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--- |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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- mteb |
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model-index: |
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- name: bge-large-en-v1.5 |
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results: |
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- task: |
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type: Classification |
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dataset: |
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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: |
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- type: accuracy |
|
value: 75.8507462686567 |
|
- type: ap |
|
value: 38.566457320228245 |
|
- type: f1 |
|
value: 69.69386648043475 |
|
- 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: 92.416675 |
|
- type: ap |
|
value: 89.1928861155922 |
|
- type: f1 |
|
value: 92.39477019574215 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
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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: 48.175999999999995 |
|
- type: f1 |
|
value: 47.80712792870253 |
|
- task: |
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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 |
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metrics: |
|
- type: map_at_1 |
|
value: 40.184999999999995 |
|
- type: map_at_10 |
|
value: 55.654 |
|
- type: map_at_100 |
|
value: 56.25 |
|
- type: map_at_1000 |
|
value: 56.255 |
|
- type: map_at_3 |
|
value: 51.742999999999995 |
|
- type: map_at_5 |
|
value: 54.129000000000005 |
|
- type: mrr_at_1 |
|
value: 40.967 |
|
- type: mrr_at_10 |
|
value: 55.96 |
|
- type: mrr_at_100 |
|
value: 56.54900000000001 |
|
- type: mrr_at_1000 |
|
value: 56.554 |
|
- type: mrr_at_3 |
|
value: 51.980000000000004 |
|
- type: mrr_at_5 |
|
value: 54.44 |
|
- type: ndcg_at_1 |
|
value: 40.184999999999995 |
|
- type: ndcg_at_10 |
|
value: 63.542 |
|
- type: ndcg_at_100 |
|
value: 65.96499999999999 |
|
- type: ndcg_at_1000 |
|
value: 66.08699999999999 |
|
- type: ndcg_at_3 |
|
value: 55.582 |
|
- type: ndcg_at_5 |
|
value: 59.855000000000004 |
|
- type: precision_at_1 |
|
value: 40.184999999999995 |
|
- type: precision_at_10 |
|
value: 8.841000000000001 |
|
- type: precision_at_100 |
|
value: 0.987 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 22.238 |
|
- type: precision_at_5 |
|
value: 15.405 |
|
- type: recall_at_1 |
|
value: 40.184999999999995 |
|
- type: recall_at_10 |
|
value: 88.407 |
|
- type: recall_at_100 |
|
value: 98.72 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 66.714 |
|
- type: recall_at_5 |
|
value: 77.027 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
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name: MTEB ArxivClusteringP2P |
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config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
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metrics: |
|
- type: v_measure |
|
value: 48.567077926750066 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
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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: 43.19453389182364 |
|
- task: |
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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: |
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- type: map |
|
value: 64.46555939623092 |
|
- type: mrr |
|
value: 77.82361605768807 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
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name: MTEB BIOSSES |
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config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
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metrics: |
|
- type: cos_sim_pearson |
|
value: 84.9554128814735 |
|
- type: cos_sim_spearman |
|
value: 84.65373612172036 |
|
- type: euclidean_pearson |
|
value: 83.2905059954138 |
|
- type: euclidean_spearman |
|
value: 84.52240782811128 |
|
- type: manhattan_pearson |
|
value: 82.99533802997436 |
|
- type: manhattan_spearman |
|
value: 84.20673798475734 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
|
- type: accuracy |
|
value: 87.78896103896103 |
|
- type: f1 |
|
value: 87.77189310964883 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
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name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
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metrics: |
|
- type: v_measure |
|
value: 39.714538337650495 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
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config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 36.90108349284447 |
|
- 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: |
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- type: map_at_1 |
|
value: 32.795 |
|
- type: map_at_10 |
|
value: 43.669000000000004 |
|
- type: map_at_100 |
|
value: 45.151 |
|
- type: map_at_1000 |
|
value: 45.278 |
|
- type: map_at_3 |
|
value: 40.006 |
|
- type: map_at_5 |
|
value: 42.059999999999995 |
|
- type: mrr_at_1 |
|
value: 39.771 |
|
- type: mrr_at_10 |
|
value: 49.826 |
|
- type: mrr_at_100 |
|
value: 50.504000000000005 |
|
- type: mrr_at_1000 |
|
value: 50.549 |
|
- type: mrr_at_3 |
|
value: 47.115 |
|
- type: mrr_at_5 |
|
value: 48.832 |
|
- type: ndcg_at_1 |
|
value: 39.771 |
|
- type: ndcg_at_10 |
|
value: 50.217999999999996 |
|
- type: ndcg_at_100 |
|
value: 55.454 |
|
- type: ndcg_at_1000 |
|
value: 57.37 |
|
- type: ndcg_at_3 |
|
value: 44.885000000000005 |
|
- type: ndcg_at_5 |
|
value: 47.419 |
|
- type: precision_at_1 |
|
value: 39.771 |
|
- type: precision_at_10 |
|
value: 9.642000000000001 |
|
- type: precision_at_100 |
|
value: 1.538 |
|
- type: precision_at_1000 |
|
value: 0.198 |
|
- type: precision_at_3 |
|
value: 21.268 |
|
- type: precision_at_5 |
|
value: 15.536 |
|
- type: recall_at_1 |
|
value: 32.795 |
|
- type: recall_at_10 |
|
value: 62.580999999999996 |
|
- type: recall_at_100 |
|
value: 84.438 |
|
- type: recall_at_1000 |
|
value: 96.492 |
|
- type: recall_at_3 |
|
value: 47.071000000000005 |
|
- type: recall_at_5 |
|
value: 54.079 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackEnglishRetrieval |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.671 |
|
- type: map_at_10 |
|
value: 43.334 |
|
- type: map_at_100 |
|
value: 44.566 |
|
- type: map_at_1000 |
|
value: 44.702999999999996 |
|
- type: map_at_3 |
|
value: 40.343 |
|
- type: map_at_5 |
|
value: 41.983 |
|
- type: mrr_at_1 |
|
value: 40.764 |
|
- type: mrr_at_10 |
|
value: 49.382 |
|
- type: mrr_at_100 |
|
value: 49.988 |
|
- type: mrr_at_1000 |
|
value: 50.03300000000001 |
|
- type: mrr_at_3 |
|
value: 47.293 |
|
- type: mrr_at_5 |
|
value: 48.51 |
|
- type: ndcg_at_1 |
|
value: 40.764 |
|
- type: ndcg_at_10 |
|
value: 49.039 |
|
- type: ndcg_at_100 |
|
value: 53.259 |
|
- type: ndcg_at_1000 |
|
value: 55.253 |
|
- type: ndcg_at_3 |
|
value: 45.091 |
|
- type: ndcg_at_5 |
|
value: 46.839999999999996 |
|
- type: precision_at_1 |
|
value: 40.764 |
|
- type: precision_at_10 |
|
value: 9.191 |
|
- type: precision_at_100 |
|
value: 1.476 |
|
- type: precision_at_1000 |
|
value: 0.19499999999999998 |
|
- type: precision_at_3 |
|
value: 21.72 |
|
- type: precision_at_5 |
|
value: 15.299 |
|
- type: recall_at_1 |
|
value: 32.671 |
|
- type: recall_at_10 |
|
value: 58.816 |
|
- type: recall_at_100 |
|
value: 76.654 |
|
- type: recall_at_1000 |
|
value: 89.05999999999999 |
|
- type: recall_at_3 |
|
value: 46.743 |
|
- type: recall_at_5 |
|
value: 51.783 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.328 |
|
- type: map_at_10 |
|
value: 53.32599999999999 |
|
- type: map_at_100 |
|
value: 54.37499999999999 |
|
- type: map_at_1000 |
|
value: 54.429 |
|
- type: map_at_3 |
|
value: 49.902 |
|
- type: map_at_5 |
|
value: 52.002 |
|
- type: mrr_at_1 |
|
value: 46.332 |
|
- type: mrr_at_10 |
|
value: 56.858 |
|
- type: mrr_at_100 |
|
value: 57.522 |
|
- type: mrr_at_1000 |
|
value: 57.54899999999999 |
|
- type: mrr_at_3 |
|
value: 54.472 |
|
- type: mrr_at_5 |
|
value: 55.996 |
|
- type: ndcg_at_1 |
|
value: 46.332 |
|
- type: ndcg_at_10 |
|
value: 59.313 |
|
- type: ndcg_at_100 |
|
value: 63.266999999999996 |
|
- type: ndcg_at_1000 |
|
value: 64.36 |
|
- type: ndcg_at_3 |
|
value: 53.815000000000005 |
|
- type: ndcg_at_5 |
|
value: 56.814 |
|
- type: precision_at_1 |
|
value: 46.332 |
|
- type: precision_at_10 |
|
value: 9.53 |
|
- type: precision_at_100 |
|
value: 1.238 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 24.054000000000002 |
|
- type: precision_at_5 |
|
value: 16.589000000000002 |
|
- type: recall_at_1 |
|
value: 40.328 |
|
- type: recall_at_10 |
|
value: 73.421 |
|
- type: recall_at_100 |
|
value: 90.059 |
|
- type: recall_at_1000 |
|
value: 97.81 |
|
- type: recall_at_3 |
|
value: 59.009 |
|
- type: recall_at_5 |
|
