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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- finetuner |
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- mteb |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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datasets: |
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- jinaai/negation-dataset |
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language: en |
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license: apache-2.0 |
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model-index: |
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- name: jina-embedding-l-en-v1 |
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results: |
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- task: |
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type: Classification |
|
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: 61.64179104477612 |
|
- type: ap |
|
value: 24.63675721041911 |
|
- type: f1 |
|
value: 55.10036810049116 |
|
- 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: 60.708125 |
|
- type: ap |
|
value: 57.491681452557344 |
|
- type: f1 |
|
value: 58.046023443205655 |
|
- task: |
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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: |
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- type: accuracy |
|
value: 28.12 |
|
- type: f1 |
|
value: 26.904734434317966 |
|
- 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: 26.031 |
|
- type: map_at_10 |
|
value: 40.742 |
|
- type: map_at_100 |
|
value: 41.832 |
|
- type: map_at_1000 |
|
value: 41.844 |
|
- type: map_at_3 |
|
value: 35.526 |
|
- type: map_at_5 |
|
value: 38.567 |
|
- type: mrr_at_1 |
|
value: 26.316 |
|
- type: mrr_at_10 |
|
value: 40.855999999999995 |
|
- type: mrr_at_100 |
|
value: 41.946 |
|
- type: mrr_at_1000 |
|
value: 41.957 |
|
- type: mrr_at_3 |
|
value: 35.621 |
|
- type: mrr_at_5 |
|
value: 38.644 |
|
- type: ndcg_at_1 |
|
value: 26.031 |
|
- type: ndcg_at_10 |
|
value: 49.483 |
|
- type: ndcg_at_100 |
|
value: 54.074999999999996 |
|
- type: ndcg_at_1000 |
|
value: 54.344 |
|
- type: ndcg_at_3 |
|
value: 38.792 |
|
- type: ndcg_at_5 |
|
value: 44.24 |
|
- type: precision_at_1 |
|
value: 26.031 |
|
- type: precision_at_10 |
|
value: 7.76 |
|
- type: precision_at_100 |
|
value: 0.975 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 16.098000000000003 |
|
- type: precision_at_5 |
|
value: 12.29 |
|
- type: recall_at_1 |
|
value: 26.031 |
|
- type: recall_at_10 |
|
value: 77.596 |
|
- type: recall_at_100 |
|
value: 97.51100000000001 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 48.293 |
|
- type: recall_at_5 |
|
value: 61.451 |
|
- 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: 41.76036539849672 |
|
- 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: 34.27585676831497 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
|
- type: map |
|
value: 63.47328704612227 |
|
- type: mrr |
|
value: 76.63182078002022 |
|
- task: |
|
type: STS |
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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: 87.42072640664271 |
|
- type: cos_sim_spearman |
|
value: 84.31336692039407 |
|
- type: euclidean_pearson |
|
value: 54.93250871487246 |
|
- type: euclidean_spearman |
|
value: 55.91091252228738 |
|
- type: manhattan_pearson |
|
value: 54.78812442894107 |
|
- type: manhattan_spearman |
|
value: 55.35005636930548 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
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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: 86.28896103896103 |
|
- type: f1 |
|
value: 86.23389676482913 |
|
- 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: 33.73729294301578 |
|
- 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: 30.641078215958288 |
|
- task: |
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type: Retrieval |
|
dataset: |
|
type: climate-fever |
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name: MTEB ClimateFEVER |
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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 |
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value: 8.258000000000001 |
|
- type: map_at_10 |
|
value: 14.57 |
|
