|
--- |
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tags: |
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- finetuner |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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datasets: |
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- jinaai/negation-dataset |
|
language: en |
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license: apache-2.0 |
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model-index: |
|
- name: jina-embedding-b-en-v1 |
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results: |
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- task: |
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type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 66.58208955223881 |
|
- type: ap |
|
value: 28.455148149555754 |
|
- type: f1 |
|
value: 59.973775371110385 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 65.09505 |
|
- type: ap |
|
value: 61.387245649832614 |
|
- type: f1 |
|
value: 62.96831291412068 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 30.633999999999993 |
|
- type: f1 |
|
value: 29.638828990078647 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
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split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.889 |
|
- type: map_at_10 |
|
value: 40.604 |
|
- type: map_at_100 |
|
value: 41.697 |
|
- type: map_at_1000 |
|
value: 41.705999999999996 |
|
- type: map_at_3 |
|
value: 35.217999999999996 |
|
- type: map_at_5 |
|
value: 38.326 |
|
- type: mrr_at_1 |
|
value: 26.245 |
|
- type: mrr_at_10 |
|
value: 40.736 |
|
- type: mrr_at_100 |
|
value: 41.829 |
|
- type: mrr_at_1000 |
|
value: 41.837999999999994 |
|
- type: mrr_at_3 |
|
value: 35.349000000000004 |
|
- type: mrr_at_5 |
|
value: 38.425 |
|
- type: ndcg_at_1 |
|
value: 25.889 |
|
- type: ndcg_at_10 |
|
value: 49.347 |
|
- type: ndcg_at_100 |
|
value: 53.956 |
|
- type: ndcg_at_1000 |
|
value: 54.2 |
|
- type: ndcg_at_3 |
|
value: 38.282 |
|
- type: ndcg_at_5 |
|
value: 43.895 |
|
- type: precision_at_1 |
|
value: 25.889 |
|
- type: precision_at_10 |
|
value: 7.752000000000001 |
|
- type: precision_at_100 |
|
value: 0.976 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 15.717999999999998 |
|
- type: precision_at_5 |
|
value: 12.162 |
|
- type: recall_at_1 |
|
value: 25.889 |
|
- type: recall_at_10 |
|
value: 77.525 |
|
- type: recall_at_100 |
|
value: 97.58200000000001 |
|
- type: recall_at_1000 |
|
value: 99.502 |
|
- type: recall_at_3 |
|
value: 47.155 |
|
- type: recall_at_5 |
|
value: 60.81100000000001 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 39.2179862062943 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 29.87826673088078 |
|
- 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 |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 62.72401299412015 |
|
- type: mrr |
|
value: 75.45167743921206 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
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name: MTEB BIOSSES |
|
config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.96510928112639 |
|
- type: cos_sim_spearman |
|
value: 82.64224450538681 |
|
- type: euclidean_pearson |
|
value: 52.03458755006108 |
|
- type: euclidean_spearman |
|
value: 52.83192670285616 |
|
- type: manhattan_pearson |
|
value: 52.14561955040935 |
|
- type: manhattan_spearman |
|
value: 52.9584356095438 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 84.11363636363636 |
|
- type: f1 |
|
value: 84.01098114920124 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 32.991971466919026 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 26.48807922559519 |
|
- task: |
|
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: |
|
- type: map_at_1 |
|
value: 8.014000000000001 |
|
- type: map_at_10 |
|
value: 14.149999999999999 |
|
- type: map_at_100 |
|
value: 15.539 |
|
- type: map_at_1000 |
|
value: 15.711 |
|
- type: map_at_3 |
|
value: 11.913 |
|
- type: map_at_5 |
|
value: 12.982 |
|
- type: mrr_at_1 |
|
value: 18.046 |
|
- type: mrr_at_10 |
|
value: 28.224 |
|
- type: mrr_at_100 |
|
value: 29.293000000000003 |
|
- type: mrr_at_1000 |
|
value: 29.348999999999997 |
|
