|
--- |
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tags: |
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- mteb |
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- Sentence Transformers |
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- sentence-similarity |
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- sentence-transformers |
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model-index: |
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- name: e5-small |
|
results: |
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- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 76.22388059701493 |
|
- type: ap |
|
value: 40.27466219523129 |
|
- type: f1 |
|
value: 70.60533006025108 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 87.525775 |
|
- type: ap |
|
value: 83.51063993897611 |
|
- type: f1 |
|
value: 87.49342736805572 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 42.611999999999995 |
|
- type: f1 |
|
value: 42.05088045932892 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
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name: MTEB ArguAna |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.826 |
|
- type: map_at_10 |
|
value: 38.269 |
|
- type: map_at_100 |
|
value: 39.322 |
|
- type: map_at_1000 |
|
value: 39.344 |
|
- type: map_at_3 |
|
value: 33.428000000000004 |
|
- type: map_at_5 |
|
value: 36.063 |
|
- type: mrr_at_1 |
|
value: 24.253 |
|
- type: mrr_at_10 |
|
value: 38.425 |
|
- type: mrr_at_100 |
|
value: 39.478 |
|
- type: mrr_at_1000 |
|
value: 39.5 |
|
- type: mrr_at_3 |
|
value: 33.606 |
|
- type: mrr_at_5 |
|
value: 36.195 |
|
- type: ndcg_at_1 |
|
value: 23.826 |
|
- type: ndcg_at_10 |
|
value: 46.693 |
|
- type: ndcg_at_100 |
|
value: 51.469 |
|
- type: ndcg_at_1000 |
|
value: 52.002 |
|
- type: ndcg_at_3 |
|
value: 36.603 |
|
- type: ndcg_at_5 |
|
value: 41.365 |
|
- type: precision_at_1 |
|
value: 23.826 |
|
- type: precision_at_10 |
|
value: 7.383000000000001 |
|
- type: precision_at_100 |
|
value: 0.9530000000000001 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 15.268 |
|
- type: precision_at_5 |
|
value: 11.479000000000001 |
|
- type: recall_at_1 |
|
value: 23.826 |
|
- type: recall_at_10 |
|
value: 73.82600000000001 |
|
- type: recall_at_100 |
|
value: 95.306 |
|
- type: recall_at_1000 |
|
value: 99.431 |
|
- type: recall_at_3 |
|
value: 45.804 |
|
- type: recall_at_5 |
|
value: 57.397 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 44.13995374767436 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
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split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 37.13950072624313 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
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config: default |
|
split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
|
- type: map |
|
value: 59.35843292105327 |
|
- type: mrr |
|
value: 73.72312359846987 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.55140418324174 |
|
- type: cos_sim_spearman |
|
value: 84.21637675860022 |
|
- type: euclidean_pearson |
|
value: 81.26069614610006 |
|
- type: euclidean_spearman |
|
value: 83.25069210421785 |
|
- type: manhattan_pearson |
|
value: 80.17441422581014 |
|
- type: manhattan_spearman |
|
value: 81.87596198487877 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 81.87337662337661 |
|
- type: f1 |
|
value: 81.76647866926402 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 35.80600542614507 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 31.86321613256603 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.054 |
|
- type: map_at_10 |
|
value: 40.699999999999996 |
|
- type: map_at_100 |
|
value: 41.818 |
|
- type: map_at_1000 |
|
value: 41.959999999999994 |
|
- type: map_at_3 |
|
value: 37.742 |
|
- type: map_at_5 |
|
value: 39.427 |
|
- type: mrr_at_1 |
|
value: 38.769999999999996 |
|
- type: mrr_at_10 |
|
value: 46.150000000000006 |
|
- type: mrr_at_100 |
|
value: 46.865 |
|
- type: mrr_at_1000 |
|
value: 46.925 |
|
- type: mrr_at_3 |
|
value: 43.705 |
|
- type: mrr_at_5 |
|
value: 45.214999999999996 |
|
- type: ndcg_at_1 |
|
value: 38.769999999999996 |
|
- type: ndcg_at_10 |
|
value: 45.778 |
|
- type: ndcg_at_100 |
|
value: 50.38 |
|
- type: ndcg_at_1000 |
|
value: 52.922999999999995 |
|
- type: ndcg_at_3 |
|
value: 41.597 |
|
- type: ndcg_at_5 |
|
value: 43.631 |
|
- type: precision_at_1 |
|
value: 38.769999999999996 |
|
- type: precision_at_10 |
|
value: 8.269 |
|
- type: precision_at_100 |
|
value: 1.278 |
|
- type: precision_at_1000 |
|
value: 0.178 |
|
- type: precision_at_3 |
|
value: 19.266 |
|
- type: precision_at_5 |
|
value: 13.705 |
|
- type: recall_at_1 |
|
value: 32.054 |
|
- type: recall_at_10 |
|
value: 54.947 |
|
- type: recall_at_100 |
|
value: 74.79599999999999 |
|
- type: recall_at_1000 |
|
value: 91.40899999999999 |
|
- type: recall_at_3 |
|
value: 42.431000000000004 |
|
- type: recall_at_5 |
|
value: 48.519 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.035 |
|
- type: map_at_10 |
|
value: 38.007000000000005 |
|
- type: map_at_100 |
|
value: 39.125 |
|
- type: map_at_1000 |
|
value: 39.251999999999995 |
|
- type: map_at_3 |
|
value: 35.77 |
|
- type: map_at_5 |
|
value: 37.057 |
|
- type: mrr_at_1 |
|
value: 36.497 |
|
- type: mrr_at_10 |
|
value: 44.077 |
|
- type: mrr_at_100 |
|
value: 44.743 |
|
- type: mrr_at_1000 |
|
value: 44.79 |
|
- type: mrr_at_3 |
|
value: 42.123 |
|
- type: mrr_at_5 |
|
value: 43.308 |
|
- type: ndcg_at_1 |
|
value: 36.497 |
|
- type: ndcg_at_10 |
|
value: 42.986000000000004 |
|
- type: ndcg_at_100 |
|
value: 47.323 |
|
- type: ndcg_at_1000 |
|
value: 49.624 |
|
- type: ndcg_at_3 |
|
value: 39.805 |
|
- type: ndcg_at_5 |
|
value: 41.286 |
|
- type: precision_at_1 |
|
value: 36.497 |
|
- type: precision_at_10 |
|
value: 7.8340000000000005 |
|
- type: precision_at_100 |
|
value: 1.269 |
|
- type: precision_at_1000 |
|
value: 0.178 |
|
- type: precision_at_3 |
|
value: 19.023 |
|
- type: precision_at_5 |
|
value: 13.248 |
|
- type: recall_at_1 |
|
value: 29.035 |
|
- type: recall_at_10 |
|
value: 51.06 |
|
- type: recall_at_100 |
|
value: 69.64099999999999 |
|
- type: recall_at_1000 |
|
value: 84.49 |
|
- type: recall_at_3 |
|
value: 41.333999999999996 |
|
- type: recall_at_5 |
|
value: 45.663 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.239 |
|
- type: map_at_10 |
|
value: 47.873 |
|
- type: map_at_100 |
|