- type: map_at_100 |
|
value: 15.98 |
|
- type: map_at_1000 |
|
value: 16.149 |
|
- type: map_at_3 |
|
value: 11.993 |
|
- type: map_at_5 |
|
value: 13.383000000000001 |
|
- type: mrr_at_1 |
|
value: 18.176000000000002 |
|
- type: mrr_at_10 |
|
value: 28.560000000000002 |
|
- type: mrr_at_100 |
|
value: 29.656 |
|
- type: mrr_at_1000 |
|
value: 29.709999999999997 |
|
- type: mrr_at_3 |
|
value: 25.255 |
|
- type: mrr_at_5 |
|
value: 27.128000000000004 |
|
- type: ndcg_at_1 |
|
value: 18.176000000000002 |
|
- type: ndcg_at_10 |
|
value: 21.36 |
|
- type: ndcg_at_100 |
|
value: 27.619 |
|
- type: ndcg_at_1000 |
|
value: 31.086000000000002 |
|
- type: ndcg_at_3 |
|
value: 16.701 |
|
- type: ndcg_at_5 |
|
value: 18.559 |
|
- type: precision_at_1 |
|
value: 18.176000000000002 |
|
- type: precision_at_10 |
|
value: 6.683999999999999 |
|
- type: precision_at_100 |
|
value: 1.3339999999999999 |
|
- type: precision_at_1000 |
|
value: 0.197 |
|
- type: precision_at_3 |
|
value: 12.269 |
|
- type: precision_at_5 |
|
value: 9.798 |
|
- type: recall_at_1 |
|
value: 8.258000000000001 |
|
- type: recall_at_10 |
|
value: 27.060000000000002 |
|
- type: recall_at_100 |
|
value: 48.833 |
|
- type: recall_at_1000 |
|
value: 68.636 |
|
- type: recall_at_3 |
|
value: 15.895999999999999 |
|
- type: recall_at_5 |
|
value: 20.625 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
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name: MTEB DBPedia |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.241 |
|
- type: map_at_10 |
|
value: 17.141000000000002 |
|
- type: map_at_100 |
|
value: 22.805 |
|
- type: map_at_1000 |
|
value: 24.189 |
|
- type: map_at_3 |
|
value: 12.940999999999999 |
|
- type: map_at_5 |
|
value: 14.607000000000001 |
|
- type: mrr_at_1 |
|
value: 62.25000000000001 |
|
- type: mrr_at_10 |
|
value: 70.537 |
|
- type: mrr_at_100 |
|
value: 70.851 |
|
- type: mrr_at_1000 |
|
value: 70.875 |
|
- type: mrr_at_3 |
|
value: 68.75 |
|
- type: mrr_at_5 |
|
value: 69.77499999999999 |
|
- type: ndcg_at_1 |
|
value: 50.125 |
|
- type: ndcg_at_10 |
|
value: 36.032 |
|
- type: ndcg_at_100 |
|
value: 39.428999999999995 |
|
- type: ndcg_at_1000 |
|
value: 47.138999999999996 |
|
- type: ndcg_at_3 |
|
value: 40.99 |
|
- type: ndcg_at_5 |
|
value: 37.772 |
|
- type: precision_at_1 |
|
value: 62.25000000000001 |
|
- type: precision_at_10 |
|
value: 28.050000000000004 |
|
- type: precision_at_100 |
|
value: 8.527999999999999 |
|
- type: precision_at_1000 |
|
value: 1.82 |
|
- type: precision_at_3 |
|
value: 45.0 |
|
- type: precision_at_5 |
|
value: 36.0 |
|
- type: recall_at_1 |
|
value: 8.241 |
|
- type: recall_at_10 |
|
value: 22.583000000000002 |
|
- type: recall_at_100 |
|
value: 44.267 |
|
- type: recall_at_1000 |
|
value: 69.497 |
|
- type: recall_at_3 |
|
value: 14.326 |
|
- type: recall_at_5 |
|
value: 17.29 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 42.295 |
|
- type: f1 |
|
value: 38.32403088027173 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 58.553 |
|
- type: map_at_10 |
|
value: 69.632 |
|
- type: map_at_100 |
|
value: 69.95400000000001 |
|
- type: map_at_1000 |
|
value: 69.968 |
|
- type: map_at_3 |
|
value: 67.656 |
|
- type: map_at_5 |
|
value: 68.86 |
|
- type: mrr_at_1 |
|
value: 63.156 |
|
- type: mrr_at_10 |
|
value: 74.37700000000001 |
|
- type: mrr_at_100 |
|
value: 74.629 |
|
- type: mrr_at_1000 |
|
value: 74.63300000000001 |
|
- type: mrr_at_3 |
|
value: 72.577 |
|
- type: mrr_at_5 |
|
value: 73.71 |
|
- type: ndcg_at_1 |
|
value: 63.156 |
|
- type: ndcg_at_10 |
|
value: 75.345 |
|
- type: ndcg_at_100 |
|
value: 76.728 |
|
- type: ndcg_at_1000 |
|
value: 77.006 |
|
- type: ndcg_at_3 |
|
value: 71.67099999999999 |
|
- type: ndcg_at_5 |
|
value: 73.656 |
|
- type: precision_at_1 |
|
value: 63.156 |
|
- type: precision_at_10 |
|
value: 9.673 |
|
- type: precision_at_100 |
|
value: 1.045 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 28.393 |
|
- type: precision_at_5 |
|
value: 18.160999999999998 |
|
- type: recall_at_1 |
|
value: 58.553 |
|
- type: recall_at_10 |
|
value: 88.362 |
|
- type: recall_at_100 |
|
value: 94.401 |
|
- type: recall_at_1000 |
|
value: 96.256 |
|
- type: recall_at_3 |
|
value: 78.371 |
|
- type: recall_at_5 |
|
value: 83.32300000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.302 |