value: 66.352 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.424 |
|
- type: map_at_10 |
|
value: 36.332 |
|
- type: map_at_100 |
|
value: 37.347 |
|
- type: map_at_1000 |
|
value: 37.422 |
|
- type: map_at_3 |
|
value: 33.743 |
|
- type: map_at_5 |
|
value: 35.176 |
|
- type: mrr_at_1 |
|
value: 29.153000000000002 |
|
- type: mrr_at_10 |
|
value: 38.233 |
|
- type: mrr_at_100 |
|
value: 39.109 |
|
- type: mrr_at_1000 |
|
value: 39.164 |
|
- type: mrr_at_3 |
|
value: 35.876000000000005 |
|
- type: mrr_at_5 |
|
value: 37.169000000000004 |
|
- type: ndcg_at_1 |
|
value: 29.153000000000002 |
|
- type: ndcg_at_10 |
|
value: 41.439 |
|
- type: ndcg_at_100 |
|
value: 46.42 |
|
- type: ndcg_at_1000 |
|
value: 48.242000000000004 |
|
- type: ndcg_at_3 |
|
value: 36.362 |
|
- type: ndcg_at_5 |
|
value: 38.743 |
|
- type: precision_at_1 |
|
value: 29.153000000000002 |
|
- type: precision_at_10 |
|
value: 6.315999999999999 |
|
- type: precision_at_100 |
|
value: 0.927 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 15.443000000000001 |
|
- type: precision_at_5 |
|
value: 10.644 |
|
- type: recall_at_1 |
|
value: 27.424 |
|
- type: recall_at_10 |
|
value: 55.364000000000004 |
|
- type: recall_at_100 |
|
value: 78.211 |
|
- type: recall_at_1000 |
|
value: 91.74600000000001 |
|
- type: recall_at_3 |
|
value: 41.379 |
|
- type: recall_at_5 |
|
value: 47.14 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.601 |
|
- type: map_at_10 |
|
value: 27.826 |
|
- type: map_at_100 |
|
value: 29.017 |
|
- type: map_at_1000 |
|
value: 29.137 |
|
- type: map_at_3 |
|
value: 25.125999999999998 |
|
- type: map_at_5 |
|
value: 26.765 |
|
- type: mrr_at_1 |
|
value: 24.005000000000003 |
|
- type: mrr_at_10 |
|
value: 32.716 |
|
- type: mrr_at_100 |
|
value: 33.631 |
|
- type: mrr_at_1000 |
|
value: 33.694 |
|
- type: mrr_at_3 |
|
value: 29.934 |
|
- type: mrr_at_5 |
|
value: 31.630999999999997 |
|
- type: ndcg_at_1 |
|
value: 24.005000000000003 |
|
- type: ndcg_at_10 |
|
value: 33.158 |
|
- type: ndcg_at_100 |
|
value: 38.739000000000004 |
|
- type: ndcg_at_1000 |
|
value: 41.495 |
|
- type: ndcg_at_3 |
|
value: 28.185 |
|
- type: ndcg_at_5 |
|
value: 30.796 |
|
- type: precision_at_1 |
|
value: 24.005000000000003 |
|
- type: precision_at_10 |
|
value: 5.908 |
|
- type: precision_at_100 |
|
value: 1.005 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 13.391 |
|
- type: precision_at_5 |
|
value: 9.876 |
|
- type: recall_at_1 |
|
value: 19.601 |
|
- type: recall_at_10 |
|
value: 44.746 |
|
- type: recall_at_100 |
|
value: 68.82300000000001 |
|
- type: recall_at_1000 |
|
value: 88.215 |
|
- type: recall_at_3 |
|
value: 31.239 |
|
- type: recall_at_5 |
|
value: 37.695 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.130000000000003 |
|
- type: map_at_10 |
|
value: 40.96 |
|
- type: map_at_100 |
|
value: 42.282 |
|
- type: map_at_1000 |
|
value: 42.392 |
|
- type: map_at_3 |
|
value: 37.889 |
|
- type: map_at_5 |
|
value: 39.661 |
|
- type: mrr_at_1 |
|
value: 36.958999999999996 |
|
- type: mrr_at_10 |
|
value: 46.835 |
|
- type: mrr_at_100 |
|
value: 47.644 |
|
- type: mrr_at_1000 |
|
value: 47.688 |
|
- type: mrr_at_3 |
|
value: 44.562000000000005 |
|
- type: mrr_at_5 |
|
value: 45.938 |
|
- type: ndcg_at_1 |
|
value: 36.958999999999996 |
|
- type: ndcg_at_10 |
|
value: 47.06 |
|
- type: ndcg_at_100 |
|
value: 52.345 |
|
- type: ndcg_at_1000 |
|
value: 54.35 |
|
- type: ndcg_at_3 |
|
value: 42.301 |
|
- type: ndcg_at_5 |
|
value: 44.635999999999996 |
|
- type: precision_at_1 |
|
value: 36.958999999999996 |
|
- type: precision_at_10 |
|
value: 8.479000000000001 |
|
- type: precision_at_100 |
|
value: 1.284 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 20.244 |
|
- type: precision_at_5 |
|
value: 14.224999999999998 |
|
- type: recall_at_1 |
|
value: 30.130000000000003 |
|
- type: recall_at_10 |
|
value: 59.27 |
|
- type: recall_at_100 |
|
value: 81.195 |
|
- type: recall_at_1000 |
|
value: 94.21199999999999 |
|
- type: recall_at_3 |
|
value: 45.885 |
|
- type: recall_at_5 |
|
value: 52.016 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.169999999999998 |
|
- type: map_at_10 |
|
value: 36.451 |
|
- type: map_at_100 |
|
value: 37.791000000000004 |
|
- type: map_at_1000 |
|
value: 37.897 |
|
- type: map_at_3 |
|
value: 33.109 |
|
- type: map_at_5 |
|
value: 34.937000000000005 |
|
- type: mrr_at_1 |
|
value: 32.877 |
|
- type: mrr_at_10 |
|
value: 42.368 |
|
- type: mrr_at_100 |
|
value: 43.201 |
|
- type: mrr_at_1000 |
|
value: 43.259 |
|
- type: mrr_at_3 |
|
value: 39.763999999999996 |
|
- type: mrr_at_5 |
|
value: 41.260000000000005 |
|
- type: ndcg_at_1 |
|
value: 32.877 |
|
- type: ndcg_at_10 |
|
value: 42.659000000000006 |
|
- type: ndcg_at_100 |
|
value: 48.161 |
|
- type: ndcg_at_1000 |
|
value: 50.345 |
|
- type: ndcg_at_3 |
|
value: 37.302 |
|
- type: ndcg_at_5 |
|
value: 39.722 |
|
- type: precision_at_1 |
|
value: 32.877 |
|
- type: precision_at_10 |
|
value: 7.9 |
|
- type: precision_at_100 |
|
value: 1.236 |
|
- type: precision_at_1000 |
|
value: 0.158 |
|
- type: precision_at_3 |
|
value: 17.846 |
|
- type: precision_at_5 |
|
value: 12.9 |
|
- type: recall_at_1 |
|
value: 26.169999999999998 |
|
- type: recall_at_10 |
|
value: 55.35 |
|
- type: recall_at_100 |
|
value: 78.755 |
|
- type: recall_at_1000 |
|
value: 93.518 |
|
- type: recall_at_3 |
|
value: 40.176 |
|
- type: recall_at_5 |
|
value: 46.589000000000006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.15516666666667 |
|
- type: map_at_10 |
|
value: 36.65741666666667 |
|
- type: map_at_100 |
|
value: 37.84991666666666 |
|
- type: map_at_1000 |
|
value: 37.96316666666667 |
|
- type: map_at_3 |
|
value: 33.74974999999999 |
|
- type: map_at_5 |
|
value: 35.3765 |
|
- type: mrr_at_1 |
|
value: 32.08233333333334 |
|
- type: mrr_at_10 |
|
value: 41.033833333333334 |
|
- type: mrr_at_100 |
|
value: 41.84524999999999 |
|
- type: mrr_at_1000 |
|
value: 41.89983333333333 |
|
- type: mrr_at_3 |
|
value: 38.62008333333333 |
|
- type: mrr_at_5 |
|
value: 40.03441666666666 |
|
- type: ndcg_at_1 |
|
value: 32.08233333333334 |
|
- type: ndcg_at_10 |
|
value: 42.229 |
|
- type: ndcg_at_100 |
|
value: 47.26716666666667 |
|
- type: ndcg_at_1000 |
|
value: 49.43466666666667 |
|
- type: ndcg_at_3 |
|
value: 37.36408333333333 |
|
- type: ndcg_at_5 |
|
value: 39.6715 |
|
- type: precision_at_1 |
|
value: 32.08233333333334 |
|
- type: precision_at_10 |
|
value: 7.382583333333334 |
|
- type: precision_at_100 |
|
value: 1.16625 |
|
- type: precision_at_1000 |
|
value: 0.15408333333333332 |
|
- type: precision_at_3 |
|
value: 17.218 |
|
- type: precision_at_5 |
|
value: 12.21875 |
|
- type: recall_at_1 |
|
value: 27.15516666666667 |
|
- type: recall_at_10 |
|
value: 54.36683333333333 |
|
- type: recall_at_100 |
|
value: 76.37183333333333 |
|
- type: recall_at_1000 |
|
value: 91.26183333333333 |
|
- type: recall_at_3 |
|
value: 40.769916666666674 |
|
- type: recall_at_5 |
|
value: 46.702333333333335 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.749 |
|
- type: map_at_10 |
|
value: 33.001999999999995 |
|
- type: map_at_100 |
|
value: 33.891 |
|
- type: map_at_1000 |
|
value: 33.993 |
|
- type: map_at_3 |
|
value: 30.703999999999997 |
|
- type: map_at_5 |
|
value: 31.959 |
|
- type: mrr_at_1 |
|
value: 28.834 |
|
- type: mrr_at_10 |
|
value: 35.955 |
|
- type: mrr_at_100 |
|
value: 36.709 |
|
- type: mrr_at_1000 |
|
value: 36.779 |
|
- type: mrr_at_3 |
|
value: 33.947 |
|
- type: mrr_at_5 |
|
value: 35.089 |
|
- type: ndcg_at_1 |
|
value: 28.834 |
|
- type: ndcg_at_10 |
|
value: 37.329 |
|
- type: ndcg_at_100 |
|
value: 41.79 |
|
- type: ndcg_at_1000 |
|
value: 44.169000000000004 |
|
- type: ndcg_at_3 |
|
value: 33.184999999999995 |
|
- type: ndcg_at_5 |
|
value: 35.107 |
|
- type: precision_at_1 |
|
value: 28.834 |
|
- type: precision_at_10 |
|
value: 5.7669999999999995 |
|
- type: precision_at_100 |
|
value: 0.876 |
|
- type: precision_at_1000 |
|
value: 0.11399999999999999 |
|
- type: precision_at_3 |
|
value: 14.213000000000001 |
|
- type: precision_at_5 |
|
value: 9.754999999999999 |
|
- type: recall_at_1 |
|
value: 25.749 |
|
- type: recall_at_10 |
|
value: 47.791 |
|
- type: recall_at_100 |
|
value: 68.255 |
|
- type: recall_at_1000 |
|
value: 85.749 |
|
- type: recall_at_3 |
|
value: 36.199 |
|
- type: recall_at_5 |
|
value: 41.071999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.777 |
|
- type: map_at_10 |
|
value: 25.201 |
|
- type: map_at_100 |
|
value: 26.423999999999996 |
|
- type: map_at_1000 |
|
value: 26.544 |
|
- type: map_at_3 |
|
value: 22.869 |
|
- type: map_at_5 |
|
value: 24.023 |
|
- type: mrr_at_1 |
|
value: 21.473 |
|
- type: mrr_at_10 |
|
value: 29.12 |
|
- type: mrr_at_100 |
|
value: 30.144 |
|
- type: mrr_at_1000 |
|
value: 30.215999999999998 |
|
- type: mrr_at_3 |
|
value: 26.933 |
|
- type: mrr_at_5 |
|
value: 28.051 |
|
- type: ndcg_at_1 |
|
value: 21.473 |
|
- type: ndcg_at_10 |
|
value: 30.003 |
|
- type: ndcg_at_100 |
|
value: 35.766 |
|
- type: ndcg_at_1000 |
|
value: 38.501000000000005 |
|
- type: ndcg_at_3 |
|