value: 48.842999999999996 |
|
- type: map_at_1000 |
|
value: 48.913000000000004 |
|
- type: map_at_3 |
|
value: 45.050000000000004 |
|
- type: map_at_5 |
|
value: 46.498 |
|
- type: mrr_at_1 |
|
value: 42.508 |
|
- type: mrr_at_10 |
|
value: 51.44 |
|
- type: mrr_at_100 |
|
value: 52.087 |
|
- type: mrr_at_1000 |
|
value: 52.129999999999995 |
|
- type: mrr_at_3 |
|
value: 49.164 |
|
- type: mrr_at_5 |
|
value: 50.343 |
|
- type: ndcg_at_1 |
|
value: 42.508 |
|
- type: ndcg_at_10 |
|
value: 53.31399999999999 |
|
- type: ndcg_at_100 |
|
value: 57.245000000000005 |
|
- type: ndcg_at_1000 |
|
value: 58.794000000000004 |
|
- type: ndcg_at_3 |
|
value: 48.295 |
|
- type: ndcg_at_5 |
|
value: 50.415 |
|
- type: precision_at_1 |
|
value: 42.508 |
|
- type: precision_at_10 |
|
value: 8.458 |
|
- type: precision_at_100 |
|
value: 1.133 |
|
- type: precision_at_1000 |
|
value: 0.132 |
|
- type: precision_at_3 |
|
value: 21.191 |
|
- type: precision_at_5 |
|
value: 14.307 |
|
- type: recall_at_1 |
|
value: 37.239 |
|
- type: recall_at_10 |
|
value: 65.99000000000001 |
|
- type: recall_at_100 |
|
value: 82.99499999999999 |
|
- type: recall_at_1000 |
|
value: 94.128 |
|
- type: recall_at_3 |
|
value: 52.382 |
|
- type: recall_at_5 |
|
value: 57.648999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.039 |
|
- type: map_at_10 |
|
value: 29.694 |
|
- type: map_at_100 |
|
value: 30.587999999999997 |
|
- type: map_at_1000 |
|
value: 30.692999999999998 |
|
- type: map_at_3 |
|
value: 27.708 |
|
- type: map_at_5 |
|
value: 28.774 |
|
- type: mrr_at_1 |
|
value: 24.633 |
|
- type: mrr_at_10 |
|
value: 31.478 |
|
- type: mrr_at_100 |
|
value: 32.299 |
|
- type: mrr_at_1000 |
|
value: 32.381 |
|
- type: mrr_at_3 |
|
value: 29.435 |
|
- type: mrr_at_5 |
|
value: 30.446 |
|
- type: ndcg_at_1 |
|
value: 24.633 |
|
- type: ndcg_at_10 |
|
value: 33.697 |
|
- type: ndcg_at_100 |
|
value: 38.080000000000005 |
|
- type: ndcg_at_1000 |
|
value: 40.812 |
|
- type: ndcg_at_3 |
|
value: 29.654000000000003 |
|
- type: ndcg_at_5 |
|
value: 31.474000000000004 |
|
- type: precision_at_1 |
|
value: 24.633 |
|
- type: precision_at_10 |
|
value: 5.0729999999999995 |
|
- type: precision_at_100 |
|
value: 0.753 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 12.279 |
|
- type: precision_at_5 |
|
value: 8.452 |
|
- type: recall_at_1 |
|
value: 23.039 |
|
- type: recall_at_10 |
|
value: 44.275999999999996 |
|
- type: recall_at_100 |
|
value: 64.4 |
|
- type: recall_at_1000 |
|
value: 85.135 |
|
- type: recall_at_3 |
|
value: 33.394 |
|
- type: recall_at_5 |
|
value: 37.687 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.594999999999999 |
|
- type: map_at_10 |
|
value: 19.933999999999997 |
|
- type: map_at_100 |
|
value: 20.966 |
|
- type: map_at_1000 |
|
value: 21.087 |
|
- type: map_at_3 |
|
value: 17.749000000000002 |
|
- type: map_at_5 |
|
value: 19.156000000000002 |
|
- type: mrr_at_1 |
|
value: 17.662 |
|
- type: mrr_at_10 |
|
value: 24.407 |
|
- type: mrr_at_100 |
|
value: 25.385 |
|
- type: mrr_at_1000 |
|
value: 25.465 |
|
- type: mrr_at_3 |
|
value: 22.056 |
|
- type: mrr_at_5 |
|
value: 23.630000000000003 |
|
- type: ndcg_at_1 |
|
value: 17.662 |
|
- type: ndcg_at_10 |
|
value: 24.391 |
|
- type: ndcg_at_100 |
|
value: 29.681 |
|
- type: ndcg_at_1000 |
|
value: 32.923 |
|
- type: ndcg_at_3 |
|
value: 20.271 |
|
- type: ndcg_at_5 |
|
value: 22.621 |
|
- type: precision_at_1 |
|
value: 17.662 |
|
- type: precision_at_10 |
|
value: 4.44 |
|
- type: precision_at_100 |
|
value: 0.8200000000000001 |
|
- type: precision_at_1000 |
|
value: 0.125 |
|
- type: precision_at_3 |
|
value: 9.577 |
|
- type: precision_at_5 |
|
value: 7.313 |
|
- type: recall_at_1 |
|
value: 13.594999999999999 |
|
- type: recall_at_10 |
|
value: 33.976 |
|
- type: recall_at_100 |
|
value: 57.43000000000001 |
|
- type: recall_at_1000 |
|
value: 80.958 |
|
- type: recall_at_3 |
|
value: 22.897000000000002 |
|
- type: recall_at_5 |
|
value: 28.714000000000002 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.683 |
|
- type: map_at_10 |
|
value: 35.068 |
|
- type: map_at_100 |
|
value: 36.311 |
|
- type: map_at_1000 |
|
value: 36.436 |
|
- type: map_at_3 |
|
value: 32.371 |
|
- type: map_at_5 |
|
value: 33.761 |
|
- type: mrr_at_1 |
|
value: 32.435 |
|
- type: mrr_at_10 |
|
value: 40.721000000000004 |
|
- type: mrr_at_100 |
|
value: 41.535 |
|
- type: mrr_at_1000 |
|
value: 41.593 |
|
- type: mrr_at_3 |
|
value: 38.401999999999994 |
|
- type: mrr_at_5 |
|
value: 39.567 |
|
- type: ndcg_at_1 |
|
value: 32.435 |
|
- type: ndcg_at_10 |
|
value: 40.538000000000004 |
|
- type: ndcg_at_100 |
|
value: 45.963 |
|
- type: ndcg_at_1000 |
|
value: 48.400999999999996 |
|
- type: ndcg_at_3 |
|
value: 36.048 |
|
- type: ndcg_at_5 |
|
value: 37.899 |
|
- type: precision_at_1 |
|
value: 32.435 |
|
- type: precision_at_10 |
|
value: 7.1129999999999995 |
|
- type: precision_at_100 |
|
value: 1.162 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 16.683 |
|
- type: precision_at_5 |
|
value: 11.684 |
|
- type: recall_at_1 |
|
value: 26.683 |
|
- type: recall_at_10 |
|
value: 51.517 |
|
- type: recall_at_100 |
|
value: 74.553 |
|
- type: recall_at_1000 |
|
value: 90.649 |
|
- type: recall_at_3 |
|
value: 38.495000000000005 |
|
- type: recall_at_5 |
|
value: 43.495 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.186 |
|
- type: map_at_10 |
|
value: 31.972 |
|
- type: map_at_100 |
|
value: 33.117000000000004 |
|
- type: map_at_1000 |
|
value: 33.243 |
|
- type: map_at_3 |
|
value: 29.423 |
|
- type: map_at_5 |
|
value: 30.847 |
|
- type: mrr_at_1 |
|
value: 29.794999999999998 |
|
- type: mrr_at_10 |
|
value: 36.767 |
|
- type: mrr_at_100 |
|
value: 37.645 |
|
- type: mrr_at_1000 |
|
value: 37.716 |
|
- type: mrr_at_3 |
|
value: 34.513 |
|
- type: mrr_at_5 |
|
value: 35.791000000000004 |
|
- type: ndcg_at_1 |
|
value: 29.794999999999998 |
|
- type: ndcg_at_10 |
|
value: 36.786 |
|
- type: ndcg_at_100 |
|
value: 41.94 |
|
- type: ndcg_at_1000 |
|
value: 44.830999999999996 |
|
- type: ndcg_at_3 |
|
value: 32.504 |
|
- type: ndcg_at_5 |
|
value: 34.404 |
|
- type: precision_at_1 |
|
value: 29.794999999999998 |
|
- type: precision_at_10 |
|
value: 6.518 |
|
- type: precision_at_100 |
|
value: 1.0659999999999998 |
|
- type: precision_at_1000 |
|
value: 0.149 |
|
- type: precision_at_3 |
|
value: 15.296999999999999 |
|
- type: precision_at_5 |
|
value: 10.731 |
|
- type: recall_at_1 |
|
value: 24.186 |
|
