|
--- |
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tags: |
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- mteb |
|
model-index: |
|
- name: bge-base-en |
|
results: |
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- 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 |
|
pipeline_tag: sentence-similarity |
|
--- |
|
|
|
<h1 align="center">FlagEmbedding</h1> |
|
|
|
|
|
<h4 align="center"> |
|
<p> |
|
<a href=#model-list>Model List</a> | |
|
<a href=#usage>Usage</a> | |
|
<a href="#evaluation">Evaluation</a> | |
|
<a href="#train">Train</a> | |
|
<a href="#license">License</a> |
|
<p> |
|
</h4> |
|
|
|
For more details please refer to our GitHub repo: [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**🌟 ************* |
|
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
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- 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. |
|
|
|
|
|
## Model List |
|
|
|
`bge` is short for `BAAI general embedding`. |
|
|
|
| Model | Language | Description | query instruction for retrieval | |
|
|:-------------------------------|:--------:| :--------:| :--------:| |
|
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | :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 | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | 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 | :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-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | |
|
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `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 | 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：` | |
|
|
|
|
|
|
|
## Usage |
|
|
|
* **Using FlagEmbedding** |
|
``` |
|
pip install -U FlagEmbedding |
|
``` |
|
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 = ["样例数据-1", "样例数据-2"] |
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model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：") |
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embeddings = model.encode(sentences) |
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print(embeddings) |
|
|
|
# for retrieval task, please use encode_queries() which will automatically add the instruction to each query |
|
# corpus in retrieval task can still use encode() or encode_corpus() |
|
queries = ['query_1', 'query_2'] |
|
passages = ["样例段落-1", "样例段落-2"] |
|
q_embeddings = model.encode_queries(queries) |
|
p_embeddings = model.encode(passages) |
|
scores = q_embeddings @ p_embeddings.T |
|
``` |
|
The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
|
|
|
FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU. |
|
|
|
|
|
* **Using Sentence-Transformers** |
|
|
|
Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
model = SentenceTransformer('BAAI/bge-large-zh') |
|
embeddings = model.encode(sentences, normalize_embeddings=True) |
|
print(embeddings) |
|
``` |
|
For retrieval task, |
|
each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
queries = ["手机开不了机怎么办？"] |
|
passages = ["样例段落-1", "样例段落-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') |
|
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 HuggingFace Transformers** |
|
|
|
With 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 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') |
|
model = AutoModel.from_pretrained('BAAI/bge-large-zh') |
|
|
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
# for retrieval task, add an instruction to query |
|
# 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) |
|
``` |
|
|
|
|
|
## Evaluation |
|
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
|
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) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**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 | |
|
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 384 | 512 | 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 | |
|
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 | |
|
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 768 | 512 | 56.00 | 41.88 | 41.1 | 82.54 | 53.14 | 76.51 | 30.36 | 66.68 | |
|
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 768 | 512 | 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 | |
|
|
|
|
|
|
|
- **C-MTEB**: |
|
We create a 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 | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 | |
|
| [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** | |
|
| [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 | |
|
| [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 | |
|
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 | |
|
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 | |
|
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 | |
|
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | |
|
| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 | |
|
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 | |
|
|
|
|
|
|
|
## Train |
|
This section will introduce the way we used to train the general embedding. |
|
The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), |
|
and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md). |
|
|
|
|
|
**1. RetroMAE Pre-train** |
|
We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), |
|
which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). |
|
The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. |
|
In retromae, the mask ratio of encoder and decoder are 0.3, and 0.5 respectively. |
|
We used the AdamW optimizer and the learning rate is 2e-5. |
|
|
|
**Pre-training data**: |
|
- English: |
|
- [Pile](https://pile.eleuther.ai/) |
|
- [wikipedia](https://huggingface.co/datasets/wikipedia) |
|
- [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus) |
|
- Chinese: |
|
- Subset of [wudao](https://github.com/BAAI-WuDao/Data) |
|
- [baidu-baike](https://baike.baidu.com/) |
|
|
|
|
|
**2. Finetune** |
|
We fine-tune the model using a contrastive objective. |
|
The format of input data is a triple`(query, positive, negative)`. |
|
Besides the negative in the triple, we also adopt in-batch negatives strategy. |
|
We employ the cross-device negatives sharing method to share negatives among different GPUs, |
|
which can dramatically **increase the number of negatives**. |
|
|
|
We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). |
|
We used the AdamW optimizer and the learning rate is 1e-5. |
|
The temperature for contrastive loss is 0.01. |
|
|
|
For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training. |
|
For english, the instruction is `Represent this sentence for searching relevant passages: `; |
|
For chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`. |
|
In the evaluation, the instruction should be added for sentence to passages retrieval task, not be added for other tasks. |
|
|
|
|
|
The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
|
You can easily finetune your model with it. |
|
|
|
**Training data**: |
|
|
|
- For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on. |
|
|
|
- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on. |
|
|
|
**The data collection is to be released in the future.** |
|
|
|
We will continually update the embedding models and training codes, |
|
hoping to promote the development of the embedding model community. |
|
|
|
## License |
|
FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
