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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- mteb |
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- sentence-transformers |
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- transformers |
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model-index: |
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- name: e5-mistral-7b-instruct |
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results: |
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- task: |
|
type: STS |
|
dataset: |
|
name: MTEB AFQMC |
|
type: C-MTEB/AFQMC |
|
config: default |
|
split: validation |
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revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 37.863226091673866 |
|
- type: cos_sim_spearman |
|
value: 38.98733013335281 |
|
- type: euclidean_pearson |
|
value: 37.51783380497874 |
|
- type: euclidean_spearman |
|
value: 38.98733012753365 |
|
- type: manhattan_pearson |
|
value: 37.26706888081721 |
|
- type: manhattan_spearman |
|
value: 38.709750161903834 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB ATEC |
|
type: C-MTEB/ATEC |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 43.33924583134623 |
|
- type: cos_sim_spearman |
|
value: 42.84316155158754 |
|
- type: euclidean_pearson |
|
value: 45.62709879515238 |
|
- type: euclidean_spearman |
|
value: 42.843155921732404 |
|
- type: manhattan_pearson |
|
value: 45.4786950991229 |
|
- type: manhattan_spearman |
|
value: 42.657334751855984 |
|
- task: |
|
type: Classification |
|
dataset: |
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name: MTEB AmazonCounterfactualClassification (en) |
|
type: mteb/amazon_counterfactual |
|
config: en |
|
split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 78.68656716417911 |
|
- type: ap |
|
value: 41.71522322900398 |
|
- type: f1 |
|
value: 72.37207703532552 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonCounterfactualClassification (de) |
|
type: mteb/amazon_counterfactual |
|
config: de |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 74.04710920770879 |
|
- type: ap |
|
value: 83.42622221864045 |
|
- type: f1 |
|
value: 72.14388257905772 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonCounterfactualClassification (en-ext) |
|
type: mteb/amazon_counterfactual |
|
config: en-ext |
|
split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 77.93103448275862 |
|
- type: ap |
|
value: 26.039284760509513 |
|
- type: f1 |
|
value: 64.81092954450712 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonCounterfactualClassification (ja) |
|
type: mteb/amazon_counterfactual |
|
config: ja |
|
split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 77.21627408993577 |
|
- type: ap |
|
value: 24.876490553983036 |
|
- type: f1 |
|
value: 63.8773359684989 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonPolarityClassification |
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type: mteb/amazon_polarity |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 95.90679999999999 |
|
- type: ap |
|
value: 94.32357863164454 |
|
- type: f1 |
|
value: 95.90485634708557 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonReviewsClassification (en) |
|
type: mteb/amazon_reviews_multi |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 55.786 |
|
- type: f1 |
|
value: 55.31211995815146 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonReviewsClassification (de) |
|
type: mteb/amazon_reviews_multi |
|
config: de |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 53.26 |
|
- type: f1 |
|
value: 52.156230111544986 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonReviewsClassification (es) |
|
type: mteb/amazon_reviews_multi |
|
config: es |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 50.33 |
|
- type: f1 |
|
value: 49.195023008878145 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonReviewsClassification (fr) |
|
type: mteb/amazon_reviews_multi |
|
config: fr |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 49.3 |
|
- type: f1 |
|
value: 48.434470184108 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonReviewsClassification (ja) |
|
type: mteb/amazon_reviews_multi |
|
