| --- |
| tags: |
| - mteb |
| model-index: |
| - name: bge-base-en |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 75.73134328358209 |
| - type: ap |
| value: 38.97277232632892 |
| - type: f1 |
| value: 69.81740361139785 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 92.56522500000001 |
| - type: ap |
| value: 88.88821771869553 |
| - type: f1 |
| value: 92.54817512659696 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 46.91 |
| - type: f1 |
| value: 46.28536394320311 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 38.834 |
| - type: map_at_10 |
| value: 53.564 |
| - type: map_at_100 |
| value: 54.230000000000004 |
| - type: map_at_1000 |
| value: 54.235 |
| - type: map_at_3 |
| value: 49.49 |
| - type: map_at_5 |
| value: 51.784 |
| - type: mrr_at_1 |
| value: 39.26 |
| - type: mrr_at_10 |
| value: 53.744 |
| - type: mrr_at_100 |
| value: 54.410000000000004 |
| - type: mrr_at_1000 |
| value: 54.415 |
| - type: mrr_at_3 |
| value: 49.656 |
| - type: mrr_at_5 |
| value: 52.018 |
| - type: ndcg_at_1 |
| value: 38.834 |
| - type: ndcg_at_10 |
| value: 61.487 |
| - type: ndcg_at_100 |
| value: 64.303 |
| - type: ndcg_at_1000 |
| value: 64.408 |
| - type: ndcg_at_3 |
| value: 53.116 |
| - type: ndcg_at_5 |
| value: 57.248 |
| - type: precision_at_1 |
| value: 38.834 |
| - type: precision_at_10 |
| value: 8.663 |
| - type: precision_at_100 |
| value: 0.989 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 21.218999999999998 |
| - type: precision_at_5 |
| value: 14.737 |
| - type: recall_at_1 |
| value: 38.834 |
| - type: recall_at_10 |
| value: 86.629 |
| - type: recall_at_100 |
| value: 98.86200000000001 |
| - type: recall_at_1000 |
| value: 99.644 |
| - type: recall_at_3 |
| value: 63.656 |
| - type: recall_at_5 |
| value: 73.68400000000001 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 48.88475477433035 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 42.85053138403176 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 62.23221013208242 |
| - type: mrr |
| value: 74.64857318735436 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.4403443247284 |
| - type: cos_sim_spearman |
| value: 85.5326718115169 |
| - type: euclidean_pearson |
| value: 86.0114007449595 |
| - type: euclidean_spearman |
| value: 86.05979225604875 |
| - type: manhattan_pearson |
| value: 86.05423806568598 |
| - type: manhattan_spearman |
| value: 86.02485170086835 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 86.44480519480518 |
| - type: f1 |
| value: 86.41301900941988 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 40.17547250880036 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 37.74514172687293 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 32.096000000000004 |
| - type: map_at_10 |
| value: 43.345 |
| - type: map_at_100 |
| value: 44.73 |
| - type: map_at_1000 |
| value: 44.85 |
| - type: map_at_3 |
| value: 39.956 |
| - type: map_at_5 |
| value: 41.727 |
| - type: mrr_at_1 |
| value: 38.769999999999996 |
| - type: mrr_at_10 |
| value: 48.742000000000004 |
| - type: mrr_at_100 |
| value: 49.474000000000004 |
| - type: mrr_at_1000 |
| value: 49.513 |
| - type: mrr_at_3 |
| value: 46.161 |
| - type: mrr_at_5 |
| value: 47.721000000000004 |
| - type: ndcg_at_1 |
| value: 38.769999999999996 |
| - type: ndcg_at_10 |
| value: 49.464999999999996 |
| - type: ndcg_at_100 |
| value: 54.632000000000005 |
| - type: ndcg_at_1000 |
| value: 56.52 |
| - type: ndcg_at_3 |
| value: 44.687 |
| - type: ndcg_at_5 |
| value: 46.814 |
| - type: precision_at_1 |
| value: 38.769999999999996 |
| - type: precision_at_10 |
| value: 9.471 |
| - type: precision_at_100 |
| value: 1.4909999999999999 |
| - type: precision_at_1000 |
| value: 0.194 |
| - type: precision_at_3 |
| value: 21.268 |
| - type: precision_at_5 |
| value: 15.079 |
| - type: recall_at_1 |
| value: 32.096000000000004 |
| - type: recall_at_10 |
| value: 60.99099999999999 |
| - type: recall_at_100 |
| value: 83.075 |
| - type: recall_at_1000 |
| value: 95.178 |
| - type: recall_at_3 |
| value: 47.009 |
| - type: recall_at_5 |
| value: 53.348 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 32.588 |
| - type: map_at_10 |
| value: 42.251 |
| - type: map_at_100 |
| value: 43.478 |
| - type: map_at_1000 |
| value: 43.617 |
| - type: map_at_3 |
| value: 39.381 |
| - type: map_at_5 |
| value: 41.141 |
| - type: mrr_at_1 |
| value: 41.21 |
| - type: mrr_at_10 |
| value: 48.765 |
| - type: mrr_at_100 |
| value: 49.403000000000006 |
| - type: mrr_at_1000 |
| value: 49.451 |
| - type: mrr_at_3 |
| value: 46.73 |
| - type: mrr_at_5 |
| value: 47.965999999999994 |
| - type: ndcg_at_1 |
| value: 41.21 |
| - type: ndcg_at_10 |
| value: 47.704 |
| - type: ndcg_at_100 |
| value: 51.916 |
| - type: ndcg_at_1000 |
| value: 54.013999999999996 |
| - type: ndcg_at_3 |
| value: 44.007000000000005 |
| - type: ndcg_at_5 |
| value: 45.936 |
| - type: precision_at_1 |
| value: 41.21 |
| - type: precision_at_10 |
| value: 8.885 |
| - type: precision_at_100 |
| value: 1.409 |
| - type: precision_at_1000 |
| value: 0.189 |
| - type: precision_at_3 |
| value: 21.274 |
| - type: precision_at_5 |
| value: 15.045 |
| - type: recall_at_1 |
| value: 32.588 |
| - type: recall_at_10 |
| value: 56.333 |
| - type: recall_at_100 |
| value: 74.251 |
| - type: recall_at_1000 |
| value: 87.518 |
| - type: recall_at_3 |
| value: 44.962 |
| - type: recall_at_5 |
| value: 50.609 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 40.308 |
| - type: map_at_10 |
| value: 53.12 |
| - type: map_at_100 |
| value: 54.123 |
| - type: map_at_1000 |
| value: 54.173 |
| - type: map_at_3 |
| value: 50.017999999999994 |
| - type: map_at_5 |
| value: 51.902 |
| - type: mrr_at_1 |
| value: 46.394999999999996 |
| - type: mrr_at_10 |
| value: 56.531 |
| - type: mrr_at_100 |
| value: 57.19800000000001 |
| - type: mrr_at_1000 |
| value: 57.225 |
| - type: mrr_at_3 |
| value: 54.368 |
| - type: mrr_at_5 |
| value: 55.713 |
| - type: ndcg_at_1 |
| value: 46.394999999999996 |
| - type: ndcg_at_10 |
| value: 58.811 |
| - type: ndcg_at_100 |
| value: 62.834 |
| - type: ndcg_at_1000 |
| value: 63.849999999999994 |
| - type: ndcg_at_3 |
| value: 53.88699999999999 |
| - type: ndcg_at_5 |
| value: 56.477999999999994 |
| - type: precision_at_1 |
| value: 46.394999999999996 |
| - type: precision_at_10 |
| value: 9.398 |
| - type: precision_at_100 |
| value: 1.2309999999999999 |
| - type: precision_at_1000 |
| value: 0.136 |
| - type: precision_at_3 |
| value: 24.221999999999998 |
| - type: precision_at_5 |
| value: 16.539 |
| - type: recall_at_1 |
| value: 40.308 |
| - type: recall_at_10 |
| value: 72.146 |
| - type: recall_at_100 |
| value: 89.60900000000001 |
| - type: recall_at_1000 |
| value: 96.733 |
| - type: recall_at_3 |
| value: 58.91499999999999 |
| - type: recall_at_5 |
| value: 65.34299999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 27.383000000000003 |
| - type: map_at_10 |
| value: 35.802 |
| - type: map_at_100 |
| value: 36.756 |
| - type: map_at_1000 |
| value: 36.826 |
| - type: map_at_3 |
| value: 32.923 |
| - type: map_at_5 |
| value: 34.577999999999996 |
| - type: mrr_at_1 |
| value: 29.604999999999997 |
| - type: mrr_at_10 |
| value: 37.918 |
| - type: mrr_at_100 |
| value: 38.732 |
| - type: mrr_at_1000 |
| value: 38.786 |
| - type: mrr_at_3 |
| value: 35.198 |
| - type: mrr_at_5 |
| value: 36.808 |
| - type: ndcg_at_1 |
| value: 29.604999999999997 |
| - type: ndcg_at_10 |
| value: 40.836 |
| - type: ndcg_at_100 |
| value: 45.622 |
| - type: ndcg_at_1000 |
| value: 47.427 |
| - type: ndcg_at_3 |
| value: 35.208 |
| - type: ndcg_at_5 |
| value: 38.066 |
| - type: precision_at_1 |
| value: 29.604999999999997 |
| - type: precision_at_10 |
| value: 6.226 |
| - type: precision_at_100 |
