggingface.co/silk-road/luotuo-bert-medium", + "LASER2": "https://github.com/facebookresearch/LASER", + "LaBSE": "https://huggingface.co/sentence-transformers/LaBSE", + "m3e-base": "https://huggingface.co/moka-ai/m3e-base", + "m3e-large": "https://huggingface.co/moka-ai/m3e-large", + "mistral-embed": "https://docs.mistral.ai/guides/embeddings", + "msmarco-bert-co-condensor": "https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor", + "multi-qa-MiniLM-L6-cos-v1": "https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1", + "multilingual-e5-base": "https://huggingface.co/intfloat/multilingual-e5-base", + "multilingual-e5-large": "https://huggingface.co/intfloat/multilingual-e5-large", + "multilingual-e5-small": "https://huggingface.co/intfloat/multilingual-e5-small", + "nb-bert-base": "https://huggingface.co/NbAiLab/nb-bert-base", + "nb-bert-large": "https://huggingface.co/NbAiLab/nb-bert-large", + "nomic-embed-text-v1.5-64": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", + "nomic-embed-text-v1.5-128": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", + "nomic-embed-text-v1.5-256": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", + "nomic-embed-text-v1.5-512": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5", + "norbert3-base": "https://huggingface.co/ltg/norbert3-base", + "norbert3-large": "https://huggingface.co/ltg/norbert3-large", + "OpenSearch-text-hybrid": "https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval", + "paraphrase-multilingual-mpnet-base-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2", + "paraphrase-multilingual-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", + "sentence-camembert-base": "https://huggingface.co/dangvantuan/sentence-camembert-base", + "sentence-camembert-large": "https://huggingface.co/dangvantuan/sentence-camembert-large", + "sentence-croissant-llm-base": "https://huggingface.co/Wissam42/sentence-croissant-llm-base", + "sentence-bert-swedish-cased": "https://huggingface.co/KBLab/sentence-bert-swedish-cased", + "sentence-t5-base": "https://huggingface.co/sentence-transformers/sentence-t5-base", + "sentence-t5-large": "https://huggingface.co/sentence-transformers/sentence-t5-large", + "sentence-t5-xl": "https://huggingface.co/sentence-transformers/sentence-t5-xl", + "sentence-t5-xxl": "https://huggingface.co/sentence-transformers/sentence-t5-xxl", + "silver-retriever-base-v1": "https://huggingface.co/ipipan/silver-retriever-base-v1", + "sup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased", + "st-polish-paraphrase-from-distilroberta": "https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta", + "st-polish-paraphrase-from-mpnet": "https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet", + "text2vec-base-chinese": "https://huggingface.co/shibing624/text2vec-base-chinese", + "text2vec-large-chinese": "https://huggingface.co/GanymedeNil/text2vec-large-chinese", + "text-embedding-3-small": "https://openai.com/blog/new-embedding-models-and-api-updates", + "text-embedding-3-large": "https://openai.com/blog/new-embedding-models-and-api-updates", + "text-embedding-3-large-256": "https://openai.com/blog/new-embedding-models-and-api-updates", + "text-embedding-ada-002": "https://openai.com/blog/new-and-improved-embedding-model", + "text-similarity-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-similarity-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-similarity-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-similarity-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-search-ada-doc-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-search-ada-query-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-search-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-search-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-search-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "text-search-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings", + "titan-embed-text-v1": "https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html", + "udever-bloom-1b1": "https://huggingface.co/izhx/udever-bloom-1b1", + "udever-bloom-560m": "https://huggingface.co/izhx/udever-bloom-560m", + "universal-sentence-encoder-multilingual-3": "https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3", + "universal-sentence-encoder-multilingual-large-3": "https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-large-3", + "unsup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased", + "use-cmlm-multilingual": "https://huggingface.co/sentence-transformers/use-cmlm-multilingual", + "voyage-2": "https://docs.voyageai.com/embeddings/", + "voyage-code-2": "https://docs.voyageai.com/embeddings/", + "voyage-law-2": "https://docs.voyageai.com/embeddings/", + "voyage-lite-01-instruct": "https://docs.voyageai.com/embeddings/", + "voyage-lite-02-instruct": "https://docs.voyageai.com/embeddings/", + "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base", + "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large", +} + +EXTERNAL_MODEL_TO_DIM = { + "Cohere-embed-english-v3.0": 1024, + "Cohere-embed-multilingual-v3.0": 1024, + "Cohere-embed-multilingual-light-v3.0": 384, + "all-MiniLM-L12-v2": 384, + "all-MiniLM-L6-v2": 384, + "all-mpnet-base-v2": 768, + "allenai-specter": 768, + "Baichuan-text-embedding": 1024, + "bert-base-10lang-cased": 768, + "bert-base-15lang-cased": 768, + "bert-base-25lang-cased": 768, + "bert-base-multilingual-cased": 768, + "bert-base-multilingual-uncased": 768, + "bert-base-swedish-cased": 768, + "bert-base-uncased": 768, + "bge-base-zh-v1.5": 768, + "bge-large-en-v1.5": 1024, + "bge-large-zh-v1.5": 1024, + "bge-large-zh-noinstruct": 1024, + "bge-small-zh-v1.5": 512, + "camembert-base": 512, + "camembert-large": 768, + "contriever-base-msmarco": 768, + "cross-en-de-roberta-sentence-transformer": 768, + "DanskBERT": 768, + "distilbert-base-25lang-cased": 768, + "distilbert-base-en-fr-cased": 768, + "distilbert-base-en-fr-es-pt-it-cased": 768, + "distilbert-base-fr-cased": 768, + "distilbert-base-uncased": 768, + "distiluse-base-multilingual-cased-v2": 512, + "dfm-encoder-large-v1": 1024, + "dfm-sentence-encoder-large-1": 1024, + "e5-base": 768, + "e5-large": 1024, + "e5-mistral-7b-instruct": 4096, + "e5-small": 384, + "electra-small-nordic": 256, + "electra-small-swedish-cased-discriminator": 256, + "flaubert_base_cased": 768, + "flaubert_base_uncased": 768, + "flaubert_large_cased": 1024, + "luotuo-bert-medium": 768, + "LASER2": 1024, + "LaBSE": 768, + "gbert-base": 768, + "gbert-large": 1024, + "gelectra-base": 768, + "gelectra-large": 1024, + "glove.6B.300d": 300, + "google-gecko.text-embedding-preview-0409": 768, + "google-gecko-256.text-embedding-preview-0409": 256, + "gottbert-base": 768, + "gtr-t5-base": 768, + "gtr-t5-large": 768, + "gtr-t5-xl": 768, + "gtr-t5-xxl": 768, + "herbert-base-retrieval-v2": 768, + "komninos": 300, + "m3e-base": 768, + "m3e-large": 768, + "mistral-embed": 1024, + "msmarco-bert-co-condensor": 768, + "multi-qa-MiniLM-L6-cos-v1": 384, + "multilingual-e5-base": 768, + "multilingual-e5-small": 384, + "multilingual-e5-large": 1024, + "nb-bert-base": 768, + "nb-bert-large": 1024, + "nomic-embed-text-v1.5-64": 64, + "nomic-embed-text-v1.5-128": 128, + "nomic-embed-text-v1.5-256": 256, + "nomic-embed-text-v1.5-512": 512, + "norbert3-base": 768, + "norbert3-large": 1024, + "OpenSearch-text-hybrid": 1792, + "paraphrase-multilingual-MiniLM-L12-v2": 384, + "paraphrase-multilingual-mpnet-base-v2": 768, + "sentence-camembert-base": 768, + "sentence-camembert-large": 1024, + "sentence-croissant-llm-base": 2048, + "sentence-bert-swedish-cased": 768, + "sentence-t5-base": 768, + "sentence-t5-large": 768, + "sentence-t5-xl": 768, + "sentence-t5-xxl": 768, + "silver-retriever-base-v1": 768, + "sup-simcse-bert-base-uncased": 768, + "st-polish-paraphrase-from-distilroberta": 768, + "st-polish-paraphrase-from-mpnet": 768, + "text2vec-base-chinese": 768, + "text2vec-large-chinese": 1024, + "text-embedding-3-large": 3072, + "text-embedding-3-large-256": 256, + "text-embedding-3-small": 1536, + "text-embedding-ada-002": 1536, + "text-similarity-ada-001": 1024, + "text-similarity-babbage-001": 2048, + "text-similarity-curie-001": 4096, + "text-similarity-davinci-001": 12288, + "text-search-ada-doc-001": 1024, + "text-search-ada-query-001": 1024, + "text-search-ada-001": 1024, + "text-search-babbage-001": 2048, + "text-search-curie-001": 4096, + "text-search-davinci-001": 12288, + "titan-embed-text-v1": 1536, + "udever-bloom-1b1": 1536, + "udever-bloom-560m": 1024, + "universal-sentence-encoder-multilingual-3": 512, + "universal-sentence-encoder-multilingual-large-3": 512, + "unsup-simcse-bert-base-uncased": 768, + "use-cmlm-multilingual": 768, + "voyage-2": 1024, + "voyage-code-2": 1536, + "voyage-law-2": 1024, + "voyage-lite-01-instruct": 1024, + "voyage-lite-02-instruct": 1024, + "xlm-roberta-base": 768, + "xlm-roberta-large": 1024, +} + +EXTERNAL_MODEL_TO_SEQLEN = { + "Cohere-embed-english-v3.0": 512, + "Cohere-embed-multilingual-v3.0": 512, + "Cohere-embed-multilingual-light-v3.0": 512, + "all-MiniLM-L12-v2": 512, + "all-MiniLM-L6-v2": 512, + "all-mpnet-base-v2": 514, + "allenai-specter": 512, + "Baichuan-text-embedding": 512, + "bert-base-10lang-cased": 512, + "bert-base-15lang-cased": 512, + "bert-base-25lang-cased": 512, + "bert-base-multilingual-cased": 512, + "bert-base-multilingual-uncased": 512, + "bert-base-swedish-cased": 512, + "bert-base-uncased": 512, + "bge-base-zh-v1.5": 512, + "bge-large-en-v1.5": 512, + "bge-large-zh-v1.5": 512, + "bge-large-zh-noinstruct": 512, + "bge-small-zh-v1.5": 512, + "camembert-base": 512, + "camembert-large": 512, + "contriever-base-msmarco": 512, + "cross-en-de-roberta-sentence-transformer": 514, + "distilbert-base-25lang-cased": 512, + "distilbert-base-en-fr-cased": 512, + "distilbert-base-en-fr-es-pt-it-cased": 512, + "distilbert-base-fr-cased": 512, + "distilbert-base-uncased": 512, + "DanskBERT": 514, + "dfm-encoder-large-v1": 512, + "dfm-sentence-encoder-large-1": 512, + "distiluse-base-multilingual-cased-v2": 512, + "e5-base": 512, + "e5-large": 512, + "e5-mistral-7b-instruct": 32768, + "e5-small": 512, + "electra-small-nordic": 512, + "electra-small-swedish-cased-discriminator": 512, + "flaubert_base_cased": 512, + "flaubert_base_uncased": 512, + "flaubert_large_cased": 512, + "gbert-base": 512, + "gbert-large": 512, + "gelectra-base": 512, + "gelectra-large": 512, + "google-gecko.text-embedding-preview-0409": 2048, + "google-gecko-256.text-embedding-preview-0409": 2048, + "gottbert-base": 512, + "glove.6B.300d": "N/A", + "gtr-t5-base": 512, + "gtr-t5-large": 512, + "gtr-t5-xl": 512, + "gtr-t5-xxl": 512, + "herbert-base-retrieval-v2": 514, + "komninos": "N/A", + "luotuo-bert-medium": 512, + "LASER2": "N/A", + "LaBSE": 512, + "m3e-base": 512, + "m3e-large": 512, +# "mistral-embed": "?", + "msmarco-bert-co-condensor": 512, + "multi-qa-MiniLM-L6-cos-v1": 512, + "multilingual-e5-base": 514, + "multilingual-e5-large": 514, + "multilingual-e5-small": 512, + "nb-bert-base": 512, + "nb-bert-large": 512, + "nomic-embed-text-v1.5-64": 8192, + "nomic-embed-text-v1.5-128": 8192, + "nomic-embed-text-v1.5-256": 8192, + "nomic-embed-text-v1.5-512": 8192, + "norbert3-base": 512, + "norbert3-large": 512, + "OpenSearch-text-hybrid": 512, + "paraphrase-multilingual-MiniLM-L12-v2": 512, + "paraphrase-multilingual-mpnet-base-v2": 514, + "sentence-camembert-base": 512, + "sentence-camembert-large": 512, + "sentence-croissant-llm-base": 2048, + "sentence-bert-swedish-cased": 512, + "sentence-t5-base": 512, + "sentence-t5-large": 512, + "sentence-t5-xl": 512, + "sentence-t5-xxl": 512, + "silver-retriever-base-v1": 514, + "sup-simcse-bert-base-uncased": 512, + "st-polish-paraphrase-from-distilroberta": 514, + "st-polish-paraphrase-from-mpnet": 514, + "text2vec-base-chinese": 512, + "text2vec-large-chinese": 512, + "text-embedding-3-large": 8191, + "text-embedding-3-large-256": 8191, + "text-embedding-3-small": 8191, + "text-embedding-ada-002": 8191, + "text-similarity-ada-001": 2046, + "text-similarity-babbage-001": 2046, + "text-similarity-curie-001": 2046, + "text-similarity-davinci-001": 2046, + "text-search-ada-doc-001": 2046, + "text-search-ada-query-001": 2046, + "text-search-ada-001": 2046, + "text-search-babbage-001": 2046, + "text-search-curie-001": 2046, + "text-search-davinci-001": 2046, + "titan-embed-text-v1": 8000, + "udever-bloom-1b1": 2048, + "udever-bloom-560m": 2048, + "universal-sentence-encoder-multilingual-3": 512, + "universal-sentence-encoder-multilingual-large-3": 512, + "use-cmlm-multilingual": 512, + "unsup-simcse-bert-base-uncased": 512, + "voyage-2": 1024, + "voyage-code-2": 16000, + "voyage-law-2": 4000, + "voyage-lite-01-instruct": 4000, + "voyage-lite-02-instruct": 4000, + "xlm-roberta-base": 514, + "xlm-roberta-large": 514, +} + +EXTERNAL_MODEL_TO_SIZE = { + "allenai-specter": 110, + "all-MiniLM-L12-v2": 33, + "all-MiniLM-L6-v2": 23, + "all-mpnet-base-v2": 110, + "bert-base-10lang-cased": 138, + "bert-base-15lang-cased": 138, + "bert-base-25lang-cased": 138, + "bert-base-multilingual-cased": 179, + "bert-base-multilingual-uncased": 168, + "bert-base-uncased": 110, + "bert-base-swedish-cased": 125, + "bge-base-zh-v1.5": 102, + "bge-large-zh-v1.5": 326, + "bge-large-zh-noinstruct": 326, + "bge-small-zh-v1.5": 24, + "camembert-base": 111, + "camembert-large": 338, + "cross-en-de-roberta-sentence-transformer": 278, + "contriever-base-msmarco": 110, + "distilbert-base-25lang-cased": 110, + "distilbert-base-en-fr-cased": 110, + "distilbert-base-en-fr-es-pt-it-cased": 110, + "distilbert-base-fr-cased": 110, + "distilbert-base-uncased": 110, + "DanskBERT": 125, + "distiluse-base-multilingual-cased-v2": 135, + "dfm-encoder-large-v1": 355, + "dfm-sentence-encoder-large-1": 355, + "e5-base": 110, + "e5-large": 335, + "e5-mistral-7b-instruct": 7111, + "e5-small": 33, + "electra-small-nordic": 23, + "electra-small-swedish-cased-discriminator": 16, + "flaubert_base_cased": 138, + "flaubert_base_uncased": 138, + "flaubert_large_cased": 372, + "gbert-base": 110, + "gbert-large": 337, + "gelectra-base": 110, + "gelectra-large": 335, + "glove.6B.300d": 120, + "google-gecko.text-embedding-preview-0409": 1200, + "google-gecko-256.text-embedding-preview-0409": 1200, + "gottbert-base": 127, + "gtr-t5-base": 110, + "gtr-t5-large": 168, + "gtr-t5-xl": 1240, + "gtr-t5-xxl": 4865, + "herbert-base-retrieval-v2": 125, + "komninos": 134, + "luotuo-bert-medium": 328, + "LASER2": 43, + "LaBSE": 471, + "m3e-base": 102, + "m3e-large": 102, + "msmarco-bert-co-condensor": 110, + "multi-qa-MiniLM-L6-cos-v1": 23, + "multilingual-e5-base": 278, + "multilingual-e5-small": 118, + "multilingual-e5-large": 560, + "nb-bert-base": 179, + "nb-bert-large": 355, + "nomic-embed-text-v1.5-64": 138, + "nomic-embed-text-v1.5-128": 138, + "nomic-embed-text-v1.5-256": 138, + "nomic-embed-text-v1.5-512": 138, + "norbert3-base": 131, + "norbert3-large": 368, + "paraphrase-multilingual-mpnet-base-v2": 278, + "paraphrase-multilingual-MiniLM-L12-v2": 118, + "sentence-camembert-base": 110, + "sentence-camembert-large": 337, + "sentence-croissant-llm-base": 1280, + "sentence-bert-swedish-cased": 125, + "sentence-t5-base": 110, + "sentence-t5-large": 168, + "sentence-t5-xl": 1240, + "sentence-t5-xxl": 4865, + "silver-retriever-base-v1": 125, + "sup-simcse-bert-base-uncased": 110, + "st-polish-paraphrase-from-distilroberta": 125, + "st-polish-paraphrase-from-mpnet": 125, + "text2vec-base-chinese": 102, + "text2vec-large-chinese": 326, + "unsup-simcse-bert-base-uncased": 110, + "use-cmlm-multilingual": 472, + #"voyage-law-2": 1220, + "voyage-lite-02-instruct": 1220, + "xlm-roberta-base": 279, + "xlm-roberta-large": 560, +} PROPRIETARY_MODELS = { - make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}")) + "Cohere-embed-english-v3.0", + "Cohere-embed-multilingual-v3.0", + "Cohere-embed-multilingual-light-v3.0", + "Baichuan-text-embedding", + "mistral-embed", + "OpenSearch-text-hybrid", + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-3-large-256", + "text-embedding-ada-002", + "text-similarity-ada-001", + "text-similarity-babbage-001", + "text-similarity-curie-001", + "text-similarity-davinci-001", + "text-search-ada-doc-001", + "text-search-ada-query-001", + "text-search-ada-001", + "text-search-curie-001", + "text-search-babbage-001", + "text-search-davinci-001", + "titan-embed-text-v1", + "voyage-2", + "voyage-code-2", + "voyage-law-2", + "voyage-lite-01-instruct", + "voyage-lite-02-instruct", + "google-gecko.text-embedding-preview-0409", + "google-gecko-256.text-embedding-preview-0409", +} +PROPRIETARY_MODELS = { + make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard")) for model in PROPRIETARY_MODELS } + SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = { - make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}")) - for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS -} -INSTRUCT_MODELS = { - make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}")) - for model in INSTRUCT_MODELS -} -NOINSTRUCT_MODELS = { - make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}")) - for model in NOINSTRUCT_MODELS + "allenai-specter", + "allenai-specter", + "all-MiniLM-L12-v2", + "all-MiniLM-L6-v2", + "all-mpnet-base-v2", + "bert-base-10lang-cased", + "bert-base-15lang-cased", + "bert-base-25lang-cased", + "bert-base-multilingual-cased", + "bert-base-multilingual-uncased", + "bert-base-swedish-cased", + "bert-base-uncased", + "bge-base-zh-v1.5", + "bge-large-zh-v1.5", + "bge-large-zh-noinstruct", + "bge-small-zh-v1.5", + "camembert-base", + "camembert-large", + "contriever-base-msmarco", + "cross-en-de-roberta-sentence-transformer", + "DanskBERT", + "distilbert-base-25lang-cased", + "distilbert-base-en-fr-cased", + "distilbert-base-en-fr-es-pt-it-cased", + "distilbert-base-fr-cased", + "distilbert-base-uncased", + "distiluse-base-multilingual-cased-v2", + "dfm-encoder-large-v1", + "dfm-sentence-encoder-large-1", + "e5-base", + "e5-large", + "e5-mistral-7b-instruct", + "e5-small", + "electra-small-nordic", + "electra-small-swedish-cased-discriminator", + "flaubert_base_cased", + "flaubert_base_uncased", + "flaubert_large_cased", + "gbert-base", + "gbert-large", + "gelectra-base", + "gelectra-large", + "glove.6B.300d", + "gottbert-base", + "gtr-t5-base", + "gtr-t5-large", + "gtr-t5-xl", + "gtr-t5-xxl", + "herbert-base-retrieval-v2", + "komninos", + "luotuo-bert-medium", + "LaBSE", + "m3e-base", + "m3e-large", + "msmarco-bert-co-condensor", + "multi-qa-MiniLM-L6-cos-v1", + "multilingual-e5-base", + "multilingual-e5-large", + "multilingual-e5-small", + "nb-bert-base", + "nb-bert-large", + "nomic-embed-text-v1.5-64", + "nomic-embed-text-v1.5-128", + "nomic-embed-text-v1.5-256", + "nomic-embed-text-v1.5-512", + "norbert3-base", + "norbert3-large", + "paraphrase-multilingual-mpnet-base-v2", + "paraphrase-multilingual-MiniLM-L12-v2", + "sentence-camembert-base", + "sentence-camembert-large", + "sentence-croissant-llm-base", + "sentence-bert-swedish-cased", + "sentence-t5-base", + "sentence-t5-large", + "sentence-t5-xl", + "sentence-t5-xxl", + "silver-retriever-base-v1", + "sup-simcse-bert-base-uncased", + "st-polish-paraphrase-from-distilroberta", + "st-polish-paraphrase-from-mpnet", + "text2vec-base-chinese", + "text2vec-large-chinese", + "udever-bloom-1b1", + "udever-bloom-560m", + "universal-sentence-encoder-multilingual-3", + "universal-sentence-encoder-multilingual-large-3", + "unsup-simcse-bert-base-uncased", + "use-cmlm-multilingual", + "xlm-roberta-base", + "xlm-roberta-large", } -CROSS_ENCODERS = { - make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}")) - for model in CROSS_ENCODERS +SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = { + make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard")) + for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS } -BI_ENCODERS = { - make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}")) - for model in BI_ENCODERS + +MODELS_TO_SKIP = { + "baseplate/instructor-large-1", # Duplicate + "radames/e5-large", # Duplicate + "gentlebowl/instructor-large-safetensors", # Duplicate + "Consensus/instructor-base", # Duplicate + "GovCompete/instructor-xl", # Duplicate + "GovCompete/e5-large-v2", # Duplicate + "t12e/instructor-base", # Duplicate + "michaelfeil/ct2fast-e5-large-v2", + "michaelfeil/ct2fast-e5-large", + "michaelfeil/ct2fast-e5-small-v2", + "newsrx/instructor-xl-newsrx", + "newsrx/instructor-large-newsrx", + "fresha/e5-large-v2-endpoint", + "ggrn/e5-small-v2", + "michaelfeil/ct2fast-e5-small", + "jncraton/e5-small-v2-ct2-int8", + "anttip/ct2fast-e5-small-v2-hfie", + "newsrx/instructor-large", + "newsrx/instructor-xl", + "dmlls/all-mpnet-base-v2", + "cgldo/semanticClone", + "Malmuk1/e5-large-v2_Sharded", + "jncraton/gte-small-ct2-int8", + "Einas/einas_ashkar", + "gruber/e5-small-v2-ggml", + "jncraton/bge-small-en-ct2-int8", + "vectoriseai/bge-small-en", + "recipe/embeddings", + "dhairya0907/thenlper-get-large", + "Narsil/bge-base-en", + "kozistr/fused-large-en", + "sionic-ai/sionic-ai-v2", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1 + "sionic-ai/sionic-ai-v1", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1 + "BAAI/bge-large-en", # Deprecated in favor of v1.5 + "BAAI/bge-base-en", # Deprecated in favor of v1.5 + "BAAI/bge-small-en", # Deprecated in favor of v1.5 + "d0rj/e5-large-en-ru", + "d0rj/e5-base-en-ru", + "d0rj/e5-small-en-ru", + "aident-ai/bge-base-en-onnx", + "barisaydin/bge-base-en", + "barisaydin/gte-large", + "barisaydin/gte-base", + "barisaydin/gte-small", + "barisaydin/bge-small-en", + "odunola/e5-base-v2", + "goldenrooster/multilingual-e5-large", + "davidpeer/gte-small", + "barisaydin/bge-large-en", + "jamesgpt1/english-large-v1", + "vectoriseai/bge-large-en-v1.5", + "vectoriseai/bge-base-en-v1.5", + "vectoriseai/instructor-large", + "vectoriseai/instructor-base", + "vectoriseai/gte-large", + "vectoriseai/gte-base", + "vectoriseai/e5-large-v2", + "vectoriseai/bge-small-en-v1.5", + "vectoriseai/e5-base-v2", + "vectoriseai/e5-large", + "vectoriseai/multilingual-e5-large", + "vectoriseai/gte-small", + "vectoriseai/ember-v1", + "vectoriseai/e5-base", + "vectoriseai/e5-small-v2", + "michaelfeil/ct2fast-bge-large-en-v1.5", + "michaelfeil/ct2fast-bge-large-en-v1.5", + "michaelfeil/ct2fast-bge-base-en-v1.5", + "michaelfeil/ct2fast-gte-large", + "michaelfeil/ct2fast-gte-base", + "michaelfeil/ct2fast-bge-small-en-v1.5", + "rizki/bgr-tf", + "ef-zulla/e5-multi-sml-torch", + "cherubhao/yogamodel", + "morgendigital/multilingual-e5-large-quantized", + "jncraton/gte-tiny-ct2-int8", + "Research2NLP/electrical_stella", + "Intel/bge-base-en-v1.5-sts-int8-static", + "Intel/bge-base-en-v1.5-sts-int8-dynamic", + "Intel/bge-base-en-v1.5-sst2", + "Intel/bge-base-en-v1.5-sst2-int8-static", + "Intel/bge-base-en-v1.5-sst2-int8-dynamic", + "Intel/bge-small-en-v1.5-sst2", + "Intel/bge-small-en-v1.5-sst2-int8-dynamic", + "Intel/bge-small-en-v1.5-sst2-int8-static", + "binqiangliu/EmbeddingModlebgelargeENv1.5", + "DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext", + "woody72/multilingual-e5-base", + "Severian/embed", + "Frazic/udever-bloom-3b-sentence", + "jamesgpt1/zzz", + "karrar-alwaili/UAE-Large-V1", + "odunola/UAE-Large-VI", + "shubham-bgi/UAE-Large", + "retrainai/instructor-xl", + "weakit-v/bge-base-en-v1.5-onnx", + "ieasybooks/multilingual-e5-large-onnx", + "gizmo-ai/Cohere-embed-multilingual-v3.0", + "jingyeom/korean_embedding_model", + "barisaydin/text2vec-base-multilingual", + "mlx-community/multilingual-e5-large-mlx", + "mlx-community/multilingual-e5-base-mlx", + "mlx-community/multilingual-e5-small-mlx", + "maiyad/multilingual-e5-small", + "khoa-klaytn/bge-base-en-v1.5-angle", + "khoa-klaytn/bge-small-en-v1.5-angle", + "mixamrepijey/instructor-small", + "mixamrepijey/instructor-models", + "lsf1000/bge-evaluation", # Empty + "giulio98/placeholder", # Empty + "Severian/nomic", # Copy + "atian-chapters/Chapters-SFR-Embedding-Mistral", # Copy + "rlsChapters/Chapters-SFR-Embedding-Mistral", # Copy + "TitanML/jina-v2-base-en-embed", # Copy + "MaziyarPanahi/GritLM-8x7B-GGUF", # GGUF variant + "Geolumina/instructor-xl", # Duplicate + "krilecy/e5-mistral-7b-instruct", + "beademiguelperez/sentence-transformers-multilingual-e5-small", + "arcdev/SFR-Embedding-Mistral", + "arcdev/e5-mistral-7b-instruct", + "Koat/gte-tiny", + "SmartComponents/bge-micro-v2", } +def add_lang(examples): + if not(examples["eval_language"]): + examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] + else: + examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] + f' ({examples["eval_language"]})' + return examples + +def norm(names): return set([name.split(" ")[0] for name in names]) + +def add_task(examples): + # Could be added to the dataset loading script instead + if examples["mteb_dataset_name"] in norm(TASK_LIST_CLASSIFICATION + TASK_LIST_CLASSIFICATION_DA + TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLASSIFICATION_NB + TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLASSIFICATION_SV + TASK_LIST_CLASSIFICATION_ZH): + examples["mteb_task"] = "Classification" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_CLUSTERING + TASK_LIST_CLUSTERING_DE + TASK_LIST_CLUSTERING_FR + TASK_LIST_CLUSTERING_PL + TASK_LIST_CLUSTERING_ZH): + examples["mteb_task"] = "Clustering" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_PAIR_CLASSIFICATION_ZH): + examples["mteb_task"] = "PairClassification" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_RERANKING + TASK_LIST_RERANKING_FR + TASK_LIST_RERANKING_ZH): + examples["mteb_task"] = "Reranking" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_RETRIEVAL_NORM + TASK_LIST_RETRIEVAL_FR + TASK_LIST_RETRIEVAL_PL + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_RETRIEVAL_LAW): + examples["mteb_task"] = "Retrieval" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_STS + TASK_LIST_STS_FR + TASK_LIST_STS_PL + TASK_LIST_STS_ZH): + examples["mteb_task"] = "STS" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_SUMMARIZATION + TASK_LIST_SUMMARIZATION_FR): + examples["mteb_task"] = "Summarization" + elif examples["mteb_dataset_name"] in norm(TASK_LIST_BITEXT_MINING + TASK_LIST_BITEXT_MINING_DA): + examples["mteb_task"] = "BitextMining" + else: + print("WARNING: Task not found for dataset", examples["mteb_dataset_name"]) + examples["mteb_task"] = "Unknown" + return examples + +if os.path.exists("EXTERNAL_MODEL_RESULTS.json"): + with open("EXTERNAL_MODEL_RESULTS.json") as f: + EXTERNAL_MODEL_RESULTS = json.load(f) + # Update with models not contained + models_to_run = [] + for model in EXTERNAL_MODELS: + if model not in EXTERNAL_MODEL_RESULTS: + models_to_run.append(model) + EXTERNAL_MODEL_RESULTS[model] = {k: {v: []} for k, v in TASK_TO_METRIC.items()} +else: + EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS} + models_to_run = EXTERNAL_MODELS + +pbar = tqdm(models_to_run, desc="Fetching external model results") +for model in pbar: + pbar.set_description(f"Fetching external model results for {model!r}") + ds = load_dataset("mteb/results", model, trust_remote_code=True) + # For local debugging: + #, download_mode='force_redownload', verification_mode="no_checks") + ds = ds.map(add_lang) + ds = ds.map(add_task) + base_dict = {"Model": make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))} + # For now only one metric per task - Could add more metrics lateron + for task, metric in TASK_TO_METRIC.items(): + ds_dict = ds.filter(lambda x: (x["mteb_task"] == task) and (x["metric"] == metric))["test"].to_dict() + ds_dict = {k: round(v, 2) for k, v in zip(ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"])} + EXTERNAL_MODEL_RESULTS[model][task][metric].append({**base_dict, **ds_dict}) + +# Save & cache EXTERNAL_MODEL_RESULTS +with open("EXTERNAL_MODEL_RESULTS.json", "w") as f: + json.dump(EXTERNAL_MODEL_RESULTS, f) + +def get_dim_seq_size(model): + filenames = [sib.rfilename for sib in model.siblings] + dim, seq = "", "" + for filename in filenames: + if re.match("\d+_Pooling/config.json", filename): + st_config_path = hf_hub_download(model.modelId, filename=filename) + dim = json.load(open(st_config_path)).get("word_embedding_dimension", "") + break + for filename in filenames: + if re.match("\d+_Dense/config.json", filename): + st_config_path = hf_hub_download(model.modelId, filename=filename) + dim = json.load(open(st_config_path)).get("out_features", dim) + if "config.json" in filenames: + config_path = hf_hub_download(model.modelId, filename="config.json") + config = json.load(open(config_path)) + if not dim: + dim = config.get("hidden_dim", config.get("hidden_size", config.get("d_model", ""))) + seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", "")))) + # Get model file size without downloading. Parameters in million parameters and memory in GB + parameters, memory = get_model_parameters_memory(model) + return dim, seq, parameters, memory + def make_datasets_clickable(df): """Does not work""" if "BornholmBitextMining" in df.columns: @@ -43,6 +1266,405 @@ def make_datasets_clickable(df): columns={f'BornholmBitextMining': 'BornholmBitextMining',}) return df +def add_rank(df): + cols_to_rank = [col for col in df.columns if col not in ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]] + if len(cols_to_rank) == 1: + df.sort_values(cols_to_rank[0], ascending=False, inplace=True) + else: + df.insert(len(df.columns) - len(cols_to_rank), "Average", df[cols_to_rank].mean(axis=1, skipna=False)) + df.sort_values("Average", ascending=False, inplace=True) + df.insert(0, "Rank", list(range(1, len(df) + 1))) + df = df.round(2) + # Fill NaN after averaging + df.fillna("", inplace=True) + return df + +def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_emb_dim=True, task_to_metric=TASK_TO_METRIC, rank=True): + api = HfApi() + models = api.list_models(filter="mteb") + # Initialize list to models that we cannot fetch metadata from + df_list = [] + for model in EXTERNAL_MODEL_RESULTS: + results_list = [res for task in tasks for res in EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]] + if len(datasets) > 0: + res = {k: v for d in results_list for k, v in d.items() if (k == "Model") or any([x in k for x in datasets])} + elif langs: + # Would be cleaner to rely on an extra language column instead + langs_format = [f"({lang})" for lang in langs] + res = {k: v for d in results_list for k, v in d.items() if any([k.split(" ")[-1] in (k, x) for x in langs_format])} + else: + res = {k: v for d in results_list for k, v in d.items()} + # Model & at least one result + if len(res) > 1: + if add_emb_dim: + res["Model Size (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "") + res["Memory Usage (GB, fp32)"] = round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2) if res["Model Size (Million Parameters)"] != "" else "" + res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "") + res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "") + df_list.append(res) + + for model in models: + if model.modelId in MODELS_TO_SKIP: continue + print("MODEL", model) + readme_path = hf_hub_download(model.modelId, filename="README.md") + meta = metadata_load(readme_path) + if "model-index" not in meta: + continue + # meta['model-index'][0]["results"] is list of elements like: + # { + # "task": {"type": "Classification"}, + # "dataset": { + # "type": "mteb/amazon_massive_intent", + # "name": "MTEB MassiveIntentClassification (nb)", + # "config": "nb", + # "split": "test", + # }, + # "metrics": [ + # {"type": "accuracy", "value": 39.81506388702084}, + # {"type": "f1", "value": 38.809586587791664}, + # ], + # }, + # Use "get" instead of dict indexing to skip incompat metadata instead of erroring out + if len(datasets) > 0: + task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and any([x in sub_res.get("dataset", {}).get("name", "") for x in datasets])] + elif langs: + task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and (sub_res.get("dataset", {}).get("config", "default") in ("default", *langs))] + else: + task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks)] + out = [{res["dataset"]["name"].replace("MTEB ", ""): [round(score["value"], 2) for score in res["metrics"] if score["type"] == task_to_metric.get(res["task"]["type"])][0]} for res in task_results] + out = {k: v for d in out for k, v in d.items()} + out["Model"] = make_clickable_model(model.modelId) + # Model & at least one result + if len(out) > 1: + if add_emb_dim: + try: + # Fails on gated repos, so we only include scores for them + out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = get_dim_seq_size(model) + except: + pass + df_list.append(out) + if model.library_name == "sentence-transformers" or "sentence-transformers" in model.tags or "modules.json" in {file.rfilename for file in model.siblings}: + SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"]) + df = pd.DataFrame(df_list) + # If there are any models that are the same, merge them + # E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one + df = df.groupby("Model", as_index=False).first() + # Put 'Model' column first + cols = sorted(list(df.columns)) + cols.insert(0, cols.pop(cols.index("Model"))) + df = df[cols] + if rank: + df = add_rank(df) + if fillna: + df.fillna("", inplace=True) + return df + +def get_mteb_average(): + global DATA_OVERALL, DATA_CLASSIFICATION_EN, DATA_CLUSTERING, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS_EN, DATA_SUMMARIZATION + DATA_OVERALL = get_mteb_data( + tasks=[ + "Classification", + "Clustering", + "PairClassification", + "Reranking", + "Retrieval", + "STS", + "Summarization", + ], + datasets=TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION, + fillna=False, + add_emb_dim=True, + rank=False, + ) + # Debugging: + # DATA_OVERALL.to_csv("overall.csv") + + DATA_OVERALL.insert(1, f"Average ({len(TASK_LIST_EN)} datasets)", DATA_OVERALL[TASK_LIST_EN].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_CLASSIFICATION].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", DATA_OVERALL[TASK_LIST_CLUSTERING].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_PAIR_CLASSIFICATION].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", DATA_OVERALL[TASK_LIST_RERANKING].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", DATA_OVERALL[TASK_LIST_RETRIEVAL].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(7, f"STS Average ({len(TASK_LIST_STS)} datasets)", DATA_OVERALL[TASK_LIST_STS].mean(axis=1, skipna=False)) + DATA_OVERALL.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)", DATA_OVERALL[TASK_LIST_SUMMARIZATION].mean(axis=1, skipna=False)) + DATA_OVERALL.sort_values(f"Average ({len(TASK_LIST_EN)} datasets)", ascending=False, inplace=True) + # Start ranking from 1 + DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1))) + + DATA_OVERALL = DATA_OVERALL.round(2) + + DATA_CLASSIFICATION_EN = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION]) + # Only keep rows with at least one score in addition to the "Model" & rank column + DATA_CLASSIFICATION_EN = DATA_CLASSIFICATION_EN[DATA_CLASSIFICATION_EN.iloc[:, 4:].ne("").any(axis=1)] + + DATA_CLUSTERING = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING]) + DATA_CLUSTERING = DATA_CLUSTERING[DATA_CLUSTERING.iloc[:, 4:].ne("").any(axis=1)] + + DATA_PAIR_CLASSIFICATION = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION]) + DATA_PAIR_CLASSIFICATION = DATA_PAIR_CLASSIFICATION[DATA_PAIR_CLASSIFICATION.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RERANKING = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING]) + DATA_RERANKING = DATA_RERANKING[DATA_RERANKING.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RETRIEVAL = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL]) + DATA_RETRIEVAL = DATA_RETRIEVAL[DATA_RETRIEVAL.iloc[:, 4:].ne("").any(axis=1)] + + DATA_STS_EN = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS]) + DATA_STS_EN = DATA_STS_EN[DATA_STS_EN.iloc[:, 4:].ne("").any(axis=1)] + + DATA_SUMMARIZATION = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_SUMMARIZATION]) + DATA_SUMMARIZATION = DATA_SUMMARIZATION[DATA_SUMMARIZATION.iloc[:, 1:].ne("").any(axis=1)] + + # Fill NaN after averaging + DATA_OVERALL.fillna("", inplace=True) + + DATA_OVERALL = DATA_OVERALL[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_EN)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", f"STS Average ({len(TASK_LIST_STS)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)"]] + DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)] + + return DATA_OVERALL + +def get_mteb_average_zh(): + global DATA_OVERALL_ZH, DATA_CLASSIFICATION_ZH, DATA_CLUSTERING_ZH, DATA_PAIR_CLASSIFICATION_ZH, DATA_RERANKING_ZH, DATA_RETRIEVAL_ZH, DATA_STS_ZH + DATA_OVERALL_ZH = get_mteb_data( + tasks=[ + "Classification", + "Clustering", + "PairClassification", + "Reranking", + "Retrieval", + "STS", + ], + datasets=TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH, + fillna=False, + add_emb_dim=True, + rank=False, + ) + # Debugging: + # DATA_OVERALL_ZH.to_csv("overall.csv") + + DATA_OVERALL_ZH.insert(1, f"Average ({len(TASK_LIST_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLASSIFICATION_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLUSTERING_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_PAIR_CLASSIFICATION_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RERANKING_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RETRIEVAL_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.insert(7, f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_STS_ZH].mean(axis=1, skipna=False)) + DATA_OVERALL_ZH.sort_values(f"Average ({len(TASK_LIST_ZH)} datasets)", ascending=False, inplace=True) + # Start ranking from 1 + DATA_OVERALL_ZH.insert(0, "Rank", list(range(1, len(DATA_OVERALL_ZH) + 1))) + + DATA_OVERALL_ZH = DATA_OVERALL_ZH.round(2) + + DATA_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_ZH]) + # Only keep rows with at least one score in addition to the "Model" & rank column + DATA_CLASSIFICATION_ZH = DATA_CLASSIFICATION_ZH[DATA_CLASSIFICATION_ZH.iloc[:, 4:].ne("").any(axis=1)] + + DATA_CLUSTERING_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_ZH]) + DATA_CLUSTERING_ZH = DATA_CLUSTERING_ZH[DATA_CLUSTERING_ZH.iloc[:, 4:].ne("").any(axis=1)] + + DATA_PAIR_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_ZH]) + DATA_PAIR_CLASSIFICATION_ZH = DATA_PAIR_CLASSIFICATION_ZH[DATA_PAIR_CLASSIFICATION_ZH.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RERANKING_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING_ZH]) + DATA_RERANKING_ZH = DATA_RERANKING_ZH[DATA_RERANKING_ZH.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RETRIEVAL_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_ZH]) + DATA_RETRIEVAL_ZH = DATA_RETRIEVAL_ZH[DATA_RETRIEVAL_ZH.iloc[:, 4:].ne("").any(axis=1)] + + DATA_STS_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_ZH]) + DATA_STS_ZH = DATA_STS_ZH[DATA_STS_ZH.iloc[:, 4:].ne("").any(axis=1)] + + # Fill NaN after averaging + DATA_OVERALL_ZH.fillna("", inplace=True) + + DATA_OVERALL_ZH = DATA_OVERALL_ZH[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_ZH)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)"]] + DATA_OVERALL_ZH = DATA_OVERALL_ZH[DATA_OVERALL_ZH.iloc[:, 5:].ne("").any(axis=1)] + + return DATA_OVERALL_ZH + +def get_mteb_average_fr(): + global DATA_OVERALL_FR, DATA_CLASSIFICATION_FR, DATA_CLUSTERING_FR, DATA_PAIR_CLASSIFICATION_FR, DATA_RERANKING_FR, DATA_RETRIEVAL_FR, DATA_STS_FR, DATA_SUMMARIZATION_FR + DATA_OVERALL_FR = get_mteb_data( + tasks=[ + "Classification", + "Clustering", + "PairClassification", + "Reranking", + "Retrieval", + "STS", + "Summarization" + ], + datasets=TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLUSTERING_FR + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_RERANKING_FR + TASK_LIST_RETRIEVAL_FR + TASK_LIST_STS_FR + TASK_LIST_SUMMARIZATION_FR, + fillna=False, + add_emb_dim=True, + rank=False, + ) + # Debugging: + # DATA_OVERALL_FR.to_csv("overall.csv") + + DATA_OVERALL_FR.insert(1, f"Average ({len(TASK_LIST_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_CLASSIFICATION_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_CLUSTERING_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_PAIR_CLASSIFICATION_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_RERANKING_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_RETRIEVAL_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(7, f"STS Average ({len(TASK_LIST_STS_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_STS_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION_FR)} dataset)", DATA_OVERALL_FR[TASK_LIST_SUMMARIZATION_FR].mean(axis=1, skipna=False)) + DATA_OVERALL_FR.sort_values(f"Average ({len(TASK_LIST_FR)} datasets)", ascending=False, inplace=True) + # Start ranking from 1 + DATA_OVERALL_FR.insert(0, "Rank", list(range(1, len(DATA_OVERALL_FR) + 1))) + DATA_OVERALL_FR = DATA_OVERALL_FR.round(2) + + DATA_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_FR]) + DATA_CLASSIFICATION_FR = DATA_CLASSIFICATION_FR[DATA_CLASSIFICATION_FR.iloc[:, 4:].ne("").any(axis=1)] + + DATA_CLUSTERING_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_FR]) + DATA_CLUSTERING_FR = DATA_CLUSTERING_FR[DATA_CLUSTERING_FR.iloc[:, 4:].ne("").any(axis=1)] + + DATA_PAIR_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_FR]) + DATA_PAIR_CLASSIFICATION_FR = DATA_PAIR_CLASSIFICATION_FR[DATA_PAIR_CLASSIFICATION_FR.