- type: recall_at_10 |
|
value: 46.617 |
|
- type: recall_at_100 |
|
value: 68.75 |
|
- type: recall_at_1000 |
|
value: 88.864 |
|
- type: recall_at_3 |
|
value: 34.199 |
|
- type: recall_at_5 |
|
value: 39.462 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.22083333333333 |
|
- type: map_at_10 |
|
value: 31.606666666666662 |
|
- type: map_at_100 |
|
value: 32.6195 |
|
- type: map_at_1000 |
|
value: 32.739999999999995 |
|
- type: map_at_3 |
|
value: 29.37825 |
|
- type: map_at_5 |
|
value: 30.596083333333336 |
|
- type: mrr_at_1 |
|
value: 28.607916666666668 |
|
- type: mrr_at_10 |
|
value: 35.54591666666666 |
|
- type: mrr_at_100 |
|
value: 36.33683333333333 |
|
- type: mrr_at_1000 |
|
value: 36.40624999999999 |
|
- type: mrr_at_3 |
|
value: 33.526250000000005 |
|
- type: mrr_at_5 |
|
value: 34.6605 |
|
- type: ndcg_at_1 |
|
value: 28.607916666666668 |
|
- type: ndcg_at_10 |
|
value: 36.07966666666667 |
|
- type: ndcg_at_100 |
|
value: 40.73308333333333 |
|
- type: ndcg_at_1000 |
|
value: 43.40666666666666 |
|
- type: ndcg_at_3 |
|
value: 32.23525 |
|
- type: ndcg_at_5 |
|
value: 33.97083333333333 |
|
- type: precision_at_1 |
|
value: 28.607916666666668 |
|
- type: precision_at_10 |
|
value: 6.120333333333335 |
|
- type: precision_at_100 |
|
value: 0.9921666666666668 |
|
- type: precision_at_1000 |
|
value: 0.14091666666666666 |
|
- type: precision_at_3 |
|
value: 14.54975 |
|
- type: precision_at_5 |
|
value: 10.153166666666667 |
|
- type: recall_at_1 |
|
value: 24.22083333333333 |
|
- type: recall_at_10 |
|
value: 45.49183333333334 |
|
- type: recall_at_100 |
|
value: 66.28133333333332 |
|
- type: recall_at_1000 |
|
value: 85.16541666666667 |
|
- type: recall_at_3 |
|
value: 34.6485 |
|
- type: recall_at_5 |
|
value: 39.229749999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.842 |
|
- type: map_at_10 |
|
value: 27.573999999999998 |
|
- type: map_at_100 |
|
value: 28.410999999999998 |
|
- type: map_at_1000 |
|
value: 28.502 |
|
- type: map_at_3 |
|
value: 25.921 |
|
- type: map_at_5 |
|
value: 26.888 |
|
- type: mrr_at_1 |
|
value: 24.08 |
|
- type: mrr_at_10 |
|
value: 29.915999999999997 |
|
- type: mrr_at_100 |
|
value: 30.669 |
|
- type: mrr_at_1000 |
|
value: 30.746000000000002 |
|
- type: mrr_at_3 |
|
value: 28.349000000000004 |
|
- type: mrr_at_5 |
|
value: 29.246 |
|
- type: ndcg_at_1 |
|
value: 24.08 |
|
- type: ndcg_at_10 |
|
value: 30.898999999999997 |
|
- type: ndcg_at_100 |
|
value: 35.272999999999996 |
|
- type: ndcg_at_1000 |
|
value: 37.679 |
|
- type: ndcg_at_3 |
|
value: 27.881 |
|
- type: ndcg_at_5 |
|
value: 29.432000000000002 |
|
- type: precision_at_1 |
|
value: 24.08 |
|
- type: precision_at_10 |
|
value: 4.678 |
|
- type: precision_at_100 |
|
value: 0.744 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 11.860999999999999 |
|
- type: precision_at_5 |
|
value: 8.16 |
|
- type: recall_at_1 |
|
value: 21.842 |
|
- type: recall_at_10 |
|
value: 38.66 |
|
- type: recall_at_100 |
|
value: 59.169000000000004 |
|
- type: recall_at_1000 |
|
value: 76.887 |
|
- type: recall_at_3 |
|
value: 30.532999999999998 |
|
- type: recall_at_5 |
|
value: 34.354 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.145 |
|
- type: map_at_10 |
|
value: 22.729 |
|
- type: map_at_100 |
|
value: 23.574 |
|
- type: map_at_1000 |
|
value: 23.695 |
|
- type: map_at_3 |
|
value: 21.044 |
|
- type: map_at_5 |
|
value: 21.981 |
|
- type: mrr_at_1 |
|
value: 20.888 |
|
- type: mrr_at_10 |
|
value: 26.529000000000003 |
|
- type: mrr_at_100 |
|
value: 27.308 |
|
- type: mrr_at_1000 |
|
value: 27.389000000000003 |
|
- type: mrr_at_3 |
|
value: 24.868000000000002 |
|
- type: mrr_at_5 |
|
value: 25.825 |
|
- type: ndcg_at_1 |
|
value: 20.888 |
|
- type: ndcg_at_10 |
|
value: 26.457000000000004 |
|
- type: ndcg_at_100 |
|
value: 30.764000000000003 |
|
- type: ndcg_at_1000 |
|
value: 33.825 |
|
- type: ndcg_at_3 |
|
value: 23.483999999999998 |
|
- type: ndcg_at_5 |
|
value: 24.836 |
|
- type: precision_at_1 |
|
value: 20.888 |
|
- type: precision_at_10 |
|
value: 4.58 |
|
- type: precision_at_100 |
|
value: 0.784 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 10.874 |
|
- type: precision_at_5 |
|
value: 7.639 |
|
- type: recall_at_1 |
|
value: 17.145 |
|
- type: recall_at_10 |
|
value: 33.938 |
|
- type: recall_at_100 |
|
value: 53.672 |
|
- type: recall_at_1000 |
|
value: 76.023 |
|
- type: recall_at_3 |
|
value: 25.363000000000003 |
|
- type: recall_at_5 |
|
value: 29.023 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.275 |
|
- type: map_at_10 |
|
value: 30.438 |
|
- type: map_at_100 |
|
value: 31.489 |
|
- type: map_at_1000 |
|
value: 31.601000000000003 |
|
- type: map_at_3 |
|
value: 28.647 |
|
- type: map_at_5 |
|
value: 29.660999999999998 |
|
- type: mrr_at_1 |
|
value: 28.077999999999996 |
|
- type: mrr_at_10 |
|
value: 34.098 |
|
- type: mrr_at_100 |
|
value: 35.025 |
|
- type: mrr_at_1000 |
|
value: 35.109 |
|
- type: mrr_at_3 |
|
value: 32.4 |
|
- type: mrr_at_5 |
|
value: 33.379999999999995 |
|
- type: ndcg_at_1 |
|
value: 28.077999999999996 |
|
- type: ndcg_at_10 |
|
value: 34.271 |
|
- type: ndcg_at_100 |
|
value: 39.352 |
|
- type: ndcg_at_1000 |
|
value: 42.199 |
|
- type: ndcg_at_3 |
|
value: 30.978 |
|
- type: ndcg_at_5 |
|
value: 32.498 |
|
- type: precision_at_1 |
|
value: 28.077999999999996 |
|
- type: precision_at_10 |
|
value: 5.345 |
|
- type: precision_at_100 |
|
value: 0.897 |
|
- type: precision_at_1000 |
|
value: 0.125 |
|
- type: precision_at_3 |
|
value: 13.526 |
|
- type: precision_at_5 |
|
value: 9.16 |
|
- type: recall_at_1 |
|
value: 24.275 |
|
- type: recall_at_10 |
|
value: 42.362 |
|
- type: recall_at_100 |
|
value: 64.461 |
|
- type: recall_at_1000 |
|
value: 84.981 |
|
- type: recall_at_3 |
|
value: 33.249 |
|
- type: recall_at_5 |
|
value: 37.214999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.358 |
|
- type: map_at_10 |
|
value: 30.062 |
|
- type: map_at_100 |
|
value: 31.189 |
|
- type: map_at_1000 |
|
value: 31.386999999999997 |
|
- type: map_at_3 |
|
value: 27.672 |
|
- type: map_at_5 |
|
value: 28.76 |
|
- type: mrr_at_1 |
|
value: 26.877000000000002 |
|
- type: mrr_at_10 |
|
value: 33.948 |
|
- type: mrr_at_100 |
|
value: 34.746 |
|
- type: mrr_at_1000 |
|
value: 34.816 |
|
- type: mrr_at_3 |
|
value: 31.884 |
|
- type: mrr_at_5 |
|
value: 33.001000000000005 |
|
- type: ndcg_at_1 |
|
value: 26.877000000000002 |
|
- type: ndcg_at_10 |
|
value: 34.977000000000004 |
|
- type: ndcg_at_100 |