| value: 0.9079999999999999 |
| - type: precision_at_1000 |
| value: 0.11 |
| - type: precision_at_3 |
| value: 14.463000000000001 |
| - type: precision_at_5 |
| value: 10.35 |
| - type: recall_at_1 |
| value: 27.383000000000003 |
| - type: recall_at_10 |
| value: 54.434000000000005 |
| - type: recall_at_100 |
| value: 76.632 |
| - type: recall_at_1000 |
| value: 90.25 |
| - type: recall_at_3 |
| value: 39.275 |
| - type: recall_at_5 |
| value: 46.225 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 17.885 |
| - type: map_at_10 |
| value: 25.724000000000004 |
| - type: map_at_100 |
| value: 26.992 |
| - type: map_at_1000 |
| value: 27.107999999999997 |
| - type: map_at_3 |
| value: 23.04 |
| - type: map_at_5 |
| value: 24.529 |
| - type: mrr_at_1 |
| value: 22.264 |
| - type: mrr_at_10 |
| value: 30.548 |
| - type: mrr_at_100 |
| value: 31.593 |
| - type: mrr_at_1000 |
| value: 31.657999999999998 |
| - type: mrr_at_3 |
| value: 27.756999999999998 |
| - type: mrr_at_5 |
| value: 29.398999999999997 |
| - type: ndcg_at_1 |
| value: 22.264 |
| - type: ndcg_at_10 |
| value: 30.902 |
| - type: ndcg_at_100 |
| value: 36.918 |
| - type: ndcg_at_1000 |
| value: 39.735 |
| - type: ndcg_at_3 |
| value: 25.915 |
| - type: ndcg_at_5 |
| value: 28.255999999999997 |
| - type: precision_at_1 |
| value: 22.264 |
| - type: precision_at_10 |
| value: 5.634 |
| - type: precision_at_100 |
| value: 0.9939999999999999 |
| - type: precision_at_1000 |
| value: 0.13699999999999998 |
| - type: precision_at_3 |
| value: 12.396 |
| - type: precision_at_5 |
| value: 9.055 |
| - type: recall_at_1 |
| value: 17.885 |
| - type: recall_at_10 |
| value: 42.237 |
| - type: recall_at_100 |
| value: 68.489 |
| - type: recall_at_1000 |
| value: 88.721 |
| - type: recall_at_3 |
| value: 28.283 |
| - type: recall_at_5 |
| value: 34.300000000000004 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 29.737000000000002 |
| - type: map_at_10 |
| value: 39.757 |
| - type: map_at_100 |
| value: 40.992 |
| - type: map_at_1000 |
| value: 41.102 |
| - type: map_at_3 |
| value: 36.612 |
| - type: map_at_5 |
| value: 38.413000000000004 |
| - type: mrr_at_1 |
| value: 35.804 |
| - type: mrr_at_10 |
| value: 45.178000000000004 |
| - type: mrr_at_100 |
| value: 45.975 |
| - type: mrr_at_1000 |
| value: 46.021 |
| - type: mrr_at_3 |
| value: 42.541000000000004 |
| - type: mrr_at_5 |
| value: 44.167 |
| - type: ndcg_at_1 |
| value: 35.804 |
| - type: ndcg_at_10 |
| value: 45.608 |
| - type: ndcg_at_100 |
| value: 50.746 |
| - type: ndcg_at_1000 |
| value: 52.839999999999996 |
| - type: ndcg_at_3 |
| value: 40.52 |
| - type: ndcg_at_5 |
| value: 43.051 |
| - type: precision_at_1 |
| value: 35.804 |
| - type: precision_at_10 |
| value: 8.104 |
| - type: precision_at_100 |
| value: 1.256 |
| - type: precision_at_1000 |
| value: 0.161 |
| - type: precision_at_3 |
| value: 19.121 |
| - type: precision_at_5 |
| value: 13.532 |
| - type: recall_at_1 |
| value: 29.737000000000002 |
| - type: recall_at_10 |
| value: 57.66 |
| - type: recall_at_100 |
| value: 79.121 |
| - type: recall_at_1000 |
| value: 93.023 |
| - type: recall_at_3 |
| value: 43.13 |
| - type: recall_at_5 |
| value: 49.836000000000006 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.299 |
| - type: map_at_10 |
| value: 35.617 |
| - type: map_at_100 |
| value: 36.972 |
| - type: map_at_1000 |
| value: 37.096000000000004 |
| - type: map_at_3 |
| value: 32.653999999999996 |
| - type: map_at_5 |
| value: 34.363 |
| - type: mrr_at_1 |
| value: 32.877 |
| - type: mrr_at_10 |
| value: 41.423 |
| - type: mrr_at_100 |
| value: 42.333999999999996 |
| - type: mrr_at_1000 |
| value: 42.398 |
| - type: mrr_at_3 |
| value: 39.193 |
| - type: mrr_at_5 |
| value: 40.426 |
| - type: ndcg_at_1 |
| value: 32.877 |
| - type: ndcg_at_10 |
| value: 41.271 |
| - type: ndcg_at_100 |
| value: 46.843 |
| - type: ndcg_at_1000 |
| value: 49.366 |
| - type: ndcg_at_3 |
| value: 36.735 |
| - type: ndcg_at_5 |
| value: 38.775999999999996 |
| - type: precision_at_1 |
| value: 32.877 |
| - type: precision_at_10 |
| value: 7.580000000000001 |
| - type: precision_at_100 |
| value: 1.192 |
| - type: precision_at_1000 |
| value: 0.158 |
| - type: precision_at_3 |
| value: 17.541999999999998 |
| - type: precision_at_5 |
| value: 12.443 |
| - type: recall_at_1 |
| value: 26.299 |
| - type: recall_at_10 |
| value: 52.256 |
| - type: recall_at_100 |
| value: 75.919 |
| - type: recall_at_1000 |
| value: 93.185 |
| - type: recall_at_3 |
| value: 39.271 |
| - type: recall_at_5 |
| value: 44.901 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 27.05741666666667 |
| - type: map_at_10 |
| value: 36.086416666666665 |
| - type: map_at_100 |
| value: 37.26916666666667 |
| - type: map_at_1000 |
| value: 37.38191666666666 |
| - type: map_at_3 |
| value: 33.34225 |
| - type: map_at_5 |
| value: 34.86425 |
| - type: mrr_at_1 |
| value: 32.06008333333333 |
| - type: mrr_at_10 |
| value: 40.36658333333333 |
| - type: mrr_at_100 |
| value: 41.206500000000005 |
| - type: mrr_at_1000 |
| value: 41.261083333333325 |
| - type: mrr_at_3 |
| value: 38.01208333333334 |
| - type: mrr_at_5 |
| value: 39.36858333333333 |
| - type: ndcg_at_1 |
| value: 32.06008333333333 |
| - type: ndcg_at_10 |
| value: 41.3535 |
| - type: ndcg_at_100 |
| value: 46.42066666666666 |
| - type: ndcg_at_1000 |
| value: 48.655166666666666 |
| - type: ndcg_at_3 |
| value: 36.78041666666667 |
| - type: ndcg_at_5 |
| value: 38.91783333333334 |
| - type: precision_at_1 |
| value: 32.06008333333333 |
| - type: precision_at_10 |
| value: 7.169833333333332 |
| - type: precision_at_100 |
| value: 1.1395 |
| - type: precision_at_1000 |
| value: 0.15158333333333332 |
| - type: precision_at_3 |
| value: 16.852 |
| - type: precision_at_5 |
| value: 11.8645 |
| - type: recall_at_1 |
| value: 27.05741666666667 |
| - type: recall_at_10 |
| value: 52.64491666666666 |
| - type: recall_at_100 |
| value: 74.99791666666667 |
| - type: recall_at_1000 |
| value: 90.50524999999999 |
| - type: recall_at_3 |
| value: 39.684000000000005 |
| - type: recall_at_5 |
| value: 45.37225 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.607999999999997 |
| - type: map_at_10 |
| value: 32.28 |
| - type: map_at_100 |
| value: 33.261 |
| - type: map_at_1000 |
| value: 33.346 |
| - type: map_at_3 |
| value: 30.514999999999997 |
| - type: map_at_5 |
| value: 31.415 |
| - type: mrr_at_1 |
| value: 28.988000000000003 |
| - type: mrr_at_10 |
| value: 35.384 |
| - type: mrr_at_100 |
| value: 36.24 |
| - type: mrr_at_1000 |
| value: 36.299 |
| - type: mrr_at_3 |
| value: 33.717000000000006 |
| - type: mrr_at_5 |
| value: 34.507 |
| - type: ndcg_at_1 |
| value: 28.988000000000003 |
| - type: ndcg_at_10 |
| value: 36.248000000000005 |
| - type: ndcg_at_100 |
| value: 41.034 |
| - type: ndcg_at_1000 |
| value: 43.35 |
| - type: ndcg_at_3 |
| value: 32.987 |
| - type: ndcg_at_5 |
| value: 34.333999999999996 |
| - type: precision_at_1 |
| value: 28.988000000000003 |
| - type: precision_at_10 |
| value: 5.506 |
| - type: precision_at_100 |
| value: 0.853 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 14.11 |
| - type: precision_at_5 |
| value: 9.417 |
| - type: recall_at_1 |
| value: 25.607999999999997 |
| - type: recall_at_10 |
| value: 45.344 |
| - type: recall_at_100 |
| value: 67.132 |
| - type: recall_at_1000 |
| value: 84.676 |
| - type: recall_at_3 |
| value: 36.02 |
| - type: recall_at_5 |
| value: 39.613 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 18.44 |
| - type: map_at_10 |
| value: 25.651000000000003 |
| - type: map_at_100 |
| value: 26.735 |
| - type: map_at_1000 |
| value: 26.86 |
| - type: map_at_3 |
| value: 23.409 |
| - type: map_at_5 |
| value: 24.604 |
| - type: mrr_at_1 |
| value: 22.195 |
| - type: mrr_at_10 |
| value: 29.482000000000003 |