|
- type: map_at_10 |
|
value: 31.887 |
|
- type: map_at_100 |
|
value: 33.727000000000004 |
|
- type: map_at_1000 |
|
value: 33.914 |
|
- type: map_at_3 |
|
value: 27.254 |
|
- type: map_at_5 |
|
value: 29.904999999999998 |
|
- type: mrr_at_1 |
|
value: 39.043 |
|
- type: mrr_at_10 |
|
value: 47.858000000000004 |
|
- type: mrr_at_100 |
|
value: 48.636 |
|
- type: mrr_at_1000 |
|
value: 48.677 |
|
- type: mrr_at_3 |
|
value: 45.062000000000005 |
|
- type: mrr_at_5 |
|
value: 46.775 |
|
- type: ndcg_at_1 |
|
value: 39.043 |
|
- type: ndcg_at_10 |
|
value: 39.899 |
|
- type: ndcg_at_100 |
|
value: 46.719 |
|
- type: ndcg_at_1000 |
|
value: 49.739 |
|
- type: ndcg_at_3 |
|
value: 35.666 |
|
- type: ndcg_at_5 |
|
value: 37.232 |
|
- type: precision_at_1 |
|
value: 39.043 |
|
- type: precision_at_10 |
|
value: 11.265 |
|
- type: precision_at_100 |
|
value: 1.864 |
|
- type: precision_at_1000 |
|
value: 0.23800000000000002 |
|
- type: precision_at_3 |
|
value: 24.227999999999998 |
|
- type: precision_at_5 |
|
value: 18.148 |
|
- type: recall_at_1 |
|
value: 19.302 |
|
- type: recall_at_10 |
|
value: 47.278 |
|
- type: recall_at_100 |
|
value: 72.648 |
|
- type: recall_at_1000 |
|
value: 90.793 |
|
- type: recall_at_3 |
|
value: 31.235000000000003 |
|
- type: recall_at_5 |
|
value: 38.603 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.398 |
|
- type: map_at_10 |
|
value: 44.635000000000005 |
|
- type: map_at_100 |
|
value: 45.513 |
|
- type: map_at_1000 |
|
value: 45.595 |
|
- type: map_at_3 |
|
value: 41.894 |
|
- type: map_at_5 |
|
value: 43.514 |
|
- type: mrr_at_1 |
|
value: 62.795 |
|
- type: mrr_at_10 |
|
value: 70.001 |
|
- type: mrr_at_100 |
|
value: 70.378 |
|
- type: mrr_at_1000 |
|
value: 70.399 |
|
- type: mrr_at_3 |
|
value: 68.542 |
|
- type: mrr_at_5 |
|
value: 69.394 |
|
- type: ndcg_at_1 |
|
value: 62.795 |
|
- type: ndcg_at_10 |
|
value: 53.635 |
|
- type: ndcg_at_100 |
|
value: 57.05 |
|
- type: ndcg_at_1000 |
|
value: 58.755 |
|
- type: ndcg_at_3 |
|
value: 49.267 |
|
- type: ndcg_at_5 |
|
value: 51.522 |
|
- type: precision_at_1 |
|
value: 62.795 |
|
- type: precision_at_10 |
|
value: 11.196 |
|
- type: precision_at_100 |
|
value: 1.389 |
|
- type: precision_at_1000 |
|
value: 0.16199999999999998 |
|
- type: precision_at_3 |
|
value: 30.804 |
|
- type: precision_at_5 |
|
value: 20.265 |
|
- type: recall_at_1 |
|
value: 31.398 |
|
- type: recall_at_10 |
|
value: 55.982 |
|
- type: recall_at_100 |
|
value: 69.453 |
|
- type: recall_at_1000 |
|
value: 80.756 |
|
- type: recall_at_3 |
|
value: 46.205 |
|
- type: recall_at_5 |
|
value: 50.662 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 63.803200000000004 |
|
- type: ap |
|
value: 59.04397034963468 |
|
- type: f1 |
|
value: 63.4675375611795 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.671 |
|
- type: map_at_10 |
|
value: 29.152 |
|
- type: map_at_100 |
|
value: 30.422 |
|
- type: map_at_1000 |
|
value: 30.481 |
|
- type: map_at_3 |
|
value: 25.417 |
|
- type: map_at_5 |
|
value: 27.448 |
|
- type: mrr_at_1 |
|
value: 18.195 |
|
- type: mrr_at_10 |
|
value: 29.67 |
|
- type: mrr_at_100 |
|
value: 30.891999999999996 |
|
- type: mrr_at_1000 |
|
value: 30.944 |
|
- type: mrr_at_3 |
|
value: 25.974000000000004 |
|
- type: mrr_at_5 |
|
value: 27.996 |
|
- type: ndcg_at_1 |
|
value: 18.195 |
|
- type: ndcg_at_10 |
|
value: 35.795 |
|
- type: ndcg_at_100 |
|
value: 42.117 |
|
- type: ndcg_at_1000 |
|
value: 43.585 |
|
- type: ndcg_at_3 |
|
value: 28.122000000000003 |
|
- type: ndcg_at_5 |
|
value: 31.757 |
|
- type: precision_at_1 |
|
value: 18.195 |
|
- type: precision_at_10 |
|
value: 5.89 |
|
- type: precision_at_100 |
|
value: 0.9079999999999999 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 12.24 |
|
- type: precision_at_5 |
|
value: 9.178 |
|
- type: recall_at_1 |
|
value: 17.671 |
|
- type: recall_at_10 |
|
value: 56.373 |
|
- type: recall_at_100 |
|
value: 86.029 |
|
- type: recall_at_1000 |
|
value: 97.246 |
|
- type: recall_at_3 |
|
value: 35.414 |
|
- type: recall_at_5 |
|
value: 44.149 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 90.80255357957135 |
|
- type: f1 |
|
value: 90.79256308087807 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 71.20611035111719 |
|
- type: f1 |
|