value: 25.773000000000003 |
|
- type: ndcg_at_5 |
|
value: 27.462999999999997 |
|
- type: precision_at_1 |
|
value: 21.473 |
|
- type: precision_at_10 |
|
value: 5.482 |
|
- type: precision_at_100 |
|
value: 0.975 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 12.205 |
|
- type: precision_at_5 |
|
value: 8.692 |
|
- type: recall_at_1 |
|
value: 17.777 |
|
- type: recall_at_10 |
|
value: 40.582 |
|
- type: recall_at_100 |
|
value: 66.305 |
|
- type: recall_at_1000 |
|
value: 85.636 |
|
- type: recall_at_3 |
|
value: 28.687 |
|
- type: recall_at_5 |
|
value: 33.089 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.677 |
|
- type: map_at_10 |
|
value: 36.309000000000005 |
|
- type: map_at_100 |
|
value: 37.403999999999996 |
|
- type: map_at_1000 |
|
value: 37.496 |
|
- type: map_at_3 |
|
value: 33.382 |
|
- type: map_at_5 |
|
value: 34.98 |
|
- type: mrr_at_1 |
|
value: 31.343 |
|
- type: mrr_at_10 |
|
value: 40.549 |
|
- type: mrr_at_100 |
|
value: 41.342 |
|
- type: mrr_at_1000 |
|
value: 41.397 |
|
- type: mrr_at_3 |
|
value: 38.029 |
|
- type: mrr_at_5 |
|
value: 39.451 |
|
- type: ndcg_at_1 |
|
value: 31.343 |
|
- type: ndcg_at_10 |
|
value: 42.1 |
|
- type: ndcg_at_100 |
|
value: 47.089999999999996 |
|
- type: ndcg_at_1000 |
|
value: 49.222 |
|
- type: ndcg_at_3 |
|
value: 36.836999999999996 |
|
- type: ndcg_at_5 |
|
value: 39.21 |
|
- type: precision_at_1 |
|
value: 31.343 |
|
- type: precision_at_10 |
|
value: 7.164 |
|
- type: precision_at_100 |
|
value: 1.0959999999999999 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 16.915 |
|
- type: precision_at_5 |
|
value: 11.940000000000001 |
|
- type: recall_at_1 |
|
value: 26.677 |
|
- type: recall_at_10 |
|
value: 55.54599999999999 |
|
- type: recall_at_100 |
|
value: 77.094 |
|
- type: recall_at_1000 |
|
value: 92.01 |
|
- type: recall_at_3 |
|
value: 41.191 |
|
- type: recall_at_5 |
|
value: 47.006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.501 |
|
- type: map_at_10 |
|
value: 33.102 |
|
- type: map_at_100 |
|
value: 34.676 |
|
- type: map_at_1000 |
|
value: 34.888000000000005 |
|
- type: map_at_3 |
|
value: 29.944 |
|
- type: map_at_5 |
|
value: 31.613999999999997 |
|
- type: mrr_at_1 |
|
value: 29.447000000000003 |
|
- type: mrr_at_10 |
|
value: 37.996 |
|
- type: mrr_at_100 |
|
value: 38.946 |
|
- type: mrr_at_1000 |
|
value: 38.995000000000005 |
|
- type: mrr_at_3 |
|
value: 35.079 |
|
- type: mrr_at_5 |
|
value: 36.69 |
|
- type: ndcg_at_1 |
|
value: 29.447000000000003 |
|
- type: ndcg_at_10 |
|
value: 39.232 |
|
- type: ndcg_at_100 |
|
value: 45.247 |
|
- type: ndcg_at_1000 |
|
value: 47.613 |
|
- type: ndcg_at_3 |
|
value: 33.922999999999995 |
|
- type: ndcg_at_5 |
|
value: 36.284 |
|
- type: precision_at_1 |
|
value: 29.447000000000003 |
|
- type: precision_at_10 |
|
value: 7.648000000000001 |
|
- type: precision_at_100 |
|
value: 1.516 |
|
- type: precision_at_1000 |
|
value: 0.23900000000000002 |
|
- type: precision_at_3 |
|
value: 16.008 |
|
- type: precision_at_5 |
|
value: 11.779 |
|
- type: recall_at_1 |
|
value: 24.501 |
|
- type: recall_at_10 |
|
value: 51.18899999999999 |
|
- type: recall_at_100 |
|
value: 78.437 |
|
- type: recall_at_1000 |
|
value: 92.842 |
|
- type: recall_at_3 |
|
value: 35.808 |
|
- type: recall_at_5 |
|
value: 42.197 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.039 |
|
- type: map_at_10 |
|
value: 30.377 |
|
- type: map_at_100 |
|
value: 31.275 |
|
- type: map_at_1000 |
|
value: 31.379 |
|
- type: map_at_3 |
|
value: 27.98 |
|
- type: map_at_5 |
|
value: 29.358 |
|
- type: mrr_at_1 |
|
value: 24.03 |
|
- type: mrr_at_10 |
|
value: 32.568000000000005 |
|
- type: mrr_at_100 |
|
value: 33.403 |
|
- type: mrr_at_1000 |
|
value: 33.475 |
|
- type: mrr_at_3 |
|
value: 30.436999999999998 |
|
- type: mrr_at_5 |
|
value: 31.796000000000003 |
|
- type: ndcg_at_1 |
|
value: 24.03 |
|
- type: ndcg_at_10 |
|
value: 35.198 |
|
- type: ndcg_at_100 |
|
value: 39.668 |
|
- type: ndcg_at_1000 |
|
value: 42.296 |
|
- type: ndcg_at_3 |
|
value: 30.709999999999997 |
|
- type: ndcg_at_5 |
|
value: 33.024 |
|
- type: precision_at_1 |
|
value: 24.03 |
|
- type: precision_at_10 |
|
value: 5.564 |
|
- type: precision_at_100 |
|
value: 0.828 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 13.309000000000001 |
|
- type: precision_at_5 |
|
value: 9.39 |
|
- type: recall_at_1 |
|
value: 22.039 |
|
- type: recall_at_10 |
|
value: 47.746 |
|
- type: recall_at_100 |
|
value: 68.23599999999999 |
|
- type: recall_at_1000 |
|
value: 87.852 |
|
- type: recall_at_3 |
|
value: 35.852000000000004 |
|
- type: recall_at_5 |
|
value: 41.410000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.692999999999998 |
|
- type: map_at_10 |
|
value: 26.903 |
|
- type: map_at_100 |
|
value: 28.987000000000002 |
|
- type: map_at_1000 |
|
value: 29.176999999999996 |
|
- type: map_at_3 |
|
value: 22.137 |
|
- type: map_at_5 |
|
value: 24.758 |
|
- type: mrr_at_1 |
|
value: 35.57 |
|
- type: mrr_at_10 |
|
value: 47.821999999999996 |
|
- type: mrr_at_100 |
|
value: 48.608000000000004 |
|
- type: mrr_at_1000 |
|
value: 48.638999999999996 |
|
- type: mrr_at_3 |
|
value: 44.452000000000005 |
|
- type: mrr_at_5 |
|
value: 46.546 |
|
- type: ndcg_at_1 |
|
value: 35.57 |
|
- type: ndcg_at_10 |
|
value: 36.567 |
|
- type: ndcg_at_100 |
|
value: 44.085 |
|
- type: ndcg_at_1000 |
|
value: 47.24 |
|
- type: ndcg_at_3 |
|
value: 29.964000000000002 |
|
- type: ndcg_at_5 |
|
value: 32.511 |
|
- type: precision_at_1 |
|
value: 35.57 |
|
- type: precision_at_10 |
|
value: 11.485 |
|
- type: precision_at_100 |
|
value: 1.9619999999999997 |
|
- type: precision_at_1000 |
|
value: 0.256 |
|
- type: precision_at_3 |
|
value: 22.237000000000002 |
|
- type: precision_at_5 |
|
value: 17.471999999999998 |
|
- type: recall_at_1 |
|
value: 15.692999999999998 |
|
- type: recall_at_10 |
|
value: 43.056 |
|
- type: recall_at_100 |
|
value: 68.628 |
|
- type: recall_at_1000 |
|
value: 86.075 |
|
- type: recall_at_3 |
|
value: 26.918999999999997 |
|
- type: recall_at_5 |
|
value: 34.14 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.53 |
|
- type: map_at_10 |
|
value: 20.951 |
|
- type: map_at_100 |
|
value: 30.136000000000003 |
|
- type: map_at_1000 |
|
value: 31.801000000000002 |
|
- type: map_at_3 |
|
value: 15.021 |
|
- type: map_at_5 |
|
value: 17.471999999999998 |
|
- type: mrr_at_1 |
|
value: 71.0 |
|
- type: mrr_at_10 |
|
value: 79.176 |
|
- type: mrr_at_100 |
|
value: 79.418 |
|
- type: mrr_at_1000 |
|
value: 79.426 |
|
- type: mrr_at_3 |
|
value: 78.125 |
|
- type: mrr_at_5 |
|
value: 78.61200000000001 |
|
- type: ndcg_at_1 |
|
value: 58.5 |
|
- type: ndcg_at_10 |
|
value: 44.106 |
|
- type: ndcg_at_100 |
|
value: 49.268 |
|
- type: ndcg_at_1000 |
|
value: 56.711999999999996 |
|
- type: ndcg_at_3 |
|
value: 48.934 |
|
- type: ndcg_at_5 |
|
value: 45.826 |
|
- type: precision_at_1 |
|
value: 71.0 |
|
- type: precision_at_10 |
|
value: 35.0 |
|
- type: precision_at_100 |
|
value: 11.360000000000001 |
|
- type: precision_at_1000 |
|
value: 2.046 |
|
- type: precision_at_3 |
|
value: 52.833 |
|
- type: precision_at_5 |
|
value: 44.15 |
|
- type: recall_at_1 |
|
value: 9.53 |
|
- type: recall_at_10 |
|
value: 26.811 |
|
- type: recall_at_100 |
|
value: 55.916999999999994 |
|
- type: recall_at_1000 |
|
value: 79.973 |
|
- type: recall_at_3 |
|
value: 16.413 |
|
- type: recall_at_5 |
|
value: 19.980999999999998 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 51.519999999999996 |
|
- type: f1 |
|
value: 46.36601294761231 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 74.413 |
|
- type: map_at_10 |
|
value: 83.414 |
|
- type: map_at_100 |
|
value: 83.621 |
|
- type: map_at_1000 |
|
value: 83.635 |
|
- type: map_at_3 |
|
value: 82.337 |
|
- type: map_at_5 |
|
value: 83.039 |
|
- type: mrr_at_1 |
|
value: 80.19800000000001 |
|
- type: mrr_at_10 |
|
value: 87.715 |
|
- type: mrr_at_100 |
|
value: 87.778 |
|
- type: mrr_at_1000 |
|
value: 87.779 |
|
- type: mrr_at_3 |
|
value: 87.106 |
|
- type: mrr_at_5 |
|
value: 87.555 |
|
- type: ndcg_at_1 |
|
value: 80.19800000000001 |
|
- type: ndcg_at_10 |
|
value: 87.182 |
|
- type: ndcg_at_100 |
|
value: 87.90299999999999 |
|
- type: ndcg_at_1000 |
|
value: 88.143 |
|
- type: ndcg_at_3 |
|
value: 85.60600000000001 |
|
- type: ndcg_at_5 |
|
value: 86.541 |
|
- type: precision_at_1 |
|
value: 80.19800000000001 |
|
- type: precision_at_10 |
|
value: 10.531 |
|
- type: precision_at_100 |
|
value: 1.113 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 32.933 |
|
- type: precision_at_5 |
|
value: 20.429 |
|
- type: recall_at_1 |
|
value: 74.413 |
|
- type: recall_at_10 |
|
value: 94.363 |
|
- type: recall_at_100 |
|
value: 97.165 |
|
- type: recall_at_1000 |
|
value: 98.668 |
|
- type: recall_at_3 |
|
value: 90.108 |
|
- type: recall_at_5 |
|
value: 92.52 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.701 |
|
- type: map_at_10 |
|
value: 37.122 |
|
- type: map_at_100 |
|
value: 39.178000000000004 |
|
- type: map_at_1000 |
|
value: 39.326 |
|
- type: map_at_3 |
|
value: 32.971000000000004 |