| - type: mrr_at_100 |
| value: 30.395 |
| - type: mrr_at_1000 |
| value: 30.471999999999998 |
| - type: mrr_at_3 |
| value: 27.409 |
| - type: mrr_at_5 |
| value: 28.553 |
| - type: ndcg_at_1 |
| value: 22.195 |
| - type: ndcg_at_10 |
| value: 30.242 |
| - type: ndcg_at_100 |
| value: 35.397 |
| - type: ndcg_at_1000 |
| value: 38.287 |
| - type: ndcg_at_3 |
| value: 26.201 |
| - type: ndcg_at_5 |
| value: 28.008 |
| - type: precision_at_1 |
| value: 22.195 |
| - type: precision_at_10 |
| value: 5.372 |
| - type: precision_at_100 |
| value: 0.9259999999999999 |
| - type: precision_at_1000 |
| value: 0.135 |
| - type: precision_at_3 |
| value: 12.228 |
| - type: precision_at_5 |
| value: 8.727 |
| - type: recall_at_1 |
| value: 18.44 |
| - type: recall_at_10 |
| value: 40.325 |
| - type: recall_at_100 |
| value: 63.504000000000005 |
| - type: recall_at_1000 |
| value: 83.909 |
| - type: recall_at_3 |
| value: 28.925 |
| - type: recall_at_5 |
| value: 33.641 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.535999999999998 |
| - type: map_at_10 |
| value: 35.358000000000004 |
| - type: map_at_100 |
| value: 36.498999999999995 |
| - type: map_at_1000 |
| value: 36.597 |
| - type: map_at_3 |
| value: 32.598 |
| - type: map_at_5 |
| value: 34.185 |
| - type: mrr_at_1 |
| value: 31.25 |
| - type: mrr_at_10 |
| value: 39.593 |
| - type: mrr_at_100 |
| value: 40.443 |
| - type: mrr_at_1000 |
| value: 40.498 |
| - type: mrr_at_3 |
| value: 37.018 |
| - type: mrr_at_5 |
| value: 38.492 |
| - type: ndcg_at_1 |
| value: 31.25 |
| - type: ndcg_at_10 |
| value: 40.71 |
| - type: ndcg_at_100 |
| value: 46.079 |
| - type: ndcg_at_1000 |
| value: 48.287 |
| - type: ndcg_at_3 |
| value: 35.667 |
| - type: ndcg_at_5 |
| value: 38.080000000000005 |
| - type: precision_at_1 |
| value: 31.25 |
| - type: precision_at_10 |
| value: 6.847 |
| - type: precision_at_100 |
| value: 1.079 |
| - type: precision_at_1000 |
| value: 0.13699999999999998 |
| - type: precision_at_3 |
| value: 16.262 |
| - type: precision_at_5 |
| value: 11.455 |
| - type: recall_at_1 |
| value: 26.535999999999998 |
| - type: recall_at_10 |
| value: 52.92099999999999 |
| - type: recall_at_100 |
| value: 76.669 |
| - type: recall_at_1000 |
| value: 92.096 |
| - type: recall_at_3 |
| value: 38.956 |
| - type: recall_at_5 |
| value: 45.239000000000004 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.691 |
| - type: map_at_10 |
| value: 33.417 |
| - type: map_at_100 |
| value: 35.036 |
| - type: map_at_1000 |
| value: 35.251 |
| - type: map_at_3 |
| value: 30.646 |
| - type: map_at_5 |
| value: 32.177 |
| - type: mrr_at_1 |
| value: 30.04 |
| - type: mrr_at_10 |
| value: 37.905 |
| - type: mrr_at_100 |
| value: 38.929 |
| - type: mrr_at_1000 |
| value: 38.983000000000004 |
| - type: mrr_at_3 |
| value: 35.276999999999994 |
| - type: mrr_at_5 |
| value: 36.897000000000006 |
| - type: ndcg_at_1 |
| value: 30.04 |
| - type: ndcg_at_10 |
| value: 39.037 |
| - type: ndcg_at_100 |
| value: 44.944 |
| - type: ndcg_at_1000 |
| value: 47.644 |
| - type: ndcg_at_3 |
| value: 34.833999999999996 |
| - type: ndcg_at_5 |
| value: 36.83 |
| - type: precision_at_1 |
| value: 30.04 |
| - type: precision_at_10 |
| value: 7.4510000000000005 |
| - type: precision_at_100 |
| value: 1.492 |
| - type: precision_at_1000 |
| value: 0.234 |
| - type: precision_at_3 |
| value: 16.337 |
| - type: precision_at_5 |
| value: 11.897 |
| - type: recall_at_1 |
| value: 24.691 |
| - type: recall_at_10 |
| value: 49.303999999999995 |
| - type: recall_at_100 |
| value: 76.20400000000001 |
| - type: recall_at_1000 |
| value: 93.30000000000001 |
| - type: recall_at_3 |
| value: 36.594 |
| - type: recall_at_5 |
| value: 42.41 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.118 |
| - type: map_at_10 |
| value: 30.714999999999996 |
| - type: map_at_100 |
| value: 31.656000000000002 |
| - type: map_at_1000 |
| value: 31.757 |
| - type: map_at_3 |
| value: 28.355000000000004 |
| - type: map_at_5 |
| value: 29.337000000000003 |
| - type: mrr_at_1 |
| value: 25.323 |
| - type: mrr_at_10 |
| value: 32.93 |
| - type: mrr_at_100 |
| value: 33.762 |
| - type: mrr_at_1000 |
| value: 33.829 |
| - type: mrr_at_3 |
| value: 30.775999999999996 |
| - type: mrr_at_5 |
| value: 31.774 |
| - type: ndcg_at_1 |
| value: 25.323 |
| - type: ndcg_at_10 |
| value: 35.408 |
| - type: ndcg_at_100 |
| value: 40.083 |
| - type: ndcg_at_1000 |
| value: 42.542 |
| - type: ndcg_at_3 |
| value: 30.717 |
| - type: ndcg_at_5 |
| value: 32.385000000000005 |
| - type: precision_at_1 |
| value: 25.323 |
| - type: precision_at_10 |
| value: 5.564 |
| - type: precision_at_100 |
| value: 0.843 |
| - type: precision_at_1000 |
| value: 0.116 |
| - type: precision_at_3 |
| value: 13.001 |
| - type: precision_at_5 |
| value: 8.834999999999999 |
| - type: recall_at_1 |
| value: 23.118 |
| - type: recall_at_10 |
| value: 47.788000000000004 |
| - type: recall_at_100 |
| value: 69.37 |
| - type: recall_at_1000 |
| value: 87.47399999999999 |
| - type: recall_at_3 |
| value: 34.868 |
| - type: recall_at_5 |
| value: 39.001999999999995 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 14.288 |
| - type: map_at_10 |
| value: 23.256 |
| - type: map_at_100 |
| value: 25.115 |
| - type: map_at_1000 |
| value: 25.319000000000003 |
| - type: map_at_3 |
| value: 20.005 |
| - type: map_at_5 |
| value: 21.529999999999998 |
| - type: mrr_at_1 |
| value: 31.401 |
| - type: mrr_at_10 |
| value: 42.251 |
| - type: mrr_at_100 |
| value: 43.236999999999995 |
| - type: mrr_at_1000 |
| value: 43.272 |
| - type: mrr_at_3 |
| value: 39.164 |
| - type: mrr_at_5 |
| value: 40.881 |
| - type: ndcg_at_1 |
| value: 31.401 |
| - type: ndcg_at_10 |
| value: 31.615 |
| - type: ndcg_at_100 |
| value: 38.982 |
| - type: ndcg_at_1000 |
| value: 42.496 |
| - type: ndcg_at_3 |
| value: 26.608999999999998 |
| - type: ndcg_at_5 |
| value: 28.048000000000002 |
| - type: precision_at_1 |
| value: 31.401 |
| - type: precision_at_10 |
| value: 9.536999999999999 |
| - type: precision_at_100 |
| value: 1.763 |
| - type: precision_at_1000 |
| value: 0.241 |
| - type: precision_at_3 |
| value: 19.153000000000002 |
| - type: precision_at_5 |
| value: 14.228 |
| - type: recall_at_1 |
| value: 14.288 |
| - type: recall_at_10 |
| value: 36.717 |
| - type: recall_at_100 |
| value: 61.9 |
| - type: recall_at_1000 |
| value: 81.676 |
| - type: recall_at_3 |
| value: 24.203 |
| - type: recall_at_5 |
| value: 28.793999999999997 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 9.019 |
| - type: map_at_10 |
| value: 19.963 |
| - type: map_at_100 |
| value: 28.834 |
| - type: map_at_1000 |
| value: 30.537999999999997 |
| - type: map_at_3 |
| value: 14.45 |
| - type: map_at_5 |
| value: 16.817999999999998 |
| - type: mrr_at_1 |
| value: 65.75 |
| - type: mrr_at_10 |
| value: 74.646 |
| - type: mrr_at_100 |
| value: 74.946 |
| - type: mrr_at_1000 |
| value: 74.95100000000001 |
| - type: mrr_at_3 |
| value: 72.625 |
| - type: mrr_at_5 |
| value: 74.012 |
| - type: ndcg_at_1 |
| value: 54 |
| - type: ndcg_at_10 |
| value: 42.014 |
| - type: ndcg_at_100 |
| value: 47.527 |
| - type: ndcg_at_1000 |
| value: 54.911 |
| - type: ndcg_at_3 |
| value: 46.586 |
| - type: ndcg_at_5 |
| value: 43.836999999999996 |
| - type: precision_at_1 |
| value: 65.75 |
| - type: precision_at_10 |
| value: 33.475 |
| - type: precision_at_100 |
| value: 11.16 |
| - type: precision_at_1000 |
| value: 2.145 |
| - type: precision_at_3 |
| value: 50.083 |
| - type: precision_at_5 |
| value: 42.55 |
| - type: recall_at_1 |
| value: 9.019 |
| - type: recall_at_10 |
| value: 25.558999999999997 |
| - type: recall_at_100 |
| value: 53.937999999999995 |
| - type: recall_at_1000 |
| value: 77.67399999999999 |
| - type: recall_at_3 |
| value: 15.456 |