value: 54.075483897190836 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 70.79354404841965 |
|
- type: f1 |
|
value: 68.53816551555609 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 76.6072629455279 |
|
- type: f1 |
|
value: 77.04997715738867 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 30.432745003633016 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.95493811839366 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.63516074152514 |
|
- type: mrr |
|
value: 32.73091425241894 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.379 |
|
- type: map_at_10 |
|
value: 12.051 |
|
- type: map_at_100 |
|
value: 15.176 |
|
- type: map_at_1000 |
|
value: 16.662 |
|
- type: map_at_3 |
|
value: 8.588 |
|
- type: map_at_5 |
|
value: 10.274 |
|
- type: mrr_at_1 |
|
value: 44.891999999999996 |
|
- type: mrr_at_10 |
|
value: 53.06999999999999 |
|
- type: mrr_at_100 |
|
value: 53.675 |
|
- type: mrr_at_1000 |
|
value: 53.717999999999996 |
|
- type: mrr_at_3 |
|
value: 50.671 |
|
- type: mrr_at_5 |
|
value: 52.25 |
|
- type: ndcg_at_1 |
|
value: 42.879 |
|
- type: ndcg_at_10 |
|
value: 33.291 |
|
- type: ndcg_at_100 |
|
value: 30.567 |
|
- type: ndcg_at_1000 |
|
value: 39.598 |
|
- type: ndcg_at_3 |
|
value: 37.713 |
|
- type: ndcg_at_5 |
|
value: 36.185 |
|
- type: precision_at_1 |
|
value: 44.891999999999996 |
|
- type: precision_at_10 |
|
value: 24.923000000000002 |
|
- type: precision_at_100 |
|
value: 8.015 |
|
- type: precision_at_1000 |
|
value: 2.083 |
|
- type: precision_at_3 |
|
value: 35.088 |
|
- type: precision_at_5 |
|
value: 31.765 |
|
- type: recall_at_1 |
|
value: 5.379 |
|
- type: recall_at_10 |
|
value: 16.346 |
|
- type: recall_at_100 |
|
value: 31.887999999999998 |
|
- type: recall_at_1000 |
|
value: 64.90599999999999 |
|
- type: recall_at_3 |
|
value: 9.543 |
|
- type: recall_at_5 |
|
value: 12.369 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.654 |
|
- type: map_at_10 |
|
value: 40.163 |
|
- type: map_at_100 |
|
value: 41.376000000000005 |
|
- type: map_at_1000 |
|
value: 41.411 |
|
- type: map_at_3 |
|
value: 35.677 |
|
- type: map_at_5 |
|
value: 38.238 |
|
- type: mrr_at_1 |
|
value: 29.055999999999997 |
|
- type: mrr_at_10 |
|
value: 42.571999999999996 |
|
- type: mrr_at_100 |
|
value: 43.501 |
|
- type: mrr_at_1000 |
|
value: 43.527 |
|
- type: mrr_at_3 |
|
value: 38.775 |
|
- type: mrr_at_5 |
|
value: 40.953 |
|
- type: ndcg_at_1 |
|
value: 29.026999999999997 |
|
- type: ndcg_at_10 |
|
value: 47.900999999999996 |
|
- type: ndcg_at_100 |
|
value: 52.941 |
|
- type: ndcg_at_1000 |
|
value: 53.786 |
|
- type: ndcg_at_3 |
|
value: 39.387 |
|
- type: ndcg_at_5 |
|
value: 43.65 |
|
- type: precision_at_1 |
|
value: 29.026999999999997 |
|
- type: precision_at_10 |
|
value: 8.247 |
|
- type: precision_at_100 |
|
value: 1.102 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 18.231 |
|
- type: precision_at_5 |
|
value: 13.378 |
|
- type: recall_at_1 |
|
value: 25.654 |
|
- type: recall_at_10 |
|
value: 69.175 |
|
- type: recall_at_100 |
|
value: 90.85600000000001 |
|
- type: recall_at_1000 |
|
value: 97.18 |
|
- type: recall_at_3 |
|
value: 47.043 |
|
- type: recall_at_5 |
|
value: 56.86600000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.785 |
|
- type: map_at_10 |
|
value: 84.509 |
|
- type: map_at_100 |
|
value: 85.17 |
|
- type: map_at_1000 |
|
value: 85.187 |
|
- type: map_at_3 |
|
value: 81.628 |
|
- type: map_at_5 |
|
value: 83.422 |
|
- type: mrr_at_1 |
|
value: 81.43 |
|
- type: mrr_at_10 |
|
value: 87.506 |
|
- type: mrr_at_100 |
|
value: 87.616 |
|
- type: mrr_at_1000 |
|
value: 87.617 |
|
- type: mrr_at_3 |
|
value: 86.598 |
|
- type: mrr_at_5 |
|
value: 87.215 |
|
- type: ndcg_at_1 |
|
value: 81.44 |
|
- type: ndcg_at_10 |
|
value: 88.208 |
|
- type: ndcg_at_100 |
|
value: 89.49000000000001 |
|
- type: ndcg_at_1000 |
|
value: 89.59700000000001 |
|
- type: ndcg_at_3 |
|
value: 85.471 |
|
- type: ndcg_at_5 |
|
value: 86.955 |
|
- type: precision_at_1 |
|
value: 81.44 |
|
- type: precision_at_10 |
|
value: 13.347000000000001 |
|
- type: precision_at_100 |
|
value: 1.53 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.330000000000005 |
|
- type: precision_at_5 |
|
value: 24.506 |
|
- type: recall_at_1 |
|
value: 70.785 |
|
- type: recall_at_10 |
|
value: 95.15 |
|
