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--- |
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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-large-v2 |
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results: |
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- task: |
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type: Classification |
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dataset: |
|
type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
|
- type: accuracy |
|
value: 79.22388059701493 |
|
- type: ap |
|
value: 43.20816505595132 |
|
- type: f1 |
|
value: 73.27811303522058 |
|
- 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 |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
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metrics: |
|
- type: accuracy |
|
value: 93.748325 |
|
- type: ap |
|
value: 90.72534979701297 |
|
- type: f1 |
|
value: 93.73895874282185 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
|
- type: accuracy |
|
value: 48.612 |
|
- type: f1 |
|
value: 47.61157345898393 |
|
- task: |
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type: Retrieval |
|
dataset: |
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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 |
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metrics: |
|
- type: map_at_1 |
|
value: 23.541999999999998 |
|
- type: map_at_10 |
|
value: 38.208 |
|
- type: map_at_100 |
|
value: 39.417 |
|
- type: map_at_1000 |
|
value: 39.428999999999995 |
|
- type: map_at_3 |
|
value: 33.95 |
|
- type: map_at_5 |
|
value: 36.329 |
|
- type: mrr_at_1 |
|
value: 23.755000000000003 |
|
- type: mrr_at_10 |
|
value: 38.288 |
|
- type: mrr_at_100 |
|
value: 39.511 |
|
- type: mrr_at_1000 |
|
value: 39.523 |
|
- type: mrr_at_3 |
|
value: 34.009 |
|
- type: mrr_at_5 |
|
value: 36.434 |
|
- type: ndcg_at_1 |
|
value: 23.541999999999998 |
|
- type: ndcg_at_10 |
|
value: 46.417 |
|
- type: ndcg_at_100 |
|
value: 51.812000000000005 |
|
- type: ndcg_at_1000 |
|
value: 52.137 |
|
- type: ndcg_at_3 |
|
value: 37.528 |
|
- type: ndcg_at_5 |
|
value: 41.81 |
|
- type: precision_at_1 |
|
value: 23.541999999999998 |
|
- type: precision_at_10 |
|
value: 7.269 |
|
- type: precision_at_100 |
|
value: 0.9690000000000001 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 15.979 |
|
- type: precision_at_5 |
|
value: 11.664 |
|
- type: recall_at_1 |
|
value: 23.541999999999998 |
|
- type: recall_at_10 |
|
value: 72.688 |
|
- type: recall_at_100 |
|
value: 96.871 |
|
- type: recall_at_1000 |
|
value: 99.431 |
|
- type: recall_at_3 |
|
value: 47.937000000000005 |
|
- type: recall_at_5 |
|
value: 58.321 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
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config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 45.546499570522094 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
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config: default |
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split: test |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
|
- type: v_measure |
|
value: 41.01607489943561 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
|
- type: map |
|
value: 59.616107510107774 |
|
- type: mrr |
|
value: 72.75106626214661 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
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metrics: |
|
- type: cos_sim_pearson |
|
value: 84.33018094733868 |
|
- type: cos_sim_spearman |
|
value: 83.60190492611737 |
|
- type: euclidean_pearson |
|
value: 82.1492450218961 |
|
- type: euclidean_spearman |
|
value: 82.70308926526991 |
|
- type: manhattan_pearson |
|
value: 81.93959600076842 |
|
- type: manhattan_spearman |
|
value: 82.73260801016369 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 84.54545454545455 |
|
- type: f1 |
|
value: 84.49582530928923 |
|
- 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: 37.362725540120096 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 34.849509608178145 |
|
- 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 |
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metrics: |
|
- type: map_at_1 |
|
value: 31.502999999999997 |
|
- type: map_at_10 |
|
value: 43.323 |
|
- type: map_at_100 |
|
value: 44.708999999999996 |
|
- type: map_at_1000 |
|
value: 44.838 |
|
- type: map_at_3 |
|
value: 38.987 |
|
- type: map_at_5 |
|
value: 41.516999999999996 |
|
- type: mrr_at_1 |
|
value: 38.769999999999996 |
|
- type: mrr_at_10 |
|
value: 49.13 |
|
- type: mrr_at_100 |
|
value: 49.697 |
|
- type: mrr_at_1000 |
|
value: 49.741 |
|
- type: mrr_at_3 |
|
value: 45.804 |
|
- type: mrr_at_5 |
|
value: 47.842 |
|
- type: ndcg_at_1 |
|
value: 38.769999999999996 |
|
- type: ndcg_at_10 |
|
value: 50.266999999999996 |
|
- type: ndcg_at_100 |
|
value: 54.967 |
|
- type: ndcg_at_1000 |
|
value: 56.976000000000006 |
|
- type: ndcg_at_3 |
|
value: 43.823 |
|
- type: ndcg_at_5 |
|
value: 47.12 |
|
- type: precision_at_1 |
|
value: 38.769999999999996 |
|
- type: precision_at_10 |
|
value: 10.057 |
|
- type: precision_at_100 |
|
value: 1.554 |
|
- type: precision_at_1000 |
|
value: 0.202 |
|
- type: precision_at_3 |
|
value: 21.125 |
|
- type: precision_at_5 |
|
value: 15.851 |
|
- type: recall_at_1 |
|
value: 31.502999999999997 |
|
- type: recall_at_10 |
|
value: 63.715999999999994 |
|
- type: recall_at_100 |
|
value: 83.61800000000001 |
|
- type: recall_at_1000 |
|
value: 96.63199999999999 |
|
- type: recall_at_3 |
|
value: 45.403 |
|
- type: recall_at_5 |
|
value: 54.481 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.833000000000002 |
|
- type: map_at_10 |
|
value: 37.330999999999996 |
|
- type: map_at_100 |
|
value: 38.580999999999996 |
|
- type: map_at_1000 |
|
value: 38.708 |
|
- type: map_at_3 |
|
value: 34.713 |
|
- type: map_at_5 |
|
value: 36.104 |
|
- type: mrr_at_1 |
|
value: 35.223 |
|
- type: mrr_at_10 |
|
value: 43.419000000000004 |
|
- type: mrr_at_100 |
|
value: 44.198 |
|
- type: mrr_at_1000 |
|
value: 44.249 |
|
- type: mrr_at_3 |
|
value: 41.614000000000004 |
|
- type: mrr_at_5 |
|
value: 42.553000000000004 |
|