- type: mrr_at_3 |
|
value: 25.179000000000002 |
|
- type: mrr_at_5 |
|
value: 26.827 |
|
- type: ndcg_at_1 |
|
value: 18.046 |
|
- type: ndcg_at_10 |
|
value: 20.784 |
|
- type: ndcg_at_100 |
|
value: 26.939999999999998 |
|
- type: ndcg_at_1000 |
|
value: 30.453999999999997 |
|
- type: ndcg_at_3 |
|
value: 16.694 |
|
- type: ndcg_at_5 |
|
value: 18.049 |
|
- type: precision_at_1 |
|
value: 18.046 |
|
- type: precision_at_10 |
|
value: 6.5280000000000005 |
|
- type: precision_at_100 |
|
value: 1.2959999999999998 |
|
- type: precision_at_1000 |
|
value: 0.19499999999999998 |
|
- type: precision_at_3 |
|
value: 12.465 |
|
- type: precision_at_5 |
|
value: 9.511 |
|
- type: recall_at_1 |
|
value: 8.014000000000001 |
|
- type: recall_at_10 |
|
value: 26.021 |
|
- type: recall_at_100 |
|
value: 47.692 |
|
- type: recall_at_1000 |
|
value: 67.63 |
|
- type: recall_at_3 |
|
value: 16.122 |
|
- type: recall_at_5 |
|
value: 19.817 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
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split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.396 |
|
- type: map_at_10 |
|
value: 14.543000000000001 |
|
- type: map_at_100 |
|
value: 19.235 |
|
- type: map_at_1000 |
|
value: 20.384 |
|
- type: map_at_3 |
|
value: 10.886 |
|
- type: map_at_5 |
|
value: 12.61 |
|
- type: mrr_at_1 |
|
value: 55.50000000000001 |
|
- type: mrr_at_10 |
|
value: 63.731 |
|
- type: mrr_at_100 |
|
value: 64.256 |
|
- type: mrr_at_1000 |
|
value: 64.27000000000001 |
|
- type: mrr_at_3 |
|
value: 61.583 |
|
- type: mrr_at_5 |
|
value: 62.92100000000001 |
|
- type: ndcg_at_1 |
|
value: 43.375 |
|
- type: ndcg_at_10 |
|
value: 31.352000000000004 |
|
- type: ndcg_at_100 |
|
value: 34.717999999999996 |
|
- type: ndcg_at_1000 |
|
value: 41.959 |
|
- type: ndcg_at_3 |
|
value: 35.319 |
|
- type: ndcg_at_5 |
|
value: 33.222 |
|
- type: precision_at_1 |
|
value: 55.50000000000001 |
|
- type: precision_at_10 |
|
value: 24.15 |
|
- type: precision_at_100 |
|
value: 7.42 |
|
- type: precision_at_1000 |
|
value: 1.66 |
|
- type: precision_at_3 |
|
value: 37.917 |
|
- type: precision_at_5 |
|
value: 31.900000000000002 |
|
- type: recall_at_1 |
|
value: 7.396 |
|
- type: recall_at_10 |
|
value: 19.686999999999998 |
|
- type: recall_at_100 |
|
value: 40.465 |
|
- type: recall_at_1000 |
|
value: 63.79899999999999 |
|
- type: recall_at_3 |
|
value: 12.124 |
|
- type: recall_at_5 |
|
value: 15.28 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 41.33 |
|
- type: f1 |
|
value: 37.682972473685496 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 49.019 |
|
- type: map_at_10 |
|
value: 61.219 |
|
- type: map_at_100 |
|
value: 61.753 |
|
- type: map_at_1000 |
|
value: 61.771 |
|
- type: map_at_3 |
|
value: 58.952000000000005 |
|
- type: map_at_5 |
|
value: 60.239 |
|
- type: mrr_at_1 |
|
value: 53 |
|
- type: mrr_at_10 |
|
value: 65.678 |
|
- type: mrr_at_100 |
|
value: 66.147 |
|
- type: mrr_at_1000 |
|
value: 66.155 |
|
- type: mrr_at_3 |
|
value: 63.495999999999995 |
|
- type: mrr_at_5 |
|
value: 64.75800000000001 |
|
- type: ndcg_at_1 |
|
value: 53 |
|
- type: ndcg_at_10 |
|
value: 67.587 |
|
- type: ndcg_at_100 |
|
value: 69.877 |
|
- type: ndcg_at_1000 |
|
value: 70.25200000000001 |
|
- type: ndcg_at_3 |
|
value: 63.174 |
|
- type: ndcg_at_5 |
|
value: 65.351 |
|
- type: precision_at_1 |
|
value: 53 |
|
- type: precision_at_10 |
|
value: 9.067 |
|
- type: precision_at_100 |
|
value: 1.026 |
|
- type: precision_at_1000 |
|
value: 0.107 |
|
- type: precision_at_3 |
|
value: 25.728 |
|
- type: precision_at_5 |
|
value: 16.637 |
|
- type: recall_at_1 |
|
value: 49.019 |
|
- type: recall_at_10 |
|
value: 82.962 |
|
- type: recall_at_100 |
|
value: 92.917 |
|
- type: recall_at_1000 |
|
value: 95.511 |
|
- type: recall_at_3 |
|
value: 70.838 |
|
- type: recall_at_5 |
|
value: 76.201 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.714000000000002 |
|
- type: map_at_10 |
|
value: 28.041 |
|
- type: map_at_100 |
|
value: 29.75 |
|
- type: map_at_1000 |
|
value: 29.944 |
|
- type: map_at_3 |
|
value: 23.884 |
|
- type: map_at_5 |
|
value: 26.468000000000004 |
|
- type: mrr_at_1 |
|
value: 33.796 |
|
- type: mrr_at_10 |
|
value: 42.757 |