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RERANKING_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING_FR]) + DATA_RERANKING_FR = DATA_RERANKING_FR[DATA_RERANKING_FR.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RETRIEVAL_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_FR]) + DATA_RETRIEVAL_FR = DATA_RETRIEVAL_FR[DATA_RETRIEVAL_FR.iloc[:, 4:].ne("").any(axis=1)] + + DATA_STS_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_FR]) + DATA_STS_FR = DATA_STS_FR[DATA_STS_FR.iloc[:, 4:].ne("").any(axis=1)] + + DATA_SUMMARIZATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_SUMMARIZATION_FR]) + DATA_SUMMARIZATION_FR = DATA_SUMMARIZATION_FR[DATA_SUMMARIZATION_FR.iloc[:, 1:].ne("").any(axis=1)] + + # Fill NaN after averaging + DATA_OVERALL_FR.fillna("", inplace=True) + + DATA_OVERALL_FR = DATA_OVERALL_FR[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_FR)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_FR)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_FR)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_FR)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_FR)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_FR)} datasets)", f"STS Average ({len(TASK_LIST_STS_FR)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION_FR)} dataset)"]] + DATA_OVERALL_FR = DATA_OVERALL_FR[DATA_OVERALL_FR.iloc[:, 5:].ne("").any(axis=1)] + + return DATA_OVERALL_FR + +def get_mteb_average_pl(): + global DATA_OVERALL_PL, DATA_CLASSIFICATION_PL, DATA_CLUSTERING_PL, DATA_PAIR_CLASSIFICATION_PL, DATA_RETRIEVAL_PL, DATA_STS_PL + DATA_OVERALL_PL = get_mteb_data( + tasks=[ + "Classification", + "Clustering", + "PairClassification", + "Retrieval", + "STS", + ], + datasets=TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL, + fillna=False, + add_emb_dim=True, + rank=False, + ) + # Debugging: + # DATA_OVERALL_PL.to_csv("overall.csv") + + DATA_OVERALL_PL.insert(1, f"Average ({len(TASK_LIST_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PL].mean(axis=1, skipna=False)) + DATA_OVERALL_PL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLASSIFICATION_PL].mean(axis=1, skipna=False)) + DATA_OVERALL_PL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLUSTERING_PL].mean(axis=1, skipna=False)) + DATA_OVERALL_PL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PAIR_CLASSIFICATION_PL].mean(axis=1, skipna=False)) + DATA_OVERALL_PL.insert(5, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_RETRIEVAL_PL].mean(axis=1, skipna=False)) + DATA_OVERALL_PL.insert(6, f"STS Average ({len(TASK_LIST_STS_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_STS_PL].mean(axis=1, skipna=False)) + DATA_OVERALL_PL.sort_values(f"Average ({len(TASK_LIST_PL)} datasets)", ascending=False, inplace=True) + # Start ranking from 1 + DATA_OVERALL_PL.insert(0, "Rank", list(range(1, len(DATA_OVERALL_PL) + 1))) + + DATA_OVERALL_PL = DATA_OVERALL_PL.round(2) + + DATA_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_PL]) + # Only keep rows with at least one score in addition to the "Model" & rank column + DATA_CLASSIFICATION_PL = DATA_CLASSIFICATION_PL[DATA_CLASSIFICATION_PL.iloc[:, 4:].ne("").any(axis=1)] + + DATA_CLUSTERING_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_PL]) + DATA_CLUSTERING_PL = DATA_CLUSTERING_PL[DATA_CLUSTERING_PL.iloc[:, 4:].ne("").any(axis=1)] + + DATA_PAIR_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_PL]) + DATA_PAIR_CLASSIFICATION_PL = DATA_PAIR_CLASSIFICATION_PL[DATA_PAIR_CLASSIFICATION_PL.iloc[:, 4:].ne("").any(axis=1)] + + DATA_RETRIEVAL_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_PL]) + DATA_RETRIEVAL_PL = DATA_RETRIEVAL_PL[DATA_RETRIEVAL_PL.iloc[:, 4:].ne("").any(axis=1)] + + DATA_STS_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_PL]) + DATA_STS_PL = DATA_STS_PL[DATA_STS_PL.iloc[:, 4:].ne("").any(axis=1)] + + # Fill NaN after averaging + DATA_OVERALL_PL.fillna("", inplace=True) + + DATA_OVERALL_PL = DATA_OVERALL_PL[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_PL)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", f"STS Average ({len(TASK_LIST_STS_PL)} datasets)"]] + DATA_OVERALL_PL = DATA_OVERALL_PL[DATA_OVERALL_PL.iloc[:, 5:].ne("").any(axis=1)] + + return DATA_OVERALL_PL + +get_mteb_average() +get_mteb_average_fr() +get_mteb_average_pl() +get_mteb_average_zh() +DATA_BITEXT_MINING = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_BITEXT_MINING] +DATA_BITEXT_MINING_DA = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING_DA)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_BITEXT_MINING_DA] +DATA_CLASSIFICATION_DA = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_DA)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_DA] +DATA_CLASSIFICATION_NB = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_NB)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_NB] +DATA_CLASSIFICATION_SV = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_SV)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_SV] +DATA_CLASSIFICATION_OTHER = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_OTHER)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_OTHER] +DATA_CLUSTERING_DE = get_mteb_data(["Clustering"], [], TASK_LIST_CLUSTERING_DE)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLUSTERING_DE] +DATA_STS_OTHER = get_mteb_data(["STS"], [], TASK_LIST_STS_OTHER)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_STS_OTHER] +DATA_RETRIEVAL_LAW = get_mteb_data(["Retrieval"], [], TASK_LIST_RETRIEVAL_LAW)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_RETRIEVAL_LAW] + +# Exact, add all non-nan integer values for every dataset +NUM_SCORES = 0 +DATASETS = [] +MODELS = [] +# LANGUAGES = [] +for d in [ + DATA_BITEXT_MINING, + DATA_BITEXT_MINING_DA, + DATA_CLASSIFICATION_EN, + DATA_CLASSIFICATION_DA, + DATA_CLASSIFICATION_FR, + DATA_CLASSIFICATION_NB, + DATA_CLASSIFICATION_PL, + DATA_CLASSIFICATION_SV, + DATA_CLASSIFICATION_ZH, + DATA_CLASSIFICATION_OTHER, + DATA_CLUSTERING, + DATA_CLUSTERING_DE, + DATA_CLUSTERING_FR, + DATA_CLUSTERING_PL, + DATA_CLUSTERING_ZH, + DATA_PAIR_CLASSIFICATION, + DATA_PAIR_CLASSIFICATION_FR, + DATA_PAIR_CLASSIFICATION_PL, + DATA_PAIR_CLASSIFICATION_ZH, + DATA_RERANKING, + DATA_RERANKING_FR, + DATA_RERANKING_ZH, + DATA_RETRIEVAL, + DATA_RETRIEVAL_FR, + DATA_RETRIEVAL_PL, + DATA_RETRIEVAL_ZH, + DATA_RETRIEVAL_LAW, + DATA_STS_EN, + DATA_STS_FR, + DATA_STS_PL, + DATA_STS_ZH, + DATA_STS_OTHER, + DATA_SUMMARIZATION, + DATA_SUMMARIZATION_FR, +]: + # NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum() + cols_to_ignore = 4 if "Average" in d.columns else 3 + # Count number of scores including only non-nan floats & excluding the rank column + NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum() + # Exclude rank & model name column (first two); Do not count different language versions as different datasets + DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]] + # LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]] + MODELS += d["Model"].tolist() + +NUM_DATASETS = len(set(DATASETS)) +# NUM_LANGUAGES = len(set(LANGUAGES)) +NUM_MODELS = len(set(MODELS)) # 1. Force headers to wrap # 2. Force model column (maximum) width @@ -72,98 +1694,323 @@ Each inner tab can have the following keys: - language_long: [optional] The long form of the language - description: The description of the leaderboard - credits: [optional] The credits for the leaderboard -- desc: [optional] The description of the leaderboard - data: The data for the leaderboard +- refresh: The function to refresh the leaderboard """ -# No more refreshing manually, happens daily -# def get_refresh_function(task_category, task_list): -# def _refresh(): -# data_task_category = get_mteb_data(tasks=[task_category], datasets=task_list) -# data_task_category.drop(columns=["Embedding Dimensions", "Max Tokens"], inplace=True) -# return data_task_category -# return _refresh - - -# def get_refresh_overall_function(tasks): -# return lambda: get_mteb_average(tasks)[0] - - -# load in the pre-calculated `all_data_tasks` and `boards_data` -print(f"Loading pre-calculated data....") -all_data_tasks = load_results("all_data_tasks") -boards_data = load_results("boards_data") - -#### Caclulate Metadata -# Exact, add all non-nan integer values for every dataset -NUM_SCORES = 0 -DATASETS = [] -MODELS = [] -# LANGUAGES = [] -for d in all_data_tasks: - if isinstance(d, list) and len(d) == 0: - continue - # NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum() - cols_to_ignore = 4 if "Average" in d.columns else 3 - # Count number of scores including only non-nan floats & excluding the rank column - NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum() - # Exclude rank & model name column (first two); Do not count different language versions as different datasets - DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]] - # LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]] - MODELS += d["Model"].tolist() - - -NUM_DATASETS = len(set(DATASETS)) -# NUM_LANGUAGES = len(set(LANGUAGES)) -NUM_MODELS = len(set(MODELS)) +chinese_credits = "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)" +french_credits = "[Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)" +danish_credits = "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)" +norwegian_credits = "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)" +polish_credits = "[Rafał Poświata](https://github.com/rafalposwiata)" data = { - "Overall": {"metric": "Various, refer to task tabs", "data": []} + "Overall": { + "metric": "Various, refer to task tabs", + "data": [ + { + "language": "English", + "description": "**Overall MTEB English leaderboard** 🔮", + "data": DATA_OVERALL, + "refresh": get_mteb_average, + }, + { + "language": "Chinese", + "data": DATA_OVERALL_ZH, + "description": "**Overall MTEB Chinese leaderboard (C-MTEB)** 🔮🇨🇳", + "credits": chinese_credits, + "refresh": get_mteb_average_zh, + }, + { + "language": "French", + "data": DATA_OVERALL_FR, + "description": "**Overall MTEB French leaderboard (F-MTEB)** 🔮🇫🇷", + "credits": french_credits, + "refresh": get_mteb_average_fr, + }, + { + "language": "Polish", + "data": DATA_OVERALL_PL, + "description": "**Overall MTEB Polish leaderboard** 🔮🇵🇱", + "refresh": get_mteb_average_pl, + }, + ] + }, + "Bitext Mining": { + "metric": "[F1](https://huggingface.co/spaces/evaluate-metric/f1)", + "data": [ + { + "language": "English-X", + "language_long": "117 (Pairs of: English & other language)", + "description": "**Bitext Mining English-X Leaderboard** 🎌", + "data": DATA_BITEXT_MINING, + "refresh": partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING), + }, + { + "language": "Danish", + "language_long": "Danish & Bornholmsk (Danish Dialect)", + "description": "**Bitext Mining Danish Leaderboard** 🎌🇩🇰", + "credits": danish_credits, + "data": DATA_BITEXT_MINING_DA, + "refresh": partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING_DA), + } + ] + }, + "Classification": { + "metric": "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)", + "data": [ + { + "language": "English", + "description": "**Classification English Leaderboard** ❤️", + "data": DATA_CLASSIFICATION_EN, + "refresh": partial(get_mteb_data, tasks=["Classification"], langs=["en"]) + }, + { + "language": "Chinese", + "description": "**Classification Chinese Leaderboard** 🧡🇨🇳", + "credits": chinese_credits, + "data": DATA_CLASSIFICATION_ZH, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_ZH) + }, + { + "language": "Danish", + "description": "**Classification Danish Leaderboard** 🤍🇩🇰", + "credits": danish_credits, + "data": DATA_CLASSIFICATION_DA, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_DA) + }, + { + "language": "French", + "description": "**Classification French Leaderboard** 💙🇫🇷", + "credits": french_credits, + "data": DATA_CLASSIFICATION_FR, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_FR) + }, + { + "language": "Norwegian", + "language_long": "Norwegian Bokmål", + "description": "**Classification Norwegian Leaderboard** 💙🇳🇴", + "credits": norwegian_credits, + "data": DATA_CLASSIFICATION_NB, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_NB) + }, + { + "language": "Polish", + "description": "**Classification Polish Leaderboard** 🤍🇵🇱", + "credits": polish_credits, + "data": DATA_CLASSIFICATION_PL, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_PL) + }, + { + "language": "Swedish", + "description": "**Classification Swedish Leaderboard** 💛🇸🇪", + "credits": norwegian_credits, + "data": DATA_CLASSIFICATION_SV, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_SV) + }, + { + "language": "Other", + "language_long": "47 (Only languages not included in the other tabs)", + "description": "**Classification Other Languages Leaderboard** 💜💚💙", + "data": DATA_CLASSIFICATION_OTHER, + "refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_OTHER) + } + ] + }, + "Clustering": { + "metric": "Validity Measure (v_measure)", + "data": [ + { + "language": "English", + "description": "**Clustering Leaderboard** ✨", + "data": DATA_CLUSTERING, + "refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING) + }, + { + "language": "Chinese", + "description": "**Clustering Chinese Leaderboard** ✨🇨🇳", + "credits": chinese_credits, + "data": DATA_CLUSTERING_ZH, + "refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_ZH) + }, + { + "language": "French", + "description": "**Clustering French Leaderboard** ✨🇫🇷", + "credits": french_credits, + "data": DATA_CLUSTERING_FR, + "refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_FR) + }, + { + "language": "German", + "description": "**Clustering German Leaderboard** ✨🇩🇪", + "credits": "[Silvan](https://github.com/slvnwhrl)", + "data": DATA_CLUSTERING_DE, + "refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_DE) + }, + { + "language": "Polish", + "description": "**Clustering Polish Leaderboard** ✨🇵🇱", + "credits": polish_credits, + "data": DATA_CLUSTERING_PL, + "refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_PL) + }, + ] + }, + "Pair Classification": { + "metric": "Average Precision based on Cosine Similarities (cos_sim_ap)", + "data": [ + { + "language": "English", + "description": "**Pair Classification English Leaderboard** 🎭", + "data": DATA_PAIR_CLASSIFICATION, + "refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION) + }, + { + "language": "Chinese", + "description": "**Pair Classification Chinese Leaderboard** 🎭🇨🇳", + "credits": chinese_credits, + "data": DATA_PAIR_CLASSIFICATION_ZH, + "refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_ZH) + }, + { + "language": "French", + "description": "**Pair Classification French Leaderboard** 🎭🇫🇷", + "credits": french_credits, + "data": DATA_PAIR_CLASSIFICATION_FR, + "refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_FR) + }, + { + "language": "Polish", + "description": "**Pair Classification Polish Leaderboard** 🎭🇵🇱", + "credits": polish_credits, + "data": DATA_PAIR_CLASSIFICATION_PL, + "refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_PL) + }, + ] + }, + "Reranking": { + "metric": "Mean Average Precision (MAP)", + "data": [ + { + "language": "English", + "description": "**Reranking English Leaderboard** 🥈", + "data": DATA_RERANKING, + "refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING) + }, + { + "language": "Chinese", + "description": "**Reranking Chinese Leaderboard** 🥈🇨🇳", + "credits": chinese_credits, + "data": DATA_RERANKING_ZH, + "refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_ZH) + }, + { + "language": "French", + "description": "**Reranking French Leaderboard** 🥈🇫🇷", + "credits": french_credits, + "data": DATA_RERANKING_FR, + "refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_FR) + } + ] + }, + "Retrieval": { + "metric": "Normalized Discounted Cumulative Gain @ k (ndcg_at_10)", + "data": [ + { + "language": "English", + "description": "**Retrieval English Leaderboard** 🔎", + "data": DATA_RETRIEVAL, + "refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL) + }, + { + "language": "Chinese", + "description": "**Retrieval Chinese Leaderboard** 🔎🇨🇳", + "credits": chinese_credits, + "data": DATA_RETRIEVAL_ZH, + "refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_ZH) + }, + { + "language": "French", + "description": "**Retrieval French Leaderboard** 🔎🇫🇷", + "credits": french_credits, + "data": DATA_RETRIEVAL_FR, + "refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_FR) + }, + { + "language": "Law", + "language_long": "English, German, Chinese", + "description": "**Retrieval Law Leaderboard** 🔎⚖️", + "credits": "[Voyage AI](https://www.voyageai.com/)", + "data": DATA_RETRIEVAL_LAW, + "refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_LAW) + }, + { + "language": "Polish", + "description": "**Retrieval Polish Leaderboard** 🔎🇵🇱", + "credits": polish_credits, + "data": DATA_RETRIEVAL_PL, + "refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_PL) + } + ] + }, + "STS": { + "metric": "Spearman correlation based on cosine similarity", + "data": [ + { + "language": "English", + "description": "**STS English Leaderboard** 🤖", + "data": DATA_STS_EN, + "refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS) + }, + { + "language": "Chinese", + "description": "**STS Chinese Leaderboard** 🤖🇨🇳", + "credits": chinese_credits, + "data": DATA_STS_ZH, + "refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_ZH) + }, + { + "language": "French", + "description": "**STS French Leaderboard** 🤖🇫🇷", + "credits": french_credits, + "data": DATA_STS_FR, + "refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_FR) + }, + { + "language": "Polish", + "description": "**STS Polish Leaderboard** 🤖🇵🇱", + "credits": polish_credits, + "data": DATA_STS_PL, + "refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_PL) + }, + { + "language": "Other", + "language_long": "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)", + "description": "**STS Other Leaderboard** 👽", + "data": DATA_STS_OTHER, + "refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_OTHER) + }, + ] + }, + "Summarization": { + "metric": "Spearman correlation based on cosine similarity", + "data": [ + { + "language": "English", + "description": "**Summarization Leaderboard** 📜", + "data": DATA_SUMMARIZATION, + "refresh": partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION) + }, + { + "language": "French", + "description": "**Summarization Leaderboard** 📜", + "credits": french_credits, + "data": DATA_SUMMARIZATION_FR, + "refresh": partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION_FR) + } + ] + } } -for task in TASKS: - data[task] = {"metric": TASKS_CONFIG[task]["metric_description"], "data": []} - -for board, board_config in BOARDS_CONFIG.items(): - init_name = board_config["title"] - if init_name in PRETTY_NAMES: - init_name = PRETTY_NAMES[init_name] - board_pretty_name = f"{init_name} leaderboard" - acronym = board_config.get("acronym", None) - board_icon = board_config.get("icon", None) - if board_icon is None: - board_icon = "" - credits = board_config.get("credits", None) - metric = board_config.get("metric", None) - desc = board_config.get("desc", None) - - if board_config["has_overall"]: - overall_pretty_name = board_pretty_name - if acronym is not None: - overall_pretty_name += f" ({board_config['acronym']})" - data["Overall"]["data"].append({ - "language": board_config["title"], - "language_long": board_config["language_long"], - "description": f"**Overall MTEB {overall_pretty_name}** 🔮{board_icon}", - "data": boards_data[board]["data_overall"], - # "refresh": get_refresh_overall_function(board_config["tasks"]), - "credits": credits, - "metric": metric, - "desc": desc, - }) - for task_category, task_category_list in board_config["tasks"].items(): - task_icon = TASKS_CONFIG[task_category]['icon'] - if "special_icons" in board_config and isinstance(board_config["special_icons"], dict): - task_icon = board_config["special_icons"].get(task_category, task_icon) - data[task_category]["data"].append({ - "language": board_config["title"], - "language_long": board_config["language_long"], - "description": f"**{task_category} {board_pretty_name}** {task_icon}{board_icon}", - "data": boards_data[board]["data_tasks"][task_category], - # "refresh": get_refresh_function(task_category, task_category_list), - "credits": credits, - "metric": metric, - "desc": desc, - }) dataframes = [] full_dataframes = [] @@ -189,11 +2036,7 @@ function(goalUrlObject) { def update_url_task(event: gr.SelectData, current_task_language: dict, language_per_task: dict): current_task_language["task"] = event.target.id # Either use the cached language for this task or the 1st language - try: - current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[1].children[0].id) - except Exception as e: # is Overall tab, no description - current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id) - + current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id) return current_task_language, language_per_task def update_url_language(event: gr.SelectData, current_task_language: dict, language_per_task: dict): @@ -215,10 +2058,6 @@ MODEL_TYPES = [ "Open", "Proprietary", "Sentence Transformers", - "Cross-Encoders", - "Bi-Encoders", - "Uses Instructions", - "No Instructions", ] def filter_data(search_query, model_types, model_sizes, *full_dataframes): @@ -229,7 +2068,7 @@ def filter_data(search_query, model_types, model_sizes, *full_dataframes): names = df["Model"].map(lambda x: re.match("(.