- type: recall_at_100 |
|
value: 99.502 |
|
- type: recall_at_1000 |
|
value: 99.993 |
|
- type: recall_at_3 |
|
value: 87.234 |
|
- type: recall_at_5 |
|
value: 91.467 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 52.40682777853522 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 56.61834429208595 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.918 |
|
- type: map_at_10 |
|
value: 11.562 |
|
- type: map_at_100 |
|
value: 13.636999999999999 |
|
- type: map_at_1000 |
|
value: 13.918 |
|
- type: map_at_3 |
|
value: 8.353 |
|
- type: map_at_5 |
|
value: 9.878 |
|
- type: mrr_at_1 |
|
value: 24.3 |
|
- type: mrr_at_10 |
|
value: 33.914 |
|
- type: mrr_at_100 |
|
value: 35.079 |
|
- type: mrr_at_1000 |
|
value: 35.134 |
|
- type: mrr_at_3 |
|
value: 30.833 |
|
- type: mrr_at_5 |
|
value: 32.528 |
|
- type: ndcg_at_1 |
|
value: 24.3 |
|
- type: ndcg_at_10 |
|
value: 19.393 |
|
- type: ndcg_at_100 |
|
value: 27.471 |
|
- type: ndcg_at_1000 |
|
value: 32.543 |
|
- type: ndcg_at_3 |
|
value: 18.648 |
|
- type: ndcg_at_5 |
|
value: 16.064999999999998 |
|
- type: precision_at_1 |
|
value: 24.3 |
|
- type: precision_at_10 |
|
value: 9.92 |
|
- type: precision_at_100 |
|
value: 2.152 |
|
- type: precision_at_1000 |
|
value: 0.338 |
|
- type: precision_at_3 |
|
value: 17.1 |
|
- type: precision_at_5 |
|
value: 13.819999999999999 |
|
- type: recall_at_1 |
|
value: 4.918 |
|
- type: recall_at_10 |
|
value: 20.102 |
|
- type: recall_at_100 |
|
value: 43.69 |
|
- type: recall_at_1000 |
|
value: 68.568 |
|
- type: recall_at_3 |
|
value: 10.383000000000001 |
|
- type: recall_at_5 |
|
value: 13.977999999999998 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.02374279770862 |
|
- type: cos_sim_spearman |
|
value: 80.3123278821752 |
|
- type: euclidean_pearson |
|
value: 78.150387301923 |
|
- type: euclidean_spearman |
|
value: 74.27020095240543 |
|
- type: manhattan_pearson |
|
value: 78.00212720962597 |
|
- type: manhattan_spearman |
|
value: 74.27996355049189 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.56832604166104 |
|
- type: cos_sim_spearman |
|
value: 73.85172437109456 |
|
- type: euclidean_pearson |
|
value: 70.77037821156355 |
|
- type: euclidean_spearman |
|
value: 58.32603602271459 |
|
- type: manhattan_pearson |
|
value: 70.6019035905572 |
|
- type: manhattan_spearman |
|
value: 58.18758998109944 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.97624603590171 |
|
- type: cos_sim_spearman |
|
value: 84.3654403570941 |
|
- type: euclidean_pearson |
|
value: 77.37734191552401 |
|
- type: euclidean_spearman |
|
value: 77.83492278107906 |
|
- type: manhattan_pearson |
|
value: 77.38406845115612 |
|
- type: manhattan_spearman |
|
value: 77.80429501178632 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.5175806484823 |
|
- type: cos_sim_spearman |
|
value: 77.84074419393815 |
|
- type: euclidean_pearson |
|
value: 75.31514179994578 |
|
- type: euclidean_spearman |
|
value: 71.06564963155697 |
|
- type: manhattan_pearson |
|
value: 75.25016497298036 |
|
- type: manhattan_spearman |
|
value: 71.0503867625097 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.15312065200007 |
|
- type: cos_sim_spearman |
|
value: 86.28786282283781 |
|
- type: euclidean_pearson |
|
value: 69.93961446583728 |
|
- type: euclidean_spearman |
|
value: 70.99565144007187 |
|
- type: manhattan_pearson |
|
value: 70.06338127800244 |
|
- type: manhattan_spearman |
|
value: 71.15328825585216 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.48261723093232 |
|
- type: cos_sim_spearman |
|
value: 82.13997187275378 |
|
- type: euclidean_pearson |
|
value: 72.01034058956992 |
|
- type: euclidean_spearman |
|
value: 72.90423890320797 |
|
- type: manhattan_pearson |
|
value: 71.91819389305805 |
|
- type: manhattan_spearman |
|
value: 72.804333901611 |
|
- 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: 89.89094326696411 |
|
- type: cos_sim_spearman |
|
value: 89.5679328484923 |
|
- type: euclidean_pearson |
|
value: 77.27326226557433 |
|
- type: euclidean_spearman |
|
value: 75.44670270858582 |
|
- type: manhattan_pearson |
|
value: 77.49623029933024 |
|
- type: manhattan_spearman |
|
value: 75.6317127686177 |
|
- 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: 67.03259798800852 |
|
- type: cos_sim_spearman |
|