|
- type: mrr_at_100 |
|
value: 43.705 |
|
- type: mrr_at_1000 |
|
value: 43.751 |
|
- type: mrr_at_3 |
|
value: 40.406 |
|
- type: mrr_at_5 |
|
value: 41.88 |
|
- type: ndcg_at_1 |
|
value: 33.796 |
|
- type: ndcg_at_10 |
|
value: 35.482 |
|
- type: ndcg_at_100 |
|
value: 42.44 |
|
- type: ndcg_at_1000 |
|
value: 45.903 |
|
- type: ndcg_at_3 |
|
value: 31.922 |
|
- type: ndcg_at_5 |
|
value: 33.516 |
|
- type: precision_at_1 |
|
value: 33.796 |
|
- type: precision_at_10 |
|
value: 10.108 |
|
- type: precision_at_100 |
|
value: 1.735 |
|
- type: precision_at_1000 |
|
value: 0.23500000000000001 |
|
- type: precision_at_3 |
|
value: 21.759 |
|
- type: precision_at_5 |
|
value: 16.605 |
|
- type: recall_at_1 |
|
value: 16.714000000000002 |
|
- type: recall_at_10 |
|
value: 42.38 |
|
- type: recall_at_100 |
|
value: 68.84700000000001 |
|
- type: recall_at_1000 |
|
value: 90.036 |
|
- type: recall_at_3 |
|
value: 28.776000000000003 |
|
- type: recall_at_5 |
|
value: 35.606 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.534 |
|
- type: map_at_10 |
|
value: 40.857 |
|
- type: map_at_100 |
|
value: 41.715999999999994 |
|
- type: map_at_1000 |
|
value: 41.795 |
|
- type: map_at_3 |
|
value: 38.415 |
|
- type: map_at_5 |
|
value: 39.833 |
|
- type: mrr_at_1 |
|
value: 59.068 |
|
- type: mrr_at_10 |
|
value: 66.034 |
|
- type: mrr_at_100 |
|
value: 66.479 |
|
- type: mrr_at_1000 |
|
value: 66.50399999999999 |
|
- type: mrr_at_3 |
|
value: 64.38000000000001 |
|
- type: mrr_at_5 |
|
value: 65.40599999999999 |
|
- type: ndcg_at_1 |
|
value: 59.068 |
|
- type: ndcg_at_10 |
|
value: 49.638 |
|
- type: ndcg_at_100 |
|
value: 53.093999999999994 |
|
- type: ndcg_at_1000 |
|
value: 54.813 |
|
- type: ndcg_at_3 |
|
value: 45.537 |
|
- type: ndcg_at_5 |
|
value: 47.671 |
|
- type: precision_at_1 |
|
value: 59.068 |
|
- type: precision_at_10 |
|
value: 10.313 |
|
- type: precision_at_100 |
|
value: 1.304 |
|
- type: precision_at_1000 |
|
value: 0.153 |
|
- type: precision_at_3 |
|
value: 28.278 |
|
- type: precision_at_5 |
|
value: 18.658 |
|
- type: recall_at_1 |
|
value: 29.534 |
|
- type: recall_at_10 |
|
value: 51.56699999999999 |
|
- type: recall_at_100 |
|
value: 65.199 |
|
- type: recall_at_1000 |
|
value: 76.678 |
|
- type: recall_at_3 |
|
value: 42.417 |
|
- type: recall_at_5 |
|
value: 46.644000000000005 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 65.74719999999999 |
|
- type: ap |
|
value: 60.57322504947344 |
|
- type: f1 |
|
value: 65.37875006542282 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.695999999999998 |
|
- type: map_at_10 |
|
value: 26.661 |
|
- type: map_at_100 |
|
value: 27.982000000000003 |
|
- type: map_at_1000 |
|
value: 28.049000000000003 |
|
- type: map_at_3 |
|
value: 23.057 |
|
- type: map_at_5 |
|
value: 25.079 |
|
- type: mrr_at_1 |
|
value: 16.16 |
|
- type: mrr_at_10 |
|
value: 27.150999999999996 |
|
- type: mrr_at_100 |
|
value: 28.423 |
|
- type: mrr_at_1000 |
|
value: 28.483999999999998 |
|
- type: mrr_at_3 |
|
value: 23.577 |
|
- type: mrr_at_5 |
|
value: 25.585 |
|
- type: ndcg_at_1 |
|
value: 16.16 |
|
- type: ndcg_at_10 |
|
value: 33.017 |
|
- type: ndcg_at_100 |
|
value: 39.582 |
|
- type: ndcg_at_1000 |
|
value: 41.28 |
|
- type: ndcg_at_3 |
|
value: 25.607000000000003 |
|
- type: ndcg_at_5 |
|
value: 29.214000000000002 |
|
- type: precision_at_1 |
|
value: 16.16 |
|
- type: precision_at_10 |
|
value: 5.506 |
|
- type: precision_at_100 |
|
value: 0.882 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_3 |
|
value: 11.199 |
|
- type: precision_at_5 |
|
value: 8.55 |
|
- type: recall_at_1 |
|
value: 15.695999999999998 |
|
- type: recall_at_10 |
|
value: 52.736000000000004 |
|
- type: recall_at_100 |
|
value: 83.523 |
|
- type: recall_at_1000 |
|
value: 96.588 |
|
- type: recall_at_3 |
|
value: 32.484 |
|
- type: recall_at_5 |
|
value: 41.117 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 91.71682626538988 |
|
- type: f1 |
|
value: 91.60647677401211 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 74.94756041951665 |
|
- type: f1 |
|
value: 57.26936028487369 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 71.43241425689307 |
|
- type: f1 |
|