config: ja |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 48.68599999999999 |
|
- type: f1 |
|
value: 47.62681775202072 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB AmazonReviewsClassification (zh) |
|
type: mteb/amazon_reviews_multi |
|
config: zh |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 46.238 |
|
- type: f1 |
|
value: 45.014030559653705 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB ArguAna |
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type: arguana |
|
config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 36.486000000000004 |
|
- type: map_at_10 |
|
value: 53.076 |
|
- type: map_at_100 |
|
value: 53.657999999999994 |
|
- type: map_at_1000 |
|
value: 53.659 |
|
- type: map_at_3 |
|
value: 48.234 |
|
- type: map_at_5 |
|
value: 51.121 |
|
- type: mrr_at_1 |
|
value: 37.269000000000005 |
|
- type: mrr_at_10 |
|
value: 53.335 |
|
- type: mrr_at_100 |
|
value: 53.916 |
|
- type: mrr_at_1000 |
|
value: 53.918 |
|
- type: mrr_at_3 |
|
value: 48.518 |
|
- type: mrr_at_5 |
|
value: 51.406 |
|
- type: ndcg_at_1 |
|
value: 36.486000000000004 |
|
- type: ndcg_at_10 |
|
value: 61.882000000000005 |
|
- type: ndcg_at_100 |
|
value: 64.165 |
|
- type: ndcg_at_1000 |
|
value: 64.203 |
|
- type: ndcg_at_3 |
|
value: 52.049 |
|
- type: ndcg_at_5 |
|
value: 57.199 |
|
- type: precision_at_1 |
|
value: 36.486000000000004 |
|
- type: precision_at_10 |
|
value: 8.982999999999999 |
|
- type: precision_at_100 |
|
value: 0.9939999999999999 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 21.029 |
|
- type: precision_at_5 |
|
value: 15.092 |
|
- type: recall_at_1 |
|
value: 36.486000000000004 |
|
- type: recall_at_10 |
|
value: 89.82900000000001 |
|
- type: recall_at_100 |
|
value: 99.36 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 63.087 |
|
- type: recall_at_5 |
|
value: 75.46199999999999 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB ArxivClusteringP2P |
|
type: mteb/arxiv-clustering-p2p |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 50.45119266859667 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB ArxivClusteringS2S |
|
type: mteb/arxiv-clustering-s2s |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 45.4958298992051 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB AskUbuntuDupQuestions |
|
type: mteb/askubuntudupquestions-reranking |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 66.98177472838887 |
|
- type: mrr |
|
value: 79.91854636591478 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB BIOSSES |
|
type: mteb/biosses-sts |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.67086498650698 |
|
- type: cos_sim_spearman |
|
value: 85.54773239564638 |
|
- type: euclidean_pearson |
|
value: 86.48229161588425 |
|
- type: euclidean_spearman |
|
value: 85.54773239564638 |
|
- type: manhattan_pearson |
|
value: 86.67533327742343 |
|
- type: manhattan_spearman |
|
value: 85.76099026691983 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB BQ |
|
type: C-MTEB/BQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 50.31998888922809 |
|
- type: cos_sim_spearman |
|
value: 50.6369940530675 |
|
- type: euclidean_pearson |
|
value: 50.055544636296055 |
|
- type: euclidean_spearman |
|
value: 50.63699405154838 |
|
- type: manhattan_pearson |
|
value: 50.00739378036807 |
|
- type: manhattan_spearman |
|
value: 50.607237418676945 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB BUCC (de-en) |
|
type: mteb/bucc-bitext-mining |
|
config: de-en |
|
split: test |
|
revision: d51519689f32196a32af33b075a01d0e7c51e252 |
|
metrics: |
|
- type: accuracy |
|
value: 99.5615866388309 |
|
- type: f1 |
|
value: 99.49895615866389 |
|
- type: precision |
|
value: 99.46764091858039 |
|
- type: recall |
|
value: 99.5615866388309 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB BUCC (fr-en) |
|
type: mteb/bucc-bitext-mining |
|
config: fr-en |
|
split: test |
|
revision: d51519689f32196a32af33b075a01d0e7c51e252 |
|
metrics: |
|
- type: accuracy |
|
value: 99.19656614571869 |
|
- type: f1 |
|
value: 99.08650671362535 |
|
- type: precision |
|
value: 99.0314769975787 |
|
- type: recall |
|
value: 99.19656614571869 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB BUCC (ru-en) |