| - type: recall_at_5 |
| value: 19.259 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 52.635 |
| - type: f1 |
| value: 47.692783881403926 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 76.893 |
| - type: map_at_10 |
| value: 84.897 |
| - type: map_at_100 |
| value: 85.122 |
| - type: map_at_1000 |
| value: 85.135 |
| - type: map_at_3 |
| value: 83.88 |
| - type: map_at_5 |
| value: 84.565 |
| - type: mrr_at_1 |
| value: 83.003 |
| - type: mrr_at_10 |
| value: 89.506 |
| - type: mrr_at_100 |
| value: 89.574 |
| - type: mrr_at_1000 |
| value: 89.575 |
| - type: mrr_at_3 |
| value: 88.991 |
| - type: mrr_at_5 |
| value: 89.349 |
| - type: ndcg_at_1 |
| value: 83.003 |
| - type: ndcg_at_10 |
| value: 88.351 |
| - type: ndcg_at_100 |
| value: 89.128 |
| - type: ndcg_at_1000 |
| value: 89.34100000000001 |
| - type: ndcg_at_3 |
| value: 86.92 |
| - type: ndcg_at_5 |
| value: 87.78200000000001 |
| - type: precision_at_1 |
| value: 83.003 |
| - type: precision_at_10 |
| value: 10.517999999999999 |
| - type: precision_at_100 |
| value: 1.115 |
| - type: precision_at_1000 |
| value: 0.11499999999999999 |
| - type: precision_at_3 |
| value: 33.062999999999995 |
| - type: precision_at_5 |
| value: 20.498 |
| - type: recall_at_1 |
| value: 76.893 |
| - type: recall_at_10 |
| value: 94.374 |
| - type: recall_at_100 |
| value: 97.409 |
| - type: recall_at_1000 |
| value: 98.687 |
| - type: recall_at_3 |
| value: 90.513 |
| - type: recall_at_5 |
| value: 92.709 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 20.829 |
| - type: map_at_10 |
| value: 32.86 |
| - type: map_at_100 |
| value: 34.838 |
| - type: map_at_1000 |
| value: 35.006 |
| - type: map_at_3 |
| value: 28.597 |
| - type: map_at_5 |
| value: 31.056 |
| - type: mrr_at_1 |
| value: 41.358 |
| - type: mrr_at_10 |
| value: 49.542 |
| - type: mrr_at_100 |
| value: 50.29900000000001 |
| - type: mrr_at_1000 |
| value: 50.334999999999994 |
| - type: mrr_at_3 |
| value: 46.579 |
| - type: mrr_at_5 |
| value: 48.408 |
| - type: ndcg_at_1 |
| value: 41.358 |
| - type: ndcg_at_10 |
| value: 40.758 |
| - type: ndcg_at_100 |
| value: 47.799 |
| - type: ndcg_at_1000 |
| value: 50.589 |
| - type: ndcg_at_3 |
| value: 36.695 |
| - type: ndcg_at_5 |
| value: 38.193 |
| - type: precision_at_1 |
| value: 41.358 |
| - type: precision_at_10 |
| value: 11.142000000000001 |
| - type: precision_at_100 |
| value: 1.8350000000000002 |
| - type: precision_at_1000 |
| value: 0.234 |
| - type: precision_at_3 |
| value: 24.023 |
| - type: precision_at_5 |
| value: 17.963 |
| - type: recall_at_1 |
| value: 20.829 |
| - type: recall_at_10 |
| value: 47.467999999999996 |
| - type: recall_at_100 |
| value: 73.593 |
| - type: recall_at_1000 |
| value: 90.122 |
| - type: recall_at_3 |
| value: 32.74 |
| - type: recall_at_5 |
| value: 39.608 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 40.324 |
| - type: map_at_10 |
| value: 64.183 |
| - type: map_at_100 |
| value: 65.037 |
| - type: map_at_1000 |
| value: 65.094 |
| - type: map_at_3 |
| value: 60.663 |
| - type: map_at_5 |
| value: 62.951 |
| - type: mrr_at_1 |
| value: 80.648 |
| - type: mrr_at_10 |
| value: 86.005 |
| - type: mrr_at_100 |
| value: 86.157 |
| - type: mrr_at_1000 |
| value: 86.162 |
| - type: mrr_at_3 |
| value: 85.116 |
| - type: mrr_at_5 |
| value: 85.703 |
| - type: ndcg_at_1 |
| value: 80.648 |
| - type: ndcg_at_10 |
| value: 72.351 |
| - type: ndcg_at_100 |
| value: 75.279 |
| - type: ndcg_at_1000 |
| value: 76.357 |
| - type: ndcg_at_3 |
| value: 67.484 |
| - type: ndcg_at_5 |
| value: 70.31500000000001 |
| - type: precision_at_1 |
| value: 80.648 |
| - type: precision_at_10 |
| value: 15.103 |
| - type: precision_at_100 |
| value: 1.7399999999999998 |
| - type: precision_at_1000 |
| value: 0.188 |
| - type: precision_at_3 |
| value: 43.232 |
| - type: precision_at_5 |
| value: 28.165000000000003 |
| - type: recall_at_1 |
| value: 40.324 |
| - type: recall_at_10 |
| value: 75.517 |
| - type: recall_at_100 |
| value: 86.982 |
| - type: recall_at_1000 |
| value: 94.072 |
| - type: recall_at_3 |
| value: 64.848 |
| - type: recall_at_5 |
| value: 70.41199999999999 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 91.4 |
| - type: ap |
| value: 87.4422032289312 |
| - type: f1 |
| value: 91.39249564302281 |
| - task: |
| type: Retrieval |
| dataset: |
| type: msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.03 |
| - type: map_at_10 |
| value: 34.402 |
| - type: map_at_100 |
| value: 35.599 |
| - type: map_at_1000 |
| value: 35.648 |
| - type: map_at_3 |
| value: 30.603 |
| - type: map_at_5 |
| value: 32.889 |
| - type: mrr_at_1 |
| value: 22.679 |
| - type: mrr_at_10 |
| value: 35.021 |
| - type: mrr_at_100 |
| value: 36.162 |
| - type: mrr_at_1000 |
| value: 36.205 |
| - type: mrr_at_3 |
| value: 31.319999999999997 |
| - type: mrr_at_5 |
| value: 33.562 |
| - type: ndcg_at_1 |
| value: 22.692999999999998 |
| - type: ndcg_at_10 |
| value: 41.258 |
| - type: ndcg_at_100 |
| value: 46.967 |
| - type: ndcg_at_1000 |
| value: 48.175000000000004 |
| - type: ndcg_at_3 |
| value: 33.611000000000004 |
| - type: ndcg_at_5 |
| value: 37.675 |
| - type: precision_at_1 |
| value: 22.692999999999998 |
| - type: precision_at_10 |
| value: 6.5089999999999995 |
| - type: precision_at_100 |
| value: 0.936 |
| - type: precision_at_1000 |
| value: 0.104 |
| - type: precision_at_3 |
| value: 14.413 |
| - type: precision_at_5 |
| value: 10.702 |
| - type: recall_at_1 |
| value: 22.03 |
| - type: recall_at_10 |
| value: 62.248000000000005 |
| - type: recall_at_100 |
| value: 88.524 |
| - type: recall_at_1000 |
| value: 97.714 |
| - type: recall_at_3 |
| value: 41.617 |
| - type: recall_at_5 |
| value: 51.359 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 94.36844505243957 |
| - type: f1 |
| value: 94.12408743818202 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 76.43410852713177 |
| - type: f1 |
| value: 58.501855709435624 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 76.04909213180902 |
| - type: f1 |
| value: 74.1800860395823 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 79.76126429051781 |
| - type: f1 |
| value: 79.85705217473232 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 34.70119520292863 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 32.33544316467486 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.75499243990726 |
| - type: mrr |
| value: 31.70602251821063 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 6.451999999999999 |
| - type: map_at_10 |
| value: 13.918 |
| - type: map_at_100 |
| value: 17.316000000000003 |
| - type: map_at_1000 |
| value: 18.747 |
| - type: map_at_3 |
| value: 10.471 |
| - type: map_at_5 |
| value: 12.104 |
| - type: mrr_at_1 |
| value: 46.749 |
| - type: mrr_at_10 |
| value: 55.717000000000006 |
| - type: mrr_at_100 |
| value: 56.249 |
| - type: mrr_at_1000 |
| value: 56.288000000000004 |
| - type: mrr_at_3 |
| value: 53.818 |
| - type: mrr_at_5 |
| value: 55.103 |
| - type: ndcg_at_1 |
| value: 45.201 |
| - type: ndcg_at_10 |
| value: 35.539 |
| - type: ndcg_at_100 |
| value: 32.586 |
| - type: ndcg_at_1000 |
| value: 41.486000000000004 |
| - type: ndcg_at_3 |
| value: 41.174 |
| - type: ndcg_at_5 |
| value: 38.939 |
| - type: precision_at_1 |
| value: 46.749 |
| - type: precision_at_10 |
| value: 25.944 |
| - type: precision_at_100 |
| value: 8.084 |
| - type: precision_at_1000 |
| value: 2.076 |
| - type: precision_at_3 |
| value: 38.7 |
| - type: precision_at_5 |
| value: 33.56 |
| - type: recall_at_1 |
| value: 6.451999999999999 |
| - type: recall_at_10 |
| value: 17.302 |
| - type: recall_at_100 |
| value: 32.14 |
| - type: recall_at_1000 |
| value: 64.12 |
| - type: recall_at_3 |
| value: 11.219 |
| - type: recall_at_5 |