|
- type: map_at_5 |
|
value: 35.332 |
|
- type: mrr_at_1 |
|
value: 44.753 |
|
- type: mrr_at_10 |
|
value: 53.452 |
|
- type: mrr_at_100 |
|
value: 54.198 |
|
- type: mrr_at_1000 |
|
value: 54.225 |
|
- type: mrr_at_3 |
|
value: 50.952 |
|
- type: mrr_at_5 |
|
value: 52.464 |
|
- type: ndcg_at_1 |
|
value: 44.753 |
|
- type: ndcg_at_10 |
|
value: 45.021 |
|
- type: ndcg_at_100 |
|
value: 52.028 |
|
- type: ndcg_at_1000 |
|
value: 54.596000000000004 |
|
- type: ndcg_at_3 |
|
value: 41.622 |
|
- type: ndcg_at_5 |
|
value: 42.736000000000004 |
|
- type: precision_at_1 |
|
value: 44.753 |
|
- type: precision_at_10 |
|
value: 12.284 |
|
- type: precision_at_100 |
|
value: 1.955 |
|
- type: precision_at_1000 |
|
value: 0.243 |
|
- type: precision_at_3 |
|
value: 27.828999999999997 |
|
- type: precision_at_5 |
|
value: 20.061999999999998 |
|
- type: recall_at_1 |
|
value: 22.701 |
|
- type: recall_at_10 |
|
value: 51.432 |
|
- type: recall_at_100 |
|
value: 77.009 |
|
- type: recall_at_1000 |
|
value: 92.511 |
|
- type: recall_at_3 |
|
value: 37.919000000000004 |
|
- type: recall_at_5 |
|
value: 44.131 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.189 |
|
- type: map_at_10 |
|
value: 66.24600000000001 |
|
- type: map_at_100 |
|
value: 67.098 |
|
- type: map_at_1000 |
|
value: 67.149 |
|
- type: map_at_3 |
|
value: 62.684 |
|
- type: map_at_5 |
|
value: 64.974 |
|
- type: mrr_at_1 |
|
value: 80.378 |
|
- type: mrr_at_10 |
|
value: 86.127 |
|
- type: mrr_at_100 |
|
value: 86.29299999999999 |
|
- type: mrr_at_1000 |
|
value: 86.297 |
|
- type: mrr_at_3 |
|
value: 85.31400000000001 |
|
- type: mrr_at_5 |
|
value: 85.858 |
|
- type: ndcg_at_1 |
|
value: 80.378 |
|
- type: ndcg_at_10 |
|
value: 74.101 |
|
- type: ndcg_at_100 |
|
value: 76.993 |
|
- type: ndcg_at_1000 |
|
value: 77.948 |
|
- type: ndcg_at_3 |
|
value: 69.232 |
|
- type: ndcg_at_5 |
|
value: 72.04599999999999 |
|
- type: precision_at_1 |
|
value: 80.378 |
|
- type: precision_at_10 |
|
value: 15.595999999999998 |
|
- type: precision_at_100 |
|
value: 1.7840000000000003 |
|
- type: precision_at_1000 |
|
value: 0.191 |
|
- type: precision_at_3 |
|
value: 44.884 |
|
- type: precision_at_5 |
|
value: 29.145 |
|
- type: recall_at_1 |
|
value: 40.189 |
|
- type: recall_at_10 |
|
value: 77.981 |
|
- type: recall_at_100 |
|
value: 89.21 |
|
- type: recall_at_1000 |
|
value: 95.48299999999999 |
|
- type: recall_at_3 |
|
value: 67.326 |
|
- type: recall_at_5 |
|
value: 72.863 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 92.84599999999999 |
|
- type: ap |
|
value: 89.4710787567357 |
|
- type: f1 |
|
value: 92.83752676932258 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.132 |
|
- type: map_at_10 |
|
value: 35.543 |
|
- type: map_at_100 |
|
value: 36.702 |
|
- type: map_at_1000 |
|
value: 36.748999999999995 |
|
- type: map_at_3 |
|
value: 31.737 |
|
- type: map_at_5 |
|
value: 33.927 |
|
- type: mrr_at_1 |
|
value: 23.782 |
|
- type: mrr_at_10 |
|
value: 36.204 |
|
- type: mrr_at_100 |
|
value: 37.29 |
|
- type: mrr_at_1000 |
|
value: 37.330999999999996 |
|
- type: mrr_at_3 |
|
value: 32.458999999999996 |
|
- type: mrr_at_5 |
|
value: 34.631 |
|
- type: ndcg_at_1 |
|
value: 23.782 |
|
- type: ndcg_at_10 |
|
value: 42.492999999999995 |
|
- type: ndcg_at_100 |
|
value: 47.985 |
|
- type: ndcg_at_1000 |
|
value: 49.141 |
|
- type: ndcg_at_3 |
|
value: 34.748000000000005 |
|
- type: ndcg_at_5 |
|
value: 38.651 |
|
- type: precision_at_1 |
|
value: 23.782 |
|
- type: precision_at_10 |
|
value: 6.665 |
|
- type: precision_at_100 |
|
value: 0.941 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.776 |
|
- type: precision_at_5 |
|
value: 10.84 |
|
- type: recall_at_1 |
|
value: 23.132 |
|
- type: recall_at_10 |
|
value: 63.794 |
|
- type: recall_at_100 |
|
value: 89.027 |
|
- type: recall_at_1000 |
|
value: 97.807 |
|
- type: recall_at_3 |
|
value: 42.765 |
|
- type: recall_at_5 |
|
value: 52.11 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 94.59188326493388 |
|
- type: f1 |
|
value: 94.3842594786827 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 79.49384404924761 |
|
- type: f1 |
|
value: 59.7580539534629 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 77.56220578345663 |
|
- type: f1 |
|
value: 75.27228165561478 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 80.53463349024884 |
|
- type: f1 |
|
value: 80.4893958236536 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 32.56100273484962 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.470380028839607 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 32.06102792457849 |
|
- type: mrr |
|
value: 33.30709199672238 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.776999999999999 |
|
- type: map_at_10 |
|
value: 14.924000000000001 |
|
- type: map_at_100 |
|
value: 18.955 |
|
- type: map_at_1000 |
|
value: 20.538999999999998 |
|
- type: map_at_3 |
|
value: 10.982 |
|
- type: map_at_5 |
|
value: 12.679000000000002 |
|
- type: mrr_at_1 |
|
value: 47.988 |
|
- type: mrr_at_10 |
|
value: 57.232000000000006 |
|
- type: mrr_at_100 |
|
value: 57.818999999999996 |
|
- type: mrr_at_1000 |
|
value: 57.847 |
|
- type: mrr_at_3 |
|
value: 54.901999999999994 |
|
- type: mrr_at_5 |
|
value: 56.481 |
|
- type: ndcg_at_1 |
|
value: 46.594 |
|
- type: ndcg_at_10 |
|
value: 38.129000000000005 |
|
- type: ndcg_at_100 |
|
value: 35.54 |
|
- type: ndcg_at_1000 |
|
value: 44.172 |
|
- type: ndcg_at_3 |
|
value: 43.025999999999996 |
|
- type: ndcg_at_5 |
|
value: 41.052 |
|
- type: precision_at_1 |
|
value: 47.988 |
|
- type: precision_at_10 |
|
value: 28.111000000000004 |
|
- type: precision_at_100 |
|
value: 8.929 |
|
- type: precision_at_1000 |
|
value: 2.185 |
|
- type: precision_at_3 |
|
value: 40.144000000000005 |
|
- type: precision_at_5 |
|
value: 35.232 |
|
- type: recall_at_1 |
|
value: 6.776999999999999 |
|
- type: recall_at_10 |
|
value: 19.289 |
|
- type: recall_at_100 |
|
value: 36.359 |
|
- type: recall_at_1000 |
|
value: 67.54 |
|
- type: recall_at_3 |
|
value: 11.869 |
|
- type: recall_at_5 |
|
value: 14.999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.108000000000004 |
|
- type: map_at_10 |
|
value: 47.126000000000005 |
|
- type: map_at_100 |
|
value: 48.171 |
|
- type: map_at_1000 |
|
value: 48.199 |
|
- type: map_at_3 |
|
value: 42.734 |
|
- type: map_at_5 |
|
value: 45.362 |
|
- type: mrr_at_1 |
|
value: 34.936 |
|
- type: mrr_at_10 |
|
value: 49.571 |
|
- type: mrr_at_100 |
|
value: 50.345 |
|
- type: mrr_at_1000 |
|
value: 50.363 |
|
- type: mrr_at_3 |
|
value: 45.959 |
|
- type: mrr_at_5 |
|
value: 48.165 |
|
- type: ndcg_at_1 |
|
value: 34.936 |
|
- type: ndcg_at_10 |
|
value: 55.028999999999996 |
|
- type: ndcg_at_100 |
|
value: 59.244 |
|
- type: ndcg_at_1000 |
|
value: 59.861 |
|
- type: ndcg_at_3 |
|
value: 46.872 |
|
- type: ndcg_at_5 |
|
value: 51.217999999999996 |
|
- type: precision_at_1 |
|
value: 34.936 |
|
- type: precision_at_10 |
|
value: 9.099 |
|
- type: precision_at_100 |
|
value: 1.145 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 21.456 |
|
- type: precision_at_5 |
|
value: 15.411 |
|
- type: recall_at_1 |
|
value: 31.108000000000004 |
|
- type: recall_at_10 |
|
value: 76.53999999999999 |
|
- type: recall_at_100 |
|
value: 94.39 |
|
- type: recall_at_1000 |
|
value: 98.947 |
|
- type: recall_at_3 |
|
value: 55.572 |
|
- type: recall_at_5 |
|
value: 65.525 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.56400000000001 |
|
- type: map_at_10 |
|
value: 85.482 |
|
- type: map_at_100 |
|
value: 86.114 |
|
- type: map_at_1000 |
|
value: 86.13 |
|
- type: map_at_3 |
|
value: 82.607 |
|
- type: map_at_5 |
|
value: 84.405 |
|
- type: mrr_at_1 |
|
value: 82.42 |
|
- type: mrr_at_10 |
|
value: 88.304 |
|
- type: mrr_at_100 |
|
value: 88.399 |
|
- type: mrr_at_1000 |
|
value: 88.399 |
|
- type: mrr_at_3 |
|
value: 87.37 |
|
- type: mrr_at_5 |
|
value: 88.024 |
|
- type: ndcg_at_1 |
|
value: 82.45 |
|
- type: ndcg_at_10 |
|
value: 89.06500000000001 |
|
- type: ndcg_at_100 |
|
value: 90.232 |
|
- type: ndcg_at_1000 |
|
value: 90.305 |
|
- type: ndcg_at_3 |
|
value: 86.375 |
|
- type: ndcg_at_5 |
|
value: 87.85300000000001 |
|
- type: precision_at_1 |
|
value: 82.45 |
|
- type: precision_at_10 |
|
value: 13.486999999999998 |
|
- type: precision_at_100 |
|
value: 1.534 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.813 |
|
- type: precision_at_5 |
|
value: 24.773999999999997 |
|
- type: recall_at_1 |
|
value: 71.56400000000001 |
|
- type: recall_at_10 |
|
value: 95.812 |
|
- type: recall_at_100 |
|
value: 99.7 |
|
- type: recall_at_1000 |
|
value: 99.979 |
|
- type: recall_at_3 |
|
value: 87.966 |
|
- type: recall_at_5 |
|
value: 92.268 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 57.241876648614145 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 64.66212576446223 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.308 |
|
- type: map_at_10 |
|
value: 13.803 |
|
- type: map_at_100 |
|
value: 16.176 |
|
- type: map_at_1000 |
|
value: 16.561 |
|
- type: map_at_3 |
|
value: 9.761000000000001 |