value: 66.17683868865686 |
|
- type: euclidean_pearson |
|
value: 49.154524473561416 |
|
- type: euclidean_spearman |
|
value: 58.82796771905756 |
|
- type: manhattan_pearson |
|
value: 48.97445679282608 |
|
- type: manhattan_spearman |
|
value: 58.69653501728678 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.01368632144246 |
|
- type: cos_sim_spearman |
|
value: 83.64169080274549 |
|
- type: euclidean_pearson |
|
value: 75.84021692605727 |
|
- type: euclidean_spearman |
|
value: 74.69132304226987 |
|
- type: manhattan_pearson |
|
value: 75.9627059404693 |
|
- type: manhattan_spearman |
|
value: 74.83616979158057 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 81.63017243645893 |
|
- type: mrr |
|
value: 94.79274900843528 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 47.094 |
|
- type: map_at_10 |
|
value: 56.047000000000004 |
|
- type: map_at_100 |
|
value: 56.701 |
|
- type: map_at_1000 |
|
value: 56.742000000000004 |
|
- type: map_at_3 |
|
value: 53.189 |
|
- type: map_at_5 |
|
value: 54.464 |
|
- type: mrr_at_1 |
|
value: 50.0 |
|
- type: mrr_at_10 |
|
value: 57.567 |
|
- type: mrr_at_100 |
|
value: 58.104 |
|
- type: mrr_at_1000 |
|
value: 58.142 |
|
- type: mrr_at_3 |
|
value: 55.222 |
|
- type: mrr_at_5 |
|
value: 56.355999999999995 |
|
- type: ndcg_at_1 |
|
value: 50.0 |
|
- type: ndcg_at_10 |
|
value: 60.84 |
|
- type: ndcg_at_100 |
|
value: 63.983999999999995 |
|
- type: ndcg_at_1000 |
|
value: 65.19500000000001 |
|
- type: ndcg_at_3 |
|
value: 55.491 |
|
- type: ndcg_at_5 |
|
value: 57.51500000000001 |
|
- type: precision_at_1 |
|
value: 50.0 |
|
- type: precision_at_10 |
|
value: 8.366999999999999 |
|
- type: precision_at_100 |
|
value: 1.013 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 21.556 |
|
- type: precision_at_5 |
|
value: 14.2 |
|
- type: recall_at_1 |
|
value: 47.094 |
|
- type: recall_at_10 |
|
value: 74.239 |
|
- type: recall_at_100 |
|
value: 89.0 |
|
- type: recall_at_1000 |
|
value: 98.667 |
|
- type: recall_at_3 |
|
value: 59.606 |
|
- type: recall_at_5 |
|
value: 64.756 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.7128712871287 |
|
- type: cos_sim_ap |
|
value: 91.8391173412632 |
|
- type: cos_sim_f1 |
|
value: 85.23421588594704 |
|
- type: cos_sim_precision |
|
value: 86.82572614107885 |
|
- type: cos_sim_recall |
|
value: 83.7 |
|
- type: dot_accuracy |
|
value: 99.23960396039604 |
|
- type: dot_ap |
|
value: 58.07268940033783 |
|
- type: dot_f1 |
|
value: 58.00486618004865 |
|
- type: dot_precision |
|
value: 56.49289099526066 |
|
- type: dot_recall |
|
value: 59.599999999999994 |
|
- type: euclidean_accuracy |
|
value: 99.62574257425743 |
|
- type: euclidean_ap |
|
value: 86.31145319031712 |
|
- type: euclidean_f1 |
|
value: 80.12486992715921 |
|
- type: euclidean_precision |
|
value: 83.51409978308027 |
|
- type: euclidean_recall |
|
value: 77.0 |
|
- type: manhattan_accuracy |
|
value: 99.62178217821783 |
|
- type: manhattan_ap |
|
value: 85.96697606381338 |
|
- type: manhattan_f1 |
|
value: 80.24193548387099 |
|
- type: manhattan_precision |
|
value: 80.89430894308943 |
|
- type: manhattan_recall |
|
value: 79.60000000000001 |
|
- type: max_accuracy |
|
value: 99.7128712871287 |
|
- type: max_ap |
|
value: 91.8391173412632 |
|
- type: max_f1 |
|
value: 85.23421588594704 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 54.98955943181893 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 32.72837687387049 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 51.02207528482775 |
|
- type: mrr |
|
value: 51.8842044393515 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.250596893094876 |
|
- type: cos_sim_spearman |
|
value: 30.609457706010158 |
|
- type: dot_pearson |
|
value: 19.739579843052162 |
|
- type: dot_spearman |
|
value: 20.27834051930579 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.187 |
|
- type: map_at_10 |
|
value: 1.239 |
|
- type: map_at_100 |
|
value: 6.388000000000001 |
|
- type: map_at_1000 |
|
value: 15.507000000000001 |
|
- type: map_at_3 |
|
value: 0.5 |
|
- type: map_at_5 |
|
value: 0.712 |
|
- type: mrr_at_1 |
|
value: 70.0 |
|
- type: mrr_at_10 |
|
value: 83.0 |
|
- type: mrr_at_100 |
|
value: 83.0 |
|
- type: mrr_at_1000 |
|
value: 83.0 |
|
- type: mrr_at_3 |
|
value: 81.667 |
|
- type: mrr_at_5 |
|