|
value: 39.753 |
|
- type: ndcg_at_1000 |
|
value: 42.866 |
|
- type: ndcg_at_3 |
|
value: 30.956 |
|
- type: ndcg_at_5 |
|
value: 32.381 |
|
- type: precision_at_1 |
|
value: 26.877000000000002 |
|
- type: precision_at_10 |
|
value: 6.7 |
|
- type: precision_at_100 |
|
value: 1.287 |
|
- type: precision_at_1000 |
|
value: 0.215 |
|
- type: precision_at_3 |
|
value: 14.360999999999999 |
|
- type: precision_at_5 |
|
value: 10.119 |
|
- type: recall_at_1 |
|
value: 22.358 |
|
- type: recall_at_10 |
|
value: 44.183 |
|
- type: recall_at_100 |
|
value: 67.14 |
|
- type: recall_at_1000 |
|
value: 87.53999999999999 |
|
- type: recall_at_3 |
|
value: 32.79 |
|
- type: recall_at_5 |
|
value: 36.829 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.198999999999998 |
|
- type: map_at_10 |
|
value: 25.229000000000003 |
|
- type: map_at_100 |
|
value: 26.003 |
|
- type: map_at_1000 |
|
value: 26.111 |
|
- type: map_at_3 |
|
value: 23.442 |
|
- type: map_at_5 |
|
value: 24.343 |
|
- type: mrr_at_1 |
|
value: 21.072 |
|
- type: mrr_at_10 |
|
value: 27.02 |
|
- type: mrr_at_100 |
|
value: 27.735 |
|
- type: mrr_at_1000 |
|
value: 27.815 |
|
- type: mrr_at_3 |
|
value: 25.416 |
|
- type: mrr_at_5 |
|
value: 26.173999999999996 |
|
- type: ndcg_at_1 |
|
value: 21.072 |
|
- type: ndcg_at_10 |
|
value: 28.862 |
|
- type: ndcg_at_100 |
|
value: 33.043 |
|
- type: ndcg_at_1000 |
|
value: 36.003 |
|
- type: ndcg_at_3 |
|
value: 25.35 |
|
- type: ndcg_at_5 |
|
value: 26.773000000000003 |
|
- type: precision_at_1 |
|
value: 21.072 |
|
- type: precision_at_10 |
|
value: 4.436 |
|
- type: precision_at_100 |
|
value: 0.713 |
|
- type: precision_at_1000 |
|
value: 0.106 |
|
- type: precision_at_3 |
|
value: 10.659 |
|
- type: precision_at_5 |
|
value: 7.32 |
|
- type: recall_at_1 |
|
value: 19.198999999999998 |
|
- type: recall_at_10 |
|
value: 38.376 |
|
- type: recall_at_100 |
|
value: 58.36900000000001 |
|
- type: recall_at_1000 |
|
value: 80.92099999999999 |
|
- type: recall_at_3 |
|
value: 28.715000000000003 |
|
- type: recall_at_5 |
|
value: 32.147 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.9319999999999995 |
|
- type: map_at_10 |
|
value: 10.483 |
|
- type: map_at_100 |
|
value: 11.97 |
|
- type: map_at_1000 |
|
value: 12.171999999999999 |
|
- type: map_at_3 |
|
value: 8.477 |
|
- type: map_at_5 |
|
value: 9.495000000000001 |
|
- type: mrr_at_1 |
|
value: 13.094 |
|
- type: mrr_at_10 |
|
value: 21.282 |
|
- type: mrr_at_100 |
|
value: 22.556 |
|
- type: mrr_at_1000 |
|
value: 22.628999999999998 |
|
- type: mrr_at_3 |
|
value: 18.218999999999998 |
|
- type: mrr_at_5 |
|
value: 19.900000000000002 |
|
- type: ndcg_at_1 |
|
value: 13.094 |
|
- type: ndcg_at_10 |
|
value: 15.811 |
|
- type: ndcg_at_100 |
|
value: 23.035 |
|
- type: ndcg_at_1000 |
|
value: 27.089999999999996 |
|
- type: ndcg_at_3 |
|
value: 11.905000000000001 |
|
- type: ndcg_at_5 |
|
value: 13.377 |
|
- type: precision_at_1 |
|
value: 13.094 |
|
- type: precision_at_10 |
|
value: 5.225 |
|
- type: precision_at_100 |
|
value: 1.2970000000000002 |
|
- type: precision_at_1000 |
|
value: 0.203 |
|
- type: precision_at_3 |
|
value: 8.86 |
|
- type: precision_at_5 |
|
value: 7.309 |
|
- type: recall_at_1 |
|
value: 5.9319999999999995 |
|
- type: recall_at_10 |
|
value: 20.305 |
|
- type: recall_at_100 |
|
value: 46.314 |
|
- type: recall_at_1000 |
|
value: 69.612 |
|
- type: recall_at_3 |
|
value: 11.21 |
|
- type: recall_at_5 |
|
value: 14.773 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.674 |
|
- type: map_at_10 |
|
value: 17.822 |
|
- type: map_at_100 |
|
value: 24.794 |
|
- type: map_at_1000 |
|
value: 26.214 |
|
- type: map_at_3 |
|
value: 12.690999999999999 |
|
- type: map_at_5 |
|
value: 15.033 |
|
- type: mrr_at_1 |
|
value: 61.75000000000001 |
|
- type: mrr_at_10 |
|
value: 71.58 |
|
- type: mrr_at_100 |
|
value: 71.923 |
|
- type: mrr_at_1000 |
|
value: 71.932 |
|
- type: mrr_at_3 |
|
value: 70.125 |
|
- type: mrr_at_5 |
|
value: 71.038 |
|
- type: ndcg_at_1 |
|
value: 51 |
|
- type: ndcg_at_10 |
|
value: 38.637 |
|
- type: ndcg_at_100 |
|
value: 42.398 |
|
- type: ndcg_at_1000 |
|
value: 48.962 |
|
- type: ndcg_at_3 |
|
value: 43.29 |
|
- type: ndcg_at_5 |
|
value: 40.763 |
|
- type: precision_at_1 |
|
value: 61.75000000000001 |
|
- type: precision_at_10 |
|
value: 30.125 |
|
- type: precision_at_100 |
|
value: 9.53 |
|
- type: precision_at_1000 |
|
value: 1.9619999999999997 |
|
- type: precision_at_3 |
|
value: 45.583 |
|
- type: precision_at_5 |
|
value: 38.95 |
|
- type: recall_at_1 |
|
value: 8.674 |
|
- type: recall_at_10 |
|
value: 23.122 |
|
- type: recall_at_100 |
|
value: 47.46 |
|
- type: recall_at_1000 |
|
value: 67.662 |
|
- type: recall_at_3 |
|
value: 13.946 |
|
- type: recall_at_5 |
|
value: 17.768 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 46.86000000000001 |
|
- type: f1 |
|
value: 41.343580452760776 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 36.609 |
|
- type: map_at_10 |
|
value: 47.552 |
|
- type: map_at_100 |
|
value: 48.283 |
|
- type: map_at_1000 |
|
value: 48.321 |
|
- type: map_at_3 |
|
value: 44.869 |
|
- type: map_at_5 |
|
value: 46.509 |
|
- type: mrr_at_1 |
|
value: 39.214 |
|
- type: mrr_at_10 |
|
value: 50.434999999999995 |
|
- type: mrr_at_100 |
|
value: 51.122 |
|
- type: mrr_at_1000 |
|
value: 51.151 |
|
- type: mrr_at_3 |
|
value: 47.735 |
|
- type: mrr_at_5 |
|
value: 49.394 |
|
- type: ndcg_at_1 |
|
value: 39.214 |
|
- type: ndcg_at_10 |
|
value: 53.52400000000001 |
|
- type: ndcg_at_100 |
|
value: 56.997 |
|
- type: ndcg_at_1000 |
|
value: 57.975 |
|
- type: ndcg_at_3 |
|
value: 48.173 |
|
- type: ndcg_at_5 |
|
value: 51.05800000000001 |
|
- type: precision_at_1 |
|
value: 39.214 |
|
- type: precision_at_10 |
|
value: 7.573 |
|
- type: precision_at_100 |
|
value: 0.9440000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 19.782 |
|
- type: precision_at_5 |
|
value: 13.453000000000001 |
|
- type: recall_at_1 |
|
value: 36.609 |
|
- type: recall_at_10 |
|
value: 69.247 |
|
- type: recall_at_100 |
|
value: 84.99600000000001 |
|
- type: recall_at_1000 |
|
value: 92.40899999999999 |
|
- type: recall_at_3 |
|
value: 54.856 |
|
- type: recall_at_5 |
|
value: 61.797000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.466 |
|
- type: map_at_10 |
|
value: 27.060000000000002 |
|
- type: map_at_100 |
|
value: 28.511999999999997 |
|
- type: map_at_1000 |
|
value: 28.693 |
|
- type: map_at_3 |
|
value: 22.777 |
|