|
type: mteb/bucc-bitext-mining |
|
config: ru-en |
|
split: test |
|
revision: d51519689f32196a32af33b075a01d0e7c51e252 |
|
metrics: |
|
- type: accuracy |
|
value: 98.0256321440942 |
|
- type: f1 |
|
value: 97.83743216718624 |
|
- type: precision |
|
value: 97.74390947927492 |
|
- type: recall |
|
value: 98.0256321440942 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB BUCC (zh-en) |
|
type: mteb/bucc-bitext-mining |
|
config: zh-en |
|
split: test |
|
revision: d51519689f32196a32af33b075a01d0e7c51e252 |
|
metrics: |
|
- type: accuracy |
|
value: 99.26276987888363 |
|
- type: f1 |
|
value: 99.22766368264 |
|
- type: precision |
|
value: 99.21011058451816 |
|
- type: recall |
|
value: 99.26276987888363 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB Banking77Classification |
|
type: mteb/banking77 |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 88.22727272727272 |
|
- type: f1 |
|
value: 88.17411732496673 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB BiorxivClusteringP2P |
|
type: mteb/biorxiv-clustering-p2p |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 43.530637846246975 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB BiorxivClusteringS2S |
|
type: mteb/biorxiv-clustering-s2s |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 40.23505728593893 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB CLSClusteringP2P |
|
type: C-MTEB/CLSClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 44.419028279451275 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB CLSClusteringS2S |
|
type: C-MTEB/CLSClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 42.5820277929776 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB CMedQAv1 |
|
type: C-MTEB/CMedQAv1-reranking |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 77.67811726152972 |
|
- type: mrr |
|
value: 80.99003968253969 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB CMedQAv2 |
|
type: C-MTEB/CMedQAv2-reranking |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 78.66055354534922 |
|
- type: mrr |
|
value: 81.66119047619047 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackRetrieval |
|
type: BeIR/cqadupstack |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.162333333333333 |
|
- type: map_at_10 |
|
value: 37.22291666666667 |
|
- type: map_at_100 |
|
value: 38.56733333333333 |
|
- type: map_at_1000 |
|
value: 38.684250000000006 |
|
- type: map_at_3 |
|
value: 34.22858333333333 |
|
- type: map_at_5 |
|
value: 35.852500000000006 |
|
- type: mrr_at_1 |
|
value: 32.459833333333336 |
|
- type: mrr_at_10 |
|
value: 41.65358333333333 |
|
- type: mrr_at_100 |
|
value: 42.566916666666664 |
|
- type: mrr_at_1000 |
|
value: 42.61766666666667 |
|
- type: mrr_at_3 |
|
value: 39.210499999999996 |
|
- type: mrr_at_5 |
|
value: 40.582166666666666 |
|
- type: ndcg_at_1 |
|
value: 32.459833333333336 |
|
- type: ndcg_at_10 |
|
value: 42.96758333333333 |
|
- type: ndcg_at_100 |
|
value: 48.5065 |
|
- type: ndcg_at_1000 |
|
value: 50.556583333333336 |
|
- type: ndcg_at_3 |
|
value: 38.004416666666664 |
|
- type: ndcg_at_5 |
|
value: 40.25916666666667 |
|
- type: precision_at_1 |
|
value: 32.459833333333336 |
|
- type: precision_at_10 |
|
value: 7.664583333333333 |
|
- type: precision_at_100 |
|
value: 1.2349999999999999 |
|
- type: precision_at_1000 |
|
value: 0.15966666666666668 |
|
- type: precision_at_3 |
|
value: 17.731166666666663 |
|
- type: precision_at_5 |
|
value: 12.575333333333335 |
|
- type: recall_at_1 |
|
value: 27.162333333333333 |
|
- type: recall_at_10 |
|
value: 55.44158333333334 |
|
- type: recall_at_100 |
|
value: 79.56966666666666 |
|
- type: recall_at_1000 |
|
value: 93.45224999999999 |
|
- type: recall_at_3 |
|
value: 41.433083333333336 |
|
- type: recall_at_5 |
|
value: 47.31108333333333 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB ClimateFEVER |
|
type: climate-fever |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.539 |
|
- type: map_at_10 |
|
value: 28.494999999999997 |
|
- type: map_at_100 |
|
value: 30.568 |
|
- type: map_at_1000 |
|
value: 30.741000000000003 |
|
- type: map_at_3 |
|
value: 23.846999999999998 |
|
- type: map_at_5 |
|
value: 26.275 |
|
- type: mrr_at_1 |
|
value: 37.394 |