value: 82.667 |
|
- type: ndcg_at_1 |
|
value: 65.0 |
|
- type: ndcg_at_10 |
|
value: 56.57600000000001 |
|
- type: ndcg_at_100 |
|
value: 42.054 |
|
- type: ndcg_at_1000 |
|
value: 38.269999999999996 |
|
- type: ndcg_at_3 |
|
value: 63.134 |
|
- type: ndcg_at_5 |
|
value: 58.792 |
|
- type: precision_at_1 |
|
value: 70.0 |
|
- type: precision_at_10 |
|
value: 59.8 |
|
- type: precision_at_100 |
|
value: 42.5 |
|
- type: precision_at_1000 |
|
value: 17.304 |
|
- type: precision_at_3 |
|
value: 67.333 |
|
- type: precision_at_5 |
|
value: 62.4 |
|
- type: recall_at_1 |
|
value: 0.187 |
|
- type: recall_at_10 |
|
value: 1.529 |
|
- type: recall_at_100 |
|
value: 9.673 |
|
- type: recall_at_1000 |
|
value: 35.807 |
|
- type: recall_at_3 |
|
value: 0.5459999999999999 |
|
- type: recall_at_5 |
|
value: 0.8130000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.646 |
|
- type: map_at_10 |
|
value: 6.569999999999999 |
|
- type: map_at_100 |
|
value: 11.530999999999999 |
|
- type: map_at_1000 |
|
value: 13.009 |
|
- type: map_at_3 |
|
value: 3.234 |
|
- type: map_at_5 |
|
value: 4.956 |
|
- type: mrr_at_1 |
|
value: 18.367 |
|
- type: mrr_at_10 |
|
value: 35.121 |
|
- type: mrr_at_100 |
|
value: 36.142 |
|
- type: mrr_at_1000 |
|
value: 36.153 |
|
- type: mrr_at_3 |
|
value: 29.252 |
|
- type: mrr_at_5 |
|
value: 33.434999999999995 |
|
- type: ndcg_at_1 |
|
value: 16.326999999999998 |
|
- type: ndcg_at_10 |
|
value: 17.336 |
|
- type: ndcg_at_100 |
|
value: 28.925 |
|
- type: ndcg_at_1000 |
|
value: 41.346 |
|
- type: ndcg_at_3 |
|
value: 16.131999999999998 |
|
- type: ndcg_at_5 |
|
value: 18.107 |
|
- type: precision_at_1 |
|
value: 18.367 |
|
- type: precision_at_10 |
|
value: 16.531000000000002 |
|
- type: precision_at_100 |
|
value: 6.449000000000001 |
|
- type: precision_at_1000 |
|
value: 1.451 |
|
- type: precision_at_3 |
|
value: 17.687 |
|
- type: precision_at_5 |
|
value: 20.0 |
|
- type: recall_at_1 |
|
value: 1.646 |
|
- type: recall_at_10 |
|
value: 12.113 |
|
- type: recall_at_100 |
|
value: 40.261 |
|
- type: recall_at_1000 |
|
value: 77.878 |
|
- type: recall_at_3 |
|
value: 4.181 |
|
- type: recall_at_5 |
|
value: 7.744 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 66.61500000000001 |
|
- type: ap |
|
value: 11.70707762285034 |
|
- type: f1 |
|
value: 50.53259935502312 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 54.89247311827958 |
|
- type: f1 |
|
value: 55.044186334629586 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 46.95851882042766 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.01978899684092 |
|
- type: cos_sim_ap |
|
value: 68.10404793439619 |
|
- type: cos_sim_f1 |
|
value: 63.93145891154821 |
|
- type: cos_sim_precision |
|
value: 58.905937291527685 |
|
- type: cos_sim_recall |
|
value: 69.89445910290237 |
|
- type: dot_accuracy |
|
value: 77.78506288370984 |
|
- type: dot_ap |
|
value: 38.55636213255057 |
|
- type: dot_f1 |
|
value: 44.6866485013624 |
|
- type: dot_precision |
|
value: 34.07202216066482 |
|
- type: dot_recall |
|
value: 64.90765171503958 |
|
- type: euclidean_accuracy |
|
value: 82.94093103653812 |
|
- type: euclidean_ap |
|
value: 63.65596102723866 |
|
- type: euclidean_f1 |
|
value: 61.444903916322055 |
|
- type: euclidean_precision |
|
value: 56.994584837545126 |
|
- type: euclidean_recall |
|
value: 66.64907651715039 |
|
- type: manhattan_accuracy |
|
value: 82.99457590749239 |
|
- type: manhattan_ap |
|
value: 63.77653539498376 |
|
- type: manhattan_f1 |
|
value: 61.48299483235189 |
|
- type: manhattan_precision |
|
value: 56.455528580887226 |
|
- type: manhattan_recall |
|
value: 67.4934036939314 |
|
- type: max_accuracy |
|
value: 84.01978899684092 |
|
- type: max_ap |
|
value: 68.10404793439619 |
|
- type: max_f1 |
|
value: 63.93145891154821 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.75177552683665 |
|
- type: cos_sim_ap |
|
value: 83.75899853399007 |
|
- type: cos_sim_f1 |
|
value: 76.25022931572188 |
|
- type: cos_sim_precision |
|
value: 72.83241045769958 |
|
- type: cos_sim_recall |
|
value: 80.00461964890668 |
|
- type: dot_accuracy |
|
value: 81.8197694725812 |
|
- type: dot_ap |
|
value: 67.6851675345571 |
|
- type: dot_f1 |
|
value: 64.04501820589209 |
|
- type: dot_precision |
|
value: 56.17233770758332 |
|
- type: dot_recall |