value: 68.80370629448252 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.04774714189642 |
|
- type: f1 |
|
value: 76.93545888412446 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 30.009784989313765 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 25.568442512328872 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.013959341949697 |
|
- type: mrr |
|
value: 31.998487836684575 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.316 |
|
- type: map_at_10 |
|
value: 10.287 |
|
- type: map_at_100 |
|
value: 12.817 |
|
- type: map_at_1000 |
|
value: 14.141 |
|
- type: map_at_3 |
|
value: 7.728 |
|
- type: map_at_5 |
|
value: 8.876000000000001 |
|
- type: mrr_at_1 |
|
value: 39.628 |
|
- type: mrr_at_10 |
|
value: 48.423 |
|
- type: mrr_at_100 |
|
value: 49.153999999999996 |
|
- type: mrr_at_1000 |
|
value: 49.198 |
|
- type: mrr_at_3 |
|
value: 45.666000000000004 |
|
- type: mrr_at_5 |
|
value: 47.477000000000004 |
|
- type: ndcg_at_1 |
|
value: 36.533 |
|
- type: ndcg_at_10 |
|
value: 29.304000000000002 |
|
- type: ndcg_at_100 |
|
value: 27.078000000000003 |
|
- type: ndcg_at_1000 |
|
value: 36.221 |
|
- type: ndcg_at_3 |
|
value: 33.256 |
|
- type: ndcg_at_5 |
|
value: 31.465 |
|
- type: precision_at_1 |
|
value: 39.009 |
|
- type: precision_at_10 |
|
value: 22.043 |
|
- type: precision_at_100 |
|
value: 7.115 |
|
- type: precision_at_1000 |
|
value: 1.991 |
|
- type: precision_at_3 |
|
value: 31.476 |
|
- type: precision_at_5 |
|
value: 27.616000000000003 |
|
- type: recall_at_1 |
|
value: 4.316 |
|
- type: recall_at_10 |
|
value: 14.507 |
|
- type: recall_at_100 |
|
value: 28.847 |
|
- type: recall_at_1000 |
|
value: 61.758 |
|
- type: recall_at_3 |
|
value: 8.753 |
|
- type: recall_at_5 |
|
value: 11.153 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.374 |
|
- type: map_at_10 |
|
value: 36.095 |
|
- type: map_at_100 |
|
value: 37.413999999999994 |
|
- type: map_at_1000 |
|
value: 37.46 |
|
- type: map_at_3 |
|
value: 31.711 |
|
- type: map_at_5 |
|
value: 34.294999999999995 |
|
- type: mrr_at_1 |
|
value: 25.406000000000002 |
|
- type: mrr_at_10 |
|
value: 38.424 |
|
- type: mrr_at_100 |
|
value: 39.456 |
|
- type: mrr_at_1000 |
|
value: 39.488 |
|
- type: mrr_at_3 |
|
value: 34.613 |
|
- type: mrr_at_5 |
|
value: 36.864999999999995 |
|
- type: ndcg_at_1 |
|
value: 25.406000000000002 |
|
- type: ndcg_at_10 |
|
value: 43.614000000000004 |
|
- type: ndcg_at_100 |
|
value: 49.166 |
|
- type: ndcg_at_1000 |
|
value: 50.212 |
|
- type: ndcg_at_3 |
|
value: 35.221999999999994 |
|
- type: ndcg_at_5 |
|
value: 39.571 |
|
- type: precision_at_1 |
|
value: 25.406000000000002 |
|
- type: precision_at_10 |
|
value: 7.654 |
|
- type: precision_at_100 |
|
value: 1.0699999999999998 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 16.425 |
|
- type: precision_at_5 |
|
value: 12.352 |
|
- type: recall_at_1 |
|
value: 22.374 |
|
- type: recall_at_10 |
|
value: 64.337 |
|
- type: recall_at_100 |
|
value: 88.374 |
|
- type: recall_at_1000 |
|
value: 96.101 |
|
- type: recall_at_3 |
|
value: 42.5 |
|
- type: recall_at_5 |
|
value: 52.556000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 69.301 |
|
- type: map_at_10 |
|
value: 83.128 |
|
- type: map_at_100 |
|
value: 83.779 |
|
- type: map_at_1000 |
|
value: 83.798 |
|
- type: map_at_3 |
|
value: 80.11399999999999 |
|
- type: map_at_5 |
|
value: 82.00699999999999 |
|
- type: mrr_at_1 |
|
value: 79.81 |
|
- type: mrr_at_10 |
|
value: 86.28 |
|
- type: mrr_at_100 |
|
value: 86.399 |
|
- type: mrr_at_1000 |
|
value: 86.401 |
|
- type: mrr_at_3 |
|
value: 85.26 |
|
- type: mrr_at_5 |
|
value: 85.93499999999999 |
|
- type: ndcg_at_1 |
|
value: 79.80000000000001 |
|
- type: ndcg_at_10 |
|
value: 87.06700000000001 |
|
- type: ndcg_at_100 |
|
value: 88.41799999999999 |
|
- type: ndcg_at_1000 |
|
value: 88.554 |
|
- type: ndcg_at_3 |
|
value: 84.052 |
|
- type: ndcg_at_5 |
|
value: 85.711 |
|
- type: precision_at_1 |
|
value: 79.80000000000001 |
|
- type: precision_at_10 |
|
value: 13.224 |
|
- type: precision_at_100 |
|
value: 1.5230000000000001 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 36.723 |
|
- type: precision_at_5 |
|
value: 24.192 |
|
- type: recall_at_1 |
|