- type: map_at_5 |
|
value: 25.086000000000002 |
|
- type: mrr_at_1 |
|
value: 32.716 |
|
- type: mrr_at_10 |
|
value: 41.593999999999994 |
|
- type: mrr_at_100 |
|
value: 42.370000000000005 |
|
- type: mrr_at_1000 |
|
value: 42.419000000000004 |
|
- type: mrr_at_3 |
|
value: 38.143 |
|
- type: mrr_at_5 |
|
value: 40.288000000000004 |
|
- type: ndcg_at_1 |
|
value: 32.716 |
|
- type: ndcg_at_10 |
|
value: 34.795 |
|
- type: ndcg_at_100 |
|
value: 40.58 |
|
- type: ndcg_at_1000 |
|
value: 43.993 |
|
- type: ndcg_at_3 |
|
value: 29.573 |
|
- type: ndcg_at_5 |
|
value: 31.583 |
|
- type: precision_at_1 |
|
value: 32.716 |
|
- type: precision_at_10 |
|
value: 9.937999999999999 |
|
- type: precision_at_100 |
|
value: 1.585 |
|
- type: precision_at_1000 |
|
value: 0.22 |
|
- type: precision_at_3 |
|
value: 19.496 |
|
- type: precision_at_5 |
|
value: 15.247 |
|
- type: recall_at_1 |
|
value: 16.466 |
|
- type: recall_at_10 |
|
value: 42.886 |
|
- type: recall_at_100 |
|
value: 64.724 |
|
- type: recall_at_1000 |
|
value: 85.347 |
|
- type: recall_at_3 |
|
value: 26.765 |
|
- type: recall_at_5 |
|
value: 33.603 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.025 |
|
- type: map_at_10 |
|
value: 47.343 |
|
- type: map_at_100 |
|
value: 48.207 |
|
- type: map_at_1000 |
|
value: 48.281 |
|
- type: map_at_3 |
|
value: 44.519 |
|
- type: map_at_5 |
|
value: 46.217000000000006 |
|
- type: mrr_at_1 |
|
value: 66.05 |
|
- type: mrr_at_10 |
|
value: 72.94699999999999 |
|
- type: mrr_at_100 |
|
value: 73.289 |
|
- type: mrr_at_1000 |
|
value: 73.30499999999999 |
|
- type: mrr_at_3 |
|
value: 71.686 |
|
- type: mrr_at_5 |
|
value: 72.491 |
|
- type: ndcg_at_1 |
|
value: 66.05 |
|
- type: ndcg_at_10 |
|
value: 56.338 |
|
- type: ndcg_at_100 |
|
value: 59.599999999999994 |
|
- type: ndcg_at_1000 |
|
value: 61.138000000000005 |
|
- type: ndcg_at_3 |
|
value: 52.034000000000006 |
|
- type: ndcg_at_5 |
|
value: 54.352000000000004 |
|
- type: precision_at_1 |
|
value: 66.05 |
|
- type: precision_at_10 |
|
value: 11.693000000000001 |
|
- type: precision_at_100 |
|
value: 1.425 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 32.613 |
|
- type: precision_at_5 |
|
value: 21.401999999999997 |
|
- type: recall_at_1 |
|
value: 33.025 |
|
- type: recall_at_10 |
|
value: 58.467 |
|
- type: recall_at_100 |
|
value: 71.242 |
|
- type: recall_at_1000 |
|
value: 81.452 |
|
- type: recall_at_3 |
|
value: 48.92 |
|
- type: recall_at_5 |
|
value: 53.504 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 75.5492 |
|
- type: ap |
|
value: 69.42911637216271 |
|
- type: f1 |
|
value: 75.39113704261024 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.173 |
|
- type: map_at_10 |
|
value: 35.453 |
|
- type: map_at_100 |
|
value: 36.573 |
|
- type: map_at_1000 |
|
value: 36.620999999999995 |
|
- type: map_at_3 |
|
value: 31.655 |
|
- type: map_at_5 |
|
value: 33.823 |
|
- type: mrr_at_1 |
|
value: 23.868000000000002 |
|
- type: mrr_at_10 |
|
value: 36.085 |
|
- type: mrr_at_100 |
|
value: 37.15 |
|
- type: mrr_at_1000 |
|
value: 37.193 |
|
- type: mrr_at_3 |
|
value: 32.376 |
|
- type: mrr_at_5 |
|
value: 34.501 |
|
- type: ndcg_at_1 |
|
value: 23.854 |
|
- type: ndcg_at_10 |
|
value: 42.33 |
|
- type: ndcg_at_100 |
|
value: 47.705999999999996 |
|
- type: ndcg_at_1000 |
|
value: 48.91 |
|
- type: ndcg_at_3 |
|
value: 34.604 |
|
- type: ndcg_at_5 |
|
value: 38.473 |
|
- type: precision_at_1 |
|
value: 23.854 |
|
- type: precision_at_10 |
|
value: 6.639 |
|
- type: precision_at_100 |
|
value: 0.932 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.685 |
|
- type: precision_at_5 |
|
value: 10.782 |
|
- type: recall_at_1 |
|
value: 23.173 |
|
- type: recall_at_10 |
|
value: 63.441 |
|
- type: recall_at_100 |
|
value: 88.25 |
|
- type: recall_at_1000 |
|
value: 97.438 |
|
- type: recall_at_3 |
|
value: 42.434 |
|
- type: recall_at_5 |
|
value: 51.745 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 92.05426356589147 |
|
- type: f1 |
|
value: 91.88068588063942 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 73.23985408116735 |
|
- type: f1 |
|
value: 55.858906745287506 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 72.21923335574984 |
|
- type: f1 |
|
value: 70.0174116204253 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 75.77673167451245 |
|
- type: f1 |
|
value: 75.44811354778666 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.340414710728737 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.196676760061578 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 29.564149683482206 |
|
- type: mrr |
|
value: 30.28995474250486 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.93 |
|
- type: map_at_10 |
|
value: 12.828000000000001 |
|
- type: map_at_100 |
|
value: 15.501000000000001 |
|
- type: map_at_1000 |
|
value: 16.791 |
|
- type: map_at_3 |
|
value: 9.727 |
|
- type: map_at_5 |
|
value: 11.318999999999999 |
|
- type: mrr_at_1 |
|
value: 47.678 |
|
- type: mrr_at_10 |
|
value: 55.893 |
|
- type: mrr_at_100 |
|
value: 56.491 |
|
- type: mrr_at_1000 |
|
value: 56.53 |
|
- type: mrr_at_3 |
|
value: 54.386 |
|
- type: mrr_at_5 |
|
value: 55.516 |
|
- type: ndcg_at_1 |
|
value: 45.975 |
|
- type: ndcg_at_10 |
|
value: 33.928999999999995 |
|
- type: ndcg_at_100 |
|
value: 30.164 |
|
- type: ndcg_at_1000 |
|
value: 38.756 |
|
- type: ndcg_at_3 |
|
value: 41.077000000000005 |
|
- type: ndcg_at_5 |
|
value: 38.415 |
|
- type: precision_at_1 |
|
value: 47.678 |
|
- type: precision_at_10 |
|
value: 24.365000000000002 |
|
- type: precision_at_100 |
|
value: 7.344 |
|
- type: precision_at_1000 |
|
value: 1.994 |
|
- type: precision_at_3 |
|
value: 38.184000000000005 |
|
- type: precision_at_5 |
|
value: 33.003 |
|
- type: recall_at_1 |
|
value: 5.93 |
|
- type: recall_at_10 |
|
value: 16.239 |
|
- type: recall_at_100 |
|
value: 28.782999999999998 |
|
- type: recall_at_1000 |
|
value: 60.11 |
|
- type: recall_at_3 |
|
value: 10.700999999999999 |
|
- type: recall_at_5 |
|
value: 13.584 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 36.163000000000004 |
|
- type: map_at_10 |
|
value: 51.520999999999994 |
|
- type: map_at_100 |
|
value: 52.449 |
|
- type: map_at_1000 |
|
value: 52.473000000000006 |
|
- type: map_at_3 |
|
value: 47.666 |
|
- type: map_at_5 |
|
value: 50.043000000000006 |
|
- type: mrr_at_1 |
|
value: 40.266999999999996 |
|
- type: mrr_at_10 |
|
value: 54.074 |
|
- type: mrr_at_100 |
|
value: 54.722 |
|
- type: mrr_at_1000 |