+)", x).group(1)) masks = [] for query in search_query.split(";"): - masks.append(names.str.lower().str.contains(query.lower())) + masks.append(names.str.contains(query)) df = df[reduce(lambda a, b: a | b, masks)] # Apply the model type filtering @@ -242,14 +2081,6 @@ def filter_data(search_query, model_types, model_sizes, *full_dataframes): masks.append(df["Model"].isin(PROPRIETARY_MODELS)) elif model_type == "Sentence Transformers": masks.append(df["Model"].isin(SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS)) - elif model_type == "Cross-Encoders": - masks.append(df["Model"].isin(CROSS_ENCODERS)) - elif model_type == "Bi-Encoders": - masks.append(df["Model"].isin(BI_ENCODERS)) - elif model_type == "Uses Instructions": - masks.append(df["Model"].isin(INSTRUCT_MODELS)) - elif model_type == "No Instructions": - masks.append(df["Model"].isin(NOINSTRUCT_MODELS)) if masks: df = df[reduce(lambda a, b: a | b, masks)] else: @@ -261,10 +2092,10 @@ def filter_data(search_query, model_types, model_sizes, *full_dataframes): sizes = df["Model Size (Million Parameters)"].replace('', 0) mask = sizes.apply(lambda size: any(numeric_interval.contains(size))) df = df[mask] + output_dataframes.append(df) return output_dataframes - with gr.Blocks(css=css) as block: # Store the current task and language for updating the URL. This is a bit hacky, but it works @@ -273,7 +2104,7 @@ with gr.Blocks(css=css) as block: language_per_task = gr.JSON(value=dict(), visible=False) gr.Markdown(f""" - Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models. Also check out [MTEB Arena](https://huggingface.co/spaces/mteb/arena) ⚔️ + Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models. """) with gr.Row(): @@ -286,8 +2117,7 @@ with gr.Blocks(css=css) as block: choices=MODEL_TYPES, value=MODEL_TYPES, interactive=True, - elem_classes=["filter-checkbox-group"], - scale=3, + elem_classes=["filter-checkbox-group"] ) filter_model_sizes = gr.CheckboxGroup( label="Model sizes (in number of parameters)", @@ -301,17 +2131,16 @@ with gr.Blocks(css=css) as block: with gr.Tabs() as outer_tabs: # Store the tabs for updating them on load based on URL parameters tabs.append(outer_tabs) + for task, task_values in data.items(): metric = task_values["metric"] task_tab_id = task.lower().replace(" ", "-") # Overall, Bitext Mining, Classification, etc. - pretty_task_name = task if task not in PRETTY_NAMES.keys() else PRETTY_NAMES[task] - with gr.Tab(pretty_task_name, id=task_tab_id) as task_tab: + with gr.Tab(task, id=task_tab_id) as task_tab: # For updating the 'task' in the URL task_tab.select(update_url_task, [current_task_language, language_per_task], [current_task_language, language_per_task]).then(None, [current_task_language], [], js=set_window_url_params) - if "Overall" != task: - gr.Markdown(TASK_DESCRIPTIONS[task]) + with gr.Tabs() as task_tabs: # Store the task tabs for updating them on load based on URL parameters tabs.append(task_tabs) @@ -324,18 +2153,13 @@ with gr.Blocks(css=css) as block: # For updating the 'language' in the URL item_tab.select(update_url_language, [current_task_language, language_per_task], [current_task_language, language_per_task], trigger_mode="always_last").then(None, [current_task_language], [], js=set_window_url_params) - specific_metric = metric - if item.get("metric", None) is not None: - specific_metric = item['metric'] - with gr.Row(): gr.Markdown(f""" {item['description']} - - **Metric:** {specific_metric} + - **Metric:** {metric} - **Languages:** {item['language_long'] if 'language_long' in item else item['language']} - {"- **Credits:** " + item['credits'] if ("credits" in item and item["credits"] is not None) else ''} - {"- **Description:** " + item['desc'] if ("desc" in item and item["desc"] is not None) else ''} + {"- **Credits:** " + item['credits'] if "credits" in item else ''} """) with gr.Row(): @@ -346,9 +2170,9 @@ with gr.Blocks(css=css) as block: full_dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", visible=False) full_dataframes.append(full_dataframe) - # with gr.Row(): - # refresh_button = gr.Button("Refresh") - # refresh_button.click(item["refresh"], inputs=None, outputs=dataframe, concurrency_limit=20) + with gr.Row(): + refresh_button = gr.Button("Refresh") + refresh_button.click(item["refresh"], inputs=None, outputs=dataframe) gr.Markdown(f""" - **Total Datasets**: {NUM_DATASETS} @@ -399,14 +2223,6 @@ with gr.Blocks(css=css) as block: block.queue(max_size=10) block.launch() -# Add model names here so the mteb/leaderboard space shows up on their model page -# from envs import MODEL_META -# print("','".join(MODEL_META["models_to_skip"])) -# print("','".join(list(MODEL_META['model_meta'].keys()))) -# print("','".join([x['link'].split("co/")[-1] for x in MODEL_META['model_meta'].values() if (x.get('link', None)) and ("huggingface.co" in x['link'])])) -# from envs import API; print("','".join([x.modelId for x in list(API.list_models(filter="mteb")) if x.modelId not in UNUSED])) -UNUSED = ['michaelfeil/ct2fast-e5-large-v2','McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse','newsrx/instructor-xl','sionic-ai/sionic-ai-v1','lsf1000/bge-evaluation','Intel/bge-small-en-v1.5-sst2','newsrx/instructor-xl-newsrx','McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse','McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse','davidpeer/gte-small','goldenrooster/multilingual-e5-large','kozistr/fused-large-en','mixamrepijey/instructor-small','McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised','DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext','Intel/bge-base-en-v1.5-sst2-int8-dynamic','morgendigital/multilingual-e5-large-quantized','BAAI/bge-small-en','ggrn/e5-small-v2','vectoriseai/gte-small','giulio98/placeholder','odunola/UAE-Large-VI','vectoriseai/e5-large-v2','gruber/e5-small-v2-ggml','Severian/nomic','arcdev/e5-mistral-7b-instruct','mlx-community/multilingual-e5-base-mlx','michaelfeil/ct2fast-bge-base-en-v1.5','Intel/bge-small-en-v1.5-sst2-int8-static','jncraton/stella-base-en-v2-ct2-int8','vectoriseai/multilingual-e5-large','rlsChapters/Chapters-SFR-Embedding-Mistral','arcdev/SFR-Embedding-Mistral','McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised','McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised','vectoriseai/gte-base','mixamrepijey/instructor-models','GovCompete/e5-large-v2','ef-zulla/e5-multi-sml-torch','khoa-klaytn/bge-small-en-v1.5-angle','krilecy/e5-mistral-7b-instruct','vectoriseai/bge-base-en-v1.5','vectoriseai/instructor-base','jingyeom/korean_embedding_model','rizki/bgr-tf','barisaydin/bge-base-en','jamesgpt1/zzz','Malmuk1/e5-large-v2_Sharded','vectoriseai/ember-v1','Consensus/instructor-base','barisaydin/bge-small-en','barisaydin/gte-base','woody72/multilingual-e5-base','Einas/einas_ashkar','michaelfeil/ct2fast-bge-large-en-v1.5','vectoriseai/bge-small-en-v1.5','iampanda/Test','cherubhao/yogamodel','ieasybooks/multilingual-e5-large-onnx','jncraton/e5-small-v2-ct2-int8','radames/e5-large','khoa-klaytn/bge-base-en-v1.5-angle','Intel/bge-base-en-v1.5-sst2-int8-static','vectoriseai/e5-large','TitanML/jina-v2-base-en-embed','Koat/gte-tiny','binqiangliu/EmbeddingModlebgelargeENv1.5','beademiguelperez/sentence-transformers-multilingual-e5-small','sionic-ai/sionic-ai-v2','jamesdborin/jina-v2-base-en-embed','maiyad/multilingual-e5-small','dmlls/all-mpnet-base-v2','odunola/e5-base-v2','vectoriseai/bge-large-en-v1.5','vectoriseai/bge-small-en','karrar-alwaili/UAE-Large-V1','t12e/instructor-base','Frazic/udever-bloom-3b-sentence','Geolumina/instructor-xl','hsikchi/dump','recipe/embeddings','michaelfeil/ct2fast-bge-small-en-v1.5','ildodeltaRule/multilingual-e5-large','shubham-bgi/UAE-Large','BAAI/bge-large-en','michaelfeil/ct2fast-e5-small-v2','cgldo/semanticClone','barisaydin/gte-small','aident-ai/bge-base-en-onnx','jamesgpt1/english-large-v1','michaelfeil/ct2fast-e5-small','baseplate/instructor-large-1','newsrx/instructor-large','Narsil/bge-base-en','michaelfeil/ct2fast-e5-large','mlx-community/multilingual-e5-small-mlx','lightbird-ai/nomic','MaziyarPanahi/GritLM-8x7B-GGUF','newsrx/instructor-large-newsrx','dhairya0907/thenlper-get-large','barisaydin/bge-large-en','jncraton/bge-small-en-ct2-int8','retrainai/instructor-xl','BAAI/bge-base-en','gentlebowl/instructor-large-safetensors','d0rj/e5-large-en-ru','atian-chapters/Chapters-SFR-Embedding-Mistral','Intel/bge-base-en-v1.5-sts-int8-static','Intel/bge-base-en-v1.5-sts-int8-dynamic','jncraton/GIST-small-Embedding-v0-ct2-int8','jncraton/gte-tiny-ct2-int8','d0rj/e5-small-en-ru','vectoriseai/e5-small-v2','SmartComponents/bge-micro-v2','michaelfeil/ct2fast-gte-base','vectoriseai/e5-base-v2','Intel/bge-base-en-v1.5-sst2','McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised','Research2NLP/electrical_stella','weakit-v/bge-base-en-v1.5-onnx','GovCompete/instructor-xl','barisaydin/text2vec-base-multilingual','Intel/bge-small-en-v1.5-sst2-int8-dynamic','jncraton/gte-small-ct2-int8','d0rj/e5-base-en-ru','barisaydin/gte-large','fresha/e5-large-v2-endpoint','vectoriseai/instructor-large','Severian/embed','vectoriseai/e5-base','mlx-community/multilingual-e5-large-mlx','vectoriseai/gte-large','anttip/ct2fast-e5-small-v2-hfie','michaelfeil/ct2fast-gte-large','gizmo-ai/Cohere-embed-multilingual-v3.0','McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse','Kenknight1999/tungdd7_ft_e5','joteqwork/new_gsev0','vantagediscovery/jina-embeddings-v2-base-en','vantagediscovery/nomic-embed-text-v1','vantagediscovery/nomic-embed-text-v1.5','srikanthmalla/hkunlp-instructor-xl','afrideva/GIST-all-MiniLM-L6-v2-GGUF','nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF','agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF','ekorman-strive/bge-large-en-v1.5','raghavlight/SE_v1','liddlefish/privacyembeddingv2_bge_small','ahmet1338/finetuned_embedder','radia/snowflake-arctic-embed-l-Q4_K_M-GGUF','GregorBiswanger/GritLM-7B-Q4_K_M-GGUF','powermove72/GritLM-7B-Q4_K_M-GGUF','sunzx0810/gte-Qwen2-7B-instruct-Q5_K_M-GGUF','nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF','nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF','fishbone64/gte-Qwen2-7B-instruct-Q8_0-GGUF','tobchef/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF','liddlefish/privacy_embedding_rag','liddlefish/privacy_embedding_rag_10k_tmp','liddlefish/privacy_embedding_bge_small_synthetic','mxs980/gte-Qwen2-1.5B-instruct-Q8_0-GGUF','leonn71/gte-Qwen2-1.5B-instruct-Q6_K-GGUF', 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- # Possible changes: # Could add graphs / other visual content # Could add verification marks diff --git a/boards_data/bright/data_overall/default.txt b/boards_data/bright/data_overall/default.txt deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/boards_data/bright/data_tasks/Retrieval/default.jsonl b/boards_data/bright/data_tasks/Retrieval/default.jsonl deleted file mode 100644 index 0ce61322ddf399a1899ccddb95414a90d4177c0f..0000000000000000000000000000000000000000 --- a/boards_data/bright/data_tasks/Retrieval/default.jsonl +++ /dev/null @@ -1,14 +0,0 @@ -{"index":4,"Rank":1,"Model":"gte-Qwen2-7B-instruct<\/a>","Model Size (Million Parameters)":7613,"Memory Usage (GB, fp32)":28.36,"Embedding Dimensions":3584,"Max Tokens":32768,"Average":22.89,"BrightRetrieval (aops)":15.1,"BrightRetrieval (biology)":32.09,"BrightRetrieval (earth_science)":40.66,"BrightRetrieval (economics)":16.18,"BrightRetrieval 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Dimensions":30522,"Max Tokens":512,"Average":"","AFQMC":"","ATEC":"","BQ":"","LCQMC":"","PAWSX":"","QBQTC":"","STS22 (zh)":"","STSB":""} diff --git a/config.yaml b/config.yaml deleted file mode 100644 index be1e9b4d6be1d231659288ece458746b32d77b1a..0000000000000000000000000000000000000000 --- a/config.yaml +++ /dev/null @@ -1,552 +0,0 @@ -config: - REPO_ID: "mteb/leaderboard" - RESULTS_REPO: mteb/results - LEADERBOARD_NAME: "MTEB Leaderboard" -tasks: - BitextMining: - icon: "🎌" - metric: f1 - metric_description: "[F1](https://huggingface.co/spaces/evaluate-metric/f1)" - task_description: "Bitext mining is the task of finding parallel sentences in two languages." - Classification: - icon: "❤️" - metric: accuracy - metric_description: "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)" - task_description: "Classification is the task of assigning a label to a text." - Clustering: - icon: "✨" - metric: v_measure - metric_description: "Validity Measure (V-measure)" - task_description: "Clustering is the task of grouping similar documents together." - PairClassification: - icon: "🎭" - metric: max_ap - metric_description: "Average Precision (AP) based on the models similarity metric (usually cosine)" - task_description: "Pair classification is the task of determining whether two texts are similar." - Reranking: - icon: "🥈" - metric: map - metric_description: "Mean Average Precision (MAP)" - task_description: "Reranking is the task of reordering a list of documents to improve relevance." - Retrieval: - icon: "🔎" - metric: ndcg_at_10 - metric_description: "Normalized Discounted Cumulative Gain @ 10 (nDCG@10)" - task_description: "Retrieval is the task of finding relevant documents for a query." - STS: - icon: "☘️" - metric: cosine_spearman - metric_description: "Spearman correlation based on the model's similarity metric (usually cosine)" - task_description: "Semantic Textual Similarity is the task of determining how similar two texts are." - Summarization: - icon: "📜" - metric: cosine_spearman - metric_description: "Spearman correlation based on the model's similarity metric (usually cosine)" - task_description: "Summarization is the task of generating a summary of a text." - MultilabelClassification: - icon: "🏷️" - metric: accuracy - metric_description: "Accuracy" - task_description: "Multilabel classification is the task of assigning multiple labels to a text." - InstructionRetrieval: - icon: "🔎📋" - metric: "p-MRR" - metric_description: "paired mean reciprocal rank (p-MRR)" - task_description: "Retrieval w/Instructions is the task of finding relevant documents for a query that has detailed instructions." -boards: - en: - title: English - language_long: "English" - has_overall: true - acronym: null - icon: null - special_icons: null - credits: null - tasks: - Classification: - - AmazonCounterfactualClassification (en) - - AmazonPolarityClassification - - AmazonReviewsClassification (en) - - Banking77Classification - - EmotionClassification - - ImdbClassification - - MassiveIntentClassification (en) - - MassiveScenarioClassification (en) - - MTOPDomainClassification (en) - - MTOPIntentClassification (en) - - ToxicConversationsClassification - - TweetSentimentExtractionClassification - Clustering: - - ArxivClusteringP2P - - ArxivClusteringS2S - - BiorxivClusteringP2P - - BiorxivClusteringS2S - - MedrxivClusteringP2P - - MedrxivClusteringS2S - - RedditClustering - - RedditClusteringP2P - - StackExchangeClustering - - StackExchangeClusteringP2P - - TwentyNewsgroupsClustering - PairClassification: - - SprintDuplicateQuestions - - TwitterSemEval2015 - - TwitterURLCorpus - Reranking: - - AskUbuntuDupQuestions - - MindSmallReranking - - SciDocsRR - - StackOverflowDupQuestions - Retrieval: - - ArguAna - - ClimateFEVER - - CQADupstackRetrieval - - DBPedia - - FEVER - - FiQA2018 - - HotpotQA - - MSMARCO - - NFCorpus - - NQ - - QuoraRetrieval - - SCIDOCS - - SciFact - - Touche2020 - - TRECCOVID - STS: - - BIOSSES - - SICK-R - - STS12 - - STS13 - - STS14 - - STS15 - - STS16 - - STS17 (en-en) - - STS22 (en) - - STSBenchmark - Summarization: - - SummEval - en-x: - title: "English-X" - language_long: "117 (Pairs of: English & other language)" - has_overall: false - acronym: null - icon: null - special_icons: null - credits: null - tasks: - BitextMining: ['BUCC (de-en)', 'BUCC (fr-en)', 'BUCC (ru-en)', 'BUCC (zh-en)', 'Tatoeba (afr-eng)', 'Tatoeba (amh-eng)', 'Tatoeba (ang-eng)', 'Tatoeba (ara-eng)', 'Tatoeba (arq-eng)', 'Tatoeba (arz-eng)', 'Tatoeba (ast-eng)', 'Tatoeba (awa-eng)', 'Tatoeba (aze-eng)', 'Tatoeba (bel-eng)', 'Tatoeba (ben-eng)', 'Tatoeba (ber-eng)', 'Tatoeba (bos-eng)', 'Tatoeba (bre-eng)', 'Tatoeba (bul-eng)', 'Tatoeba (cat-eng)', 'Tatoeba (cbk-eng)', 'Tatoeba (ceb-eng)', 'Tatoeba (ces-eng)', 'Tatoeba (cha-eng)', 'Tatoeba (cmn-eng)', 'Tatoeba (cor-eng)', 'Tatoeba (csb-eng)', 'Tatoeba (cym-eng)', 'Tatoeba (dan-eng)', 'Tatoeba (deu-eng)', 'Tatoeba (dsb-eng)', 'Tatoeba (dtp-eng)', 'Tatoeba (ell-eng)', 'Tatoeba (epo-eng)', 'Tatoeba (est-eng)', 'Tatoeba (eus-eng)', 'Tatoeba (fao-eng)', 'Tatoeba (fin-eng)', 'Tatoeba (fra-eng)', 'Tatoeba (fry-eng)', 'Tatoeba (gla-eng)', 'Tatoeba (gle-eng)', 'Tatoeba (glg-eng)', 'Tatoeba (gsw-eng)', 'Tatoeba (heb-eng)', 'Tatoeba (hin-eng)', 'Tatoeba (hrv-eng)', 'Tatoeba (hsb-eng)', 'Tatoeba (hun-eng)', 'Tatoeba (hye-eng)', 'Tatoeba (ido-eng)', 'Tatoeba (ile-eng)', 'Tatoeba (ina-eng)', 'Tatoeba (ind-eng)', 'Tatoeba (isl-eng)', 'Tatoeba (ita-eng)', 'Tatoeba (jav-eng)', 'Tatoeba (jpn-eng)', 'Tatoeba (kab-eng)', 'Tatoeba (kat-eng)', 'Tatoeba (kaz-eng)', 'Tatoeba (khm-eng)', 'Tatoeba (kor-eng)', 'Tatoeba (kur-eng)', 'Tatoeba (kzj-eng)', 'Tatoeba (lat-eng)', 'Tatoeba (lfn-eng)', 'Tatoeba (lit-eng)', 'Tatoeba (lvs-eng)', 'Tatoeba (mal-eng)', 'Tatoeba (mar-eng)', 'Tatoeba (max-eng)', 'Tatoeba (mhr-eng)', 'Tatoeba (mkd-eng)', 'Tatoeba (mon-eng)', 'Tatoeba (nds-eng)', 'Tatoeba (nld-eng)', 'Tatoeba (nno-eng)', 'Tatoeba (nob-eng)', 'Tatoeba (nov-eng)', 'Tatoeba (oci-eng)', 'Tatoeba (orv-eng)', 'Tatoeba (pam-eng)', 'Tatoeba (pes-eng)', 'Tatoeba (pms-eng)', 'Tatoeba (pol-eng)', 'Tatoeba (por-eng)', 'Tatoeba (ron-eng)', 'Tatoeba (rus-eng)', 'Tatoeba (slk-eng)', 'Tatoeba (slv-eng)', 'Tatoeba (spa-eng)', 'Tatoeba (sqi-eng)', 'Tatoeba (srp-eng)', 'Tatoeba (swe-eng)', 'Tatoeba (swg-eng)', 'Tatoeba (swh-eng)', 'Tatoeba (tam-eng)', 'Tatoeba (tat-eng)', 'Tatoeba (tel-eng)', 'Tatoeba (tgl-eng)', 'Tatoeba (tha-eng)', 'Tatoeba (tuk-eng)', 'Tatoeba (tur-eng)', 'Tatoeba (tzl-eng)', 'Tatoeba (uig-eng)', 'Tatoeba (ukr-eng)', 'Tatoeba (urd-eng)', 'Tatoeba (uzb-eng)', 'Tatoeba (vie-eng)', 'Tatoeba (war-eng)', 'Tatoeba (wuu-eng)', 'Tatoeba (xho-eng)', 'Tatoeba (yid-eng)', 'Tatoeba (yue-eng)', 'Tatoeba (zsm-eng)'] - zh: - title: Chinese - language_long: Chinese - has_overall: true - acronym: C-MTEB - icon: "🇨🇳" - special_icons: - Classification: "🧡" - credits: "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)" - tasks: - Classification: - - AmazonReviewsClassification (zh) - - IFlyTek - - JDReview - - MassiveIntentClassification (zh-CN) - - MassiveScenarioClassification (zh-CN) - - MultilingualSentiment - - OnlineShopping - - TNews - - Waimai - Clustering: - - CLSClusteringP2P - - CLSClusteringS2S - - ThuNewsClusteringP2P - - ThuNewsClusteringS2S - PairClassification: - - Cmnli - - Ocnli - Reranking: - - CMedQAv1 - - CMedQAv2 - - MMarcoReranking - - T2Reranking - Retrieval: - - CmedqaRetrieval - - CovidRetrieval - - DuRetrieval - - EcomRetrieval - - MedicalRetrieval - - MMarcoRetrieval - - T2Retrieval - - VideoRetrieval - STS: - - AFQMC - - ATEC - - BQ - - LCQMC - - PAWSX - - QBQTC - - STS22 (zh) - - STSB - da: - title: Danish - language_long: Danish - has_overall: false - acronym: null - icon: "🇩🇰" - special_icons: - Classification: "🤍" - credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)" - tasks: - BitextMining: - - BornholmBitextMining - Classification: - - AngryTweetsClassification - - DanishPoliticalCommentsClassification - - DKHateClassification - - LccSentimentClassification - - MassiveIntentClassification (da) - - MassiveScenarioClassification (da) - - NordicLangClassification - - ScalaDaClassification - fr: - title: French - language_long: "French" - has_overall: true - acronym: "F-MTEB" - icon: "🇫🇷" - special_icons: - Classification: "💙" - credits: "[Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)" - tasks: - Classification: - - AmazonReviewsClassification (fr) - - MasakhaNEWSClassification (fra) - - MassiveIntentClassification (fr) - - MassiveScenarioClassification (fr) - - MTOPDomainClassification (fr) - - MTOPIntentClassification (fr) - Clustering: - - AlloProfClusteringP2P - - AlloProfClusteringS2S - - HALClusteringS2S - - MLSUMClusteringP2P (fr) - - MLSUMClusteringS2S (fr) - - MasakhaNEWSClusteringP2P (fra) - - MasakhaNEWSClusteringS2S (fra) - PairClassification: - - OpusparcusPC (fr) - - PawsXPairClassification (fr) - Reranking: - - AlloprofReranking - - SyntecReranking - Retrieval: - - AlloprofRetrieval - - BSARDRetrieval - - MintakaRetrieval (fr) - - SyntecRetrieval - - XPQARetrieval (fr) - STS: - - STS22 (fr) - - STSBenchmarkMultilingualSTS (fr) - - SICKFr - Summarization: - - SummEvalFr - 'no': - title: Norwegian - language_long: "Norwegian Bokmål" - has_overall: false - acronym: null - icon: "🇳🇴" - special_icons: - Classification: "💙" - credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)" - tasks: - Classification: &id001 - - NoRecClassification - - NordicLangClassification - - NorwegianParliament - - MassiveIntentClassification (nb) - - MassiveScenarioClassification (nb) - - ScalaNbClassification - instructions: - title: English - language_long: "English" - has_overall: false - acronym: null - icon: null - credits: "[Orion Weller, FollowIR](https://arxiv.org/abs/2403.15246)" - tasks: - InstructionRetrieval: - - Robust04InstructionRetrieval - - News21InstructionRetrieval - - Core17InstructionRetrieval - de: - title: German - language_long: "German" - has_overall: false - acronym: null - icon: "🇩🇪" - special_icons: null - credits: "[Silvan](https://github.com/slvnwhrl)" - tasks: - Clustering: - - BlurbsClusteringP2P - - BlurbsClusteringS2S - - TenKGnadClusteringP2P - - TenKGnadClusteringS2S - pl: - title: Polish - language_long: Polish - has_overall: true - acronym: null - icon: "🇵🇱" - special_icons: - Classification: "🤍" - credits: "[Rafał Poświata](https://github.com/rafalposwiata)" - tasks: - Classification: - - AllegroReviews - - CBD - - MassiveIntentClassification (pl) - - MassiveScenarioClassification (pl) - - PAC - - PolEmo2.0-IN - - PolEmo2.0-OUT - Clustering: - - 8TagsClustering - PairClassification: - - CDSC-E - - PPC - - PSC - - SICK-E-PL - Retrieval: - - ArguAna-PL - - DBPedia-PL - - FiQA-PL - - HotpotQA-PL - - MSMARCO-PL - - NFCorpus-PL - - NQ-PL - - Quora-PL - - SCIDOCS-PL - - SciFact-PL - - TRECCOVID-PL - STS: - - CDSC-R - - SICK-R-PL - - STS22 (pl) - ru: - title: Russian - language_long: "Russian" - has_overall: true - acronym: null - icon: "🇷🇺" - special_icons: null - credits: "[Roman Solomatin](https://github.com/Samoed) and SaluteDevices: [Alena Fenogenova](https://github.com/Alenush), [Aleksandr Abramov](https://github.com/Ab1992ao), [Artem Snegirev](https://github.com/artemsnegirev), [Anna Maksimova](https://github.com/anpalmak2003), [Maria Tikhonova](https://github.com/MariyaTikhonova)" - tasks: - Classification: - - GeoreviewClassification - - HeadlineClassification - - InappropriatenessClassification - - KinopoiskClassification - - RuReviewsClassification - - RuSciBenchGRNTIClassification - - RuSciBenchOECDClassification - - MassiveIntentClassification (ru) - - MassiveScenarioClassification (ru) - Clustering: - - GeoreviewClusteringP2P - - RuSciBenchGRNTIClusteringP2P - - RuSciBenchOECDClusteringP2P - PairClassification: - - TERRa - Reranking: - - RuBQReranking - - MIRACLReranking (ru) - Retrieval: - - RiaNewsRetrieval - - RuBQRetrieval - - MIRACLRetrieval (ru) - STS: - - RUParaPhraserSTS - - RuSTSBenchmarkSTS - - STS22 (ru) - MultilabelClassification: - - CEDRClassification - - SensitiveTopicsClassification - se: - title: Swedish - language_long: Swedish - has_overall: false - acronym: null - icon: "🇸🇪" - special_icons: - Classification: "💛" - credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)" - tasks: - Classification: - - NoRecClassification - - NordicLangClassification - - NorwegianParliament - - MassiveIntentClassification (nb) - - MassiveScenarioClassification (nb) - - ScalaNbClassification - other-cls: - title: "Other Languages" - language_long: "47 (Only languages not included in the other tabs)" - has_overall: false - acronym: null - icon: null - special_icons: - Classification: "💜💚💙" - credits: null - tasks: - Classification: ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)'] - other-sts: - title: Other - language_long: "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)" - has_overall: false - acronym: null - icon: null - special_icons: null - credits: null - tasks: - STS: ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark"] - law: - title: Law - language_long: "English, German, Chinese" - has_overall: false - acronym: null - icon: "⚖️" - special_icons: null - credits: "[Voyage AI](https://www.voyageai.com/)" - tasks: - Retrieval: - - AILACasedocs - - AILAStatutes - - GerDaLIRSmall - - LeCaRDv2 - - LegalBenchConsumerContractsQA - - LegalBenchCorporateLobbying - - LegalQuAD - - LegalSummarization - longembed: - title: LongEmbed - language_long: "English" - has_overall: false - acronym: null - icon: "📚" - special_icons: null - credits: "[LongEmbed (Dawei Zhu et al.)](https://arxiv.org/abs/2404.12096v2)" - metric: nDCG@10 (for NarrativeQA, QMSum, SummScreenFD, WikimQA) & nDCG@1 (for passkey and needle) - tasks: - Retrieval: - - LEMBNarrativeQARetrieval - - LEMBNeedleRetrieval - - LEMBPasskeyRetrieval - - LEMBQMSumRetrieval - - LEMBSummScreenFDRetrieval - - LEMBWikimQARetrieval - rar-b: - title: RAR-b - language_long: "English" - has_overall: false - acronym: null - icon: "📚" - special_icons: null - credits: "[RAR-b (Chenghao Xiao et al.)](https://arxiv.org/abs/2404.06347/)" - metric: nDCG@10 - tasks: - Retrieval: - - ARCChallenge - - AlphaNLI - - HellaSwag - - PIQA - - Quail - - RARbCode - - RARbMath - - SIQA - - SpartQA - - TempReasonL1 - - TempReasonL2Fact - - TempReasonL2Pure - - TempReasonL3Fact - - TempReasonL3Pure - - WinoGrande - bright: - title: BRIGHT - language_long: "English" - has_overall: false - acronym: null - icon: "🌟" - special_icons: null - credits: "[BRIGHT (Hongjin Su, Howard Yen, Mengzhou Xia et al.)](https://brightbenchmark.github.io/)" - metric: nDCG@10 - split: standard - desc: This tab only allows submissions with the original queries; not results from LLM rewritten queries or using reranking. - tasks: - Retrieval: - - BrightRetrieval (biology) - - BrightRetrieval (earth_science) - - BrightRetrieval (economics) - - BrightRetrieval (psychology) - - BrightRetrieval (robotics) - - BrightRetrieval (stackoverflow) - - BrightRetrieval (sustainable_living) - - BrightRetrieval (pony) - - BrightRetrieval (leetcode) - - BrightRetrieval (aops) - - BrightRetrieval (theoremqa_theorems) - - BrightRetrieval (theoremqa_questions) - bright_long: - title: BRIGHT Long - language_long: "English" - has_overall: false - acronym: null - icon: "🌟" - special_icons: null - credits: "[BRIGHT (Hongjin Su, Howard Yen, Mengzhou Xia et al.)](https://brightbenchmark.github.io/)" - metric: Recall@1 - split: long - desc: This tab is for the long document setting of BRIGHT. - tasks: - Retrieval: - - BrightRetrieval (biology) - - BrightRetrieval (earth_science) - - BrightRetrieval (economics) - - BrightRetrieval (psychology) - - BrightRetrieval (robotics) - - BrightRetrieval (stackoverflow) - - BrightRetrieval (sustainable_living) - - BrightRetrieval (pony) - coir: - title: CoIR - language_long: "Code" - has_overall: false - acronym: null - icon: "💻" - special_icons: null - credits: "[Samoed](https://github.com/Samoed) and [monikernemo](https://github.com/monikernemo) and [CoIR (Xiangyang Li, Kuicai Dong, Yi Quan Lee et al.)](https://arxiv.org/abs/2407.02883)" - metric: nDCG@10 - tasks: - Retrieval: - - AppsRetrieval - - CodeFeedbackMT - - CodeFeedbackST - - CodeSearchNetCCRetrieval (python) - - CodeSearchNetCCRetrieval (javascript) - - CodeSearchNetCCRetrieval (go) - - CodeSearchNetCCRetrieval (ruby) - - CodeSearchNetCCRetrieval (java) - - CodeSearchNetCCRetrieval (php) - - CodeSearchNetRetrieval (python) - - CodeSearchNetRetrieval (javascript) - - CodeSearchNetRetrieval (go) - - CodeSearchNetRetrieval (ruby) - - CodeSearchNetRetrieval (java) - - CodeSearchNetRetrieval (php) - - CodeTransOceanContest - - CodeTransOceanDL - - CosQA - - StackOverflowQA - - SyntheticText2SQL diff --git a/envs.py b/envs.py deleted file mode 100644 index 0ff0be03cf8d4ed4fdcb8f2fb665eac760238b99..0000000000000000000000000000000000000000 --- a/envs.py +++ /dev/null @@ -1,50 +0,0 @@ -import os -from yaml import safe_load - -from huggingface_hub import HfApi - -LEADERBOARD_CONFIG_PATH = "config.yaml" -with open(LEADERBOARD_CONFIG_PATH, 'r', encoding='utf-8') as f: - LEADERBOARD_CONFIG = safe_load(f) -MODEL_META_PATH = "model_meta.yaml" -with open(MODEL_META_PATH, 'r', encoding='utf-8') as f: - MODEL_META = safe_load(f) - -# Try first to get the config from the environment variables, then from the config.yaml file -def get_config(name, default): - res = None - - if name in os.environ: - res = os.environ[name] - elif 'config' in LEADERBOARD_CONFIG: - res = LEADERBOARD_CONFIG['config'].get(name, None) - - if res is None: - return default - return res - - -def str2bool(v): - return str(v).lower() in ("yes", "true", "t", "1") - - -# clone / pull the lmeh eval data -HF_TOKEN = get_config("HF_TOKEN", None) - -LEADERBOARD_NAME = get_config("LEADERBOARD_NAME", "MTEB Leaderboard") - -REPO_ID = get_config("REPO_ID", "mteb/leaderboard") -RESULTS_REPO = get_config("RESULTS_REPO", "mteb/results") - -CACHE_PATH = get_config("HF_HOME", ".") -os.environ["HF_HOME"] = CACHE_PATH - -# Check if it is using persistent storage -if not os.access(CACHE_PATH, os.W_OK): - print(f"No write access to HF_HOME: {CACHE_PATH}. Resetting to current directory.") - CACHE_PATH = "." - os.environ["HF_HOME"] = CACHE_PATH -else: - print(f"Write access confirmed for HF_HOME") - -API = HfApi(token=HF_TOKEN) diff --git a/model_meta.yaml b/model_meta.yaml deleted file mode 100644 index 60c5e182214025a7659d1d47901c7feeb05d877e..0000000000000000000000000000000000000000 --- a/model_meta.yaml +++ /dev/null @@ -1,2106 +0,0 @@ -model_meta: - baichuan-ai__text-embedding: - link: https://platform.baichuan-ai.com/docs/text-Embedding - seq_len: 512 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - Cohere__Cohere-embed-english-v3.0: - link: https://huggingface.co/Cohere/Cohere-embed-english-v3.0 - seq_len: 512 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - Cohere__Cohere-embed-english-v3.0-instruct: - link: https://huggingface.co/Cohere/Cohere-embed-english-v3.0 - seq_len: 512 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - Cohere__Cohere-embed-multilingual-light-v3.0: - link: https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0 - seq_len: 512 - size: null - dim: 384 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - Cohere__Cohere-embed-multilingual-v3.0: - link: https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0 - seq_len: 512 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - vesteinn__DanskBERT: - link: https://huggingface.co/vesteinn/DanskBERT - seq_len: 514 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - jhu-clsp__FollowIR-7B: - link: https://huggingface.co/jhu-clsp/FollowIR-7B - seq_len: 4096 - size: 7240 - is_external: true - is_propietary: false - is_sentence_transformer_compatible: false - uses_instruct: true - GritLM__GritLM-7B: - link: https://huggingface.co/GritLM/GritLM-7B - seq_len: 4096 - size: 7240 - is_external: true - is_propietary: false - is_sentence_transformer_compatible: false - uses_instruct: true - GritLM__GritLM-7B-noinstruct: - link: https://huggingface.co/GritLM/GritLM-7B - seq_len: 4096 - size: 7240 - is_external: true - is_propietary: false - is_sentence_transformer_compatible: false - uses_instruct: false - facebookresearch__LASER2: - link: https://github.com/facebookresearch/LASER - seq_len: N/A - size: 43 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - McGill-NLP__LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised - seq_len: 4096 - size: 6607 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Llama-2-unsupervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse - seq_len: 4096 - size: 6607 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Meta-Llama-3-supervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised - seq_len: 8192 - size: 7505 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Meta-Llama-3-unsupervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse - seq_len: 8192 - size: 7505 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Mistral-supervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Mistral-unsupervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Sheared-Llama-supervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised - seq_len: 4096 - size: 1280 - dim: 2048 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - McGill-NLP__LLM2Vec-Sheared-Llama-unsupervised: - link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse - seq_len: 4096 - size: 1280 - dim: 2048 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - sentence-transformers__LaBSE: - link: https://huggingface.co/sentence-transformers/LaBSE - seq_len: 512 - size: 471 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Linq-Embed-Mistral: - link: https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - NV-Embed-v1: - link: https://huggingface.co/nvidia/NV-Embed-v1 - seq_len: 32768 - size: 7851 - dim: 4096 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - NV-Embed-v2: - link: https://huggingface.co/nvidia/NV-Embed-v2 - seq_len: 32768 - size: 7851 - dim: 4096 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - NV-Retriever-v1: - link: https://huggingface.co/nvidia/NV-Retriever-v1 - seq_len: 512 - size: 7111 - dim: 4096 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - aliyun__OpenSearch-text-hybrid: - link: https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval - seq_len: 512 - size: null - dim: 1792 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - Salesforce__SFR-Embedding-Mistral: - link: https://huggingface.co/Salesforce/SFR-Embedding-Mistral - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - SFR-Embedding-2_R: - link: https://huggingface.co/Salesforce/SFR-Embedding-2_R - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - sentence-transformers__all-MiniLM-L12-v2: - link: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2 - seq_len: 512 - size: 33 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__all-MiniLM-L6-v2: - link: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 - seq_len: 512 - size: 23 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__all-MiniLM-L6-v2-instruct: - link: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 - seq_len: 512 - size: 23 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - sentence-transformers__all-mpnet-base-v2: - link: https://huggingface.co/sentence-transformers/all-mpnet-base-v2 - seq_len: 514 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__all-mpnet-base-v2-instruct: - link: https://huggingface.co/sentence-transformers/all-mpnet-base-v2 - seq_len: 514 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - sentence-transformers__allenai-specter: - link: https://huggingface.co/sentence-transformers/allenai-specter - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__bert-base-10lang-cased: - link: https://huggingface.co/Geotrend/bert-base-10lang-cased - seq_len: 512 - size: 138 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__bert-base-15lang-cased: - link: https://huggingface.co/Geotrend/bert-base-15lang-cased - seq_len: 512 - size: 138 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__bert-base-25lang-cased: - link: https://huggingface.co/Geotrend/bert-base-25lang-cased - seq_len: 512 - size: 138 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - google-bert__bert-base-multilingual-cased: - link: https://huggingface.co/google-bert/bert-base-multilingual-cased - seq_len: 512 - size: 179 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - google-bert__bert-base-multilingual-uncased: - link: https://huggingface.co/google-bert/bert-base-multilingual-uncased - seq_len: 512 - size: 168 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - KB__bert-base-swedish-cased: - link: https://huggingface.co/KB/bert-base-swedish-cased - seq_len: 512 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - google-bert__bert-base-uncased: - link: https://huggingface.co/google-bert/bert-base-uncased - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - BAAI__bge-base-en-v1.5: - link: https://huggingface.co/BAAI/bge-base-en-v1.5 - seq_len: 512 - size: 109 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - BAAI__bge-base-en-v1.5-instruct: - link: https://huggingface.co/BAAI/bge-base-en-v1.5 - seq_len: 512 - size: 109 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - BAAI__bge-base-zh-v1.5: - link: https://huggingface.co/BAAI/bge-base-zh-v1.5 - seq_len: 512 - size: 102 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - bge-en-icl: - link: https://huggingface.co/BAAI/bge-en-icl - seq_len: 32768 - size: 7110 - dim: 4096 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - bge-multilingual-gemma2: - link: https://huggingface.co/BAAI/bge-multilingual-gemma2 - seq_len: 8192 - size: 9240 - dim: 3584 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - BAAI__bge-large-en-v1.5: - link: https://huggingface.co/BAAI/bge-large-en-v1.5 - seq_len: 512 - size: 1340 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - BAAI__bge-large-en-v1.5-instruct: - link: https://huggingface.co/BAAI/bge-large-en-v1.5 - seq_len: 512 - size: 1340 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - BAAI__bge-large-zh-noinstruct: - link: https://huggingface.co/BAAI/bge-large-zh-noinstruct - seq_len: 512 - size: 326 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - BAAI__bge-large-zh-v1.5: - link: https://huggingface.co/BAAI/bge-large-zh-v1.5 - seq_len: 512 - size: 326 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - BAAI__bge-m3: - link: https://huggingface.co/BAAI/bge-m3 - seq_len: 8192 - size: 567 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - BAAI__bge-m3-instruct: - link: https://huggingface.co/BAAI/bge-m3 - seq_len: 8192 - size: 567 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - BAAI__bge-small-en-v1.5: - link: https://huggingface.co/BAAI/bge-small-en-v1.5 - seq_len: 512 - size: 24 - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - BAAI__bge-small-en-v1.5-instruct: - link: https://huggingface.co/BAAI/bge-small-en-v1.5 - seq_len: 512 - size: 24 - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - BAAI__bge-small-zh-v1.5: - link: https://huggingface.co/BAAI/bge-small-zh-v1.5 - seq_len: 512 - size: 24 - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - bm25: - link: https://en.wikipedia.org/wiki/Okapi_BM25 - size: 0 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - bm25s: - link: https://github.com/xhluca/bm25s - size: 0 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - almanach__camembert-base: - link: https://huggingface.co/almanach/camembert-base - seq_len: 512 - size: 111 - dim: 512 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - almanach__camembert-large: - link: https://huggingface.co/almanach/camembert-large - seq_len: 512 - size: 338 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - nthakur__contriever-base-msmarco: - link: https://huggingface.co/nthakur/contriever-base-msmarco - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - facebook__contriever: - link: https://huggingface.co/facebook/contriever - seq_len: 512 - size: 438 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - facebook__contriever-instruct: - link: https://huggingface.co/facebook/contriever - seq_len: 512 - size: 438 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - T-Systems-onsite__cross-en-de-roberta-sentence-transformer: - link: https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer - seq_len: 514 - size: 278 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - chcaa__dfm-encoder-large-v1: - link: https://huggingface.co/chcaa/dfm-encoder-large-v1 - seq_len: 512 - size: 355 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__distilbert-base-25lang-cased: - link: https://huggingface.co/Geotrend/distilbert-base-25lang-cased - seq_len: 512 - size: 110 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__distilbert-base-en-fr-cased: - link: https://huggingface.co/Geotrend/distilbert-base-en-fr-cased - seq_len: 512 - size: 110 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__distilbert-base-en-fr-es-pt-it-cased: - link: https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased - seq_len: 512 - size: 110 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Geotrend__distilbert-base-fr-cased: - link: https://huggingface.co/Geotrend/distilbert-base-fr-cased - seq_len: 512 - size: 110 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - distilbert__distilbert-base-uncased: - link: https://huggingface.co/distilbert/distilbert-base-uncased - seq_len: 512 - size: 110 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__distiluse-base-multilingual-cased-v2: - link: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2 - seq_len: 512 - size: 135 - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - facebookresearch__dragon-plus: - link: https://github.com/facebookresearch/dpr-scale/tree/main/dragon - seq_len: 512 - size: 438 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - facebookresearch__dragon-plus-instruct: - link: https://github.com/facebookresearch/dpr-scale/tree/main/dragon - seq_len: 512 - size: 438 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - dwzhu__e5-base-4k: - link: https://huggingface.co/dwzhu/e5-base-4k - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__e5-base-v2: - link: https://huggingface.co/intfloat/e5-base-v2 - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__e5-base: - link: https://huggingface.co/intfloat/e5-base - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__e5-large-v2: - link: https://huggingface.co/intfloat/e5-large-v2 - seq_len: 512 - size: 335 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__e5-large: - link: https://huggingface.co/intfloat/e5-large - seq_len: 512 - size: 335 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__e5-mistral-7b-instruct: - link: https://huggingface.co/intfloat/e5-mistral-7b-instruct - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - intfloat__e5-mistral-7b-instruct-noinstruct: - link: https://huggingface.co/intfloat/e5-mistral-7b-instruct-noinstruct - seq_len: 32768 - size: 7111 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__e5-small: - link: https://huggingface.co/intfloat/e5-small - seq_len: 512 - size: 33 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - jonfd__electra-small-nordic: - link: https://huggingface.co/jonfd/electra-small-nordic - seq_len: 512 - size: 23 - dim: 256 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - KBLab__electra-small-swedish-cased-discriminator: - link: https://huggingface.co/KBLab/electra-small-swedish-cased-discriminator - seq_len: 512 - size: 16 - dim: 256 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - elastic__elser-v2: - link: https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html - seq_len: 512 - size: 110 - dim: 30522 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - google__flan-t5-base: - link: https://huggingface.co/google/flan-t5-base - seq_len: 512 - size: 220 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - google__flan-t5-large: - link: https://huggingface.co/google/flan-t5-large - seq_len: 512 - size: 770 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - flaubert__flaubert_base_cased: - link: https://huggingface.co/flaubert/flaubert_base_cased - seq_len: 512 - size: 138 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - flaubert__flaubert_base_uncased: - link: https://huggingface.co/flaubert/flaubert_base_uncased - seq_len: 512 - size: 138 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - flaubert__flaubert_large_cased: - link: https://huggingface.co/flaubert/flaubert_large_cased - seq_len: 512 - size: 372 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepset__gbert-base: - link: https://huggingface.co/deepset/gbert-base - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepset__gbert-large: - link: https://huggingface.co/deepset/gbert-large - seq_len: 512 - size: 337 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepset__gelectra-base: - link: https://huggingface.co/deepset/gelectra-base - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepset__gelectra-large: - link: https://huggingface.co/deepset/gelectra-large - seq_len: 512 - size: 335 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__average_word_embeddings_glove.6B.300d: - link: https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d - seq_len: N/A - size: 120 - dim: 300 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - google-gecko__text-embedding-004-256: - link: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models - seq_len: 2048 - size: 1200 - dim: 256 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - google-gecko__text-embedding-004: - link: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models - seq_len: 2048 - size: 1200 - dim: 768 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - uklfr__gottbert-base: - link: https://huggingface.co/uklfr/gottbert-base - seq_len: 512 - size: 127 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Alibaba-NLP__gte-Qwen1.5-7B-instruct: - link: https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct - seq_len: 32768 - size: 7099 - dim: 4096 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - Alibaba-NLP__gte-Qwen2-1.5B-instruct: - link: https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct - seq_len: 32768 - size: 1780 - dim: 1536 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - Alibaba-NLP__gte-Qwen2-7B-instruct: - link: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct - seq_len: 32768 - size: 7613 - dim: 3584 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - sentence-transformers__gtr-t5-base: - link: https://huggingface.co/sentence-transformers/gtr-t5-base - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__gtr-t5-large: - link: https://huggingface.co/sentence-transformers/gtr-t5-large - seq_len: 512 - size: 168 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__gtr-t5-xl: - link: https://huggingface.co/sentence-transformers/gtr-t5-xl - seq_len: 512 - size: 1240 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__gtr-t5-xxl: - link: https://huggingface.co/sentence-transformers/gtr-t5-xxl - seq_len: 512 - size: 4865 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - ipipan__herbert-base-retrieval-v2: - link: https://huggingface.co/ipipan/herbert-base-retrieval-v2 - seq_len: 514 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - hkunlp__instructor-base: - link: https://huggingface.co/hkunlp/instructor-base - seq_len: N/A - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - hkunlp__instructor-large: - link: https://huggingface.co/hkunlp/instructor-large - seq_len: 512 - size: 335 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - hkunlp__instructor-xl: - link: https://huggingface.co/hkunlp/instructor-xl - seq_len: 512 - size: 1241 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - jinaai__jina-embeddings-v2-base-en: - link: https://huggingface.co/jinaai/jina-embeddings-v2-base-en - seq_len: 8192 - size: 275 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__average_word_embeddings_komninos: - link: https://huggingface.co/sentence-transformers/average_word_embeddings_komninos - seq_len: N/A - size: 134 - dim: 300 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - meta-llama__llama-2-7b-chat: - link: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf - seq_len: 4096 - size: 7000 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - silk-road__luotuo-bert-medium: - link: https://huggingface.co/silk-road/luotuo-bert-medium - seq_len: 512 - size: 328 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - moka-ai__m3e-base: - link: https://huggingface.co/moka-ai/m3e-base - seq_len: 512 - size: 102 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - moka-ai__m3e-large: - link: https://huggingface.co/moka-ai/m3e-large - seq_len: 512 - size: 102 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - mistralai__mistral-7b-instruct-v0.2: - link: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 - seq_len: 4096 - size: 7240 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - mistral__mistral-embed: - link: https://docs.mistral.ai/guides/embeddings - seq_len: null - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - castorini__monobert-large-msmarco: - link: https://huggingface.co/castorini/monobert-large-msmarco - seq_len: 512 - size: 770 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - castorini__monot5-3b-msmarco-10k: - link: https://huggingface.co/castorini/monot5-3b-msmarco-10k - seq_len: 512 - size: 2480 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - castorini__monot5-base-msmarco-10k: - link: https://huggingface.co/castorini/monot5-base-msmarco-10k - seq_len: 512 - size: 220 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - sentence-transformers__msmarco-bert-co-condensor: - link: https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__multi-qa-MiniLM-L6-cos-v1: - link: https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1 - seq_len: 512 - size: 23 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__multilingual-e5-base: - link: https://huggingface.co/intfloat/multilingual-e5-base - seq_len: 514 - size: 278 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__multilingual-e5-large: - link: https://huggingface.co/intfloat/multilingual-e5-large - seq_len: 514 - size: 560 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - intfloat__multilingual-e5-large-instruct: - link: https://huggingface.co/intfloat/multilingual-e5-large-instruct - seq_len: 514 - size: 560 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - intfloat__multilingual-e5-small: - link: https://huggingface.co/intfloat/multilingual-e5-small - seq_len: 512 - size: 118 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - NbAiLab__nb-bert-base: - link: https://huggingface.co/NbAiLab/nb-bert-base - seq_len: 512 - size: 179 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - NbAiLab__nb-bert-large: - link: https://huggingface.co/NbAiLab/nb-bert-large - seq_len: 512 - size: 355 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - nomic-ai__nomic-embed-text-v1: - link: https://huggingface.co/nomic-ai/nomic-embed-text-v1 - seq_len: 8192 - size: 547 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - nomic-ai__nomic-embed-text-v1.5-128: - link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 - seq_len: 8192 - size: 138 - dim: 128 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - nomic-ai__nomic-embed-text-v1.5-256: - link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 - seq_len: 8192 - size: 138 - dim: 256 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - nomic-ai__nomic-embed-text-v1.5-512: - link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 - seq_len: 8192 - size: 138 - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - nomic-ai__nomic-embed-text-v1.5-64: - link: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 - seq_len: 8192 - size: 138 - dim: 64 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - ltg__norbert3-base: - link: https://huggingface.co/ltg/norbert3-base - seq_len: 512 - size: 131 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - ltg__norbert3-large: - link: https://huggingface.co/ltg/norbert3-large - seq_len: 512 - size: 368 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2: - link: https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 - seq_len: 512 - size: 118 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__paraphrase-multilingual-mpnet-base-v2: - link: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2 - seq_len: 514 - size: 278 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - KBLab__sentence-bert-swedish-cased: - link: https://huggingface.co/KBLab/sentence-bert-swedish-cased - seq_len: 512 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - dangvantuan__sentence-camembert-base: - link: https://huggingface.co/dangvantuan/sentence-camembert-base - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - dangvantuan__sentence-camembert-large: - link: https://huggingface.co/dangvantuan/sentence-camembert-large - seq_len: 512 - size: 337 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - Wissam42__sentence-croissant-llm-base: - link: https://huggingface.co/Wissam42/sentence-croissant-llm-base - seq_len: 2048 - size: 1280 - dim: 2048 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__sentence-t5-base: - link: https://huggingface.co/sentence-transformers/sentence-t5-base - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__sentence-t5-large: - link: https://huggingface.co/sentence-transformers/sentence-t5-large - seq_len: 512 - size: 168 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__sentence-t5-xl: - link: https://huggingface.co/sentence-transformers/sentence-t5-xl - seq_len: 512 - size: 1240 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__sentence-t5-xxl: - link: https://huggingface.co/sentence-transformers/sentence-t5-xxl - seq_len: 512 - size: 4865 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - ipipan__silver-retriever-base-v1: - link: https://huggingface.co/ipipan/silver-retriever-base-v1 - seq_len: 514 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sdadas__st-polish-paraphrase-from-distilroberta: - link: https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta - seq_len: 514 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sdadas__st-polish-paraphrase-from-mpnet: - link: https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet - seq_len: 514 - size: 125 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - stella_en_1.5B_v5: - link: https://huggingface.co/dunzhang/stella_en_1.5B_v5 - seq_len: 512 - size: 1500 - dim: 8192 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - stella_en_400M_v5: - link: https://huggingface.co/dunzhang/stella_en_400M_v5 - seq_len: 512 - size: 400 - dim: 8192 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: true - princeton-nlp__sup-simcse-bert-base-uncased: - link: https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - openai__text-embedding-3-large: - link: https://openai.com/blog/new-embedding-models-and-api-updates - seq_len: 8191 - size: null - dim: 3072 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-embedding-3-large-instruct: - link: https://openai.com/blog/new-embedding-models-and-api-updates - seq_len: 8191 - size: null - dim: 3072 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - openai__text-embedding-3-large-256: - link: https://openai.com/blog/new-embedding-models-and-api-updates - seq_len: 8191 - size: null - dim: 256 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-embedding-3-small: - link: https://openai.com/blog/new-embedding-models-and-api-updates - seq_len: 8191 - size: null - dim: 1536 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-embedding-3-small-instruct: - link: https://openai.com/blog/new-embedding-models-and-api-updates - seq_len: 8191 - size: null - dim: 1536 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - openai__text-embedding-ada-002: - link: https://openai.com/blog/new-and-improved-embedding-model - seq_len: 8191 - size: null - dim: 1536 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-embedding-ada-002-instruct: - link: https://openai.com/blog/new-and-improved-embedding-model - seq_len: 8191 - size: null - dim: 1536 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - openai__text-search-ada-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-search-ada-doc-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - text-search-ada-query-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 1024 - is_external: false - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-search-babbage-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 2048 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-search-curie-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 4096 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-search-davinci-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 12288 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-similarity-ada-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-similarity-babbage-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 2048 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-similarity-curie-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 4096 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - openai__text-similarity-davinci-001: - link: https://openai.com/blog/introducing-text-and-code-embeddings - seq_len: 2046 - size: null - dim: 12288 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - orionweller__tart-dual-contriever-msmarco: - link: https://huggingface.co/orionweller/tart-dual-contriever-msmarco - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - facebook__tart-full-flan-t5-xl: - link: https://huggingface.co/facebook/tart-full-flan-t5-xl - seq_len: 512 - size: 2480 - dim: -1 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: true - shibing624__text2vec-base-chinese: - link: https://huggingface.co/shibing624/text2vec-base-chinese - seq_len: 512 - size: 102 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - shibing624__text2vec-base-multilingual: - link: https://huggingface.co/shibing624/text2vec-base-multilingual - seq_len: 256 - size: 118 - dim: 384 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: false - uses_instruct: false - shibing624__text2vec-large-chinese: - link: https://huggingface.co/GanymedeNil/text2vec-large-chinese - seq_len: 512 - size: 326 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - amazon__titan-embed-text-v1: - link: https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html - seq_len: 8000 - size: null - dim: 1536 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - izhx__udever-bloom-1b1: - link: https://huggingface.co/izhx/udever-bloom-1b1 - seq_len: 2048 - size: null - dim: 1536 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - izhx__udever-bloom-560m: - link: https://huggingface.co/izhx/udever-bloom-560m - seq_len: 2048 - size: null - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - vprelovac__universal-sentence-encoder-multilingual-3: - link: https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3 - seq_len: 512 - size: null - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - vprelovac__universal-sentence-encoder-multilingual-large-3: - link: https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-large-3 - seq_len: 512 - size: null - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - princeton-nlp__unsup-simcse-bert-base-uncased: - link: https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased - seq_len: 512 - size: 110 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sentence-transformers__use-cmlm-multilingual: - link: https://huggingface.co/sentence-transformers/use-cmlm-multilingual - seq_len: 512 - size: 472 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - voyageai__voyage-2: - link: https://docs.voyageai.com/embeddings/ - seq_len: 4000 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - voyageai__voyage-code-2: - link: https://docs.voyageai.com/embeddings/ - seq_len: 16000 - size: null - dim: 1536 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - voyageai__voyage-large-2-instruct: - link: https://docs.voyageai.com/embeddings/ - seq_len: 16000 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - voyageai__voyage-law-2: - link: https://docs.voyageai.com/embeddings/ - seq_len: 16000 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - voyageai__voyage-lite-01-instruct: - link: https://docs.voyageai.com/embeddings/ - seq_len: 4000 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - voyageai__voyage-lite-02-instruct: - link: https://docs.voyageai.com/embeddings/ - seq_len: 4000 - size: 1220 - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: true - voyageai__voyage-multilingual-2: - link: https://docs.voyageai.com/embeddings/ - seq_len: 32000 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - voyageai__voyage-3: - link: https://docs.voyageai.com/embeddings/ - seq_len: 32000 - size: null - dim: 1024 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - voyageai__voyage-3-lite: - link: https://docs.voyageai.com/embeddings/ - seq_len: 32000 - size: null - dim: 512 - is_external: true - is_proprietary: true - is_sentence_transformers_compatible: false - uses_instruct: false - FacebookAI__xlm-roberta-base: - link: https://huggingface.co/FacebookAI/xlm-roberta-base - seq_len: 514 - size: 279 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - FacebookAI__xlm-roberta-large: - link: https://huggingface.co/FacebookAI/xlm-roberta-large - seq_len: 514 - size: 560 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - cointegrated__rubert-tiny: - link: https://huggingface.co/cointegrated/rubert-tiny - seq_len: 514 - size: 12 - dim: 512 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - cointegrated__rubert-tiny2: - link: https://huggingface.co/cointegrated/rubert-tiny2 - seq_len: 514 - size: 29 - dim: 2048 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - ai-forever__sbert_large_mt_nlu_ru: - link: https://huggingface.co/ai-forever/sbert_large_mt_nlu_ru - seq_len: 514 - size: 427 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - ai-forever__sbert_large_nlu_ru: - link: https://huggingface.co/ai-forever/sbert_large_nlu_ru - seq_len: 512 - size: 427 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepvk__USER-base: - link: https://huggingface.co/deepvk/USER-base - seq_len: 512 - size: 124 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepvk__USER-bge-m3: - link: https://huggingface.co/deepvk/USER-bge-m3 - seq_len: 8192 - size: 359 - dim: 1024 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - deepvk__deberta-v1-base: - link: https://huggingface.co/deepvk/deberta-v1-base - seq_len: 512 - size: 124 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sergeyzh__rubert-tiny-turbo: - link: https://huggingface.co/sergeyzh/rubert-tiny-turbo - seq_len: 2048 - size: 29 - dim: 312 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - sergeyzh__LaBSE-ru-turbo: - link: https://huggingface.co/sergeyzh/LaBSE-ru-turbo - seq_len: 512 - size: 128 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - DeepPavlov__distilrubert-small-cased-conversational: - link: https://huggingface.co/DeepPavlov/distilrubert-small-cased-conversational - seq_len: 512 - size: 106 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - DeepPavlov__rubert-base-cased: - link: https://huggingface.co/DeepPavlov/rubert-base-cased - seq_len: 512 - size: 180 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - DeepPavlov__rubert-base-cased-sentence: - link: https://huggingface.co/DeepPavlov/rubert-base-cased-sentence - seq_len: 512 - size: 180 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - cointegrated__LaBSE-en-ru: - link: https://huggingface.co/cointegrated/LaBSE-en-ru - seq_len: 512 - size: 129 - dim: 768 - is_external: true - is_proprietary: false - is_sentence_transformers_compatible: true - uses_instruct: false - cde-small-v1: - link: https://huggingface.co/jxm/cde-small-v1 - seq_len: 512 - size: 143 - dim: 768 - is_external: false - is_proprietary: false - is_sentence_transformers_compatible: false -models_to_skip: -- michaelfeil/ct2fast-e5-large-v2 -- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse -- newsrx/instructor-xl -- sionic-ai/sionic-ai-v1 -- lsf1000/bge-evaluation -- Intel/bge-small-en-v1.5-sst2 -- newsrx/instructor-xl-newsrx -- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse -- McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse -- davidpeer/gte-small -- goldenrooster/multilingual-e5-large -- kozistr/fused-large-en -- mixamrepijey/instructor-small -- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised -- DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext -- Intel/bge-base-en-v1.5-sst2-int8-dynamic -- morgendigital/multilingual-e5-large-quantized -- BAAI/bge-small-en -- ggrn/e5-small-v2 -- vectoriseai/gte-small -- giulio98/placeholder -- odunola/UAE-Large-VI -- vectoriseai/e5-large-v2 -- gruber/e5-small-v2-ggml -- Severian/nomic -- arcdev/e5-mistral-7b-instruct -- mlx-community/multilingual-e5-base-mlx -- michaelfeil/ct2fast-bge-base-en-v1.5 -- Intel/bge-small-en-v1.5-sst2-int8-static -- jncraton/stella-base-en-v2-ct2-int8 -- vectoriseai/multilingual-e5-large -- rlsChapters/Chapters-SFR-Embedding-Mistral -- arcdev/SFR-Embedding-Mistral -- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised -- McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised -- vectoriseai/gte-base -- mixamrepijey/instructor-models -- GovCompete/e5-large-v2 -- ef-zulla/e5-multi-sml-torch -- khoa-klaytn/bge-small-en-v1.5-angle -- krilecy/e5-mistral-7b-instruct -- vectoriseai/bge-base-en-v1.5 -- vectoriseai/instructor-base -- jingyeom/korean_embedding_model -- rizki/bgr-tf -- barisaydin/bge-base-en -- jamesgpt1/zzz -- Malmuk1/e5-large-v2_Sharded -- vectoriseai/ember-v1 -- Consensus/instructor-base -- barisaydin/bge-small-en -- barisaydin/gte-base -- woody72/multilingual-e5-base -- Einas/einas_ashkar -- michaelfeil/ct2fast-bge-large-en-v1.5 -- vectoriseai/bge-small-en-v1.5 -- iampanda/Test -- cherubhao/yogamodel -- ieasybooks/multilingual-e5-large-onnx -- jncraton/e5-small-v2-ct2-int8 -- radames/e5-large -- khoa-klaytn/bge-base-en-v1.5-angle -- Intel/bge-base-en-v1.5-sst2-int8-static -- vectoriseai/e5-large -- TitanML/jina-v2-base-en-embed -- Koat/gte-tiny -- binqiangliu/EmbeddingModlebgelargeENv1.5 -- beademiguelperez/sentence-transformers-multilingual-e5-small -- sionic-ai/sionic-ai-v2 -- jamesdborin/jina-v2-base-en-embed -- maiyad/multilingual-e5-small -- dmlls/all-mpnet-base-v2 -- odunola/e5-base-v2 -- vectoriseai/bge-large-en-v1.5 -- vectoriseai/bge-small-en -- karrar-alwaili/UAE-Large-V1 -- t12e/instructor-base -- Frazic/udever-bloom-3b-sentence -- Geolumina/instructor-xl -- hsikchi/dump -- recipe/embeddings -- michaelfeil/ct2fast-bge-small-en-v1.5 -- ildodeltaRule/multilingual-e5-large -- shubham-bgi/UAE-Large -- BAAI/bge-large-en -- michaelfeil/ct2fast-e5-small-v2 -- cgldo/semanticClone -- barisaydin/gte-small -- aident-ai/bge-base-en-onnx -- jamesgpt1/english-large-v1 -- michaelfeil/ct2fast-e5-small -- baseplate/instructor-large-1 -- newsrx/instructor-large -- Narsil/bge-base-en -- michaelfeil/ct2fast-e5-large -- mlx-community/multilingual-e5-small-mlx -- lightbird-ai/nomic -- MaziyarPanahi/GritLM-8x7B-GGUF -- newsrx/instructor-large-newsrx -- dhairya0907/thenlper-get-large -- barisaydin/bge-large-en -- jncraton/bge-small-en-ct2-int8 -- retrainai/instructor-xl -- BAAI/bge-base-en -- gentlebowl/instructor-large-safetensors -- d0rj/e5-large-en-ru -- atian-chapters/Chapters-SFR-Embedding-Mistral -- Intel/bge-base-en-v1.5-sts-int8-static -- Intel/bge-base-en-v1.5-sts-int8-dynamic -- jncraton/GIST-small-Embedding-v0-ct2-int8 -- jncraton/gte-tiny-ct2-int8 -- d0rj/e5-small-en-ru -- vectoriseai/e5-small-v2 -- SmartComponents/bge-micro-v2 -- michaelfeil/ct2fast-gte-base -- vectoriseai/e5-base-v2 -- Intel/bge-base-en-v1.5-sst2 -- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised -- Research2NLP/electrical_stella -- weakit-v/bge-base-en-v1.5-onnx -- GovCompete/instructor-xl -- barisaydin/text2vec-base-multilingual -- Intel/bge-small-en-v1.5-sst2-int8-dynamic -- jncraton/gte-small-ct2-int8 -- d0rj/e5-base-en-ru -- barisaydin/gte-large -- fresha/e5-large-v2-endpoint -- vectoriseai/instructor-large -- Severian/embed -- vectoriseai/e5-base -- mlx-community/multilingual-e5-large-mlx -- vectoriseai/gte-large -- anttip/ct2fast-e5-small-v2-hfie -- michaelfeil/ct2fast-gte-large -- gizmo-ai/Cohere-embed-multilingual-v3.0 -- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse -- Kenknight1999/tungdd7_ft_e5 -- joteqwork/new_gsev0 -- vantagediscovery/jina-embeddings-v2-base-en -- vantagediscovery/nomic-embed-text-v1 -- vantagediscovery/nomic-embed-text-v1.5 -- srikanthmalla/hkunlp-instructor-xl -- afrideva/GIST-all-MiniLM-L6-v2-GGUF -- nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF -- agier9/gte-Qwen1.5-7B-instruct-Q5_K_M-GGUF -- ekorman-strive/bge-large-en-v1.5 -- raghavlight/SE_v1 -- liddlefish/privacyembeddingv2_bge_small -- ahmet1338/finetuned_embedder -- radia/snowflake-arctic-embed-l-Q4_K_M-GGUF -- GregorBiswanger/GritLM-7B-Q4_K_M-GGUF -- powermove72/GritLM-7B-Q4_K_M-GGUF -- sunzx0810/gte-Qwen2-7B-instruct-Q5_K_M-GGUF -- nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF -- nazimali/gte-Qwen2-7B-instruct-Q6_K-GGUF -- fishbone64/gte-Qwen2-7B-instruct-Q8_0-GGUF -- tobchef/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF -- liddlefish/privacy_embedding_rag -- liddlefish/privacy_embedding_rag_10k_tmp -- liddlefish/privacy_embedding_bge_small_synthetic -- mxs980/gte-Qwen2-1.5B-instruct-Q8_0-GGUF -- leonn71/gte-Qwen2-1.5B-instruct-Q6_K-GGUF -- niancheng/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF -- cleatherbury/gte-Qwen2-7B-instruct-Q5_K_M-GGUF -- T3LS/stella-mrl-large-zh-v3.5-1792d-1024 -- mxs980/b1ade-embed-Q8_0-GGUF -- chihlunLee/NoInstruct-small-Embedding-v0-Q4_0-GGUF -- bcastle/snowflake-arctic-embed-l-Q8_0-GGUF -- niancheng/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF -- brtaydin/gte-Qwen2-7B-instruct-Q5_K_M-GGUF -- yishan-wang/snowflake-arctic-embed-s-Q8_0-GGUF -- Pekarnick/e5-large-v2-Q4_K_M-GGUF -- agier9/UAE-Large-V1-Q5_K_S-GGUF -- archit28/bge-large-en-v1.5-Q4_K_S-GGUF -- dimcha/mxbai-embed-large-v1-Q4_K_M-GGUF -- djuna/jina-embeddings-v2-base-en-Q5_K_M-GGUF -- djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF -- djuna/stella-base-en-v2-Q5_K_M-GGUF -- huoxu/bge-large-en-v1.5-Q8_0-GGUF -- itod/UAE-Large-V1-Q8_0-GGUF -- corto-ai/nomic-embed-text-v1 -- Intel/neural-embedding-v1 -- VenkatNDivi77/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- barnowak/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF -- KeyurRamoliya/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- cedarwud/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- KeyurRamoliya/gte-Qwen2-7B-instruct-GGUF -- agier9/gte-Qwen2-1.5B-instruct-Q4_K_M-GGUF -- dopeba/gte-Qwen2-1.5B-instruct-Q8_0-GGUF -- Yingda/gte-Qwen2-7B-instruct-Q5_K_M-GGUF -- soichisumi/gte-Qwen2-7B-instruct-Q8_0-GGUF -- Bearsaerker/gte-Qwen2-7B-instruct-Q5_K_M-GGUF -- redshiva/gte-Qwen2-7B-instruct-Q8_0-GGUF -- qaz-t/gte-Qwen2-7B-instruct-Q8_0-GGUF -- biggunnyso4/stella_en_400M_v5_cpu -- PotatoOff/mxbai-embed-large-safetensors -- KeyurRamoliya/multilingual-e5-large-instruct-GGUF -- liddlefish/privacy_embedding_rag_10k_base_12_final -- liddlefish/privacy_embedding_rag_10k_base_15_final -- liddlefish/privacy_embedding_rag_10k_base_checkpoint_2 -- liddlefish/privacy_embedding_rag_10k_base_final -- tomaarsen/bge-base-en-v1.5 -- CAiRE/UniVaR-lambda-80 -- CAiRE/UniVaR-lambda-1 -- CAiRE/UniVaR-lambda-5 -- CAiRE/UniVaR-lambda-20 -- leeloolee/intention -- yixuan-chia/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF -- yixuan-chia/snowflake-arctic-embed-m-v1.5-Q4_K_M-GGUF -- yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF -- yixuan-chia/snowflake-arctic-embed-s-Q8_0-GGUF -- VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF -- chris-code/multilingual-e5-large-Q8_0-GGUF -- soichisumi/multilingual-e5-large-Q8_0-GGUF -- pruizpar/multilingual-e5-large-Q4_K_M-GGUF -- yixuan-chia/snowflake-arctic-embed-l-Q8_0-GGUF -- twadada/nmc-300-w30k-b10k -- twadada/nmc-300-w50k-b30k -- twadada/nmc-cls-200_correct -- twadada/st-mpnet_original -- twadada/nmc-cls-500_correct -- twadada/nmc-cls-50_correct -- twadada/nmc-cls-100_correct -- twadada/nmc-wordpca -- twadada/mpn-wordpca -- twadada/lma-wordpca -- twadada/lma-nobivec -- twadada/lma-nopca -- twadada/lma_noabtt -- twadada/ft-lower-pca -- twadada/ft-lower -- twadada/nmc-prmt-and-emb-150k -- twadada/s2v-gcp-pca -- twadada/s2v-gcp -- twadada/ft-gcp-pca -- twadada/ft-gcp -- twadada/lma3-correct -- twadada/mpn-nopca -- twadada/nmc-pmpt_correct -- twadada/lma3-2 -- twadada/lma3 -- twadada/lma2 -- twadada/lma -- twadada/mpn-noabtt -- twadada/mpn-nobivec -- twadada/nmc-prmt-and-emb -- twadada/nmc-cls-300_correct -- twadada/nmc-prmpt -- twadada/kom2 -- twadada/nmc-cls-noabtt -- twadada/nmc-cls-nobiv -- twadada/nmc-cls-nopca -- twadada/mpn -- twadada/sup-cse -- twadada/mxb -- twadada/kom -- twadada/un-sim -- twadada/nmc-cls -- twadada/komni -- twadada/fasttext -- twadada/s2v -- twadada/tst2 -- twadada/tst -- markaw/NV-Embed-v2 -- linhphanff/stella_en_1.5B_v5_clone -- BN7002/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge -- Hiveurban/multilingual-e5-large-pooled -- KeyurRamoliya/multilingual-e5-large-GGUF -- SkyeTeam/stella_en_400m -- yixuan-chia/snowflake-arctic-embed-m-long-Q8_0-GGUF -- aimlresearch2023/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF -- twadada/nmc-nignore15 -- twadada/nmc-nignore30 -- twadada/nmc-nignore50 -- twadada/nmc-300-w75k-b10k -- twadada/nmc-300-w50k-b10k -- bijaygurung/stella_en_400M_v5 -- bennegeek/stella_en_1.5B_v5 -- lgbird/stella_1.5B_custom -- mmazeem/gte-Qwen2-7B-instruct-Q4_K_M-GGUF -- shanghung/stella-base-zh-v3-1792d -- mav23/gte-Qwen2-7B-instruct-GGUF -- Marqo/dunzhang-stella_en_400M_v5 -- mav23/gte-Qwen2-1.5B-instruct-GGUF -- DivineNnamdi/jina-embeddings-v3 -- tomaarsen/mxbai-embed-large-v1-exported -- yixuan-chia/multilingual-e5-large-instruct-gguf -- Nextcloud-AI/multilingual-e5-large-instruct -- aiacademyvn/multilingual-e5-large-instruct -- katanemo/bge-large-en-v1.5 -cross_encoders: -- FollowIR-7B -- flan-t5-base -- flan-t5-large -- monobert-large-msmarco -- monot5-3b-msmarco-10k -- monot5-base-msmarco-10k -- llama-2-7b-chat -- mistral-7b-instruct-v0.2 -- tart-full-flan-t5-xl diff --git a/refresh.py b/refresh.py deleted file mode 100644 index 51e8be4b113630f462ca2212869ca3ffaa7c0fb6..0000000000000000000000000000000000000000 --- a/refresh.py +++ /dev/null @@ -1,799 +0,0 @@ -from __future__ import annotations - -import json - -import os -import re -from functools import reduce -from typing import Any - -import pandas as pd -from datasets import load_dataset -from huggingface_hub import hf_hub_download -from huggingface_hub.repocard import metadata_load -from tqdm.autonotebook import tqdm - -from envs import API, LEADERBOARD_CONFIG, MODEL_META, REPO_ID, RESULTS_REPO -from utils.model_size import get_model_parameters_memory - -MODEL_CACHE = {} -TASKS_CONFIG = LEADERBOARD_CONFIG["tasks"] -BOARDS_CONFIG = LEADERBOARD_CONFIG["boards"] - -TASKS = list(TASKS_CONFIG.keys()) -PRETTY_NAMES = { - "InstructionRetrieval": "Retrieval w/Instructions", - "PairClassification": "Pair Classification", - "BitextMining": "Bitext Mining", -} - -TASK_TO_METRIC = {k: [v["metric"]] for k, v in TASKS_CONFIG.items()} -# Add legacy metric names -TASK_TO_METRIC["STS"].append("cos_sim_spearman") -TASK_TO_METRIC["STS"].append("spearman") -TASK_TO_METRIC["Summarization"].append("cos_sim_spearman") -TASK_TO_METRIC["Summarization"].append("spearman") -TASK_TO_METRIC["PairClassification"].append("ap") -TASK_TO_METRIC["PairClassification"].append("cos_sim_ap") -TASK_TO_METRIC["PairClassification"].append("cosine_ap") - - -EXTERNAL_MODELS = { - k for k, v in MODEL_META["model_meta"].items() if v.get("is_external", False) -} -EXTERNAL_MODEL_TO_LINK = { - k: v["link"] for k, v in MODEL_META["model_meta"].items() if v.get("link", False) -} -EXTERNAL_MODEL_TO_DIM = { - k: v["dim"] for k, v in MODEL_META["model_meta"].items() if v.get("dim", False) -} -EXTERNAL_MODEL_TO_SEQLEN = { - k: v["seq_len"] - for k, v in MODEL_META["model_meta"].items() - if v.get("seq_len", False) -} -EXTERNAL_MODEL_TO_SIZE = { - k: v["size"] for k, v in MODEL_META["model_meta"].items() if v.get("size", False) -} -PROPRIETARY_MODELS = { - k for k, v in MODEL_META["model_meta"].items() if v.get("is_proprietary", False) -} -TASK_DESCRIPTIONS = {k: v["task_description"] for k, v in TASKS_CONFIG.items()} -TASK_DESCRIPTIONS["Overall"] = "Overall performance across MTEB tasks." -SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = { - k - for k, v in MODEL_META["model_meta"].items() - if v.get("is_sentence_transformers_compatible", False) -} -MODELS_TO_SKIP = MODEL_META["models_to_skip"] -CROSS_ENCODERS = MODEL_META["cross_encoders"] -BI_ENCODERS = [ - k for k, _ in MODEL_META["model_meta"].items() if k not in CROSS_ENCODERS + ["bm25"] -] -INSTRUCT_MODELS = { - k for k, v in MODEL_META["model_meta"].items() if v.get("uses_instruct", False) -} -NOINSTRUCT_MODELS = { - k for k, v in MODEL_META["model_meta"].items() if not v.get("uses_instruct", False) -} - - -TASK_TO_TASK_TYPE = {task_category: [] for task_category in TASKS} -TASK_TO_SPLIT = {} -for k, board_config in BOARDS_CONFIG.items(): - for task_category, task_list in board_config["tasks"].items(): - TASK_TO_TASK_TYPE[task_category].extend(task_list) - if "split" in board_config: - TASK_TO_SPLIT[k] = board_config["split"] - - -## Don't cache this because we want to re-compute every time -# model_infos_path = "model_infos.json" -MODEL_INFOS = {} -# if os.path.exists(model_infos_path): -# with open(model_infos_path) as f: -# MODEL_INFOS = json.load(f) - - -def add_rank(df: pd.DataFrame) -> pd.DataFrame: - cols_to_rank = [ - col - for col in df.columns - if col - not in [ - "Model", - "Model Size (Million Parameters)", - "Memory Usage (GB, fp32)", - "Embedding Dimensions", - "Max Tokens", - ] - ] - if len(cols_to_rank) == 1: - df.sort_values(cols_to_rank[0], ascending=False, inplace=True) - else: - df.insert( - len(df.columns) - len(cols_to_rank), - "Average", - df[cols_to_rank].mean(axis=1, skipna=False), - ) - df.sort_values("Average", ascending=False, inplace=True) - df.insert(0, "Rank", list(range(1, len(df) + 1))) - df = df.round(2) - # Fill NaN after averaging - df.fillna("", inplace=True) - return df - - -def make_clickable_model(model_name: str, link: None | str = None) -> str: - if link is None: - link = "https://huggingface.co/" + model_name - # Remove user from model name - model_name = model_name.split("/")[-1] - model_name = model_name.split("__")[-1] - return f'{model_name}' - - -def add_subset(examples): - if not (examples["hf_subset"]) or (examples["hf_subset"] == "default"): - examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] - else: - examples["mteb_dataset_name_with_lang"] = ( - examples["mteb_dataset_name"] + f' ({examples["hf_subset"]})' - ) - return examples - - -def norm(names: list[str]) -> set[str]: - return set([name.split()[0] for name in names]) - - -def add_task(examples): - # Could be added to the dataset loading script instead - task_name = examples["mteb_dataset_name"] - task_type = None - for task_category, task_list in TASK_TO_TASK_TYPE.items(): - if task_name in norm(task_list): - task_type = task_category - break - if task_type is not None: - examples["mteb_task"] = task_type - else: - print("WARNING: Task not found for dataset", examples["mteb_dataset_name"]) - examples["mteb_task"] = "Unknown" - return examples - - -def filter_metric_external(x, task, metrics) -> bool: - # This is a hack for the passkey and needle retrieval test, which reports ndcg_at_1 (i.e. accuracy), rather than the ndcg_at_10 that is commonly used for retrieval tasks. - if x["mteb_dataset_name"] in ["LEMBNeedleRetrieval", "LEMBPasskeyRetrieval"]: - return bool(x["mteb_task"] == task and x["metric"] == "ndcg_at_1") - elif (x["mteb_dataset_name"].startswith("BrightRetrieval") and (x["split"] == "long")): - return bool(x["mteb_task"] == task and x["metric"] in ["recall_at_1"]) - elif x["mteb_dataset_name"] == "MIRACLReranking": - return bool(x["mteb_task"] == task and x["metric"] in ["NDCG@10(MIRACL)"]) - else: - return bool(x["mteb_task"] == task and x["metric"] in metrics) - - -def filter_metric_fetched(name: str, metric: str, expected_metrics, split: str) -> bool: - # This is a hack for the passkey and needle retrieval test, which reports ndcg_at_1 (i.e. accuracy), rather than the ndcg_at_10 that is commonly used for retrieval tasks. - if name in ["LEMBNeedleRetrieval", "LEMBPasskeyRetrieval"]: - return bool(metric == "ndcg_at_1") - elif (name.startswith("BrightRetrieval") and (split == "long")): - return bool(metric in ["recall_at_1"]) - elif name.startswith("MIRACLReranking"): - return bool(metric in ["NDCG@10(MIRACL)"]) - else: - return bool(metric in expected_metrics) - - -def get_dim_seq_size(model): - siblings = model.siblings or [] - filenames = [sib.rfilename for sib in siblings] - dim, seq = "", "" - for filename in filenames: - if re.match("\d+_Pooling/config.json", filename): - st_config_path = hf_hub_download(model.modelId, filename=filename) - dim = json.load(open(st_config_path)).get("word_embedding_dimension", "") - break - for filename in filenames: - if re.match("\d+_Dense/config.json", filename): - st_config_path = hf_hub_download(model.modelId, filename=filename) - dim = json.load(open(st_config_path)).get("out_features", dim) - if "config.json" in filenames: - config_path = hf_hub_download(model.modelId, filename="config.json") - config = json.load(open(config_path)) - if not dim: - dim = config.get( - "hidden_dim", config.get("hidden_size", config.get("d_model", "")) - ) - seq = config.get( - "n_positions", - config.get( - "max_position_embeddings", - config.get("n_ctx", config.get("seq_length", "")), - ), - ) - - if dim == "" or seq == "": - raise Exception(f"Could not find dim or seq for model {model.modelId}") - - # Get model file size without downloading. Parameters in million parameters and memory in GB - parameters, memory = get_model_parameters_memory(model) - return dim, seq, parameters, memory - - -def get_external_model_results(): - if os.path.exists("EXTERNAL_MODEL_RESULTS.json"): - with open("EXTERNAL_MODEL_RESULTS.json") as f: - EXTERNAL_MODEL_RESULTS = json.load(f) - # Update