value: 69.301 |
|
- type: recall_at_10 |
|
value: 94.589 |
|
- type: recall_at_100 |
|
value: 99.29299999999999 |
|
- type: recall_at_1000 |
|
value: 99.965 |
|
- type: recall_at_3 |
|
value: 86.045 |
|
- type: recall_at_5 |
|
value: 90.656 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 43.09903181165838 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 51.710378422887594 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.138 |
|
- type: map_at_10 |
|
value: 10.419 |
|
- type: map_at_100 |
|
value: 12.321 |
|
- type: map_at_1000 |
|
value: 12.605 |
|
- type: map_at_3 |
|
value: 7.445 |
|
- type: map_at_5 |
|
value: 8.859 |
|
- type: mrr_at_1 |
|
value: 20.4 |
|
- type: mrr_at_10 |
|
value: 30.148999999999997 |
|
- type: mrr_at_100 |
|
value: 31.357000000000003 |
|
- type: mrr_at_1000 |
|
value: 31.424999999999997 |
|
- type: mrr_at_3 |
|
value: 26.983 |
|
- type: mrr_at_5 |
|
value: 28.883 |
|
- type: ndcg_at_1 |
|
value: 20.4 |
|
- type: ndcg_at_10 |
|
value: 17.713 |
|
- type: ndcg_at_100 |
|
value: 25.221 |
|
- type: ndcg_at_1000 |
|
value: 30.381999999999998 |
|
- type: ndcg_at_3 |
|
value: 16.607 |
|
- type: ndcg_at_5 |
|
value: 14.559 |
|
- type: precision_at_1 |
|
value: 20.4 |
|
- type: precision_at_10 |
|
value: 9.3 |
|
- type: precision_at_100 |
|
value: 2.0060000000000002 |
|
- type: precision_at_1000 |
|
value: 0.32399999999999995 |
|
- type: precision_at_3 |
|
value: 15.5 |
|
- type: precision_at_5 |
|
value: 12.839999999999998 |
|
- type: recall_at_1 |
|
value: 4.138 |
|
- type: recall_at_10 |
|
value: 18.813 |
|
- type: recall_at_100 |
|
value: 40.692 |
|
- type: recall_at_1000 |
|
value: 65.835 |
|
- type: recall_at_3 |
|
value: 9.418 |
|
- type: recall_at_5 |
|
value: 12.983 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.25944192442188 |
|
- type: cos_sim_spearman |
|
value: 75.04296759426568 |
|
- type: euclidean_pearson |
|
value: 74.8130340249869 |
|
- type: euclidean_spearman |
|
value: 68.40180320816793 |
|
- type: manhattan_pearson |
|
value: 74.9149619199144 |
|
- type: manhattan_spearman |
|
value: 68.52380798258379 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.91983072545858 |
|
- type: cos_sim_spearman |
|
value: 73.5129498787296 |
|
- type: euclidean_pearson |
|
value: 66.76535523270856 |
|
- type: euclidean_spearman |
|
value: 56.64797879544097 |
|
- type: manhattan_pearson |
|
value: 66.12191731384162 |
|
- type: manhattan_spearman |
|
value: 56.37753861965956 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.71164758747632 |
|
- type: cos_sim_spearman |
|
value: 79.1530762030973 |
|
- type: euclidean_pearson |
|
value: 69.50621786400177 |
|
- type: euclidean_spearman |
|
value: 70.44898083428744 |
|
- type: manhattan_pearson |
|
value: 69.04018458995307 |
|
- type: manhattan_spearman |
|
value: 70.00888532086853 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.90774995778577 |
|
- type: cos_sim_spearman |
|
value: 75.24229403562713 |
|
- type: euclidean_pearson |
|
value: 68.5838924571539 |
|
- type: euclidean_spearman |
|
value: 65.06652398167358 |
|
- type: manhattan_pearson |
|
value: 68.23143277902628 |
|
- type: manhattan_spearman |
|
value: 64.79624516012709 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.78074322110155 |
|
- type: cos_sim_spearman |
|
value: 85.12071478276958 |
|
- type: euclidean_pearson |
|
value: 65.00147804089737 |
|
- type: euclidean_spearman |
|
value: 66.02559342831921 |
|
- type: manhattan_pearson |
|
value: 65.01270190203297 |
|
- type: manhattan_spearman |
|
value: 66.13038450207748 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.29395327338185 |
|
- type: cos_sim_spearman |
|
value: 80.07128686563352 |
|
- type: euclidean_pearson |
|
value: 65.97939065455975 |
|
- type: euclidean_spearman |
|
value: 66.80283051081129 |
|
- type: manhattan_pearson |
|
value: 65.6750450606584 |
|
- type: manhattan_spearman |
|
value: 66.55805829330733 |
|
- 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: 87.64956503192369 |
|
- type: cos_sim_spearman |
|
value: 87.95719598052727 |
|
- type: euclidean_pearson |
|
value: 73.35178669405819 |
|
- type: euclidean_spearman |
|
value: 71.58959083579994 |
|
- type: manhattan_pearson |
|