|
value: 54.739000000000004 |
|
- type: mrr_at_3 |
|
value: 51.043000000000006 |
|
- type: mrr_at_5 |
|
value: 52.956 |
|
- type: ndcg_at_1 |
|
value: 40.238 |
|
- type: ndcg_at_10 |
|
value: 58.73199999999999 |
|
- type: ndcg_at_100 |
|
value: 62.470000000000006 |
|
- type: ndcg_at_1000 |
|
value: 63.083999999999996 |
|
- type: ndcg_at_3 |
|
value: 51.672 |
|
- type: ndcg_at_5 |
|
value: 55.564 |
|
- type: precision_at_1 |
|
value: 40.238 |
|
- type: precision_at_10 |
|
value: 9.279 |
|
- type: precision_at_100 |
|
value: 1.139 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 23.078000000000003 |
|
- type: precision_at_5 |
|
value: 16.176 |
|
- type: recall_at_1 |
|
value: 36.163000000000004 |
|
- type: recall_at_10 |
|
value: 77.88199999999999 |
|
- type: recall_at_100 |
|
value: 93.83399999999999 |
|
- type: recall_at_1000 |
|
value: 98.465 |
|
- type: recall_at_3 |
|
value: 59.857000000000006 |
|
- type: recall_at_5 |
|
value: 68.73599999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.344 |
|
- type: map_at_10 |
|
value: 83.907 |
|
- type: map_at_100 |
|
value: 84.536 |
|
- type: map_at_1000 |
|
value: 84.557 |
|
- type: map_at_3 |
|
value: 80.984 |
|
- type: map_at_5 |
|
value: 82.844 |
|
- type: mrr_at_1 |
|
value: 81.02000000000001 |
|
- type: mrr_at_10 |
|
value: 87.158 |
|
- type: mrr_at_100 |
|
value: 87.268 |
|
- type: mrr_at_1000 |
|
value: 87.26899999999999 |
|
- type: mrr_at_3 |
|
value: 86.17 |
|
- type: mrr_at_5 |
|
value: 86.87 |
|
- type: ndcg_at_1 |
|
value: 81.02000000000001 |
|
- type: ndcg_at_10 |
|
value: 87.70700000000001 |
|
- type: ndcg_at_100 |
|
value: 89.004 |
|
- type: ndcg_at_1000 |
|
value: 89.139 |
|
- type: ndcg_at_3 |
|
value: 84.841 |
|
- type: ndcg_at_5 |
|
value: 86.455 |
|
- type: precision_at_1 |
|
value: 81.02000000000001 |
|
- type: precision_at_10 |
|
value: 13.248999999999999 |
|
- type: precision_at_100 |
|
value: 1.516 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 36.963 |
|
- type: precision_at_5 |
|
value: 24.33 |
|
- type: recall_at_1 |
|
value: 70.344 |
|
- type: recall_at_10 |
|
value: 94.75099999999999 |
|
- type: recall_at_100 |
|
value: 99.30499999999999 |
|
- type: recall_at_1000 |
|
value: 99.928 |
|
- type: recall_at_3 |
|
value: 86.506 |
|
- type: recall_at_5 |
|
value: 91.083 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 42.873718018378305 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 56.39477366450528 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.868 |
|
- type: map_at_10 |
|
value: 9.611 |
|
- type: map_at_100 |
|
value: 11.087 |
|
- type: map_at_1000 |
|
value: 11.332 |
|
- type: map_at_3 |
|
value: 6.813 |
|
- type: map_at_5 |
|
value: 8.233 |
|
- type: mrr_at_1 |
|
value: 19 |
|
- type: mrr_at_10 |
|
value: 28.457 |
|
- type: mrr_at_100 |
|
value: 29.613 |
|
- type: mrr_at_1000 |
|
value: 29.695 |
|
- type: mrr_at_3 |
|
value: 25.55 |
|
- type: mrr_at_5 |
|
value: 27.29 |
|
- type: ndcg_at_1 |
|
value: 19 |
|
- type: ndcg_at_10 |
|
value: 16.419 |
|
- type: ndcg_at_100 |
|
value: 22.817999999999998 |
|
- type: ndcg_at_1000 |
|
value: 27.72 |
|
- type: ndcg_at_3 |
|
value: 15.379000000000001 |
|
- type: ndcg_at_5 |
|
value: 13.645 |
|
- type: precision_at_1 |
|
value: 19 |
|
- type: precision_at_10 |
|
value: 8.540000000000001 |
|
- type: precision_at_100 |
|
value: 1.7819999999999998 |
|
- type: precision_at_1000 |
|
value: 0.297 |
|
- type: precision_at_3 |
|
value: 14.267 |
|
- type: precision_at_5 |
|
value: 12.04 |
|
- type: recall_at_1 |
|
value: 3.868 |
|
- type: recall_at_10 |
|
value: 17.288 |
|
- type: recall_at_100 |
|
value: 36.144999999999996 |
|
- type: recall_at_1000 |
|
value: 60.199999999999996 |
|
- type: recall_at_3 |
|
value: 8.688 |
|
- type: recall_at_5 |
|
value: 12.198 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.96614722598582 |
|
- type: cos_sim_spearman |
|
value: 78.9003023008781 |
|
- type: euclidean_pearson |
|
value: 81.01829384436505 |
|
- type: euclidean_spearman |
|
value: 78.93248416788914 |
|
- type: manhattan_pearson |
|
value: 81.1665428926402 |
|
- type: manhattan_spearman |
|
value: 78.93264116287453 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.54613363895993 |
|
- type: cos_sim_spearman |
|
value: 75.1883451602451 |
|
- type: euclidean_pearson |
|
value: 79.70320886899894 |
|
- type: euclidean_spearman |
|
value: 74.5917140136796 |
|
- type: manhattan_pearson |
|
value: 79.82157067185999 |
|
- type: manhattan_spearman |
|
value: 74.74185720594735 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.30430156721782 |
|
- type: cos_sim_spearman |
|
value: 81.79962989974364 |
|
- type: euclidean_pearson |
|
value: 80.89058823224924 |
|
- type: euclidean_spearman |
|
value: 81.35929372984597 |
|
- type: manhattan_pearson |
|
value: 81.12204370487478 |
|
- type: manhattan_spearman |
|
value: 81.6248963282232 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.13064504403134 |
|
- type: cos_sim_spearman |
|
value: 78.48371403924872 |
|
- type: euclidean_pearson |
|
value: 80.16794919665591 |
|
- type: euclidean_spearman |
|
value: 78.29216082221699 |
|
- type: manhattan_pearson |
|
value: 80.22308565207301 |
|
- type: manhattan_spearman |
|
value: 78.37829229948022 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.52918899541099 |
|
- type: cos_sim_spearman |
|
value: 87.49276894673142 |
|
- type: euclidean_pearson |
|
value: 86.77440570164254 |
|
- type: euclidean_spearman |
|
value: 87.5753295736756 |
|
- type: manhattan_pearson |
|
value: 86.86098573892133 |
|
- type: manhattan_spearman |
|
value: 87.65848591821947 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.86805307244882 |
|
- type: cos_sim_spearman |
|
value: 84.58066253757511 |
|
- type: euclidean_pearson |
|
value: 84.38377000876991 |
|
- type: euclidean_spearman |
|
value: 85.1837278784528 |
|
- type: manhattan_pearson |
|
value: 84.41903291363842 |
|
- type: manhattan_spearman |
|
value: 85.19023736251052 |
|
- 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: 86.77218560282436 |
|
- type: cos_sim_spearman |
|
value: 87.94243515296604 |
|
- type: euclidean_pearson |
|
value: 88.22800939214864 |
|
- type: euclidean_spearman |
|
value: 87.91106839439841 |
|
- type: manhattan_pearson |
|
value: 88.17063269848741 |
|
- type: manhattan_spearman |
|
value: 87.72751904126062 |
|
- 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: 60.40731554300387 |
|
- type: cos_sim_spearman |
|
value: 63.76300532966479 |