| value: 13.993 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 32.037 |
| - type: map_at_10 |
| value: 46.565 |
| - type: map_at_100 |
| value: 47.606 |
| - type: map_at_1000 |
| value: 47.636 |
| - type: map_at_3 |
| value: 42.459 |
| - type: map_at_5 |
| value: 44.762 |
| - type: mrr_at_1 |
| value: 36.181999999999995 |
| - type: mrr_at_10 |
| value: 49.291000000000004 |
| - type: mrr_at_100 |
| value: 50.059 |
| - type: mrr_at_1000 |
| value: 50.078 |
| - type: mrr_at_3 |
| value: 45.829 |
| - type: mrr_at_5 |
| value: 47.797 |
| - type: ndcg_at_1 |
| value: 36.153 |
| - type: ndcg_at_10 |
| value: 53.983000000000004 |
| - type: ndcg_at_100 |
| value: 58.347 |
| - type: ndcg_at_1000 |
| value: 59.058 |
| - type: ndcg_at_3 |
| value: 46.198 |
| - type: ndcg_at_5 |
| value: 50.022 |
| - type: precision_at_1 |
| value: 36.153 |
| - type: precision_at_10 |
| value: 8.763 |
| - type: precision_at_100 |
| value: 1.123 |
| - type: precision_at_1000 |
| value: 0.11900000000000001 |
| - type: precision_at_3 |
| value: 20.751 |
| - type: precision_at_5 |
| value: 14.646999999999998 |
| - type: recall_at_1 |
| value: 32.037 |
| - type: recall_at_10 |
| value: 74.008 |
| - type: recall_at_100 |
| value: 92.893 |
| - type: recall_at_1000 |
| value: 98.16 |
| - type: recall_at_3 |
| value: 53.705999999999996 |
| - type: recall_at_5 |
| value: 62.495 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 71.152 |
| - type: map_at_10 |
| value: 85.104 |
| - type: map_at_100 |
| value: 85.745 |
| - type: map_at_1000 |
| value: 85.761 |
| - type: map_at_3 |
| value: 82.175 |
| - type: map_at_5 |
| value: 84.066 |
| - type: mrr_at_1 |
| value: 82.03 |
| - type: mrr_at_10 |
| value: 88.115 |
| - type: mrr_at_100 |
| value: 88.21 |
| - type: mrr_at_1000 |
| value: 88.211 |
| - type: mrr_at_3 |
| value: 87.19200000000001 |
| - type: mrr_at_5 |
| value: 87.85 |
| - type: ndcg_at_1 |
| value: 82.03 |
| - type: ndcg_at_10 |
| value: 88.78 |
| - type: ndcg_at_100 |
| value: 89.96300000000001 |
| - type: ndcg_at_1000 |
| value: 90.056 |
| - type: ndcg_at_3 |
| value: 86.051 |
| - type: ndcg_at_5 |
| value: 87.63499999999999 |
| - type: precision_at_1 |
| value: 82.03 |
| - type: precision_at_10 |
| value: 13.450000000000001 |
| - type: precision_at_100 |
| value: 1.5310000000000001 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 37.627 |
| - type: precision_at_5 |
| value: 24.784 |
| - type: recall_at_1 |
| value: 71.152 |
| - type: recall_at_10 |
| value: 95.649 |
| - type: recall_at_100 |
| value: 99.58200000000001 |
| - type: recall_at_1000 |
| value: 99.981 |
| - type: recall_at_3 |
| value: 87.767 |
| - type: recall_at_5 |
| value: 92.233 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 56.48713646277477 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 63.394940772438545 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 5.043 |
| - type: map_at_10 |
| value: 12.949 |
| - type: map_at_100 |
| value: 15.146 |
| - type: map_at_1000 |
| value: 15.495000000000001 |
| - type: map_at_3 |
| value: 9.333 |
| - type: map_at_5 |
| value: 11.312999999999999 |
| - type: mrr_at_1 |
| value: 24.9 |
| - type: mrr_at_10 |
| value: 35.958 |
| - type: mrr_at_100 |
| value: 37.152 |
| - type: mrr_at_1000 |
| value: 37.201 |
| - type: mrr_at_3 |
| value: 32.667 |
| - type: mrr_at_5 |
| value: 34.567 |
| - type: ndcg_at_1 |
| value: 24.9 |
| - type: ndcg_at_10 |
| value: 21.298000000000002 |
| - type: ndcg_at_100 |
| value: 29.849999999999998 |
| - type: ndcg_at_1000 |
| value: 35.506 |
| - type: ndcg_at_3 |
| value: 20.548 |
| - type: ndcg_at_5 |
| value: 18.064 |
| - type: precision_at_1 |
| value: 24.9 |
| - type: precision_at_10 |
| value: 10.9 |
| - type: precision_at_100 |
| value: 2.331 |
| - type: precision_at_1000 |
| value: 0.367 |
| - type: precision_at_3 |
| value: 19.267 |
| - type: precision_at_5 |
| value: 15.939999999999998 |
| - type: recall_at_1 |
| value: 5.043 |
| - type: recall_at_10 |
| value: 22.092 |
| - type: recall_at_100 |
| value: 47.323 |
| - type: recall_at_1000 |
| value: 74.553 |
| - type: recall_at_3 |
| value: 11.728 |
| - type: recall_at_5 |
| value: 16.188 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.7007085938325 |
| - type: cos_sim_spearman |
| value: 80.0171084446234 |
| - type: euclidean_pearson |
| value: 81.28133218355893 |
| - type: euclidean_spearman |
| value: 79.99291731740131 |
| - type: manhattan_pearson |
| value: 81.22926922327846 |
| - type: manhattan_spearman |
| value: 79.94444878127038 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.7411883252923 |
| - type: cos_sim_spearman |
| value: 77.93462937801245 |
| - type: euclidean_pearson |
| value: 83.00858563882404 |
| - type: euclidean_spearman |
| value: 77.82717362433257 |
| - type: manhattan_pearson |
| value: 82.92887645790769 |
| - type: manhattan_spearman |
| value: 77.78807488222115 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.04222459361023 |
| - type: cos_sim_spearman |
| value: 83.85931509330395 |
| - type: euclidean_pearson |
| value: 83.26916063876055 |
| - type: euclidean_spearman |
| value: 83.98621985648353 |
| - type: manhattan_pearson |
| value: 83.14935679184327 |
| - type: manhattan_spearman |
| value: 83.87938828586304 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 81.41136639535318 |
| - type: cos_sim_spearman |
| value: 81.51200091040481 |
| - type: euclidean_pearson |
| value: 81.45382456114775 |
| - type: euclidean_spearman |
| value: 81.46201181707931 |
| - type: manhattan_pearson |
| value: 81.37243088439584 |
| - type: manhattan_spearman |
| value: 81.39828421893426 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.71942451732227 |
| - type: cos_sim_spearman |
| value: 87.33044482064973 |
| - type: euclidean_pearson |
| value: 86.58580899365178 |
| - type: euclidean_spearman |
| value: 87.09206723832895 |
| - type: manhattan_pearson |
| value: 86.47460784157013 |
| - type: manhattan_spearman |
| value: 86.98367656583076 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.55868078863449 |
| - type: cos_sim_spearman |
| value: 85.38299230074065 |
| - type: euclidean_pearson |
| value: 84.64715256244595 |
| - type: euclidean_spearman |
| value: 85.49112229604047 |
| - type: manhattan_pearson |
| value: 84.60814346792462 |
| - type: manhattan_spearman |
| value: 85.44886026766822 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-en) |
| config: en-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.99292526370614 |
| - type: cos_sim_spearman |
| value: 85.58139465695983 |
| - type: euclidean_pearson |
| value: 86.51325066734084 |
| - type: euclidean_spearman |
| value: 85.56736418284562 |
| - type: manhattan_pearson |
| value: 86.48190836601357 |
| - type: manhattan_spearman |
| value: 85.51616256224258 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (en) |
| config: en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 64.54124715078807 |
| - type: cos_sim_spearman |
| value: 65.32134275948374 |
| - type: euclidean_pearson |
| value: 67.09791698300816 |
| - type: euclidean_spearman |
| value: 65.79468982468465 |
| - type: manhattan_pearson |
| value: 67.13304723693966 |
| - type: manhattan_spearman |
| value: 65.68439995849283 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.4231099581624 |
| - type: cos_sim_spearman |
| value: 85.95475815226862 |
| - type: euclidean_pearson |
| value: 85.00339401999706 |
| - type: euclidean_spearman |
| value: 85.74133081802971 |
| - type: manhattan_pearson |
| value: 85.00407987181666 |
| - type: manhattan_spearman |
| value: 85.77509596397363 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 87.25666719585716 |
| - type: mrr |
| value: 96.32769917083642 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 57.828 |
| - type: map_at_10 |
| value: 68.369 |