value: 73.24156949179472 |
|
- type: manhattan_spearman |
|
value: 71.35933730170666 |
|
- 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: 66.61640922485357 |
|
- type: cos_sim_spearman |
|
value: 66.08406266387749 |
|
- type: euclidean_pearson |
|
value: 43.684972836995776 |
|
- type: euclidean_spearman |
|
value: 60.26686390609082 |
|
- type: manhattan_pearson |
|
value: 43.694268683941154 |
|
- type: manhattan_spearman |
|
value: 59.61419719435629 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.73624666044613 |
|
- type: cos_sim_spearman |
|
value: 81.68869881979401 |
|
- type: euclidean_pearson |
|
value: 72.47205990508046 |
|
- type: euclidean_spearman |
|
value: 71.02381428101695 |
|
- type: manhattan_pearson |
|
value: 72.4947870027535 |
|
- type: manhattan_spearman |
|
value: 71.0789806652577 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 79.53671929012175 |
|
- type: mrr |
|
value: 93.96566033820936 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 43.761 |
|
- type: map_at_10 |
|
value: 53.846000000000004 |
|
- type: map_at_100 |
|
value: 54.55799999999999 |
|
- type: map_at_1000 |
|
value: 54.620999999999995 |
|
- type: map_at_3 |
|
value: 51.513 |
|
- type: map_at_5 |
|
value: 52.591 |
|
- type: mrr_at_1 |
|
value: 46.666999999999994 |
|
- type: mrr_at_10 |
|
value: 55.461000000000006 |
|
- type: mrr_at_100 |
|
value: 56.008 |
|
- type: mrr_at_1000 |
|
value: 56.069 |
|
- type: mrr_at_3 |
|
value: 53.5 |
|
- type: mrr_at_5 |
|
value: 54.417 |
|
- type: ndcg_at_1 |
|
value: 46.666999999999994 |
|
- type: ndcg_at_10 |
|
value: 58.599000000000004 |
|
- type: ndcg_at_100 |
|
value: 61.538000000000004 |
|
- type: ndcg_at_1000 |
|
value: 63.22 |
|
- type: ndcg_at_3 |
|
value: 54.254999999999995 |
|
- type: ndcg_at_5 |
|
value: 55.861000000000004 |
|
- type: precision_at_1 |
|
value: 46.666999999999994 |
|
- type: precision_at_10 |
|
value: 8.033 |
|
- type: precision_at_100 |
|
value: 0.963 |
|
- type: precision_at_1000 |
|
value: 0.11 |
|
- type: precision_at_3 |
|
value: 21.667 |
|
- type: precision_at_5 |
|
value: 14.066999999999998 |
|
- type: recall_at_1 |
|
value: 43.761 |
|
- type: recall_at_10 |
|
value: 71.65599999999999 |
|
- type: recall_at_100 |
|
value: 84.433 |
|
- type: recall_at_1000 |
|
value: 97.5 |
|
- type: recall_at_3 |
|
value: 59.522 |
|
- type: recall_at_5 |
|
value: 63.632999999999996 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.68811881188118 |
|
- type: cos_sim_ap |
|
value: 91.08077352794682 |
|
- type: cos_sim_f1 |
|
value: 84.38570729319628 |
|
- type: cos_sim_precision |
|
value: 82.64621284755513 |
|
- type: cos_sim_recall |
|
value: 86.2 |
|
- type: dot_accuracy |
|
value: 99.14653465346535 |
|
- type: dot_ap |
|
value: 45.24942149367904 |
|
- type: dot_f1 |
|
value: 46.470062555853445 |
|
- type: dot_precision |
|
value: 42.003231017770595 |
|
- type: dot_recall |
|
value: 52 |
|
- type: euclidean_accuracy |
|
value: 99.56930693069307 |
|
- type: euclidean_ap |
|
value: 80.28575652582506 |
|
- type: euclidean_f1 |
|
value: 75.52054023635341 |
|
- type: euclidean_precision |
|
value: 86.35778635778635 |
|
- type: euclidean_recall |
|
value: 67.10000000000001 |
|
- type: manhattan_accuracy |
|
value: 99.56039603960396 |
|
- type: manhattan_ap |
|
value: 79.74630510301085 |
|
- type: manhattan_f1 |
|
value: 74.67569091934575 |
|
- type: manhattan_precision |
|
value: 85.64036222509702 |
|
- type: manhattan_recall |
|
value: 66.2 |
|
- type: max_accuracy |
|
value: 99.68811881188118 |
|
- type: max_ap |
|
value: 91.08077352794682 |
|
- type: max_f1 |
|
value: 84.38570729319628 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 52.0788049295693 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 31.606006030205545 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 50.87384988372756 |
|
- type: mrr |
|
value: 51.62476922587217 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.355859978837156 |
|
- type: cos_sim_spearman |
|
value: 30.0847548337847 |
|
- type: dot_pearson |
|
value: 19.391736817587557 |
|
- type: dot_spearman |
|
value: 20.732256259543014 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.19 |
|
- type: map_at_10 |
|
value: 1.2850000000000001 |