- type: ndcg_at_1 |
|
value: 35.223 |
|
- type: ndcg_at_10 |
|
value: 42.687999999999995 |
|
- type: ndcg_at_100 |
|
value: 47.447 |
|
- type: ndcg_at_1000 |
|
value: 49.701 |
|
- type: ndcg_at_3 |
|
value: 39.162 |
|
- type: ndcg_at_5 |
|
value: 40.557 |
|
- type: precision_at_1 |
|
value: 35.223 |
|
- type: precision_at_10 |
|
value: 7.962 |
|
- type: precision_at_100 |
|
value: 1.304 |
|
- type: precision_at_1000 |
|
value: 0.18 |
|
- type: precision_at_3 |
|
value: 19.023 |
|
- type: precision_at_5 |
|
value: 13.184999999999999 |
|
- type: recall_at_1 |
|
value: 27.833000000000002 |
|
- type: recall_at_10 |
|
value: 51.881 |
|
- type: recall_at_100 |
|
value: 72.04 |
|
- type: recall_at_1000 |
|
value: 86.644 |
|
- type: recall_at_3 |
|
value: 40.778 |
|
- type: recall_at_5 |
|
value: 45.176 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.175 |
|
- type: map_at_10 |
|
value: 51.174 |
|
- type: map_at_100 |
|
value: 52.26499999999999 |
|
- type: map_at_1000 |
|
value: 52.315999999999995 |
|
- type: map_at_3 |
|
value: 47.897 |
|
- type: map_at_5 |
|
value: 49.703 |
|
- type: mrr_at_1 |
|
value: 43.448 |
|
- type: mrr_at_10 |
|
value: 54.505 |
|
- type: mrr_at_100 |
|
value: 55.216 |
|
- type: mrr_at_1000 |
|
value: 55.242000000000004 |
|
- type: mrr_at_3 |
|
value: 51.98500000000001 |
|
- type: mrr_at_5 |
|
value: 53.434000000000005 |
|
- type: ndcg_at_1 |
|
value: 43.448 |
|
- type: ndcg_at_10 |
|
value: 57.282 |
|
- type: ndcg_at_100 |
|
value: 61.537 |
|
- type: ndcg_at_1000 |
|
value: 62.546 |
|
- type: ndcg_at_3 |
|
value: 51.73799999999999 |
|
- type: ndcg_at_5 |
|
value: 54.324 |
|
- type: precision_at_1 |
|
value: 43.448 |
|
- type: precision_at_10 |
|
value: 9.292 |
|
- type: precision_at_100 |
|
value: 1.233 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 23.218 |
|
- type: precision_at_5 |
|
value: 15.887 |
|
- type: recall_at_1 |
|
value: 38.175 |
|
- type: recall_at_10 |
|
value: 72.00999999999999 |
|
- type: recall_at_100 |
|
value: 90.155 |
|
- type: recall_at_1000 |
|
value: 97.257 |
|
- type: recall_at_3 |
|
value: 57.133 |
|
- type: recall_at_5 |
|
value: 63.424 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.405 |
|
- type: map_at_10 |
|
value: 30.043 |
|
- type: map_at_100 |
|
value: 31.191000000000003 |
|
- type: map_at_1000 |
|
value: 31.275 |
|
- type: map_at_3 |
|
value: 27.034000000000002 |
|
- type: map_at_5 |
|
value: 28.688000000000002 |
|
- type: mrr_at_1 |
|
value: 24.068 |
|
- type: mrr_at_10 |
|
value: 31.993 |
|
- type: mrr_at_100 |
|
value: 32.992 |
|
- type: mrr_at_1000 |
|
value: 33.050000000000004 |
|
- type: mrr_at_3 |
|
value: 28.964000000000002 |
|
- type: mrr_at_5 |
|
value: 30.653000000000002 |
|
- type: ndcg_at_1 |
|
value: 24.068 |
|
- type: ndcg_at_10 |
|
value: 35.198 |
|
- type: ndcg_at_100 |
|
value: 40.709 |
|
- type: ndcg_at_1000 |
|
value: 42.855 |
|
- type: ndcg_at_3 |
|
value: 29.139 |
|
- type: ndcg_at_5 |
|
value: 32.045 |
|
- type: precision_at_1 |
|
value: 24.068 |
|
- type: precision_at_10 |
|
value: 5.65 |
|
- type: precision_at_100 |
|
value: 0.885 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 12.279 |
|
- type: precision_at_5 |
|
value: 8.994 |
|
- type: recall_at_1 |
|
value: 22.405 |
|
- type: recall_at_10 |
|
value: 49.391 |
|
- type: recall_at_100 |
|
value: 74.53699999999999 |
|
- type: recall_at_1000 |
|
value: 90.605 |
|
- type: recall_at_3 |
|
value: 33.126 |
|
- type: recall_at_5 |
|
value: 40.073 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.309999999999999 |
|
- type: map_at_10 |
|
value: 20.688000000000002 |
|
- type: map_at_100 |
|
value: 22.022 |
|
- type: map_at_1000 |
|
value: 22.152 |
|
- type: map_at_3 |
|
value: 17.954 |
|
- type: map_at_5 |
|
value: 19.439 |
|
- type: mrr_at_1 |
|
value: 16.294 |
|
- type: mrr_at_10 |
|
value: 24.479 |
|
- type: mrr_at_100 |
|
value: 25.515 |
|
- type: mrr_at_1000 |
|
value: 25.593 |
|
- type: mrr_at_3 |
|
value: 21.642 |
|
- type: mrr_at_5 |
|
value: 23.189999999999998 |
|
- type: ndcg_at_1 |
|
value: 16.294 |
|
- type: ndcg_at_10 |
|
value: 25.833000000000002 |
|
- type: ndcg_at_100 |
|
value: 32.074999999999996 |
|
- type: ndcg_at_1000 |
|
value: 35.083 |
|
- type: ndcg_at_3 |
|
value: 20.493 |
|
- type: ndcg_at_5 |
|
value: 22.949 |
|
- type: precision_at_1 |
|
value: 16.294 |
|
- type: precision_at_10 |
|
value: 5.112 |
|
- type: precision_at_100 |
|
value: 0.96 |
|
- type: precision_at_1000 |
|
value: 0.134 |
|
- type: precision_at_3 |
|
value: 9.908999999999999 |
|
- type: precision_at_5 |
|
value: 7.587000000000001 |
|
- type: recall_at_1 |
|
value: 13.309999999999999 |
|
- type: recall_at_10 |
|
value: 37.851 |
|
- type: recall_at_100 |
|
value: 64.835 |
|
- type: recall_at_1000 |
|
value: 86.334 |
|
- type: recall_at_3 |
|
value: 23.493 |
|
- type: recall_at_5 |
|
value: 29.528 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.857999999999997 |
|
- type: map_at_10 |
|
value: 35.503 |
|
- type: map_at_100 |
|
value: 36.957 |
|
- type: map_at_1000 |
|
value: 37.065 |
|
- type: map_at_3 |
|
value: 32.275999999999996 |
|
- type: map_at_5 |
|
value: 34.119 |
|
- type: mrr_at_1 |
|
value: 31.954 |
|
- type: mrr_at_10 |
|
value: 40.851 |
|
- type: mrr_at_100 |
|
value: 41.863 |
|
- type: mrr_at_1000 |
|
value: 41.900999999999996 |
|
- type: mrr_at_3 |
|
value: 38.129999999999995 |
|
- type: mrr_at_5 |
|
value: 39.737 |
|
- type: ndcg_at_1 |
|
value: 31.954 |
|
- type: ndcg_at_10 |
|
value: 41.343999999999994 |
|
- type: ndcg_at_100 |
|
value: 47.397 |
|
- type: ndcg_at_1000 |
|
value: 49.501 |
|
- type: ndcg_at_3 |
|
value: 36.047000000000004 |
|
- type: ndcg_at_5 |
|
value: 38.639 |
|
- type: precision_at_1 |
|
value: 31.954 |
|
- type: precision_at_10 |
|
value: 7.68 |
|
- type: precision_at_100 |
|
value: 1.247 |
|
- type: precision_at_1000 |
|
value: 0.16199999999999998 |
|
- type: precision_at_3 |
|
value: 17.132 |
|
- type: precision_at_5 |
|
value: 12.589 |
|
- type: recall_at_1 |
|
value: 25.857999999999997 |
|
- type: recall_at_10 |
|
value: 53.43599999999999 |
|
- type: recall_at_100 |
|
value: 78.82400000000001 |
|
- type: recall_at_1000 |
|
value: 92.78999999999999 |