|
- type: euclidean_pearson |
|
value: 62.94727878229085 |
|
- type: euclidean_spearman |
|
value: 63.678039531461216 |
|
- type: manhattan_pearson |
|
value: 63.00661039863549 |
|
- type: manhattan_spearman |
|
value: 63.6282591984376 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.92731569745344 |
|
- type: cos_sim_spearman |
|
value: 86.36336704300167 |
|
- type: euclidean_pearson |
|
value: 86.09122224841195 |
|
- type: euclidean_spearman |
|
value: 86.2116149319238 |
|
- type: manhattan_pearson |
|
value: 86.07879456717032 |
|
- type: manhattan_spearman |
|
value: 86.2022069635119 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 79.75976311752326 |
|
- type: mrr |
|
value: 94.15782837351466 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 51.193999999999996 |
|
- type: map_at_10 |
|
value: 61.224999999999994 |
|
- type: map_at_100 |
|
value: 62.031000000000006 |
|
- type: map_at_1000 |
|
value: 62.066 |
|
- type: map_at_3 |
|
value: 59.269000000000005 |
|
- type: map_at_5 |
|
value: 60.159 |
|
- type: mrr_at_1 |
|
value: 53.667 |
|
- type: mrr_at_10 |
|
value: 62.74999999999999 |
|
- type: mrr_at_100 |
|
value: 63.39399999999999 |
|
- type: mrr_at_1000 |
|
value: 63.425 |
|
- type: mrr_at_3 |
|
value: 61.389 |
|
- type: mrr_at_5 |
|
value: 61.989000000000004 |
|
- type: ndcg_at_1 |
|
value: 53.667 |
|
- type: ndcg_at_10 |
|
value: 65.596 |
|
- type: ndcg_at_100 |
|
value: 68.906 |
|
- type: ndcg_at_1000 |
|
value: 69.78999999999999 |
|
- type: ndcg_at_3 |
|
value: 62.261 |
|
- type: ndcg_at_5 |
|
value: 63.453 |
|
- type: precision_at_1 |
|
value: 53.667 |
|
- type: precision_at_10 |
|
value: 8.667 |
|
- type: precision_at_100 |
|
value: 1.04 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 24.556 |
|
- type: precision_at_5 |
|
value: 15.6 |
|
- type: recall_at_1 |
|
value: 51.193999999999996 |
|
- type: recall_at_10 |
|
value: 77.156 |
|
- type: recall_at_100 |
|
value: 91.43299999999999 |
|
- type: recall_at_1000 |
|
value: 98.333 |
|
- type: recall_at_3 |
|
value: 67.994 |
|
- type: recall_at_5 |
|
value: 71.14399999999999 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81485148514851 |
|
- type: cos_sim_ap |
|
value: 95.28896513388551 |
|
- type: cos_sim_f1 |
|
value: 90.43478260869566 |
|
- type: cos_sim_precision |
|
value: 92.56544502617801 |
|
- type: cos_sim_recall |
|
value: 88.4 |
|
- type: dot_accuracy |
|
value: 99.30594059405941 |
|
- type: dot_ap |
|
value: 61.6432597455472 |
|
- type: dot_f1 |
|
value: 59.46481665014866 |
|
- type: dot_precision |
|
value: 58.93909626719057 |
|
- type: dot_recall |
|
value: 60 |
|
- type: euclidean_accuracy |
|
value: 99.81980198019802 |
|
- type: euclidean_ap |
|
value: 95.21411049527 |
|
- type: euclidean_f1 |
|
value: 91.06090373280944 |
|
- type: euclidean_precision |
|
value: 89.47876447876449 |
|
- type: euclidean_recall |
|
value: 92.7 |
|
- type: manhattan_accuracy |
|
value: 99.81782178217821 |
|
- type: manhattan_ap |
|
value: 95.32449994414968 |
|
- type: manhattan_f1 |
|
value: 90.86395233366436 |
|
- type: manhattan_precision |
|
value: 90.23668639053254 |
|
- type: manhattan_recall |
|
value: 91.5 |
|
- type: max_accuracy |
|
value: 99.81980198019802 |
|
- type: max_ap |
|
value: 95.32449994414968 |
|
- type: max_f1 |
|
value: 91.06090373280944 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 59.08045614613064 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 30.297802606804748 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 49.12801740706292 |
|
- type: mrr |
|
value: 50.05592956879722 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.523347880124497 |
|
- type: cos_sim_spearman |
|
value: 31.388214436391014 |
|
- type: dot_pearson |
|
value: 24.55403435439901 |
|
- type: dot_spearman |
|
value: 23.50153210841191 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.243 |
|
- type: map_at_10 |
|
value: 1.886 |
|
- type: map_at_100 |
|
value: 10.040000000000001 |
|
- type: map_at_1000 |
|
value: 23.768 |
|
- type: map_at_3 |
|
value: 0.674 |
|
- type: map_at_5 |
|
value: 1.079 |
|
- type: mrr_at_1 |
|
value: 88 |
|
- type: mrr_at_10 |
|
value: 93.667 |
|
- type: mrr_at_100 |
|
value: 93.667 |
|
- type: mrr_at_1000 |
|
value: 93.667 |
|
- type: mrr_at_3 |
|
value: 93.667 |
|
- type: mrr_at_5 |
|
value: 93.667 |
|
- type: ndcg_at_1 |
|
value: 83 |
|
- type: ndcg_at_10 |
|
value: 76.777 |
|
- type: ndcg_at_100 |
|
value: 55.153 |
|
- type: ndcg_at_1000 |
|
value: 47.912 |
|
- type: ndcg_at_3 |
|
value: 81.358 |
|
- type: ndcg_at_5 |
|
value: 80.74799999999999 |
|
- type: precision_at_1 |
|
value: 88 |
|
- type: precision_at_10 |
|
value: 80.80000000000001 |
|
- type: precision_at_100 |
|
value: 56.02 |
|
- type: precision_at_1000 |
|
value: 21.51 |
|
- type: precision_at_3 |
|
value: 86 |
|
- type: precision_at_5 |
|
value: 86 |
|
- type: recall_at_1 |
|
value: 0.243 |
|
- type: recall_at_10 |
|
value: 2.0869999999999997 |
|
- type: recall_at_100 |
|
value: 13.014000000000001 |
|
- type: recall_at_1000 |
|
value: 44.433 |
|
- type: recall_at_3 |
|
value: 0.6910000000000001 |
|
- type: recall_at_5 |
|
value: 1.1440000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.066 |
|
- type: map_at_10 |
|
value: 10.615 |
|
- type: map_at_100 |
|
value: 16.463 |
|
- type: map_at_1000 |
|
value: 17.815 |
|
- type: map_at_3 |
|
value: 5.7860000000000005 |
|
- type: map_at_5 |
|
value: 7.353999999999999 |
|
- type: mrr_at_1 |
|
value: 38.775999999999996 |
|
- type: mrr_at_10 |
|
value: 53.846000000000004 |
|
- type: mrr_at_100 |
|
value: 54.37 |
|
- type: mrr_at_1000 |
|
value: 54.37 |
|
- type: mrr_at_3 |
|
value: 48.980000000000004 |
|
- type: mrr_at_5 |
|
value: 51.735 |
|
- type: ndcg_at_1 |
|
value: 34.694 |
|
- type: ndcg_at_10 |
|
value: 26.811 |
|
- type: ndcg_at_100 |
|
value: 37.342999999999996 |
|
- type: ndcg_at_1000 |
|
value: 47.964 |
|
- type: ndcg_at_3 |
|
value: 30.906 |
|
- type: ndcg_at_5 |
|
value: 27.77 |
|
- type: precision_at_1 |
|
value: 38.775999999999996 |
|
- type: precision_at_10 |
|
value: 23.878 |
|
- type: precision_at_100 |
|
value: 7.632999999999999 |
|
- type: precision_at_1000 |
|
value: 1.469 |
|
- type: precision_at_3 |
|
value: 31.973000000000003 |
|
- type: precision_at_5 |
|
value: 26.939 |
|
- type: recall_at_1 |
|
value: 3.066 |
|
- type: recall_at_10 |
|
value: 17.112 |
|
- type: recall_at_100 |
|
value: 47.723 |
|
- type: recall_at_1000 |
|
value: 79.50500000000001 |
|
- type: recall_at_3 |
|