| - type: map_at_100 |
| value: 68.83399999999999 |
| - type: map_at_1000 |
| value: 68.856 |
| - type: map_at_3 |
| value: 65.38000000000001 |
| - type: map_at_5 |
| value: 67.06299999999999 |
| - type: mrr_at_1 |
| value: 61 |
| - type: mrr_at_10 |
| value: 69.45400000000001 |
| - type: mrr_at_100 |
| value: 69.785 |
| - type: mrr_at_1000 |
| value: 69.807 |
| - type: mrr_at_3 |
| value: 67 |
| - type: mrr_at_5 |
| value: 68.43299999999999 |
| - type: ndcg_at_1 |
| value: 61 |
| - type: ndcg_at_10 |
| value: 73.258 |
| - type: ndcg_at_100 |
| value: 75.173 |
| - type: ndcg_at_1000 |
| value: 75.696 |
| - type: ndcg_at_3 |
| value: 68.162 |
| - type: ndcg_at_5 |
| value: 70.53399999999999 |
| - type: precision_at_1 |
| value: 61 |
| - type: precision_at_10 |
| value: 9.8 |
| - type: precision_at_100 |
| value: 1.087 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_3 |
| value: 27 |
| - type: precision_at_5 |
| value: 17.666999999999998 |
| - type: recall_at_1 |
| value: 57.828 |
| - type: recall_at_10 |
| value: 87.122 |
| - type: recall_at_100 |
| value: 95.667 |
| - type: recall_at_1000 |
| value: 99.667 |
| - type: recall_at_3 |
| value: 73.139 |
| - type: recall_at_5 |
| value: 79.361 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.85247524752475 |
| - type: cos_sim_ap |
| value: 96.25640197639723 |
| - type: cos_sim_f1 |
| value: 92.37851662404091 |
| - type: cos_sim_precision |
| value: 94.55497382198953 |
| - type: cos_sim_recall |
| value: 90.3 |
| - type: dot_accuracy |
| value: 99.76138613861386 |
| - type: dot_ap |
| value: 93.40295864389073 |
| - type: dot_f1 |
| value: 87.64267990074441 |
| - type: dot_precision |
| value: 86.99507389162562 |
| - type: dot_recall |
| value: 88.3 |
| - type: euclidean_accuracy |
| value: 99.85049504950496 |
| - type: euclidean_ap |
| value: 96.24254350525462 |
| - type: euclidean_f1 |
| value: 92.32323232323232 |
| - type: euclidean_precision |
| value: 93.26530612244898 |
| - type: euclidean_recall |
| value: 91.4 |
| - type: manhattan_accuracy |
| value: 99.85346534653465 |
| - type: manhattan_ap |
| value: 96.2635334753325 |
| - type: manhattan_f1 |
| value: 92.37899073120495 |
| - type: manhattan_precision |
| value: 95.22292993630573 |
| - type: manhattan_recall |
| value: 89.7 |
| - type: max_accuracy |
| value: 99.85346534653465 |
| - type: max_ap |
| value: 96.2635334753325 |
| - type: max_f1 |
| value: 92.37899073120495 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 65.83905786483794 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 35.031896152126436 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 54.551326709447146 |
| - type: mrr |
| value: 55.43758222986165 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.305688567308874 |
| - type: cos_sim_spearman |
| value: 29.27135743434515 |
| - type: dot_pearson |
| value: 30.336741878796563 |
| - type: dot_spearman |
| value: 30.513365725895937 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.245 |
| - type: map_at_10 |
| value: 1.92 |
| - type: map_at_100 |
| value: 10.519 |
| - type: map_at_1000 |
| value: 23.874000000000002 |
| - type: map_at_3 |
| value: 0.629 |
| - type: map_at_5 |
| value: 1.0290000000000001 |
| - type: mrr_at_1 |
| value: 88 |
| - type: mrr_at_10 |
| value: 93.5 |
| - type: mrr_at_100 |
| value: 93.5 |
| - type: mrr_at_1000 |
| value: 93.5 |
| - type: mrr_at_3 |
| value: 93 |
| - type: mrr_at_5 |
| value: 93.5 |
| - type: ndcg_at_1 |
| value: 84 |
| - type: ndcg_at_10 |
| value: 76.447 |
| - type: ndcg_at_100 |
| value: 56.516 |
| - type: ndcg_at_1000 |
| value: 48.583999999999996 |
| - type: ndcg_at_3 |
| value: 78.877 |
| - type: ndcg_at_5 |
| value: 79.174 |
| - type: precision_at_1 |
| value: 88 |
| - type: precision_at_10 |
| value: 80.60000000000001 |
| - type: precision_at_100 |
| value: 57.64 |
| - type: precision_at_1000 |
| value: 21.227999999999998 |
| - type: precision_at_3 |
| value: 82 |
| - type: precision_at_5 |
| value: 83.6 |
| - type: recall_at_1 |
| value: 0.245 |
| - type: recall_at_10 |
| value: 2.128 |
| - type: recall_at_100 |
| value: 13.767 |
| - type: recall_at_1000 |
| value: 44.958 |
| - type: recall_at_3 |
| value: 0.654 |
| - type: recall_at_5 |
| value: 1.111 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 2.5170000000000003 |
| - type: map_at_10 |
| value: 10.915 |
| - type: map_at_100 |
| value: 17.535 |
| - type: map_at_1000 |
| value: 19.042 |
| - type: map_at_3 |
| value: 5.689 |
| - type: map_at_5 |
| value: 7.837 |
| - type: mrr_at_1 |
| value: 34.694 |
| - type: mrr_at_10 |
| value: 49.547999999999995 |
| - type: mrr_at_100 |
| value: 50.653000000000006 |
| - type: mrr_at_1000 |
| value: 50.653000000000006 |
| - type: mrr_at_3 |
| value: 44.558 |
| - type: mrr_at_5 |
| value: 48.333 |
| - type: ndcg_at_1 |
| value: 32.653 |
| - type: ndcg_at_10 |
| value: 26.543 |
| - type: ndcg_at_100 |
| value: 38.946 |
| - type: ndcg_at_1000 |
| value: 49.406 |
| - type: ndcg_at_3 |
| value: 29.903000000000002 |
| - type: ndcg_at_5 |
| value: 29.231 |
| - type: precision_at_1 |
| value: 34.694 |
| - type: precision_at_10 |
| value: 23.265 |
| - type: precision_at_100 |
| value: 8.102 |
| - type: precision_at_1000 |
| value: 1.5 |
| - type: precision_at_3 |
| value: 31.293 |
| - type: precision_at_5 |
| value: 29.796 |
| - type: recall_at_1 |
| value: 2.5170000000000003 |
| - type: recall_at_10 |
| value: 16.88 |
| - type: recall_at_100 |
| value: 49.381 |
| - type: recall_at_1000 |
| value: 81.23899999999999 |
| - type: recall_at_3 |
| value: 6.965000000000001 |
| - type: recall_at_5 |
| value: 10.847999999999999 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 71.5942 |
| - type: ap |
| value: 13.92074156956546 |
| - type: f1 |
| value: 54.671999698839066 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 59.39728353140916 |
| - type: f1 |
| value: 59.68980496759517 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 52.11181870104935 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 86.46957143708649 |
| - type: cos_sim_ap |
| value: 76.16120197845457 |
| - type: cos_sim_f1 |
| value: 69.69919295671315 |
| - type: cos_sim_precision |
| value: 64.94986326344576 |
| - type: cos_sim_recall |
| value: 75.19788918205805 |
| - type: dot_accuracy |
| value: 83.0780234845324 |
| - type: dot_ap |
| value: 64.21717343541934 |
| - type: dot_f1 |
| value: 59.48375497624245 |
| - type: dot_precision |
| value: 57.94345759319489 |
| - type: dot_recall |
| value: 61.108179419525065 |
| - type: euclidean_accuracy |
| value: 86.6543482148179 |
| - type: euclidean_ap |
| value: 76.4527555010203 |
| - type: euclidean_f1 |
| value: 70.10156056477584 |
| - type: euclidean_precision |
| value: 66.05975723622782 |
| - type: euclidean_recall |
| value: 74.67018469656992 |
| - type: manhattan_accuracy |
| value: 86.66030875603504 |
| - type: manhattan_ap |
| value: 76.40304567255436 |
| - type: manhattan_f1 |
| value: 70.05275426328058 |
| - type: manhattan_precision |
| value: 65.4666360926393 |
| - type: manhattan_recall |
| value: 75.32981530343008 |
| - type: max_accuracy |
| value: 86.66030875603504 |
| - type: max_ap |
| value: 76.4527555010203 |
| - type: max_f1 |
| value: 70.10156056477584 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.42123646524624 |
| - type: cos_sim_ap |
| value: 85.15431437761646 |
| - type: cos_sim_f1 |
| value: 76.98069301530742 |
| - type: cos_sim_precision |
| value: 72.9314502239063 |
| - type: cos_sim_recall |
| value: 81.50600554357868 |
| - type: dot_accuracy |
| value: 86.70974502270346 |
| - type: dot_ap |
| value: 80.77621563599457 |
| - type: dot_f1 |
| value: 73.87058697285117 |
| - type: dot_precision |
| value: 68.98256396552877 |
| - type: dot_recall |
| value: 79.50415768401602 |
| - type: euclidean_accuracy |