|
- type: map_at_100 |
|
value: 6.376999999999999 |
|
- type: map_at_1000 |
|
value: 15.21 |
|
- type: map_at_3 |
|
value: 0.492 |
|
- type: map_at_5 |
|
value: 0.776 |
|
- type: mrr_at_1 |
|
value: 68 |
|
- type: mrr_at_10 |
|
value: 79.783 |
|
- type: mrr_at_100 |
|
value: 79.783 |
|
- type: mrr_at_1000 |
|
value: 79.783 |
|
- type: mrr_at_3 |
|
value: 77.333 |
|
- type: mrr_at_5 |
|
value: 79.533 |
|
- type: ndcg_at_1 |
|
value: 62 |
|
- type: ndcg_at_10 |
|
value: 54.635 |
|
- type: ndcg_at_100 |
|
value: 40.939 |
|
- type: ndcg_at_1000 |
|
value: 37.716 |
|
- type: ndcg_at_3 |
|
value: 58.531 |
|
- type: ndcg_at_5 |
|
value: 58.762 |
|
- type: precision_at_1 |
|
value: 68 |
|
- type: precision_at_10 |
|
value: 58.8 |
|
- type: precision_at_100 |
|
value: 41.74 |
|
- type: precision_at_1000 |
|
value: 16.938 |
|
- type: precision_at_3 |
|
value: 64 |
|
- type: precision_at_5 |
|
value: 64.8 |
|
- type: recall_at_1 |
|
value: 0.19 |
|
- type: recall_at_10 |
|
value: 1.547 |
|
- type: recall_at_100 |
|
value: 9.739 |
|
- type: recall_at_1000 |
|
value: 35.815000000000005 |
|
- type: recall_at_3 |
|
value: 0.528 |
|
- type: recall_at_5 |
|
value: 0.894 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 1.514 |
|
- type: map_at_10 |
|
value: 7.163 |
|
- type: map_at_100 |
|
value: 11.623999999999999 |
|
- type: map_at_1000 |
|
value: 13.062999999999999 |
|
- type: map_at_3 |
|
value: 3.51 |
|
- type: map_at_5 |
|
value: 4.661 |
|
- type: mrr_at_1 |
|
value: 20.408 |
|
- type: mrr_at_10 |
|
value: 33.993 |
|
- type: mrr_at_100 |
|
value: 35.257 |
|
- type: mrr_at_1000 |
|
value: 35.313 |
|
- type: mrr_at_3 |
|
value: 30.272 |
|
- type: mrr_at_5 |
|
value: 31.701 |
|
- type: ndcg_at_1 |
|
value: 18.367 |
|
- type: ndcg_at_10 |
|
value: 18.062 |
|
- type: ndcg_at_100 |
|
value: 28.441 |
|
- type: ndcg_at_1000 |
|
value: 40.748 |
|
- type: ndcg_at_3 |
|
value: 18.651999999999997 |
|
- type: ndcg_at_5 |
|
value: 17.055 |
|
- type: precision_at_1 |
|
value: 20.408 |
|
- type: precision_at_10 |
|
value: 17.551 |
|
- type: precision_at_100 |
|
value: 6.223999999999999 |
|
- type: precision_at_1000 |
|
value: 1.427 |
|
- type: precision_at_3 |
|
value: 20.408 |
|
- type: precision_at_5 |
|
value: 17.959 |
|
- type: recall_at_1 |
|
value: 1.514 |
|
- type: recall_at_10 |
|
value: 13.447000000000001 |
|
- type: recall_at_100 |
|
value: 39.77 |
|
- type: recall_at_1000 |
|
value: 76.95 |
|
- type: recall_at_3 |
|
value: 4.806 |
|
- type: recall_at_5 |
|
value: 6.873 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 65.53179999999999 |
|
- type: ap |
|
value: 11.504743595308318 |
|
- type: f1 |
|
value: 49.74264614001562 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 56.47425014148275 |
|
- type: f1 |
|
value: 56.555750746223346 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 39.27004599453324 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.47875067056088 |
|
- type: cos_sim_ap |
|
value: 68.630858164926 |
|
- type: cos_sim_f1 |
|
value: 64.5112402121748 |
|
- type: cos_sim_precision |
|
value: 61.87015503875969 |
|
- type: cos_sim_recall |
|
value: 67.38786279683377 |
|
- type: dot_accuracy |
|
value: 77.68969422423557 |
|
- type: dot_ap |
|
value: 37.28838556128439 |
|
- type: dot_f1 |
|
value: 43.27918525376652 |
|
- type: dot_precision |
|
value: 31.776047460140898 |
|
- type: dot_recall |
|
value: 67.83641160949868 |
|
- type: euclidean_accuracy |
|
value: 82.67866722298385 |
|
- type: euclidean_ap |
|
value: 62.72011158877603 |
|
- type: euclidean_f1 |
|
value: 60.39579770339605 |
|
- type: euclidean_precision |
|
value: 56.23293903548681 |
|
- type: euclidean_recall |
|
value: 65.22427440633246 |
|
- type: manhattan_accuracy |
|
value: 82.67866722298385 |
|
- type: manhattan_ap |
|
value: 62.80364769571995 |
|
- type: manhattan_f1 |
|
value: 60.413827282864574 |
|
- type: manhattan_precision |
|
value: 56.94931090866619 |
|
- type: manhattan_recall |
|
value: 64.32717678100263 |
|
- type: max_accuracy |
|
value: 84.47875067056088 |
|
- type: max_ap |
|
value: 68.630858164926 |
|
- type: max_f1 |
|
value: 64.5112402121748 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.4192959987581 |
|