value: 6.825 |
|
- type: recall_at_5 |
|
value: 9.584 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 72.76460000000002 |
|
- type: ap |
|
value: 14.944240012137053 |
|
- type: f1 |
|
value: 55.89805777266571 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 63.30503678551217 |
|
- type: f1 |
|
value: 63.57492701921179 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 37.51066495006874 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.07021517553794 |
|
- type: cos_sim_ap |
|
value: 74.15520712370555 |
|
- type: cos_sim_f1 |
|
value: 68.64321608040201 |
|
- type: cos_sim_precision |
|
value: 65.51558752997602 |
|
- type: cos_sim_recall |
|
value: 72.0844327176781 |
|
- type: dot_accuracy |
|
value: 80.23484532395541 |
|
- type: dot_ap |
|
value: 54.298763810214176 |
|
- type: dot_f1 |
|
value: 53.22254659779924 |
|
- type: dot_precision |
|
value: 46.32525410476936 |
|
- type: dot_recall |
|
value: 62.532981530343015 |
|
- type: euclidean_accuracy |
|
value: 86.04637301066937 |
|
- type: euclidean_ap |
|
value: 73.85333854233123 |
|
- type: euclidean_f1 |
|
value: 68.77723660599845 |
|
- type: euclidean_precision |
|
value: 66.87437686939182 |
|
- type: euclidean_recall |
|
value: 70.79155672823218 |
|
- type: manhattan_accuracy |
|
value: 85.98676759849795 |
|
- type: manhattan_ap |
|
value: 73.56016090035973 |
|
- type: manhattan_f1 |
|
value: 68.48878539036647 |
|
- type: manhattan_precision |
|
value: 63.9505607690547 |
|
- type: manhattan_recall |
|
value: 73.7203166226913 |
|
- type: max_accuracy |
|
value: 86.07021517553794 |
|
- type: max_ap |
|
value: 74.15520712370555 |
|
- type: max_f1 |
|
value: 68.77723660599845 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.92769821865176 |
|
- type: cos_sim_ap |
|
value: 85.78879502899773 |
|
- type: cos_sim_f1 |
|
value: 78.14414083990464 |
|
- type: cos_sim_precision |
|
value: 74.61651607480563 |
|
- type: cos_sim_recall |
|
value: 82.0218663381583 |
|
- type: dot_accuracy |
|
value: 84.95750378390964 |
|
- type: dot_ap |
|
value: 75.80219641857563 |
|
- type: dot_f1 |
|
value: 70.13966179585681 |
|
- type: dot_precision |
|
value: 65.71140262361251 |
|
- type: dot_recall |
|
value: 75.20788420080073 |
|
- type: euclidean_accuracy |
|
value: 88.93546008460433 |
|
- type: euclidean_ap |
|
value: 85.72056428301667 |
|
- type: euclidean_f1 |
|
value: 78.14387902598124 |
|
- type: euclidean_precision |
|
value: 75.3376688344172 |
|
- type: euclidean_recall |
|
value: 81.16723129042192 |
|
- type: manhattan_accuracy |
|
value: 88.96262661543835 |
|
- type: manhattan_ap |
|
value: 85.76605136314335 |
|
- type: manhattan_f1 |
|
value: 78.26696165191743 |
|
- type: manhattan_precision |
|
value: 75.0990659496179 |
|
- type: manhattan_recall |
|
value: 81.71388974437943 |
|
- type: max_accuracy |
|
value: 88.96262661543835 |
|
- type: max_ap |
|
value: 85.78879502899773 |
|
- type: max_f1 |
|
value: 78.26696165191743 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
|
|
# E5-small |
|
|
|
**News (May 2023): please switch to [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2), which has better performance and same method of usage.** |
|
|
|
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). |
|
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 |
|
|
|
This model has 12 layers and the embedding size is 384. |
|
|
|
## Usage |
|
|
|
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
|
|
|
```python |
|
import torch.nn.functional as F |
|
|
|
from torch import Tensor |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
|
|
def average_pool(last_hidden_states: Tensor, |
|
attention_mask: Tensor) -> Tensor: |
|
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
|
|
|
|
# Each input text should start with "query: " or "passage: ". |
|
# For tasks other than retrieval, you can simply use the "query: " prefix. |
|
input_texts = ['query: how much protein should a female eat', |
|
'query: summit define', |
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
|
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small') |
|
model = AutoModel.from_pretrained('intfloat/e5-small') |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
|
|
|
# normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
## Training Details |
|
|
|
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). |
|
|
|
## Benchmark Evaluation |
|
|
|
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
|
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
|
|
|
## Support for Sentence Transformers |
|
|
|
Below is an example for usage with sentence_transformers. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
model = SentenceTransformer('intfloat/e5-small') |
|
input_texts = [ |
|
'query: how much protein should a female eat', |
|
'query: summit define', |
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
|
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
|
] |
|
embeddings = model.encode(input_texts, normalize_embeddings=True) |
|
``` |
|
|
|
Package requirements |
|
|
|
`pip install sentence_transformers~=2.2.2` |
|
|
|
Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
|
|
|
## FAQ |
|
|
|
**1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
|
|
|
Yes, this is how the model is trained, otherwise you will see a performance degradation. |
|
|
|
Here are some rules of thumb: |
|
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
|
|
|
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. |
|
|
|
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
|
|
|
**2. Why are my reproduced results slightly different from reported in the model card?** |
|
|
|
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
|
|
|
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
|
|
|
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
|
|
|
For text embedding tasks like text retrieval or semantic similarity, |
|
what matters is the relative order of the scores instead of the absolute values, |
|
so this should not be an issue. |
|
|
|
## Citation |
|
|
|
If you find our paper or models helpful, please consider cite as follows: |
|
|
|
``` |
|
@article{wang2022text, |
|
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, |
|
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, |
|
journal={arXiv preprint arXiv:2212.03533}, |
|
year={2022} |
|
} |
|
``` |
|
|
|
## Limitations |
|
|
|
This model only works for English texts. Long texts will be truncated to at most 512 tokens. |
|
|