| value: 88.46392672798541 |
| - type: euclidean_ap |
| value: 85.20370297495491 |
| - type: euclidean_f1 |
| value: 77.01372369624886 |
| - type: euclidean_precision |
| value: 73.39052800446397 |
| - type: euclidean_recall |
| value: 81.01324299353249 |
| - type: manhattan_accuracy |
| value: 88.43481973066325 |
| - type: manhattan_ap |
| value: 85.16318289864545 |
| - type: manhattan_f1 |
| value: 76.90884877182597 |
| - type: manhattan_precision |
| value: 74.01737396753062 |
| - type: manhattan_recall |
| value: 80.03541730828458 |
| - type: max_accuracy |
| value: 88.46392672798541 |
| - type: max_ap |
| value: 85.20370297495491 |
| - type: max_f1 |
| value: 77.01372369624886 |
| license: mit |
| language: |
| - en |
| --- |
| |
|
|
| **Recommend switching to newest [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5), which has more reasonable similarity distribution and same method of usage.** |
|
|
| <h1 align="center">FlagEmbedding</h1> |
|
|
|
|
| <h4 align="center"> |
| <p> |
| <a href=#model-list>Model List</a> | |
| <a href=#frequently-asked-questions>FAQ</a> | |
| <a href=#usage>Usage</a> | |
| <a href="#evaluation">Evaluation</a> | |
| <a href="#train">Train</a> | |
| <a href="#contact">Contact</a> | |
| <a href="#citation">Citation</a> | |
| <a href="#license">License</a> |
| <p> |
| </h4> |
| |
| More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
|
|
|
|
| [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
|
|
| FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. |
| And it also can be used in vector databases for LLMs. |
|
|
| ************* 🌟**Updates**🌟 ************* |
| - 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire: |
| - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released |
| - 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released |
| - 09/12/2023: New models: |
| - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. |
| - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. |
| |
|
|
| <details> |
| <summary>More</summary> |
| <!-- ### More --> |
| |
| - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. |
| - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). |
| - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
| - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: |
| - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. |
| |
| </details> |
| |
|
|
| ## Model List |
|
|
| `bge` is short for `BAAI general embedding`. |
|
|
| | Model | Language | | Description | query instruction for retrieval [1] | |
| |:-------------------------------|:--------:| :--------:| :--------:|:--------:| |
| | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | |
| | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | |
| | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | |
| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
| | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
| | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
| | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | |
| | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | |
| | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | |
| | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
| | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | |
| | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | |
| | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | |
| | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | |
| | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | |
|
|
|
|
| [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. |
|
|
| [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. |
| For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. |
|
|
| All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. |
| If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . |
|
|
|
|
| ## Frequently asked questions |
|
|
| <details> |
| <summary>1. How to fine-tune bge embedding model?</summary> |
|
|
| <!-- ### How to fine-tune bge embedding model? --> |
| Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. |
| Some suggestions: |
| - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. |
| - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. |
| - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. |
|
|
| |
| </details> |
|
|
| <details> |
| <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> |
|
|
| <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> |
| **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** |
|
|
| Since we finetune the models by contrastive learning with a temperature of 0.01, |
| the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. |
| So a similarity score greater than 0.5 does not indicate that the two sentences are similar. |
|
|
| For downstream tasks, such as passage retrieval or semantic similarity, |
| **what matters is the relative order of the scores, not the absolute value.** |
| If you need to filter similar sentences based on a similarity threshold, |
| please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). |
|
|
| </details> |
|
|
| <details> |
| <summary>3. When does the query instruction need to be used</summary> |
|
|
| <!-- ### When does the query instruction need to be used --> |
|
|
| For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. |
| No instruction only has a slight degradation in retrieval performance compared with using instruction. |
| So you can generate embedding without instruction in all cases for convenience. |
| |
| For a retrieval task that uses short queries to find long related documents, |
| it is recommended to add instructions for these short queries. |
| **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** |
| In all cases, the documents/passages do not need to add the instruction. |
|
|
| </details> |
|
|
|
|
| ## Usage |
|
|
| ### Usage for Embedding Model |
|
|
| Here are some examples for using `bge` models with |
| [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). |
|
|
| #### Using FlagEmbedding |
| ``` |
| pip install -U FlagEmbedding |
| ``` |
| If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. |
|
|
| ```python |
| from FlagEmbedding import FlagModel |
| sentences_1 = ["样例数据-1", "样例数据-2"] |
| sentences_2 = ["样例数据-3", "样例数据-4"] |
| model = FlagModel('BAAI/bge-large-zh-v1.5', |
| query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", |
| use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
| embeddings_1 = model.encode(sentences_1) |
| embeddings_2 = model.encode(sentences_2) |
| similarity = embeddings_1 @ embeddings_2.T |
| print(similarity) |
| |
| # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query |
| # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction |
| queries = ['query_1', 'query_2'] |
| passages = ["样例文档-1", "样例文档-2"] |
| q_embeddings = model.encode_queries(queries) |
| p_embeddings = model.encode(passages) |
| scores = q_embeddings @ p_embeddings.T |
| ``` |
| For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
|
|
| By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. |
| You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. |
|
|
|
|
| #### Using Sentence-Transformers |
|
|
| You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): |
|
|
| ``` |
| pip install -U sentence-transformers |
| ``` |
| ```python |
| from sentence_transformers import SentenceTransformer |
| sentences_1 = ["样例数据-1", "样例数据-2"] |
| sentences_2 = ["样例数据-3", "样例数据-4"] |
| model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
| embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) |
| embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) |
| similarity = embeddings_1 @ embeddings_2.T |
| print(similarity) |
| ``` |
| For s2p(short query to long passage) retrieval task, |