|
- type: recall_at_3 |
|
value: 38.655 |
|
- type: recall_at_5 |
|
value: 45.216 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.709 |
|
- type: map_at_10 |
|
value: 34.318 |
|
- type: map_at_100 |
|
value: 35.657 |
|
- type: map_at_1000 |
|
value: 35.783 |
|
- type: map_at_3 |
|
value: 31.326999999999998 |
|
- type: map_at_5 |
|
value: 33.021 |
|
- type: mrr_at_1 |
|
value: 30.137000000000004 |
|
- type: mrr_at_10 |
|
value: 39.093 |
|
- type: mrr_at_100 |
|
value: 39.992 |
|
- type: mrr_at_1000 |
|
value: 40.056999999999995 |
|
- type: mrr_at_3 |
|
value: 36.606 |
|
- type: mrr_at_5 |
|
value: 37.861 |
|
- type: ndcg_at_1 |
|
value: 30.137000000000004 |
|
- type: ndcg_at_10 |
|
value: 39.974 |
|
- type: ndcg_at_100 |
|
value: 45.647999999999996 |
|
- type: ndcg_at_1000 |
|
value: 48.259 |
|
- type: ndcg_at_3 |
|
value: 35.028 |
|
- type: ndcg_at_5 |
|
value: 37.175999999999995 |
|
- type: precision_at_1 |
|
value: 30.137000000000004 |
|
- type: precision_at_10 |
|
value: 7.363 |
|
- type: precision_at_100 |
|
value: 1.184 |
|
- type: precision_at_1000 |
|
value: 0.161 |
|
- type: precision_at_3 |
|
value: 16.857 |
|
- type: precision_at_5 |
|
value: 11.963 |
|
- type: recall_at_1 |
|
value: 24.709 |
|
- type: recall_at_10 |
|
value: 52.087 |
|
- type: recall_at_100 |
|
value: 76.125 |
|
- type: recall_at_1000 |
|
value: 93.82300000000001 |
|
- type: recall_at_3 |
|
value: 38.149 |
|
- type: recall_at_5 |
|
value: 43.984 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.40791666666667 |
|
- type: map_at_10 |
|
value: 32.458083333333335 |
|
- type: map_at_100 |
|
value: 33.691916666666664 |
|
- type: map_at_1000 |
|
value: 33.81191666666666 |
|
- type: map_at_3 |
|
value: 29.51625 |
|
- type: map_at_5 |
|
value: 31.168083333333335 |
|
- type: mrr_at_1 |
|
value: 27.96591666666666 |
|
- type: mrr_at_10 |
|
value: 36.528583333333344 |
|
- type: mrr_at_100 |
|
value: 37.404 |
|
- type: mrr_at_1000 |
|
value: 37.464333333333336 |
|
- type: mrr_at_3 |
|
value: 33.92883333333333 |
|
- type: mrr_at_5 |
|
value: 35.41933333333333 |
|
- type: ndcg_at_1 |
|
value: 27.96591666666666 |
|
- type: ndcg_at_10 |
|
value: 37.89141666666666 |
|
- type: ndcg_at_100 |
|
value: 43.23066666666666 |
|
- type: ndcg_at_1000 |
|
value: 45.63258333333333 |
|
- type: ndcg_at_3 |
|
value: 32.811249999999994 |
|
- type: ndcg_at_5 |
|
value: 35.22566666666667 |
|
- type: precision_at_1 |
|
value: 27.96591666666666 |
|
- type: precision_at_10 |
|
value: 6.834083333333332 |
|
- type: precision_at_100 |
|
value: 1.12225 |
|
- type: precision_at_1000 |
|
value: 0.15241666666666667 |
|
- type: precision_at_3 |
|
value: 15.264333333333335 |
|
- type: precision_at_5 |
|
value: 11.039416666666666 |
|
- type: recall_at_1 |
|
value: 23.40791666666667 |
|
- type: recall_at_10 |
|
value: 49.927083333333336 |
|
- type: recall_at_100 |
|
value: 73.44641666666668 |
|
- type: recall_at_1000 |
|
value: 90.19950000000001 |
|
- type: recall_at_3 |
|
value: 35.88341666666667 |
|
- type: recall_at_5 |
|
value: 42.061249999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.592000000000002 |
|
- type: map_at_10 |
|
value: 26.895999999999997 |
|
- type: map_at_100 |
|
value: 27.921000000000003 |
|
- type: map_at_1000 |
|
value: 28.02 |
|
- type: map_at_3 |
|
value: 24.883 |
|
- type: map_at_5 |
|
value: 25.812 |
|
- type: mrr_at_1 |
|
value: 22.698999999999998 |
|
- type: mrr_at_10 |
|
value: 29.520999999999997 |
|
- type: mrr_at_100 |
|
value: 30.458000000000002 |
|
- type: mrr_at_1000 |
|
value: 30.526999999999997 |
|
- type: mrr_at_3 |
|
value: 27.633000000000003 |
|
- type: mrr_at_5 |
|
value: 28.483999999999998 |
|
- type: ndcg_at_1 |
|
value: 22.698999999999998 |
|
- type: ndcg_at_10 |
|
value: 31.061 |
|
- type: ndcg_at_100 |
|
value: 36.398 |
|
- type: ndcg_at_1000 |
|
value: 38.89 |
|
- type: ndcg_at_3 |
|
value: 27.149 |
|
- type: ndcg_at_5 |
|
value: 28.627000000000002 |
|
- type: precision_at_1 |
|
value: 22.698999999999998 |
|
- type: precision_at_10 |
|
value: 5.106999999999999 |
|
- type: precision_at_100 |
|
value: 0.857 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 11.963 |
|
- type: precision_at_5 |
|
value: 8.221 |
|
- type: recall_at_1 |
|
value: 19.592000000000002 |
|
- type: recall_at_10 |
|
value: 41.329 |
|
- type: recall_at_100 |
|
value: 66.094 |
|
- type: recall_at_1000 |
|
value: 84.511 |
|
- type: recall_at_3 |
|
value: 30.61 |
|
- type: recall_at_5 |
|
value: 34.213 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.71 |
|
- type: map_at_10 |
|
value: 20.965 |
|
- type: map_at_100 |
|
value: 21.994 |
|
- type: map_at_1000 |
|
value: 22.133 |
|
- type: map_at_3 |
|
value: 18.741 |
|
- type: map_at_5 |
|
value: 19.951 |
|
- type: mrr_at_1 |
|
value: 18.307000000000002 |
|
- type: mrr_at_10 |
|
value: 24.66 |
|
- type: mrr_at_100 |
|
value: 25.540000000000003 |
|
- type: mrr_at_1000 |
|
value: 25.629 |
|
- type: mrr_at_3 |
|
value: 22.511 |
|
- type: mrr_at_5 |
|
value: 23.72 |
|
- type: ndcg_at_1 |
|
value: 18.307000000000002 |
|
- type: ndcg_at_10 |
|
value: 25.153 |
|
- type: ndcg_at_100 |
|
value: 30.229 |
|
- type: ndcg_at_1000 |
|
value: 33.623 |
|
- type: ndcg_at_3 |
|
value: 21.203 |
|
- type: ndcg_at_5 |
|
value: 23.006999999999998 |
|
- type: precision_at_1 |
|
value: 18.307000000000002 |
|
- type: precision_at_10 |
|
value: 4.725 |
|
- type: precision_at_100 |
|
value: 0.8659999999999999 |
|
- type: precision_at_1000 |
|
value: 0.133 |
|
- type: precision_at_3 |
|
value: 10.14 |
|
- type: precision_at_5 |
|
value: 7.481 |
|
- type: recall_at_1 |
|
value: 14.71 |
|
- type: recall_at_10 |
|
value: 34.087 |
|
- type: recall_at_100 |
|
value: 57.147999999999996 |
|
- type: recall_at_1000 |
|
value: 81.777 |
|
- type: recall_at_3 |
|
value: 22.996 |
|
- type: recall_at_5 |
|
value: 27.73 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.472 |
|
- type: map_at_10 |
|
value: 32.699 |
|
- type: map_at_100 |
|
value: 33.867000000000004 |
|
- type: map_at_1000 |
|
value: 33.967000000000006 |
|
- type: map_at_3 |
|
value: 29.718 |
|
- type: map_at_5 |
|
value: 31.345 |
|
- type: mrr_at_1 |
|
value: 28.265 |
|
- type: mrr_at_10 |