with models not contained - models_to_run = [] - for model in EXTERNAL_MODELS: - if model not in EXTERNAL_MODEL_RESULTS: - models_to_run.append(model) - EXTERNAL_MODEL_RESULTS[model] = { - k: {v[0]: []} for k, v in TASK_TO_METRIC.items() - } - - ## only if we want to re-calculate all instead of using the cache... it's likely they haven't changed - ## but if your model results have changed, delete it from the "EXTERNAL_MODEL_RESULTS.json" file - else: - EXTERNAL_MODEL_RESULTS = { - model: {k: {v[0]: []} for k, v in TASK_TO_METRIC.items()} - for model in EXTERNAL_MODELS - } - models_to_run = EXTERNAL_MODELS - - pbar = tqdm(models_to_run, desc="Fetching external model results") - for model in pbar: - pbar.set_description(f"Fetching external model results for {model!r}") - try: - ds = load_dataset( - RESULTS_REPO, - model, - trust_remote_code=True, - download_mode="force_redownload", - verification_mode="no_checks", - ) - except (KeyError, ValueError) as e: - model_tmp = "__".join([MODEL_META["model_meta"][model]["link"].split("/")[-2], model]) - ds = load_dataset( - RESULTS_REPO, - model_tmp, - trust_remote_code=True, - download_mode="force_redownload", - verification_mode="no_checks", - ) - except ValueError as e: - print(f"Can't fined model {model} in results repository. Exception: {e}") - continue - - ds = ds.map(add_subset) - ds = ds.map(add_task) - base_dict = { - "Model": make_clickable_model( - model, - link=EXTERNAL_MODEL_TO_LINK.get( - model, f"https://huggingface.co/spaces/{REPO_ID}" - ), - ) - } - - for task, metrics in TASK_TO_METRIC.items(): - ds_sub = ds.filter(lambda x: filter_metric_external(x, task, metrics))[ - "test" - ] - curent_task_metrics = ds_sub.unique("metric") - for metric in curent_task_metrics: - ds_dict = ds_sub.filter(lambda x: x["metric"] == metric).to_dict() - ds_dict = { - k: round(v, 2) - for k, v in zip( - ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"] - ) - } - # metrics[0] is the main name for this metric; other names in the list are legacy for backward-compat - # except for recall_at_1, which is the main name for BrightRetrieval (Long) - metric = metrics[0] if metric != "recall_at_1" else metric - if metric not in EXTERNAL_MODEL_RESULTS[model][task]: - EXTERNAL_MODEL_RESULTS[model][task][metric] = [] - EXTERNAL_MODEL_RESULTS[model][task][metric].append( - {**base_dict, **ds_dict} - ) - #ds_dict = ds.filter(lambda x: filter_metric_external(x, task, metrics))[ - # "test" - #].to_dict() - #ds_dict = { - # k: round(v, 2) - # for k, v in zip( - # ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"] - # ) - #} - ## metrics[0] is the main name for this metric; other names in the list are legacy for backward-compat - #EXTERNAL_MODEL_RESULTS[model][task][metrics[0]].append( - # {**base_dict, **ds_dict} - #) - - # Save & cache EXTERNAL_MODEL_RESULTS - with open("EXTERNAL_MODEL_RESULTS.json", "w") as f: - json.dump(dict(sorted(EXTERNAL_MODEL_RESULTS.items())), f, indent=4) - - return EXTERNAL_MODEL_RESULTS - - -def download_or_use_cache(modelId: str): - global MODEL_CACHE - if modelId in MODEL_CACHE: - return MODEL_CACHE[modelId] - try: - readme_path = hf_hub_download(modelId, filename="README.md", etag_timeout=30) - except Exception: - print(f"ERROR: Could not fetch metadata for {modelId}, trying again") - readme_path = hf_hub_download(modelId, filename="README.md", etag_timeout=30) - meta = metadata_load(readme_path) - MODEL_CACHE[modelId] = meta - return meta - - -def simplify_dataset_name(name): - return name.replace("MTEB ", "").replace(" (default)", "") - - -def get_mteb_data( - tasks: list = ["Clustering"], - langs: list = [], - datasets: list = [], - fillna: bool = True, - add_emb_dim: bool = True, - task_to_metric: dict = TASK_TO_METRIC, - rank: bool = True, -) -> pd.DataFrame: - global MODEL_INFOS - - with open("EXTERNAL_MODEL_RESULTS.json", "r") as f: - external_model_results = json.load(f) - - api = API - models = list(api.list_models(filter="mteb", full=True)) - # Legacy names changes; Also fetch the old results & merge later - if "MLSUMClusteringP2P (fr)" in datasets: - datasets.append("MLSUMClusteringP2P") - if "MLSUMClusteringS2S (fr)" in datasets: - datasets.append("MLSUMClusteringS2S") - if "PawsXPairClassification (fr)" in datasets: - datasets.append("PawsX (fr)") - # Initialize list to models that we cannot fetch metadata from - df_list = [] - for model in external_model_results: - results_list = [] - for task in tasks: - # Not all models have InstructionRetrieval, other new tasks - if task not in external_model_results[model]: continue - if task_to_metric[task][0] not in external_model_results[model][task]: continue - results_list += external_model_results[model][task][task_to_metric[task][0]] - - if len(datasets) > 0: - res = { - k: v - for d in results_list - for k, v in d.items() - if (k == "Model") or any([x in k for x in datasets]) - } - elif langs: - # Would be cleaner to rely on an extra language column instead - langs_format = [f"({lang})" for lang in langs] - res = { - k: v - for d in results_list - for k, v in d.items() - if any([k.split(" ")[-1] in (k, x) for x in langs_format]) - } - else: - res = {k: v for d in results_list for k, v in d.items()} - # Model & at least one result - if len(res) > 1: - if add_emb_dim: - res["Model Size (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get( - model, "" - ) - res["Memory Usage (GB, fp32)"] = ( - round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2) - if res["Model Size (Million Parameters)"] != "" - else "" - ) - res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "") - res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "") - df_list.append(res) - - pbar = tqdm(models, desc="Fetching model metadata") - for model in pbar: - if model.modelId in MODELS_TO_SKIP: - continue - pbar.set_description(f"Fetching {model.modelId!r} metadata") - meta = download_or_use_cache(model.modelId) - MODEL_INFOS[model.modelId] = {"metadata": meta} - if "model-index" not in meta: - continue - # meta['model-index'][0]["results"] is list of elements like: - # { - # "task": {"type": "Classification"}, - # "dataset": { - # "type": "mteb/amazon_massive_intent", - # "name": "MTEB MassiveIntentClassification (nb)", - # "config": "nb", - # "split": "test", - # }, - # "metrics": [ - # {"type": "accuracy", "value": 39.81506388702084}, - # {"type": "f1", "value": 38.809586587791664}, - # ], - # }, - # Use "get" instead of dict indexing to skip incompat metadata instead of erroring out - if len(datasets) > 0: - task_results = [ - sub_res - for sub_res in meta["model-index"][0]["results"] - if (sub_res.get("task", {}).get("type", "") in tasks) - and any( - [x in sub_res.get("dataset", {}).get("name", "") for x in datasets] - ) - ] - elif langs: - task_results = [ - sub_res - for sub_res in meta["model-index"][0]["results"] - if (sub_res.get("task", {}).get("type", "") in tasks) - and ( - sub_res.get("dataset", {}).get("config", "default") - in ("default", *langs) - ) - ] - else: - task_results = [ - sub_res - for sub_res in meta["model-index"][0]["results"] - if (sub_res.get("task", {}).get("type", "") in tasks) - ] - try: - out = [ - { - simplify_dataset_name(res["dataset"]["name"]): [ - round(score["value"], 2) - for score in res["metrics"] - if filter_metric_fetched( - simplify_dataset_name(res["dataset"]["name"]), - score["type"], - task_to_metric.get(res["task"]["type"]), - res["dataset"]["split"], - ) - ][0] - } - for res in task_results - ] - except Exception as e: - if 'ILKT' in model.modelId: continue - print("ERROR", model.modelId, e) - continue - out = {k: v for d in out for k, v in d.items()} - out["Model"] = make_clickable_model(model.modelId) - # Model & at least one result - if len(out) > 1: - if add_emb_dim: - # The except clause triggers on gated repos, we can use external metadata for those - try: - MODEL_INFOS[model.modelId]["dim_seq_size"] = list(get_dim_seq_size(model)) - except: - name_without_org = model.modelId.split("/")[-1] - # EXTERNAL_MODEL_TO_SIZE[name_without_org] refers to millions of parameters, so for memory usage - # we multiply by 1e6 to get just the number of parameters, then by 4 to get the number of bytes - # given fp32 precision (4 bytes per float), then divide by 1024**3 to get the number of GB - MODEL_INFOS[model.modelId]["dim_seq_size"] = ( - EXTERNAL_MODEL_TO_DIM.get(name_without_org, ""), - EXTERNAL_MODEL_TO_SEQLEN.get(name_without_org, ""), - EXTERNAL_MODEL_TO_SIZE.get(name_without_org, ""), - round( - EXTERNAL_MODEL_TO_SIZE[name_without_org] - * 1e6 - * 4 - / 1024**3, - 2, - ) - if name_without_org in EXTERNAL_MODEL_TO_SIZE - else "", - ) - ( - out["Embedding Dimensions"], - out["Max Tokens"], - out["Model Size (Million Parameters)"], - out["Memory Usage (GB, fp32)"], - ) = tuple(MODEL_INFOS[model.modelId]["dim_seq_size"]) - df_list.append(out) - model_siblings = model.siblings or [] - if ( - model.library_name == "sentence-transformers" - or "sentence-transformers" in model.tags - or "modules.json" in {file.rfilename for file in model_siblings} - ): - SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"]) - - # # Save & cache MODEL_INFOS - # with open("model_infos.json", "w") as f: - # json.dump(MODEL_INFOS, f) - - df = pd.DataFrame(df_list) - # If there are any models that are the same, merge them - # E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one - df = df.groupby("Model", as_index=False).first() - # Put 'Model' column first - cols = sorted(list(df.columns)) - base_columns = [ - "Model", - "Model Size (Million Parameters)", - "Memory Usage (GB, fp32)", - "Embedding Dimensions", - "Max Tokens", - ] - if len(datasets) > 0: - # Update legacy column names to be merged with newer ones - # Update 'MLSUMClusteringP2P (fr)' with values from 'MLSUMClusteringP2P' - if ("MLSUMClusteringP2P (fr)" in datasets) and ("MLSUMClusteringP2P" in cols): - df["MLSUMClusteringP2P (fr)"] = df["MLSUMClusteringP2P (fr)"].fillna( - df["MLSUMClusteringP2P"] - ) - datasets.remove("MLSUMClusteringP2P") - if ("MLSUMClusteringS2S (fr)" in datasets) and ("MLSUMClusteringS2S" in cols): - df["MLSUMClusteringS2S (fr)"] = df["MLSUMClusteringS2S (fr)"].fillna( - df["MLSUMClusteringS2S"] - ) - datasets.remove("MLSUMClusteringS2S") - if ("PawsXPairClassification (fr)" in datasets) and ("PawsX (fr)" in cols): - # for the first bit no model has it, hence no column for it. We can remove this in a month or so - if "PawsXPairClassification (fr)" not in cols: - df["PawsXPairClassification (fr)"] = df["PawsX (fr)"] - else: - df["PawsXPairClassification (fr)"] = df[ - "PawsXPairClassification (fr)" - ].fillna(df["PawsX (fr)"]) - # make all the columns the same - datasets.remove("PawsX (fr)") - cols.remove("PawsX (fr)") - df.drop(columns=["PawsX (fr)"], inplace=True) - - # Filter invalid columns - cols = [col for col in cols if col in base_columns + datasets] - i = 0 - for column in base_columns: - if column in cols: - cols.insert(i, cols.pop(cols.index(column))) - i += 1 - df = df[cols] - if rank: - df = add_rank(df) - if fillna: - df.fillna("", inplace=True) - return df - - -# Get dict with a task list for each task category -# E.g. {"Classification": ["AmazonMassiveIntentClassification (en)", ...], "PairClassification": ["SprintDuplicateQuestions", ...]} -def get_mteb_average(task_dict: dict) -> tuple[Any, dict]: - all_tasks = reduce(lambda x, y: x + y, task_dict.values()) - DATA_OVERALL = get_mteb_data( - tasks=list(task_dict.keys()), - datasets=all_tasks, - fillna=False, - add_emb_dim=True, - rank=False, - ) - # Debugging: - # DATA_OVERALL.to_csv("overall.csv") - DATA_OVERALL.insert( - 1, - f"Average ({len(all_tasks)} datasets)", - DATA_OVERALL[all_tasks].mean(axis=1, skipna=False), - ) - - for i, (task_category, task_category_list) in enumerate(task_dict.items()): - DATA_OVERALL.insert( - i + 2, - f"{task_category} Average ({len(task_category_list)} datasets)", - DATA_OVERALL[task_category_list].mean(axis=1, skipna=False), - ) - DATA_OVERALL.sort_values( - f"Average ({len(all_tasks)} datasets)", ascending=False, inplace=True - ) - # Start ranking from 1 - DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1))) - - DATA_OVERALL = DATA_OVERALL.round(2) - - DATA_TASKS = {} - for task_category, task_category_list in task_dict.items(): - DATA_TASKS[task_category] = add_rank( - DATA_OVERALL[ - ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"] + task_category_list - ] - ) - DATA_TASKS[task_category] = DATA_TASKS[task_category][ - DATA_TASKS[task_category].iloc[:, 4:].ne("").any(axis=1) - ] - - # Fill NaN after averaging - DATA_OVERALL.fillna("", inplace=True) - - data_overall_rows = [ - "Rank", - "Model", - "Model Size (Million Parameters)", - "Memory Usage (GB, fp32)", - "Embedding Dimensions", - "Max Tokens", - f"Average ({len(all_tasks)} datasets)", - ] - for task_category, task_category_list in task_dict.items(): - data_overall_rows.append( - f"{task_category} Average ({len(task_category_list)} datasets)" - ) - - DATA_OVERALL = DATA_OVERALL[data_overall_rows] - DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)] - - return DATA_OVERALL, DATA_TASKS - - -def refresh_leaderboard() -> tuple[list, dict]: - """ - The main code to refresh and calculate results for the leaderboard. It does this by fetching the results from the - external models and the models in the leaderboard, then calculating the average scores for each task category. - """ - - # get external model results and cache them - # NOTE: if your model results have changed, use this function to refresh them (see inside for details) - get_external_model_results() - - boards_data = {} - all_data_tasks = [] - pbar_tasks = tqdm( - BOARDS_CONFIG.items(), - desc="Fetching leaderboard results for ???", - total=len(BOARDS_CONFIG), - leave=True, - ) - for board, board_config in pbar_tasks: - # Optional fetch only for a specific board - # if board != "ru": continue - # Very hacky - should fix this as soon as possible - if board == "bright_long": - TASK_TO_METRIC["Retrieval"] = ["recall_at_1"] - boards_data[board] = {"data_overall": None, "data_tasks": {}} - pbar_tasks.set_description(f"Fetching leaderboard results for {board!r}") - pbar_tasks.refresh() - if board_config["has_overall"]: - data_overall, data_tasks = get_mteb_average(board_config["tasks"]) - boards_data[board]["data_overall"] = data_overall - boards_data[board]["data_tasks"] = data_tasks - all_data_tasks.extend(data_tasks.values()) - else: - for task_category, task_category_list in board_config["tasks"].items(): - data_task_category = get_mteb_data( - tasks=[task_category], datasets=task_category_list - ) - boards_data[board]["data_tasks"][task_category] = data_task_category - all_data_tasks.append(data_task_category) - if board == "bright_long": - TASK_TO_METRIC["Retrieval"] = ["ndcg_at_10"] - return all_data_tasks, boards_data - -def write_out_results(item: dict, item_name: str) -> None: - """ - Due to their complex structure, let's recursively create subfolders until we reach the end - of the item and then save the DFs as jsonl files - - Args: - item: The item to save - item_name: The name of the item - """ - main_folder = item_name - - if isinstance(item, list): - for i, v in enumerate(item): - write_out_results(v, os.path.join(main_folder, str(i))) - - elif isinstance(item, dict): - for key, value in item.items(): - if isinstance(value, dict): - write_out_results(value, os.path.join(main_folder, key)) - elif isinstance(value, list): - for i, v in enumerate(value): - write_out_results(v, os.path.join(main_folder, key + str(i))) - else: - write_out_results(value, os.path.join(main_folder, key)) - - elif isinstance(item, pd.DataFrame): - print(f"Saving {main_folder} to {main_folder}/default.jsonl") - os.makedirs(main_folder, exist_ok=True) - - if "index" not in item.columns: - item.reset_index(inplace=True) - item.to_json(f"{main_folder}/default.jsonl", orient="records", lines=True) - - elif isinstance(item, str): - print(f"Saving {main_folder} to {main_folder}/default.txt") - os.makedirs(main_folder, exist_ok=True) - with open(f"{main_folder}/default.txt", "w") as f: - f.write(item) - - elif item is None: - # write out an empty file - print(f"Saving {main_folder} to {main_folder}/default.txt") - os.makedirs(main_folder, exist_ok=True) - with open(f"{main_folder}/default.txt", "w") as f: - f.write("") - - else: - raise Exception(f"Unknown type {type(item)}") - - -def load_results(data_path: str) -> list | dict | pd.DataFrame | str | None: - """ - Do the reverse of `write_out_results` to reconstruct the item - - Args: - data_path: The path to the data to load - - Returns: - The loaded data - """ - if os.path.isdir(data_path): - # if the folder just has numbers from 0 to N, load as a list - all_files_in_dir = list(os.listdir(data_path)) - if set(all_files_in_dir) == set([str(i) for i in range(len(all_files_in_dir))]): - ### the list case - return [ - load_results(os.path.join(data_path, str(i))) - for i in range(len(os.listdir(data_path))) - ] - else: - if len(all_files_in_dir) == 1: - file_name = all_files_in_dir[0] - if file_name == "default.jsonl": - return load_results(os.path.join(data_path, file_name)) - else: ### the dict case - return {file_name: load_results(os.path.join(data_path, file_name))} - else: - return { - file_name: load_results(os.path.join(data_path, file_name)) - for file_name in all_files_in_dir - } - - elif data_path.endswith(".jsonl"): - df = pd.read_json(data_path, orient="records", lines=True) - if "index" in df.columns: - df = df.set_index("index") - if "Memory Usage (GB, fp32)" in df.columns: - df["Memory Usage (GB, fp32)"] = df["Memory Usage (GB, fp32)"].map(lambda value: round(value, 2) if isinstance(value, float) else value) - return df - - else: - with open(data_path, "r") as f: - data = f.read() - if data == "": - return None - else: - return data - - -if __name__ == "__main__": - print("Refreshing leaderboard statistics...") - all_data_tasks, boards_data = refresh_leaderboard() - print("Done calculating, saving...") - # save them so that the leaderboard can use them. They're quite complex though - # but we can't use pickle files because of git-lfs. - write_out_results(all_data_tasks, "all_data_tasks") - write_out_results(boards_data, "boards_data") - - # to load them use - # all_data_tasks = load_results("all_data_tasks") - # boards_data = load_results("boards_data") - print("Done saving results!") diff --git a/requirements.txt b/requirements.txt index e6a6262af70e795644f144323e47b924013328c2..44fb56ca3ca3f4014f85001ceb793fe77d1ae3ba 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -git+https://github.com/EuDs63/ffmpy.git@333a19ee4d21f32537c0508aa1942ef1aa7afe24 gradio datasets pandas diff --git a/utils/model_size.py b/utils/model_size.py index f6eb6873349ec837b6d61d39d74339b18125b11f..cd0fa206ef267ba5a8575517ba822b243ebf186f 100644 --- a/utils/model_size.py +++ b/utils/model_size.py @@ -1,33 +1,28 @@ import json import re -import traceback from huggingface_hub.hf_api import ModelInfo, get_safetensors_metadata, model_info as get_model_info, get_hf_file_metadata, hf_hub_url from huggingface_hub import hf_hub_download # Map model IDs to the number of bytes used for one parameter. So, 4 bytes for fp32, 2 bytes for fp16, etc. # By default, we assume that the model is stored in fp32. -KNOWN_BYTES_PER_PARAM = { - "dwzhu/e5-base-4k": 2, -} +KNOWN_BYTES_PER_PARAM = {} + def get_model_parameters_memory(model_info: ModelInfo): '''Get the size of the model in million of parameters.''' try: safetensors = get_safetensors_metadata(model_info.id) - except Exception as e: - pass - else: num_parameters = sum(safetensors.parameter_count.values()) return round(num_parameters / 1e6), round(num_parameters * 4 / 1024**3, 2) + except Exception as e: + pass filenames = [sib.rfilename for sib in model_info.siblings] if "pytorch_model.bin" in filenames: url = hf_hub_url(model_info.id, filename="pytorch_model.bin") meta = get_hf_file_metadata(url) bytes_per_param = KNOWN_BYTES_PER_PARAM.get(model_info.id, 4) - num_params = round(meta.size / bytes_per_param / 1e6) - size_gb = round(meta.size * (4 / bytes_per_param) / 1024**3, 2) - return num_params, size_gb + return round(meta.size / bytes_per_param / 1e6), round(meta.size / 1024**3, 2) if "pytorch_model.bin.index.json" in filenames: index_path = hf_hub_download(model_info.id, filename="pytorch_model.bin.index.json") @@ -42,4 +37,4 @@ def get_model_parameters_memory(model_info: ModelInfo): if ("metadata" in size) and ("total_size" in size["metadata"]): return round(size["metadata"]["total_size"] / bytes_per_param / 1e6), round(size["metadata"]["total_size"] / 1024**3, 2) - raise Exception(f"Could not find the model parameters for {model_info.id}") + return None, None