| each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
| But the instruction is not needed for passages. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| queries = ['query_1', 'query_2'] |
| passages = ["样例文档-1", "样例文档-2"] |
| instruction = "为这个句子生成表示以用于检索相关文章：" |
| |
| model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
| q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
| p_embeddings = model.encode(passages, normalize_embeddings=True) |
| scores = q_embeddings @ p_embeddings.T |
| ``` |
|
|
| #### Using Langchain |
|
|
| You can use `bge` in langchain like this: |
| ```python |
| from langchain.embeddings import HuggingFaceBgeEmbeddings |
| model_name = "BAAI/bge-large-en-v1.5" |
| model_kwargs = {'device': 'cuda'} |
| encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
| model = HuggingFaceBgeEmbeddings( |
| model_name=model_name, |
| model_kwargs=model_kwargs, |
| encode_kwargs=encode_kwargs, |
| query_instruction="为这个句子生成表示以用于检索相关文章：" |
| ) |
| model.query_instruction = "为这个句子生成表示以用于检索相关文章：" |
| ``` |
|
|
|
|
| #### Using HuggingFace Transformers |
|
|
| With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModel |
| import torch |
| # Sentences we want sentence embeddings for |
| sentences = ["样例数据-1", "样例数据-2"] |
| |
| # Load model from HuggingFace Hub |
| tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') |
| model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') |
| model.eval() |
| |
| # Tokenize sentences |
| encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
| # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
| # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
| |
| # Compute token embeddings |
| with torch.no_grad(): |
| model_output = model(**encoded_input) |
| # Perform pooling. In this case, cls pooling. |
| sentence_embeddings = model_output[0][:, 0] |
| # normalize embeddings |
| sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
| print("Sentence embeddings:", sentence_embeddings) |
| ``` |
|
|
| ### Usage for Reranker |
|
|
| Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. |
| You can get a relevance score by inputting query and passage to the reranker. |
| The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. |
|
|
|
|
| #### Using FlagEmbedding |
| ``` |
| pip install -U FlagEmbedding |
| ``` |
|
|
| Get relevance scores (higher scores indicate more relevance): |
| ```python |
| from FlagEmbedding import FlagReranker |
| reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
| |
| score = reranker.compute_score(['query', 'passage']) |
| print(score) |
| |
| scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) |
| print(scores) |
| ``` |
|
|
|
|
| #### Using Huggingface transformers |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') |
| model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') |
| model.eval() |
| |
| pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] |
| with torch.no_grad(): |
| inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) |
| scores = model(**inputs, return_dict=True).logits.view(-1, ).float() |
| print(scores) |
| ``` |
|
|
| ## Evaluation |
|
|
| `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
| For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
|
|
| - **MTEB**: |
|
|
| | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
| |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | |
| | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | |
| | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | |
| | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | |
| | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | |
| | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | |
| | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | |
| | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | |
| | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | |
| | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | |
| | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | |
| | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | |
| | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | |
| | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | |
| | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | |
| | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | |
| | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | |
|
|
|
|
|
|
| - **C-MTEB**: |
| We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. |
| Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. |
| |
| | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
| |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | |
| | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | |
| | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | |
| | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | |
| | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | |
| | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | |
| | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | |
| | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | |
| | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | |
| | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | |
| | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | |
| | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | |
| | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | |
| | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | |
| | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | |
| | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | |
|
|
|
|
| - **Reranking**: |
| See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. |
|
|
| | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |
| |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | |
| | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | |
| | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | |
| | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | |
| | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | |
| | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | |
| | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | |
| | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | |
| | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | |
| | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | |
|
|
| \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks |
|
|
| ## Train |
|
|
| ### BAAI Embedding |
|
|
| We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. |
| **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** |
| We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). |
| Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. |
| More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
|
|
|
|
|
|
| ### BGE Reranker |
|
|
| Cross-encoder will perform full-attention over the input pair, |
| which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. |
| Therefore, it can be used to re-rank the top-k documents returned by embedding model. |
| We train the cross-encoder on a multilingual pair data, |
| The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). |
| More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) |
|
|
|
|
| ## Contact |
| If you have any question or suggestion related to this project, feel free to open an issue or pull request. |
| You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). |
|
|
|
|
| ## Citation |
|
|
| If you find this repository useful, please consider giving a star :star: and citation |
|
|
| ``` |
| @misc{bge_embedding, |
| title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, |
| author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, |
| year={2023}, |
| eprint={2309.07597}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
|
|
| ## License |
| FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
|
|
|
|