|
value: 36.945 |
|
- type: mrr_at_100 |
|
value: 37.794 |
|
- type: mrr_at_1000 |
|
value: 37.857 |
|
- type: mrr_at_3 |
|
value: 34.266000000000005 |
|
- type: mrr_at_5 |
|
value: 35.768 |
|
- type: ndcg_at_1 |
|
value: 28.265 |
|
- type: ndcg_at_10 |
|
value: 38.35 |
|
- type: ndcg_at_100 |
|
value: 43.739 |
|
- type: ndcg_at_1000 |
|
value: 46.087 |
|
- type: ndcg_at_3 |
|
value: 33.004 |
|
- type: ndcg_at_5 |
|
value: 35.411 |
|
- type: precision_at_1 |
|
value: 28.265 |
|
- type: precision_at_10 |
|
value: 6.715999999999999 |
|
- type: precision_at_100 |
|
value: 1.059 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 15.299 |
|
- type: precision_at_5 |
|
value: 10.951 |
|
- type: recall_at_1 |
|
value: 23.472 |
|
- type: recall_at_10 |
|
value: 51.413 |
|
- type: recall_at_100 |
|
value: 75.17 |
|
- type: recall_at_1000 |
|
value: 91.577 |
|
- type: recall_at_3 |
|
value: 36.651 |
|
- type: recall_at_5 |
|
value: 42.814 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.666 |
|
- type: map_at_10 |
|
value: 32.963 |
|
- type: map_at_100 |
|
value: 34.544999999999995 |
|
- type: map_at_1000 |
|
value: 34.792 |
|
- type: map_at_3 |
|
value: 29.74 |
|
- type: map_at_5 |
|
value: 31.5 |
|
- type: mrr_at_1 |
|
value: 29.051 |
|
- type: mrr_at_10 |
|
value: 38.013000000000005 |
|
- type: mrr_at_100 |
|
value: 38.997 |
|
- type: mrr_at_1000 |
|
value: 39.055 |
|
- type: mrr_at_3 |
|
value: 34.947 |
|
- type: mrr_at_5 |
|
value: 36.815 |
|
- type: ndcg_at_1 |
|
value: 29.051 |
|
- type: ndcg_at_10 |
|
value: 39.361000000000004 |
|
- type: ndcg_at_100 |
|
value: 45.186 |
|
- type: ndcg_at_1000 |
|
value: 47.867 |
|
- type: ndcg_at_3 |
|
value: 33.797 |
|
- type: ndcg_at_5 |
|
value: 36.456 |
|
- type: precision_at_1 |
|
value: 29.051 |
|
- type: precision_at_10 |
|
value: 7.668 |
|
- type: precision_at_100 |
|
value: 1.532 |
|
- type: precision_at_1000 |
|
value: 0.247 |
|
- type: precision_at_3 |
|
value: 15.876000000000001 |
|
- type: precision_at_5 |
|
value: 11.779 |
|
- type: recall_at_1 |
|
value: 23.666 |
|
- type: recall_at_10 |
|
value: 51.858000000000004 |
|
- type: recall_at_100 |
|
value: 77.805 |
|
- type: recall_at_1000 |
|
value: 94.504 |
|
- type: recall_at_3 |
|
value: 36.207 |
|
- type: recall_at_5 |
|
value: 43.094 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.662 |
|
- type: map_at_10 |
|
value: 23.594 |
|
- type: map_at_100 |
|
value: 24.593999999999998 |
|
- type: map_at_1000 |
|
value: 24.694 |
|
- type: map_at_3 |
|
value: 20.925 |
|
- type: map_at_5 |
|
value: 22.817999999999998 |
|
- type: mrr_at_1 |
|
value: 17.375 |
|
- type: mrr_at_10 |
|
value: 25.734 |
|
- type: mrr_at_100 |
|
value: 26.586 |
|
- type: mrr_at_1000 |
|
value: 26.671 |
|
- type: mrr_at_3 |
|
value: 23.044 |
|
- type: mrr_at_5 |
|
value: 24.975 |
|
- type: ndcg_at_1 |
|
value: 17.375 |
|
- type: ndcg_at_10 |
|
value: 28.186 |
|
- type: ndcg_at_100 |
|
value: 33.436 |
|
- type: ndcg_at_1000 |
|
value: 36.203 |
|
- type: ndcg_at_3 |
|
value: 23.152 |
|
- type: ndcg_at_5 |
|
value: 26.397 |
|
- type: precision_at_1 |
|
value: 17.375 |
|
- type: precision_at_10 |
|
value: 4.677 |
|
- type: precision_at_100 |
|
value: 0.786 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_3 |
|
value: 10.351 |
|
- type: precision_at_5 |
|
value: 7.985 |
|
- type: recall_at_1 |
|
value: 15.662 |
|
- type: recall_at_10 |
|
value: 40.066 |
|
- type: recall_at_100 |
|
value: 65.006 |
|
- type: recall_at_1000 |
|
value: 85.94000000000001 |
|
- type: recall_at_3 |
|
value: 27.400000000000002 |
|
- type: recall_at_5 |
|
value: 35.002 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.853 |
|
- type: map_at_10 |
|
value: 15.568000000000001 |
|
- type: map_at_100 |
|
value: 17.383000000000003 |
|
- type: map_at_1000 |
|
value: 17.584 |
|
- type: map_at_3 |
|
value: 12.561 |
|
- type: map_at_5 |
|
value: 14.056 |
|
- type: mrr_at_1 |
|
value: 18.958 |
|
- type: mrr_at_10 |
|
value: 28.288000000000004 |
|
- type: mrr_at_100 |
|
value: 29.432000000000002 |
|
- type: mrr_at_1000 |
|
value: 29.498 |
|
- type: mrr_at_3 |
|
value: 25.049 |
|
- type: mrr_at_5 |
|
value: 26.857 |
|
- type: ndcg_at_1 |
|
value: 18.958 |
|
- type: ndcg_at_10 |
|
value: 22.21 |
|
- type: ndcg_at_100 |
|
value: 29.596 |
|
- type: ndcg_at_1000 |
|
value: 33.583 |
|
- type: ndcg_at_3 |
|
value: 16.994999999999997 |
|
- type: ndcg_at_5 |
|
value: 18.95 |
|
- type: precision_at_1 |
|
value: 18.958 |
|
- type: precision_at_10 |
|
value: 7.192 |
|
- type: precision_at_100 |
|
value: 1.5 |
|
- type: precision_at_1000 |
|
value: 0.22399999999999998 |
|
- type: precision_at_3 |
|
value: 12.573 |
|
- type: precision_at_5 |
|
value: 10.202 |
|
- type: recall_at_1 |
|
value: 8.853 |
|
- type: recall_at_10 |
|
value: 28.087 |
|
- type: recall_at_100 |
|
value: 53.701 |
|
- type: recall_at_1000 |
|
value: 76.29899999999999 |
|
- type: recall_at_3 |
|
value: 15.913 |
|
- type: recall_at_5 |
|
value: 20.658 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.077 |
|
- type: map_at_10 |
|
value: 20.788999999999998 |
|
- type: map_at_100 |
|
value: 30.429000000000002 |
|
- type: map_at_1000 |
|
value: 32.143 |
|
- type: map_at_3 |
|
value: 14.692 |
|
- type: map_at_5 |
|
value: 17.139 |
|
- type: mrr_at_1 |
|
value: 70.75 |
|
- type: mrr_at_10 |
|
value: 78.036 |
|
- type: mrr_at_100 |
|
value: 78.401 |
|
- type: mrr_at_1000 |
|
value: 78.404 |
|
- type: mrr_at_3 |
|
value: 76.75 |
|
- type: mrr_at_5 |
|
value: 77.47500000000001 |
|
- type: ndcg_at_1 |
|
value: 58.12500000000001 |
|
- type: ndcg_at_10 |
|
value: 44.015 |
|
- type: ndcg_at_100 |
|
value: 49.247 |
|
- type: ndcg_at_1000 |
|
value: 56.211999999999996 |
|
- type: ndcg_at_3 |
|
value: 49.151 |
|
- type: ndcg_at_5 |
|
value: 46.195 |
|
- type: precision_at_1 |
|
value: 70.75 |
|
- type: precision_at_10 |
|
value: 35.5 |
|
- type: precision_at_100 |
|
value: 11.355 |
|
- type: precision_at_1000 |
|
value: 2.1950000000000003 |
|
- type: precision_at_3 |
|
value: 53.083000000000006 |
|
- type: precision_at_5 |
|
value: 44.800000000000004 |
|
- type: recall_at_1 |
|
value: 9.077 |
|
- type: recall_at_10 |
|
value: 26.259 |
|