|
- type: map_at_5 |
|
value: 11.802 |
|
- type: mrr_at_1 |
|
value: 26.200000000000003 |
|
- type: mrr_at_10 |
|
value: 37.621 |
|
- type: mrr_at_100 |
|
value: 38.767 |
|
- type: mrr_at_1000 |
|
value: 38.815 |
|
- type: mrr_at_3 |
|
value: 34.117 |
|
- type: mrr_at_5 |
|
value: 36.107 |
|
- type: ndcg_at_1 |
|
value: 26.200000000000003 |
|
- type: ndcg_at_10 |
|
value: 22.64 |
|
- type: ndcg_at_100 |
|
value: 31.567 |
|
- type: ndcg_at_1000 |
|
value: 37.623 |
|
- type: ndcg_at_3 |
|
value: 21.435000000000002 |
|
- type: ndcg_at_5 |
|
value: 18.87 |
|
- type: precision_at_1 |
|
value: 26.200000000000003 |
|
- type: precision_at_10 |
|
value: 11.74 |
|
- type: precision_at_100 |
|
value: 2.465 |
|
- type: precision_at_1000 |
|
value: 0.391 |
|
- type: precision_at_3 |
|
value: 20.033 |
|
- type: precision_at_5 |
|
value: 16.64 |
|
- type: recall_at_1 |
|
value: 5.308 |
|
- type: recall_at_10 |
|
value: 23.794999999999998 |
|
- type: recall_at_100 |
|
value: 50.015 |
|
- type: recall_at_1000 |
|
value: 79.283 |
|
- type: recall_at_3 |
|
value: 12.178 |
|
- type: recall_at_5 |
|
value: 16.882 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.93231134675553 |
|
- type: cos_sim_spearman |
|
value: 81.68319292603205 |
|
- type: euclidean_pearson |
|
value: 81.8396814380367 |
|
- type: euclidean_spearman |
|
value: 81.24641903349945 |
|
- type: manhattan_pearson |
|
value: 81.84698799204274 |
|
- type: manhattan_spearman |
|
value: 81.24269997904105 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.73241671587446 |
|
- type: cos_sim_spearman |
|
value: 79.05091082971826 |
|
- type: euclidean_pearson |
|
value: 83.91146869578044 |
|
- type: euclidean_spearman |
|
value: 79.87978465370936 |
|
- type: manhattan_pearson |
|
value: 83.90888338917678 |
|
- type: manhattan_spearman |
|
value: 79.87482848584241 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.14970731146177 |
|
- type: cos_sim_spearman |
|
value: 86.37363490084627 |
|
- type: euclidean_pearson |
|
value: 83.02154218530433 |
|
- type: euclidean_spearman |
|
value: 83.80258761957367 |
|
- type: manhattan_pearson |
|
value: 83.01664495119347 |
|
- type: manhattan_spearman |
|
value: 83.77567458007952 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.40474139886784 |
|
- type: cos_sim_spearman |
|
value: 82.77768789165984 |
|
- type: euclidean_pearson |
|
value: 80.7065877443695 |
|
- type: euclidean_spearman |
|
value: 81.375940662505 |
|
- type: manhattan_pearson |
|
value: 80.6507552270278 |
|
- type: manhattan_spearman |
|
value: 81.32782179098741 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.08585968722274 |
|
- type: cos_sim_spearman |
|
value: 88.03110031451399 |
|
- type: euclidean_pearson |
|
value: 85.74012019602384 |
|
- type: euclidean_spearman |
|
value: 86.13592849438209 |
|
- type: manhattan_pearson |
|
value: 85.74404842369206 |
|
- type: manhattan_spearman |
|
value: 86.14492318960154 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.95069052788875 |
|
- type: cos_sim_spearman |
|
value: 86.4867991595147 |
|
- type: euclidean_pearson |
|
value: 84.31013325754635 |
|
- type: euclidean_spearman |
|
value: 85.01529258006482 |
|
- type: manhattan_pearson |
|
value: 84.26995570085374 |
|
- type: manhattan_spearman |
|
value: 84.96982104986162 |
|
- 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: 87.54617647971897 |
|
- type: cos_sim_spearman |
|
value: 87.49834181751034 |
|
- type: euclidean_pearson |
|
value: 86.01015322577122 |
|
- type: euclidean_spearman |
|
value: 84.63362652063199 |
|
- type: manhattan_pearson |
|
value: 86.13807574475706 |
|
- type: manhattan_spearman |
|
value: 84.7772370721132 |
|
- 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: 67.20047755786615 |
|
- type: cos_sim_spearman |
|
value: 67.05324077987636 |
|
- type: euclidean_pearson |
|
value: 66.91930642976601 |
|
- type: euclidean_spearman |
|
value: 65.21491856099105 |
|
- type: manhattan_pearson |
|
value: 66.78756851976624 |
|
- type: manhattan_spearman |
|
value: 65.12356257740728 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.19852871539686 |
|
- type: cos_sim_spearman |
|
value: 87.5161895296395 |
|
- type: euclidean_pearson |
|
value: 84.59848645207485 |
|
- type: euclidean_spearman |
|
value: 85.26427328757919 |
|
- type: manhattan_pearson |
|
value: 84.59747366996524 |
|
- type: manhattan_spearman |
|
value: 85.24045855146915 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 87.63320317811032 |
|
- type: mrr |
|
value: 96.26242947321379 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 60.928000000000004 |
|
- type: map_at_10 |
|
value: 70.112 |
|
- type: map_at_100 |
|
value: 70.59299999999999 |
|
- type: map_at_1000 |
|
value: 70.623 |
|
- type: map_at_3 |
|
value: 66.846 |
|
- type: map_at_5 |
|
value: 68.447 |
|
- type: mrr_at_1 |
|
value: 64.0 |
|
- type: mrr_at_10 |
|
value: 71.212 |
|
- type: mrr_at_100 |
|
value: 71.616 |
|
- type: mrr_at_1000 |
|
value: 71.64500000000001 |
|
- type: mrr_at_3 |
|
value: 68.77799999999999 |
|
- type: mrr_at_5 |
|
value: 70.094 |
|
- type: ndcg_at_1 |
|
value: 64.0 |
|
- type: ndcg_at_10 |
|
value: 74.607 |
|
- type: ndcg_at_100 |
|
value: 76.416 |
|
- type: ndcg_at_1000 |
|
value: 77.102 |
|
- type: ndcg_at_3 |
|
value: 69.126 |
|
- type: ndcg_at_5 |
|
value: 71.41300000000001 |
|
- type: precision_at_1 |
|
value: 64.0 |
|
- type: precision_at_10 |
|
value: 9.933 |
|
- type: precision_at_100 |
|
value: 1.077 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 26.556 |
|
- type: precision_at_5 |
|
value: 17.467 |
|
- type: recall_at_1 |
|
value: 60.928000000000004 |
|
- type: recall_at_10 |
|
value: 87.322 |
|
- type: recall_at_100 |
|
value: 94.833 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 72.628 |
|
- type: recall_at_5 |
|
value: 78.428 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.86237623762376 |
|
- type: cos_sim_ap |
|
value: 96.72586477206649 |
|
- type: cos_sim_f1 |
|
value: 93.01858362631845 |
|
- type: cos_sim_precision |
|
value: 93.4409687184662 |
|
- type: cos_sim_recall |
|
value: 92.60000000000001 |
|
- type: dot_accuracy |
|
value: 99.78019801980199 |
|
- type: dot_ap |
|
value: 93.72748205246228 |
|
- type: dot_f1 |
|
value: 89.04109589041096 |
|
- type: dot_precision |
|
value: 87.16475095785441 |
|
- type: dot_recall |
|
value: 91.0 |
|
- type: euclidean_accuracy |
|
value: 99.85445544554456 |
|
- type: euclidean_ap |
|
value: 96.6661459876145 |
|
- type: euclidean_f1 |
|
value: 92.58337481333997 |
|
- type: euclidean_precision |
|
value: 92.17046580773042 |
|
- type: euclidean_recall |
|
value: 93.0 |
|
- type: manhattan_accuracy |
|
value: 99.85445544554456 |
|
- type: manhattan_ap |
|
value: 96.6883549244056 |
|
- type: manhattan_f1 |
|
value: 92.57598405580468 |
|
- type: manhattan_precision |
|
value: 92.25422045680239 |
|
- type: manhattan_recall |
|
value: 92.9 |
|
- type: max_accuracy |
|
value: 99.86237623762376 |
|
- type: max_ap |
|
value: 96.72586477206649 |
|
- type: max_f1 |
|
value: 93.01858362631845 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 66.39930057069995 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 34.96398659903402 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 55.946944700355395 |
|
- type: mrr |
|
value: 56.97151398438164 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.541657650692905 |
|
- type: cos_sim_spearman |
|
value: 31.605804192286303 |
|
- type: dot_pearson |
|
value: 28.26905996736398 |
|
- type: dot_spearman |
|
value: 27.864801765851187 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.22599999999999998 |
|
- type: map_at_10 |
|
value: 1.8870000000000002 |
|
- type: map_at_100 |
|
value: 9.78 |
|
- type: map_at_1000 |
|
value: 22.514 |
|
- type: map_at_3 |
|
value: 0.6669999999999999 |
|
- type: map_at_5 |
|
value: 1.077 |
|
- type: mrr_at_1 |
|
value: 82.0 |
|
- type: mrr_at_10 |
|
value: 89.86699999999999 |
|
- type: mrr_at_100 |
|
value: 89.86699999999999 |
|
- type: mrr_at_1000 |
|
value: 89.86699999999999 |
|
- type: mrr_at_3 |
|
value: 89.667 |
|
- type: mrr_at_5 |
|
value: 89.667 |
|
- type: ndcg_at_1 |
|
value: 79.0 |
|
- type: ndcg_at_10 |
|
value: 74.818 |
|
- type: ndcg_at_100 |
|
value: 53.715999999999994 |
|
- type: ndcg_at_1000 |
|
value: 47.082 |
|
- type: ndcg_at_3 |
|
value: 82.134 |
|
- type: ndcg_at_5 |
|
value: 79.81899999999999 |
|
- type: precision_at_1 |
|
value: 82.0 |
|
- type: precision_at_10 |
|
value: 78.0 |
|
- type: precision_at_100 |
|
value: 54.48 |
|
- type: precision_at_1000 |
|
value: 20.518 |
|
- type: precision_at_3 |
|
value: 87.333 |
|
- type: precision_at_5 |
|
value: 85.2 |
|
- type: recall_at_1 |
|
value: 0.22599999999999998 |
|
- type: recall_at_10 |
|
value: 2.072 |
|
- type: recall_at_100 |
|
value: 13.013 |
|
- type: recall_at_1000 |
|
value: 43.462 |
|
- type: recall_at_3 |
|
value: 0.695 |
|
- type: recall_at_5 |
|
value: 1.139 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.328 |
|
- type: map_at_10 |
|
value: 9.795 |
|
- type: map_at_100 |
|
value: 15.801000000000002 |
|