|
value: 74.48413920542039 |
|
- type: euclidean_accuracy |
|
value: 83.3003454030349 |
|
- type: euclidean_ap |
|
value: 72.80186670461116 |
|
- type: euclidean_f1 |
|
value: 65.38000218078727 |
|
- type: euclidean_precision |
|
value: 61.92082616179002 |
|
- type: euclidean_recall |
|
value: 69.24853711117956 |
|
- type: manhattan_accuracy |
|
value: 83.32169053440447 |
|
- type: manhattan_ap |
|
value: 72.8243559753097 |
|
- type: manhattan_f1 |
|
value: 65.45939901157966 |
|
- type: manhattan_precision |
|
value: 61.58284124075205 |
|
- type: manhattan_recall |
|
value: 69.85679088389283 |
|
- type: max_accuracy |
|
value: 87.75177552683665 |
|
- type: max_ap |
|
value: 83.75899853399007 |
|
- type: max_f1 |
|
value: 76.25022931572188 |
|
--- |
|
|
|
<br><br> |
|
|
|
<p align="center"> |
|
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
|
</p> |
|
|
|
|
|
<p align="center"> |
|
<b>The text embedding suite trained by Jina AI, Finetuner team.</b> |
|
</p> |
|
|
|
|
|
## Intented Usage & Model Info |
|
|
|
`jina-embedding-l-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset. |
|
This dataset consists of 380 million pairs of sentences, which include both query-document pairs. |
|
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. |
|
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs. |
|
|
|
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more. |
|
|
|
With a size of 330 million parameters, |
|
the model enables single-gpu inference while delivering better performance than our small and base model. |
|
Additionally, we provide the following options: |
|
|
|
- `jina-embedding-s-en-v1`: 35 million parameters. |
|
- `jina-embedding-b-en-v1`: 110 million parameters. |
|
- `jina-embedding-l-en-v1`: 330 million parameters **(you are here)**. |
|
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10* bert-base size (soon). |
|
- `jina-embedding-6b-en-v1`: 6 billion parameters 30* bert-base size(soon). |
|
|
|
## Data & Parameters |
|
|
|
More info will be released together with the technique report. |
|
|
|
## Metrics |
|
|
|
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI: |
|
|
|
|Name|param |context| |
|
|------------------------------|-----|------| |
|
|all-minilm-l6-v2|33m |128| |
|
|all-mpnet-base-v2 |110m |128| |
|
|ada-embedding-002|Unknown/OpenAI API |8192| |
|
|jina-embedding-s-en-v1|35m |512| |
|
|jina-embedding-b-en-v1|110m |512| |
|
|jina-embedding-l-en-v1|330m |512| |
|
|
|
|
|
|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact| |
|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----| |
|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 | |
|
|all-mpnet-base-v2|0.726|0.835|**0.78** |0.857|0.8 |**0.906**|0.513 |0.875|0.656 | |
|
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** | |
|
|jina-embedding-s-en-v1|0.742|0.786|0.738|0.837|0.80|0.875|0.543 |0.857|0.608 | |
|
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.89|0.601 |0.876|0.645 | |
|
|jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.829|0.896|0.526 |**0.882**|0.652 | |
|
|
|
*update: we have updated the checkpoints for small/base model, re-evaluation of large model and BEIR is running in progress.* |
|
|
|
## Usage |
|
|
|
Use with Jina AI Finetuner |
|
|
|
```python |
|
!pip install finetuner |
|
import finetuner |
|
|
|
model = finetuner.build_model('jinaai/jina-embedding-l-en-v1') |
|
embeddings = finetuner.encode( |
|
model=model, |
|
data=['how is the weather today', 'What is the current weather like today?'] |
|
) |
|
print(finetuner.cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
Use directly with sentence-transformers: |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
sentences = ['how is the weather today', 'What is the current weather like today?'] |
|
|
|
model = SentenceTransformer('jinaai/jina-embedding-b-en-v1') |
|
embeddings = model.encode(sentences) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
## Fine-tuning |
|
|
|
Please consider [Finetuner](https://github.com/jina-ai/finetuner). |
|
|
|
## Plans |
|
|
|
1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length. |
|
2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`. |
|
|
|
## Contact |
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Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