- type: recall_at_100 |
|
value: 56.547000000000004 |
|
- type: recall_at_1000 |
|
value: 78.551 |
|
- type: recall_at_3 |
|
value: 16.162000000000003 |
|
- type: recall_at_5 |
|
value: 19.753999999999998 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 49.44500000000001 |
|
- type: f1 |
|
value: 44.67067691783401 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 68.182 |
|
- type: map_at_10 |
|
value: 78.223 |
|
- type: map_at_100 |
|
value: 78.498 |
|
- type: map_at_1000 |
|
value: 78.512 |
|
- type: map_at_3 |
|
value: 76.71 |
|
- type: map_at_5 |
|
value: 77.725 |
|
- type: mrr_at_1 |
|
value: 73.177 |
|
- type: mrr_at_10 |
|
value: 82.513 |
|
- type: mrr_at_100 |
|
value: 82.633 |
|
- type: mrr_at_1000 |
|
value: 82.635 |
|
- type: mrr_at_3 |
|
value: 81.376 |
|
- type: mrr_at_5 |
|
value: 82.182 |
|
- type: ndcg_at_1 |
|
value: 73.177 |
|
- type: ndcg_at_10 |
|
value: 82.829 |
|
- type: ndcg_at_100 |
|
value: 83.84 |
|
- type: ndcg_at_1000 |
|
value: 84.07900000000001 |
|
- type: ndcg_at_3 |
|
value: 80.303 |
|
- type: ndcg_at_5 |
|
value: 81.846 |
|
- type: precision_at_1 |
|
value: 73.177 |
|
- type: precision_at_10 |
|
value: 10.241999999999999 |
|
- type: precision_at_100 |
|
value: 1.099 |
|
- type: precision_at_1000 |
|
value: 0.11399999999999999 |
|
- type: precision_at_3 |
|
value: 31.247999999999998 |
|
- type: precision_at_5 |
|
value: 19.697 |
|
- type: recall_at_1 |
|
value: 68.182 |
|
- type: recall_at_10 |
|
value: 92.657 |
|
- type: recall_at_100 |
|
value: 96.709 |
|
- type: recall_at_1000 |
|
value: 98.184 |
|
- type: recall_at_3 |
|
value: 85.9 |
|
- type: recall_at_5 |
|
value: 89.755 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.108 |
|
- type: map_at_10 |
|
value: 33.342 |
|
- type: map_at_100 |
|
value: 35.281 |
|
- type: map_at_1000 |
|
value: 35.478 |
|
- type: map_at_3 |
|
value: 29.067 |
|
- type: map_at_5 |
|
value: 31.563000000000002 |
|
- type: mrr_at_1 |
|
value: 41.667 |
|
- type: mrr_at_10 |
|
value: 49.913000000000004 |
|
- type: mrr_at_100 |
|
value: 50.724000000000004 |
|
- type: mrr_at_1000 |
|
value: 50.766 |
|
- type: mrr_at_3 |
|
value: 47.504999999999995 |
|
- type: mrr_at_5 |
|
value: 49.033 |
|
- type: ndcg_at_1 |
|
value: 41.667 |
|
- type: ndcg_at_10 |
|
value: 41.144 |
|
- type: ndcg_at_100 |
|
value: 48.326 |
|
- type: ndcg_at_1000 |
|
value: 51.486 |
|
- type: ndcg_at_3 |
|
value: 37.486999999999995 |
|
- type: ndcg_at_5 |
|
value: 38.78 |
|
- type: precision_at_1 |
|
value: 41.667 |
|
- type: precision_at_10 |
|
value: 11.358 |
|
- type: precision_at_100 |
|
value: 1.873 |
|
- type: precision_at_1000 |
|
value: 0.244 |
|
- type: precision_at_3 |
|
value: 25 |
|
- type: precision_at_5 |
|
value: 18.519 |
|
- type: recall_at_1 |
|
value: 21.108 |
|
- type: recall_at_10 |
|
value: 47.249 |
|
- type: recall_at_100 |
|
value: 74.52 |
|
- type: recall_at_1000 |
|
value: 93.31 |
|
- type: recall_at_3 |
|
value: 33.271 |
|
- type: recall_at_5 |
|
value: 39.723000000000006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.317 |
|
- type: map_at_10 |
|
value: 64.861 |
|
- type: map_at_100 |
|
value: 65.697 |
|
- type: map_at_1000 |
|
value: 65.755 |
|
- type: map_at_3 |
|
value: 61.258 |
|
- type: map_at_5 |
|
value: 63.590999999999994 |
|
- type: mrr_at_1 |
|
value: 80.635 |
|
- type: mrr_at_10 |
|
value: 86.528 |
|
- type: mrr_at_100 |
|
value: 86.66199999999999 |
|
- type: mrr_at_1000 |
|
value: 86.666 |
|
- type: mrr_at_3 |
|
value: 85.744 |
|
- type: mrr_at_5 |
|
value: 86.24300000000001 |
|
- type: ndcg_at_1 |
|
value: 80.635 |
|
- type: ndcg_at_10 |
|
value: 73.13199999999999 |
|
- type: ndcg_at_100 |
|
value: 75.927 |
|
- type: ndcg_at_1000 |
|
value: 76.976 |
|
- type: ndcg_at_3 |
|
value: 68.241 |
|
- type: ndcg_at_5 |
|
value: 71.071 |
|
- type: precision_at_1 |
|
value: 80.635 |
|
- type: precision_at_10 |
|
value: 15.326 |
|
- type: precision_at_100 |
|
value: 1.7500000000000002 |
|
- type: precision_at_1000 |
|
value: 0.189 |
|
- type: precision_at_3 |
|
value: 43.961 |
|
- type: precision_at_5 |
|
value: 28.599999999999998 |
|
- type: recall_at_1 |
|
value: 40.317 |
|
- type: recall_at_10 |
|
value: 76.631 |
|
- type: recall_at_100 |
|
value: 87.495 |
|
- type: recall_at_1000 |
|
value: 94.362 |
|
- type: recall_at_3 |
|
value: 65.94200000000001 |
|
- type: recall_at_5 |
|
value: 71.499 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 91.686 |
|
- type: ap |
|
value: 87.5577120393173 |
|
- type: f1 |
|
value: 91.6629447355139 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.702 |
|
- type: map_at_10 |
|
value: 36.414 |
|
- type: map_at_100 |
|
value: 37.561 |
|
- type: map_at_1000 |
|
value: 37.605 |
|
- type: map_at_3 |
|
value: 32.456 |
|
- type: map_at_5 |
|
value: 34.827000000000005 |
|
- type: mrr_at_1 |
|
value: 24.355 |
|
- type: mrr_at_10 |
|
value: 37.01 |
|
- type: mrr_at_100 |
|
value: 38.085 |
|
- type: mrr_at_1000 |
|
value: 38.123000000000005 |
|
- type: mrr_at_3 |
|
value: 33.117999999999995 |
|
- type: mrr_at_5 |
|
value: 35.452 |
|
- type: ndcg_at_1 |
|
value: 24.384 |
|
- type: ndcg_at_10 |
|
value: 43.456 |
|
- type: ndcg_at_100 |
|
value: 48.892 |
|
- type: ndcg_at_1000 |
|
value: 49.964 |
|
- type: ndcg_at_3 |
|
value: 35.475 |
|
- type: ndcg_at_5 |
|
value: 39.711 |
|
- type: precision_at_1 |
|
value: 24.384 |
|
- type: precision_at_10 |
|
value: 6.7940000000000005 |
|
- type: precision_at_100 |
|
value: 0.951 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 15.052999999999999 |
|
- type: precision_at_5 |
|
value: 11.189 |
|
- type: recall_at_1 |
|
value: 23.702 |
|
- type: recall_at_10 |
|
value: 65.057 |
|
- type: recall_at_100 |
|
value: 90.021 |
|
- type: recall_at_1000 |
|
value: 98.142 |
|
- type: recall_at_3 |
|
value: 43.551 |
|
- type: recall_at_5 |
|
value: 53.738 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 94.62380300957591 |
|
- type: f1 |
|
value: 94.49871222100734 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 77.14090287277702 |
|
- type: f1 |
|
value: 60.32101258220515 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.84330867518494 |
|
- type: f1 |
|