- type: map_at_1000 |
|
value: 17.23 |
|
- type: map_at_3 |
|
value: 4.734 |
|
- type: map_at_5 |
|
value: 6.644 |
|
- type: mrr_at_1 |
|
value: 30.612000000000002 |
|
- type: mrr_at_10 |
|
value: 46.902 |
|
- type: mrr_at_100 |
|
value: 47.495 |
|
- type: mrr_at_1000 |
|
value: 47.495 |
|
- type: mrr_at_3 |
|
value: 41.156 |
|
- type: mrr_at_5 |
|
value: 44.218 |
|
- type: ndcg_at_1 |
|
value: 28.571 |
|
- type: ndcg_at_10 |
|
value: 24.806 |
|
- type: ndcg_at_100 |
|
value: 36.419000000000004 |
|
- type: ndcg_at_1000 |
|
value: 47.272999999999996 |
|
- type: ndcg_at_3 |
|
value: 25.666 |
|
- type: ndcg_at_5 |
|
value: 25.448999999999998 |
|
- type: precision_at_1 |
|
value: 30.612000000000002 |
|
- type: precision_at_10 |
|
value: 23.061 |
|
- type: precision_at_100 |
|
value: 7.714 |
|
- type: precision_at_1000 |
|
value: 1.484 |
|
- type: precision_at_3 |
|
value: 26.531 |
|
- type: precision_at_5 |
|
value: 26.122 |
|
- type: recall_at_1 |
|
value: 2.328 |
|
- type: recall_at_10 |
|
value: 16.524 |
|
- type: recall_at_100 |
|
value: 47.179 |
|
- type: recall_at_1000 |
|
value: 81.22200000000001 |
|
- type: recall_at_3 |
|
value: 5.745 |
|
- type: recall_at_5 |
|
value: 9.339 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 70.9142 |
|
- type: ap |
|
value: 14.335574772555415 |
|
- type: f1 |
|
value: 54.62839595194111 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 59.94340690435768 |
|
- type: f1 |
|
value: 60.286487936731916 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 51.26597708987974 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.48882398521786 |
|
- type: cos_sim_ap |
|
value: 79.04326607602204 |
|
- type: cos_sim_f1 |
|
value: 71.64566826860633 |
|
- type: cos_sim_precision |
|
value: 70.55512918905092 |
|
- type: cos_sim_recall |
|
value: 72.77044854881267 |
|
- type: dot_accuracy |
|
value: 84.19264469213805 |
|
- type: dot_ap |
|
value: 67.96360043562528 |
|
- type: dot_f1 |
|
value: 64.06418393006827 |
|
- type: dot_precision |
|
value: 58.64941898706424 |
|
- type: dot_recall |
|
value: 70.58047493403694 |
|
- type: euclidean_accuracy |
|
value: 87.45902127913214 |
|
- type: euclidean_ap |
|
value: 78.9742237648272 |
|
- type: euclidean_f1 |
|
value: 71.5553235908142 |
|
- type: euclidean_precision |
|
value: 70.77955601445535 |
|
- type: euclidean_recall |
|
value: 72.34828496042216 |
|
- type: manhattan_accuracy |
|
value: 87.41729749061214 |
|
- type: manhattan_ap |
|
value: 78.90073137580596 |
|
- type: manhattan_f1 |
|
value: 71.3942611553533 |
|
- type: manhattan_precision |
|
value: 68.52705653967483 |
|
- type: manhattan_recall |
|
value: 74.51187335092348 |
|
- type: max_accuracy |
|
value: 87.48882398521786 |
|
- type: max_ap |
|
value: 79.04326607602204 |
|
- type: max_f1 |
|
value: 71.64566826860633 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.68125897465751 |
|
- type: cos_sim_ap |
|
value: 85.6003454431979 |
|
- type: cos_sim_f1 |
|
value: 77.6957163958641 |
|
- type: cos_sim_precision |
|
value: 73.0110366307807 |
|
- type: cos_sim_recall |
|
value: 83.02279026793964 |
|
- type: dot_accuracy |
|
value: 87.7672992587418 |
|
- type: dot_ap |
|
value: 82.4971301112899 |
|
- type: dot_f1 |
|
value: 75.90528233151184 |
|
- type: dot_precision |
|
value: 72.0370626469368 |
|
- type: dot_recall |
|
value: 80.21250384970742 |
|
- type: euclidean_accuracy |
|
value: 88.4503434625684 |
|
- type: euclidean_ap |
|
value: 84.91949884748384 |
|
- type: euclidean_f1 |
|
value: 76.92365018444684 |
|
- type: euclidean_precision |
|
value: 74.53245721712759 |
|
- type: euclidean_recall |
|
value: 79.47336002463813 |
|
- type: manhattan_accuracy |
|
value: 88.47556952691427 |
|
- type: manhattan_ap |
|
value: 84.8963689101517 |
|
- type: manhattan_f1 |
|
value: 76.85901249256395 |
|
- type: manhattan_precision |
|
value: 74.31693989071039 |
|
- type: manhattan_recall |
|
value: 79.58115183246073 |
|
- type: max_accuracy |
|
value: 88.68125897465751 |
|
- type: max_ap |
|
value: 85.6003454431979 |
|
- type: max_f1 |
|
value: 77.6957163958641 |
|
license: mit |
|
language: |
|
- en |
|
--- |
|
|
|
|
|
<h1 align="center">FlagEmbedding</h1> |
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<h4 align="center"> |
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<p> |
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<a href=#model-list>Model List</a> | |
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<a href=#frequently-asked-questions>FAQ</a> | |
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<a href=#usage>Usage</a> | |
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<a href="#evaluation">Evaluation</a> | |
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<a href="#train">Train</a> | |
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<a href="#contact">Contact</a> | |
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<a href="#citation">Citation</a> | |
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<a href="#license">License</a> |
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<p> |
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</h4> |
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|
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For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
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|
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If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). |
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[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
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FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: |
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- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) |
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- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) |
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- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) |
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- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) |
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- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) |
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|
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## News |
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- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). |
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It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. |
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[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: |
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- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: |
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- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: |
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- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: |
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- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) |
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- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released |
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- 09/12/2023: New models: |
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- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. |
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- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. |
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<details> |
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<summary>More</summary> |
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<!-- ### More --> |
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- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. |
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- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). |
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- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
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- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: |
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- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. |
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</details> |
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## Model List |
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`bge` is short for `BAAI general embedding`. |
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| Model | Language | | Description | query instruction for retrieval [1] | |
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|:-------------------------------|:--------:| :--------:| :--------:|:--------:| |
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| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | |
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| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | |
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| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | |
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| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | |
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| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | |
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| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | |
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| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | |
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| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | |
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | |
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| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | |
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[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. |
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[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. |
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For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. |
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All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. |
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If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . |
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## Frequently asked questions |
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<details> |
|
<summary>1. How to fine-tune bge embedding model?</summary> |
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<!-- ### How to fine-tune bge embedding model? --> |
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Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. |
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Some suggestions: |
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- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. |
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- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. |
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- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. |
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|
|
</details> |
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|
<details> |
|
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> |
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<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> |
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**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** |
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Since we finetune the models by contrastive learning with a temperature of 0.01, |
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the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. |
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So a similarity score greater than 0.5 does not indicate that the two sentences are similar. |
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For downstream tasks, such as passage retrieval or semantic similarity, |
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**what matters is the relative order of the scores, not the absolute value.** |
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If you need to filter similar sentences based on a similarity threshold, |
|
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). |
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|
|
</details> |
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|
<details> |
|
<summary>3. When does the query instruction need to be used</summary> |
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|
<!-- ### When does the query instruction need to be used --> |
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For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. |
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No instruction only has a slight degradation in retrieval performance compared with using instruction. |
|
So you can generate embedding without instruction in all cases for convenience. |
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For a retrieval task that uses short queries to find long related documents, |
|
it is recommended to add instructions for these short queries. |
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**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** |
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In all cases, the documents/passages do not need to add the instruction. |
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|
|
</details> |
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|
|
## Usage |
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|
|
### Usage for Embedding Model |
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|
|
Here are some examples for using `bge` models with |
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[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). |
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|
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#### Using FlagEmbedding |
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``` |
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pip install -U FlagEmbedding |
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``` |
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If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. |
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|
|
```python |
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from FlagEmbedding import FlagModel |
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sentences_1 = ["样例数据-1", "样例数据-2"] |
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sentences_2 = ["样例数据-3", "样例数据-4"] |
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model = FlagModel('BAAI/bge-large-zh-v1.5', |
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query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", |
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use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
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embeddings_1 = model.encode(sentences_1) |
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embeddings_2 = model.encode(sentences_2) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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|
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# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query |
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# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction |
|
queries = ['query_1', 'query_2'] |
|
passages = ["样例文档-1", "样例文档-2"] |
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q_embeddings = model.encode_queries(queries) |
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p_embeddings = model.encode(passages) |
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scores = q_embeddings @ p_embeddings.T |
|
``` |
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For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
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|
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By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. |
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You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. |
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|
|
#### Using Sentence-Transformers |
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|
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You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): |
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|
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``` |
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pip install -U sentence-transformers |
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``` |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences_1 = ["样例数据-1", "样例数据-2"] |
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sentences_2 = ["样例数据-3", "样例数据-4"] |
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model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
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embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) |
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embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
|
``` |
|
For s2p(short query to long passage) retrieval task, |
|
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
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But the instruction is not needed for passages. |
|
```python |
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from sentence_transformers import SentenceTransformer |
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queries = ['query_1', 'query_2'] |
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passages = ["样例文档-1", "样例文档-2"] |
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instruction = "为这个句子生成表示以用于检索相关文章：" |