- type: cos_sim_ap |
|
value: 84.81803796578367 |
|
- type: cos_sim_f1 |
|
value: 77.1643709825528 |
|
- type: cos_sim_precision |
|
value: 73.77958839643183 |
|
- type: cos_sim_recall |
|
value: 80.874653526332 |
|
- type: dot_accuracy |
|
value: 81.99441145651414 |
|
- type: dot_ap |
|
value: 67.908510950511 |
|
- type: dot_f1 |
|
value: 64.4734255193656 |
|
- type: dot_precision |
|
value: 56.120935539075866 |
|
- type: dot_recall |
|
value: 75.74684323991376 |
|
- type: euclidean_accuracy |
|
value: 82.67163426087632 |
|
- type: euclidean_ap |
|
value: 70.1466353903414 |
|
- type: euclidean_f1 |
|
value: 62.686024087617795 |
|
- type: euclidean_precision |
|
value: 59.42738875474301 |
|
- type: euclidean_recall |
|
value: 66.32275947028026 |
|
- type: manhattan_accuracy |
|
value: 82.6483486630186 |
|
- type: manhattan_ap |
|
value: 70.12958345267741 |
|
- type: manhattan_f1 |
|
value: 62.5966218150587 |
|
- type: manhattan_precision |
|
value: 58.47820272800214 |
|
- type: manhattan_recall |
|
value: 67.33908222975053 |
|
- type: max_accuracy |
|
value: 88.4192959987581 |
|
- type: max_ap |
|
value: 84.81803796578367 |
|
- type: max_f1 |
|
value: 77.1643709825528 |
|
--- |
|
--- |
|
|
|
<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-b-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 standard size of 110 million parameters, |
|
the model enables fast inference while delivering better performance than our small model. |
|
It is recommended to use a single GPU for inference. |
|
Additionally, we provide the following options: |
|
|
|
- `jina-embedding-s-en-v1`: 35 million parameters. |
|
- `jina-embedding-b-en-v1`: 110 million parameters **(you are here)**. |
|
- `jina-embedding-l-en-v1`: 330 million parameters. |
|
- `jina-embedding-xl-en-v1`: 1.2 billion parameters (soon). |
|
- `jina-embedding-xxl-en-v1`: 6 billion parameters (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 |
|
|
|
Usage with Jina AI Finetuner: |
|
|
|
```python |
|
!pip install finetuner |
|
import finetuner |
|
|
|
model = finetuner.build_model('jinaai/jina-embedding-b-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 Huggingface Transformers: |
|
|
|
```python |
|
import torch |
|
from transformers import AutoModel, AutoTokenizer |
|
|
|
|
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output[0] |
|
input_mask_expanded = ( |
|
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
|
) |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( |
|
input_mask_expanded.sum(1), min=1e-9 |
|
) |
|
|
|
|
|
# Sentences we want sentence embeddings for |
|
sentences = ['how is the weather today', 'What is the current weather like today?'] |
|
|
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embedding-s-en-v1') |
|
model = AutoModel.from_pretrained('jinaai/jina-embedding-s-en-v1') |
|
|
|
with torch.inference_mode(): |
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encoded_input = tokenizer( |
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sentences, padding=True, truncation=True, return_tensors='pt' |
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) |
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model_output = model.encoder(**encoded_input) |
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embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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``` |
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## Fine-tuning |
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|
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Please consider [Finetuner](https://github.com/jina-ai/finetuner). |
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|
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## Plans |
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1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length. |
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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`. |
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## Contact |
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|
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Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