value: 71.92248688515255 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.10692669804976 |
|
- type: f1 |
|
value: 77.9904839122866 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.822988923078444 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 30.38394880253403 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.82504612539082 |
|
- type: mrr |
|
value: 32.84462298174977 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.029 |
|
- type: map_at_10 |
|
value: 14.088999999999999 |
|
- type: map_at_100 |
|
value: 17.601 |
|
- type: map_at_1000 |
|
value: 19.144 |
|
- type: map_at_3 |
|
value: 10.156 |
|
- type: map_at_5 |
|
value: 11.892 |
|
- type: mrr_at_1 |
|
value: 46.44 |
|
- type: mrr_at_10 |
|
value: 56.596999999999994 |
|
- type: mrr_at_100 |
|
value: 57.11000000000001 |
|
- type: mrr_at_1000 |
|
value: 57.14 |
|
- type: mrr_at_3 |
|
value: 54.334 |
|
- type: mrr_at_5 |
|
value: 55.774 |
|
- type: ndcg_at_1 |
|
value: 44.891999999999996 |
|
- type: ndcg_at_10 |
|
value: 37.134 |
|
- type: ndcg_at_100 |
|
value: 33.652 |
|
- type: ndcg_at_1000 |
|
value: 42.548 |
|
- type: ndcg_at_3 |
|
value: 41.851 |
|
- type: ndcg_at_5 |
|
value: 39.842 |
|
- type: precision_at_1 |
|
value: 46.44 |
|
- type: precision_at_10 |
|
value: 27.647 |
|
- type: precision_at_100 |
|
value: 8.309999999999999 |
|
- type: precision_at_1000 |
|
value: 2.146 |
|
- type: precision_at_3 |
|
value: 39.422000000000004 |
|
- type: precision_at_5 |
|
value: 34.675 |
|
- type: recall_at_1 |
|
value: 6.029 |
|
- type: recall_at_10 |
|
value: 18.907 |
|
- type: recall_at_100 |
|
value: 33.76 |
|
- type: recall_at_1000 |
|
value: 65.14999999999999 |
|
- type: recall_at_3 |
|
value: 11.584999999999999 |
|
- type: recall_at_5 |
|
value: 14.626 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.373000000000005 |
|
- type: map_at_10 |
|
value: 55.836 |
|
- type: map_at_100 |
|
value: 56.611999999999995 |
|
- type: map_at_1000 |
|
value: 56.63 |
|
- type: map_at_3 |
|
value: 51.747 |
|
- type: map_at_5 |
|
value: 54.337999999999994 |
|
- type: mrr_at_1 |
|
value: 44.147999999999996 |
|
- type: mrr_at_10 |
|
value: 58.42699999999999 |
|
- type: mrr_at_100 |
|
value: 58.902 |
|
- type: mrr_at_1000 |
|
value: 58.914 |
|
- type: mrr_at_3 |
|
value: 55.156000000000006 |
|
- type: mrr_at_5 |
|
value: 57.291000000000004 |
|
- type: ndcg_at_1 |
|
value: 44.119 |
|
- type: ndcg_at_10 |
|
value: 63.444 |
|
- type: ndcg_at_100 |
|
value: 66.40599999999999 |
|
- type: ndcg_at_1000 |
|
value: 66.822 |
|
- type: ndcg_at_3 |
|
value: 55.962 |
|
- type: ndcg_at_5 |
|
value: 60.228 |
|
- type: precision_at_1 |
|
value: 44.119 |
|
- type: precision_at_10 |
|
value: 10.006 |
|
- type: precision_at_100 |
|
value: 1.17 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 25.135 |
|
- type: precision_at_5 |
|
value: 17.59 |
|
- type: recall_at_1 |
|
value: 39.373000000000005 |
|
- type: recall_at_10 |
|
value: 83.78999999999999 |
|
- type: recall_at_100 |
|
value: 96.246 |
|
- type: recall_at_1000 |
|
value: 99.324 |
|
- type: recall_at_3 |
|
value: 64.71900000000001 |
|
- type: recall_at_5 |
|
value: 74.508 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 69.199 |
|
- type: map_at_10 |
|
value: 82.892 |
|
- type: map_at_100 |
|
value: 83.578 |
|
- type: map_at_1000 |
|
value: 83.598 |
|
- type: map_at_3 |
|
value: 79.948 |
|
- type: map_at_5 |
|
value: 81.779 |
|
- type: mrr_at_1 |
|
value: 79.67 |
|
- type: mrr_at_10 |
|
value: 86.115 |
|
- type: mrr_at_100 |
|
value: 86.249 |
|
- type: mrr_at_1000 |
|
value: 86.251 |
|
- type: mrr_at_3 |
|
value: 85.08200000000001 |
|
- type: mrr_at_5 |
|
value: 85.783 |
|
- type: ndcg_at_1 |
|
value: 79.67 |
|
- type: ndcg_at_10 |
|
value: 86.839 |
|
- type: ndcg_at_100 |
|
value: 88.252 |
|
- type: ndcg_at_1000 |
|
value: 88.401 |
|
- type: ndcg_at_3 |
|
value: 83.86200000000001 |
|
- type: ndcg_at_5 |
|
value: 85.473 |
|
- type: precision_at_1 |
|
value: 79.67 |
|
- type: precision_at_10 |
|
value: 13.19 |
|
- type: precision_at_100 |
|
value: 1.521 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 36.677 |
|
- type: precision_at_5 |
|
value: 24.118000000000002 |
|
- type: recall_at_1 |
|
value: 69.199 |
|
- type: recall_at_10 |
|
value: 94.321 |
|
- type: recall_at_100 |
|
value: 99.20400000000001 |
|
- type: recall_at_1000 |
|
value: 99.947 |
|
- type: recall_at_3 |
|
value: 85.787 |
|
- type: recall_at_5 |
|
value: 90.365 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 55.82810046856353 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 63.38132611783628 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.127000000000001 |
|
- type: map_at_10 |
|
value: 12.235 |
|
- type: map_at_100 |
|
value: 14.417 |
|
- type: map_at_1000 |
|
value: 14.75 |
|
- type: map_at_3 |
|
value: 8.906 |
|
- type: map_at_5 |
|
value: 10.591000000000001 |
|
- type: mrr_at_1 |
|
value: 25.2 |
|
- type: mrr_at_10 |
|
value: 35.879 |
|
- type: mrr_at_100 |
|
value: 36.935 |
|
- type: mrr_at_1000 |
|
value: 36.997 |
|
- type: mrr_at_3 |
|
value: 32.783 |
|
- type: mrr_at_5 |
|
value: 34.367999999999995 |
|
- type: ndcg_at_1 |
|
value: 25.2 |
|
- type: ndcg_at_10 |
|
value: 20.509 |
|
- type: ndcg_at_100 |
|
value: 28.67 |
|
- type: ndcg_at_1000 |
|
value: 34.42 |
|
- type: ndcg_at_3 |
|
value: 19.948 |
|
- type: ndcg_at_5 |
|
value: 17.166 |
|
- type: precision_at_1 |
|
value: 25.2 |
|
- type: precision_at_10 |
|
value: 10.440000000000001 |
|
- type: precision_at_100 |
|
value: 2.214 |
|
- type: precision_at_1000 |
|
value: 0.359 |
|
- type: precision_at_3 |
|
value: 18.533 |
|
- type: precision_at_5 |
|
value: 14.860000000000001 |
|
- type: recall_at_1 |
|
value: 5.127000000000001 |
|
- type: recall_at_10 |
|
value: 21.147 |
|
- type: recall_at_100 |
|
value: 44.946999999999996 |
|
- type: recall_at_1000 |
|
value: 72.89 |
|
- type: recall_at_3 |
|
value: 11.277 |
|
- type: recall_at_5 |
|
value: 15.042 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.0373011786213 |
|
- type: cos_sim_spearman |
|
value: 79.27889560856613 |
|
- type: euclidean_pearson |
|
value: 80.31186315495655 |
|
- type: euclidean_spearman |
|
value: 79.41630415280811 |
|