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|
|
model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
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q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
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p_embeddings = model.encode(passages, normalize_embeddings=True) |
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scores = q_embeddings @ p_embeddings.T |
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``` |
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|
|
#### Using Langchain |
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|
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You can use `bge` in langchain like this: |
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```python |
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from langchain.embeddings import HuggingFaceBgeEmbeddings |
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model_name = "BAAI/bge-large-en-v1.5" |
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model_kwargs = {'device': 'cuda'} |
|
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
|
model = HuggingFaceBgeEmbeddings( |
|
model_name=model_name, |
|
model_kwargs=model_kwargs, |
|
encode_kwargs=encode_kwargs, |
|
query_instruction="为这个句子生成表示以用于检索相关文章：" |
|
) |
|
model.query_instruction = "为这个句子生成表示以用于检索相关文章：" |
|
``` |
|
|
|
|
|
#### Using HuggingFace Transformers |
|
|
|
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModel |
|
import torch |
|
# Sentences we want sentence embeddings for |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
|
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') |
|
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') |
|
model.eval() |
|
|
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
|
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
|
|
|
# Compute token embeddings |
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
# Perform pooling. In this case, cls pooling. |
|
sentence_embeddings = model_output[0][:, 0] |
|
# normalize embeddings |
|
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
|
print("Sentence embeddings:", sentence_embeddings) |
|
``` |
|
|
|
#### Usage of the ONNX files |
|
|
|
```python |
|
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore |
|
|
|
import torch |
|
from transformers import AutoModel, AutoTokenizer |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5') |
|
model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13") |
|
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx") |
|
|
|
# Sentences we want sentence embeddings for |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
|
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
|
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
|
|
|
model_output_ort = model_ort(**encoded_input) |
|
# Compute token embeddings |
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
|
|
# model_output and model_output_ort are identical |
|
|
|
``` |
|
|
|
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. |
|
```python |
|
import asyncio |
|
from infinity_emb import AsyncEmbeddingEngine, EngineArgs |
|
|
|
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] |
|
engine = AsyncEmbeddingEngine.from_args( |
|
EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch" |
|
)) |
|
|
|
async def main(): |
|
async with engine: |
|
embeddings, usage = await engine.embed(sentences=sentences) |
|
asyncio.run(main()) |
|
``` |
|
|
|
### Usage for Reranker |
|
|
|
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. |
|
You can get a relevance score by inputting query and passage to the reranker. |
|
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. |
|
|
|
|
|
#### Using FlagEmbedding |
|
``` |
|
pip install -U FlagEmbedding |
|
``` |
|
|
|
Get relevance scores (higher scores indicate more relevance): |
|
```python |
|
from FlagEmbedding import FlagReranker |
|
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
|
|
|
score = reranker.compute_score(['query', 'passage']) |
|
print(score) |
|
|
|
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) |
|
print(scores) |
|
``` |
|
|
|
|
|
#### Using Huggingface transformers |
|
|
|
```python |
|
import torch |
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') |
|
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') |
|
model.eval() |
|
|
|
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] |
|
with torch.no_grad(): |
|
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) |
|
scores = model(**inputs, return_dict=True).logits.view(-1, ).float() |
|
print(scores) |
|
``` |
|
|
|
## Evaluation |
|
|
|
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
|
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
|
|
|
- **MTEB**: |
|
|
|
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | |
|
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | |
|
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | |
|
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | |
|
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | |
|
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | |
|
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | |
|
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | |
|
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | |
|
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | |
|
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | |
|
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | |
|
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | |
|
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | |
|
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | |
|
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | |
|
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | |
|
|
|
|
|
|
|
- **C-MTEB**: |
|
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. |
|
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. |
|
|
|
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | |
|
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | |
|
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | |
|
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | |
|
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | |
|
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | |
|
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | |
|
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | |
|
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | |
|
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | |
|
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | |
|
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | |
|
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | |
|
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | |
|
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | |
|
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | |
|
|
|
|
|
- **Reranking**: |
|
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. |
|
|
|
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | |
|
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | |
|
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | |
|
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | |
|
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | |
|
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | |
|
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | |
|
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | |
|
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | |
|
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | |
|
|
|
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks |
|
|
|
## Train |
|
|
|
### BAAI Embedding |
|
|
|
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. |
|
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** |
|
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). |
|
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. |
|
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
|
|
|
|
|
|
|
### BGE Reranker |
|
|
|
Cross-encoder will perform full-attention over the input pair, |
|
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. |
|
Therefore, it can be used to re-rank the top-k documents returned by embedding model. |
|
We train the cross-encoder on a multilingual pair data, |
|
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). |
|
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) |
|
|
|
|
|
## Contact |
|
If you have any question or suggestion related to this project, feel free to open an issue or pull request. |
|
You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). |
|
|
|
|
|
## Citation |
|
|
|
If you find this repository useful, please consider giving a star :star: and citation |
|
|
|
``` |
|
@misc{bge_embedding, |
|
title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, |
|
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, |
|
year={2023}, |
|
eprint={2309.07597}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
## License |
|
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
|
|
|
|