- type: manhattan_pearson |
|
value: 80.31755140442013 |
|
- type: manhattan_spearman |
|
value: 79.43069870027611 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.8659751342045 |
|
- type: cos_sim_spearman |
|
value: 76.95377612997667 |
|
- type: euclidean_pearson |
|
value: 81.24552945497848 |
|
- type: euclidean_spearman |
|
value: 77.18236963555253 |
|
- type: manhattan_pearson |
|
value: 81.26477607759037 |
|
- type: manhattan_spearman |
|
value: 77.13821753062756 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.34597139044875 |
|
- type: cos_sim_spearman |
|
value: 84.124169425592 |
|
- type: euclidean_pearson |
|
value: 83.68590721511401 |
|
- type: euclidean_spearman |
|
value: 84.18846190846398 |
|
- type: manhattan_pearson |
|
value: 83.57630235061498 |
|
- type: manhattan_spearman |
|
value: 84.10244043726902 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.67641885599572 |
|
- type: cos_sim_spearman |
|
value: 80.46450725650428 |
|
- type: euclidean_pearson |
|
value: 81.61645042715865 |
|
- type: euclidean_spearman |
|
value: 80.61418394236874 |
|
- type: manhattan_pearson |
|
value: 81.55712034928871 |
|
- type: manhattan_spearman |
|
value: 80.57905670523951 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.86650310886782 |
|
- type: cos_sim_spearman |
|
value: 89.76081629222328 |
|
- type: euclidean_pearson |
|
value: 89.1530747029954 |
|
- type: euclidean_spearman |
|
value: 89.80990657280248 |
|
- type: manhattan_pearson |
|
value: 89.10640563278132 |
|
- type: manhattan_spearman |
|
value: 89.76282108434047 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.93864027911118 |
|
- type: cos_sim_spearman |
|
value: 85.47096193999023 |
|
- type: euclidean_pearson |
|
value: 85.03141840870533 |
|
- type: euclidean_spearman |
|
value: 85.43124029598181 |
|
- type: manhattan_pearson |
|
value: 84.99002664393512 |
|
- type: manhattan_spearman |
|
value: 85.39169195120834 |
|
- 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: 88.7045343749832 |
|
- type: cos_sim_spearman |
|
value: 89.03262221146677 |
|
- type: euclidean_pearson |
|
value: 89.56078218264365 |
|
- type: euclidean_spearman |
|
value: 89.17827006466868 |
|
- type: manhattan_pearson |
|
value: 89.52717595468582 |
|
- type: manhattan_spearman |
|
value: 89.15878115952923 |
|
- 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: 64.20191302875551 |
|
- type: cos_sim_spearman |
|
value: 64.11446552557646 |
|
- type: euclidean_pearson |
|
value: 64.6918197393619 |
|
- type: euclidean_spearman |
|
value: 63.440182631197764 |
|
- type: manhattan_pearson |
|
value: 64.55692904121835 |
|
- type: manhattan_spearman |
|
value: 63.424877742756266 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.37793104662344 |
|
- type: cos_sim_spearman |
|
value: 87.7357802629067 |
|
- type: euclidean_pearson |
|
value: 87.4286301545109 |
|
- type: euclidean_spearman |
|
value: 87.78452920777421 |
|
- type: manhattan_pearson |
|
value: 87.42445169331255 |
|
- type: manhattan_spearman |
|
value: 87.78537677249598 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 84.31465405081792 |
|
- type: mrr |
|
value: 95.7173781193389 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 57.760999999999996 |
|
- type: map_at_10 |
|
value: 67.904 |
|
- type: map_at_100 |
|
value: 68.539 |
|
- type: map_at_1000 |
|
value: 68.562 |
|
- type: map_at_3 |
|
value: 65.415 |
|
- type: map_at_5 |
|
value: 66.788 |
|
- type: mrr_at_1 |
|
value: 60.333000000000006 |
|
- type: mrr_at_10 |
|
value: 68.797 |
|
- type: mrr_at_100 |
|
value: 69.236 |
|
- type: mrr_at_1000 |
|
value: 69.257 |
|
- type: mrr_at_3 |
|
value: 66.667 |
|
- type: mrr_at_5 |
|
value: 67.967 |
|
- type: ndcg_at_1 |
|
value: 60.333000000000006 |
|
- type: ndcg_at_10 |
|
value: 72.24199999999999 |
|
- type: ndcg_at_100 |
|
value: 74.86 |
|
- type: ndcg_at_1000 |
|
value: 75.354 |
|
- type: ndcg_at_3 |
|
value: 67.93400000000001 |
|
- type: ndcg_at_5 |
|
value: 70.02199999999999 |
|
- type: precision_at_1 |
|
value: 60.333000000000006 |
|
- type: precision_at_10 |
|
value: 9.533 |
|
- type: precision_at_100 |
|
value: 1.09 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 26.778000000000002 |
|
- type: precision_at_5 |
|
value: 17.467 |
|
- type: recall_at_1 |
|
value: 57.760999999999996 |
|
- type: recall_at_10 |
|
value: 84.383 |
|
- type: recall_at_100 |
|
value: 96.267 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 72.628 |
|
- type: recall_at_5 |
|
value: 78.094 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.8029702970297 |
|
- type: cos_sim_ap |
|
value: 94.9210324173411 |
|
- type: cos_sim_f1 |
|
value: 89.8521162672106 |
|
- type: cos_sim_precision |
|
value: 91.67533818938605 |
|
- type: cos_sim_recall |
|
value: 88.1 |
|
- type: dot_accuracy |
|
value: 99.69504950495049 |
|
- type: dot_ap |
|
value: 90.4919719146181 |
|
- type: dot_f1 |
|
value: 84.72289156626506 |
|
- type: dot_precision |
|
value: 81.76744186046511 |
|
- type: dot_recall |
|
value: 87.9 |
|
- type: euclidean_accuracy |
|
value: 99.79702970297029 |
|
- type: euclidean_ap |
|
value: 94.87827463795753 |
|
- type: euclidean_f1 |
|
value: 89.55680081507896 |
|
- type: euclidean_precision |
|
value: 91.27725856697819 |
|
- type: euclidean_recall |
|
value: 87.9 |
|
- type: manhattan_accuracy |
|
value: 99.7990099009901 |
|
- type: manhattan_ap |
|
value: 94.87587025149682 |
|
- type: manhattan_f1 |
|
value: 89.76298537569339 |
|
- type: manhattan_precision |
|
value: 90.53916581892166 |
|
- type: manhattan_recall |
|
value: 89 |
|
- type: max_accuracy |
|
value: 99.8029702970297 |
|
- type: max_ap |
|
value: 94.9210324173411 |
|
- type: max_f1 |
|
value: 89.8521162672106 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 65.92385753948724 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 33.671756975431144 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 50.677928036739004 |
|
- type: mrr |
|
value: 51.56413133435193 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.523589340819683 |
|
- type: cos_sim_spearman |
|
value: 30.187407518823235 |
|
- type: dot_pearson |
|
value: 29.039713969699015 |
|
- type: dot_spearman |
|
value: 29.114740651155508 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.211 |
|
- type: map_at_10 |
|
value: 1.6199999999999999 |
|
- type: map_at_100 |
|
value: 8.658000000000001 |
|
- type: map_at_1000 |
|
value: 21.538 |
|
- type: map_at_3 |
|
value: 0.575 |
|
- type: map_at_5 |
|
value: 0.919 |
|
- type: mrr_at_1 |
|
value: 78 |
|
- type: mrr_at_10 |
|
value: 86.18599999999999 |
|
- type: mrr_at_100 |
|
value: 86.18599999999999 |
|
- type: mrr_at_1000 |
|
value: 86.18599999999999 |
|
- type: mrr_at_3 |
|
value: 85 |
|
- type: mrr_at_5 |
|
value: 85.9 |
|
- type: ndcg_at_1 |
|
value: 74 |
|
- type: ndcg_at_10 |
|
value: 66.542 |
|
- type: ndcg_at_100 |
|
value: 50.163999999999994 |
|
- type: ndcg_at_1000 |
|
value: 45.696999999999996 |
|
- type: ndcg_at_3 |
|
value: 71.531 |
|
- type: ndcg_at_5 |
|
value: 70.45 |
|
- type: precision_at_1 |
|
value: 78 |
|
- type: precision_at_10 |
|
value: 69.39999999999999 |
|
- type: precision_at_100 |
|
value: 51.06 |
|
- type: precision_at_1000 |
|
value: 20.022000000000002 |
|
- type: precision_at_3 |
|
value: 76 |
|
- type: precision_at_5 |
|
value: 74.8 |
|
- type: recall_at_1 |
|
value: 0.211 |
|
- type: recall_at_10 |
|
value: 1.813 |
|
- type: recall_at_100 |
|
value: 12.098 |
|
- type: recall_at_1000 |
|
value: 42.618 |
|
- type: recall_at_3 |
|
value: 0.603 |
|
- type: recall_at_5 |
|
value: 0.987 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.2079999999999997 |
|
- type: map_at_10 |
|
value: 7.777000000000001 |
|
- type: map_at_100 |
|
value: 12.825000000000001 |
|
- type: map_at_1000 |
|
value: 14.196 |
|
- type: map_at_3 |
|
value: 4.285 |
|
- type: map_at_5 |
|
value: 6.177 |
|
- type: mrr_at_1 |
|
value: 30.612000000000002 |
|
- type: mrr_at_10 |
|
value: 42.635 |
|
- type: mrr_at_100 |
|
value: 43.955 |
|
- type: mrr_at_1000 |
|
value: 43.955 |
|
- type: mrr_at_3 |
|
value: 38.435 |
|
- type: mrr_at_5 |
|
value: 41.088 |
|
- type: ndcg_at_1 |
|
value: 28.571 |
|
- type: ndcg_at_10 |
|
value: 20.666999999999998 |
|
- type: ndcg_at_100 |
|
value: 31.840000000000003 |
|
- type: ndcg_at_1000 |
|
value: 43.191 |
|
- type: ndcg_at_3 |
|
value: 23.45 |
|
- type: ndcg_at_5 |
|
value: 22.994 |
|
- type: precision_at_1 |
|
value: 30.612000000000002 |
|
- type: precision_at_10 |
|
value: 17.959 |
|
- type: precision_at_100 |
|
value: 6.755 |
|
- type: precision_at_1000 |
|
value: 1.4200000000000002 |
|
- type: precision_at_3 |
|
value: 23.810000000000002 |
|
- type: precision_at_5 |
|
value: 23.673 |
|
- type: recall_at_1 |
|
value: 2.2079999999999997 |
|
- type: recall_at_10 |
|
value: 13.144 |
|
- type: recall_at_100 |
|
value: 42.491 |
|
- type: recall_at_1000 |
|
value: 77.04299999999999 |
|
- type: recall_at_3 |
|
value: 5.3469999999999995 |
|
- type: recall_at_5 |
|
value: 9.139 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 70.9044 |
|
- type: ap |
|
value: 14.625783489340755 |
|
- type: f1 |
|
value: 54.814936562590546 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 60.94227504244483 |
|
- type: f1 |
|
value: 61.22516038508854 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 49.602409155145864 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.94641473445789 |
|
- type: cos_sim_ap |
|
value: 76.91572747061197 |
|
- type: cos_sim_f1 |
|
value: 70.14348097317529 |
|
- type: cos_sim_precision |
|
value: 66.53254437869822 |
|
- type: cos_sim_recall |
|
value: 74.1688654353562 |
|
- type: dot_accuracy |
|
value: 84.80061989628658 |
|
- type: dot_ap |
|
value: 70.7952548895177 |
|
- type: dot_f1 |
|
value: 65.44780728844965 |
|
- type: dot_precision |
|
value: 61.53310104529617 |
|
- type: dot_recall |
|
value: 69.89445910290237 |
|
- type: euclidean_accuracy |
|
value: 86.94641473445789 |
|
- type: euclidean_ap |
|
value: 76.80774009393652 |
|
- type: euclidean_f1 |
|
value: 70.30522503879979 |
|
- type: euclidean_precision |
|
value: 68.94977168949772 |
|
- type: euclidean_recall |
|
value: 71.71503957783642 |
|
- type: manhattan_accuracy |
|
value: 86.8629671574179 |
|
- type: manhattan_ap |
|
value: 76.76518632600317 |
|
- type: manhattan_f1 |
|
value: 70.16056518946692 |
|
- type: manhattan_precision |
|
value: 68.360450563204 |
|
- type: manhattan_recall |
|
value: 72.0580474934037 |
|
- type: max_accuracy |
|
value: 86.94641473445789 |
|
- type: max_ap |
|
value: 76.91572747061197 |
|
- type: max_f1 |
|
value: 70.30522503879979 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 89.10428066907285 |
|
- type: cos_sim_ap |
|
value: 86.25114759921435 |
|
- type: cos_sim_f1 |
|
value: 78.37857884586856 |
|
- type: cos_sim_precision |
|
value: 75.60818546078993 |
|
- type: cos_sim_recall |
|
value: 81.35971666153372 |
|
- type: dot_accuracy |
|
value: 87.41995575736406 |
|
- type: dot_ap |
|
value: 81.51838010086782 |
|
- type: dot_f1 |
|
value: 74.77398015435503 |
|
- type: dot_precision |
|
value: 71.53002390662354 |
|
- type: dot_recall |
|
value: 78.32614721281182 |
|
- type: euclidean_accuracy |
|
value: 89.12368533395428 |
|
- type: euclidean_ap |
|
value: 86.33456799874504 |
|
- type: euclidean_f1 |
|
value: 78.45496750232127 |
|
- type: euclidean_precision |
|
value: 75.78388462366364 |
|
- type: euclidean_recall |
|
value: 81.32121958731136 |
|
- type: manhattan_accuracy |
|
value: 89.10622113556099 |
|
- type: manhattan_ap |
|
value: 86.31215061745333 |
|
- type: manhattan_f1 |
|
value: 78.40684906011539 |
|
- type: manhattan_precision |
|
value: 75.89536643366722 |
|
- type: manhattan_recall |
|
value: 81.09023714197721 |
|
- type: max_accuracy |
|
value: 89.12368533395428 |
|
- type: max_ap |
|
value: 86.33456799874504 |
|
- type: max_f1 |
|
value: 78.45496750232127 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
|
|
# E5-large-v2 |
|
|
|
[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 24 layers and the embedding size is 1024. |
|
|
|
## 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-large-v2') |
|
model = AutoModel.from_pretrained('intfloat/e5-large-v2') |
|
|
|
# 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-large-v2') |
|
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. |
|
|