|
- type: mrr_at_10 |
|
value: 50.068 |
|
- type: mrr_at_100 |
|
value: 50.727 |
|
- type: mrr_at_1000 |
|
value: 50.751000000000005 |
|
- type: mrr_at_3 |
|
value: 46.938 |
|
- type: mrr_at_5 |
|
value: 48.818 |
|
- type: ndcg_at_1 |
|
value: 37.394 |
|
- type: ndcg_at_10 |
|
value: 38.349 |
|
- type: ndcg_at_100 |
|
value: 45.512 |
|
- type: ndcg_at_1000 |
|
value: 48.321 |
|
- type: ndcg_at_3 |
|
value: 32.172 |
|
- type: ndcg_at_5 |
|
value: 34.265 |
|
- type: precision_at_1 |
|
value: 37.394 |
|
- type: precision_at_10 |
|
value: 11.927999999999999 |
|
- type: precision_at_100 |
|
value: 1.966 |
|
- type: precision_at_1000 |
|
value: 0.25 |
|
- type: precision_at_3 |
|
value: 24.126 |
|
- type: precision_at_5 |
|
value: 18.306 |
|
- type: recall_at_1 |
|
value: 16.539 |
|
- type: recall_at_10 |
|
value: 44.504 |
|
- type: recall_at_100 |
|
value: 68.605 |
|
- type: recall_at_1000 |
|
value: 84.1 |
|
- type: recall_at_3 |
|
value: 29.008 |
|
- type: recall_at_5 |
|
value: 35.58 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CmedqaRetrieval |
|
type: C-MTEB/CmedqaRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.482 |
|
- type: map_at_10 |
|
value: 28.622999999999998 |
|
- type: map_at_100 |
|
value: 30.262 |
|
- type: map_at_1000 |
|
value: 30.432 |
|
- type: map_at_3 |
|
value: 25.647 |
|
- type: map_at_5 |
|
value: 27.128000000000004 |
|
- type: mrr_at_1 |
|
value: 30.408 |
|
- type: mrr_at_10 |
|
value: 37.188 |
|
- type: mrr_at_100 |
|
value: 38.196000000000005 |
|
- type: mrr_at_1000 |
|
value: 38.273 |
|
- type: mrr_at_3 |
|
value: 35.067 |
|
- type: mrr_at_5 |
|
value: 36.124 |
|
- type: ndcg_at_1 |
|
value: 30.408 |
|
- type: ndcg_at_10 |
|
value: 34.215 |
|
- type: ndcg_at_100 |
|
value: 41.349999999999994 |
|
- type: ndcg_at_1000 |
|
value: 44.689 |
|
- type: ndcg_at_3 |
|
value: 30.264999999999997 |
|
- type: ndcg_at_5 |
|
value: 31.572 |
|
- type: precision_at_1 |
|
value: 30.408 |
|
- type: precision_at_10 |
|
value: 7.6770000000000005 |
|
- type: precision_at_100 |
|
value: 1.352 |
|
- type: precision_at_1000 |
|
value: 0.178 |
|
- type: precision_at_3 |
|
value: 17.213 |
|
- type: precision_at_5 |
|
value: 12.198 |
|
- type: recall_at_1 |
|
value: 19.482 |
|
- type: recall_at_10 |
|
value: 42.368 |
|
- type: recall_at_100 |
|
value: 72.694 |
|
- type: recall_at_1000 |
|
value: 95.602 |
|
- type: recall_at_3 |
|
value: 30.101 |
|
- type: recall_at_5 |
|
value: 34.708 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
name: MTEB Cmnli |
|
type: C-MTEB/CMNLI |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 71.16055321707758 |
|
- type: cos_sim_ap |
|
value: 80.21073839711723 |
|
- type: cos_sim_f1 |
|
value: 72.9740932642487 |
|
- type: cos_sim_precision |
|
value: 65.53136050623488 |
|
- type: cos_sim_recall |
|
value: 82.3240589198036 |
|
- type: dot_accuracy |
|
value: 71.16055321707758 |
|
- type: dot_ap |
|
value: 80.212299264122 |
|
- type: dot_f1 |
|
value: 72.9740932642487 |
|
- type: dot_precision |
|
value: 65.53136050623488 |
|
- type: dot_recall |
|
value: 82.3240589198036 |
|
- type: euclidean_accuracy |
|
value: 71.16055321707758 |
|
- type: euclidean_ap |
|
value: 80.21076298680417 |
|
- type: euclidean_f1 |
|
value: 72.9740932642487 |
|
- type: euclidean_precision |
|
value: 65.53136050623488 |
|
- type: euclidean_recall |
|
value: 82.3240589198036 |
|
- type: manhattan_accuracy |
|
value: 70.71557426337944 |
|
- type: manhattan_ap |
|
value: 79.93448977199749 |
|
- type: manhattan_f1 |
|
value: 72.83962726826877 |
|
- type: manhattan_precision |
|
value: 62.7407908077053 |
|
- type: manhattan_recall |
|
value: 86.81318681318682 |
|
- type: max_accuracy |
|
value: 71.16055321707758 |
|
- type: max_ap |
|
value: 80.212299264122 |
|
- type: max_f1 |
|
value: 72.9740932642487 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CovidRetrieval |
|
type: C-MTEB/CovidRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 60.643 |
|
- type: map_at_10 |
|
value: 69.011 |
|
- type: map_at_100 |
|
value: 69.533 |
|
- type: map_at_1000 |
|
value: 69.545 |
|
- type: map_at_3 |
|
value: 67.167 |
|
- type: map_at_5 |
|
value: 68.12700000000001 |
|
- type: mrr_at_1 |
|
value: 60.801 |
|
- type: mrr_at_10 |
|
value: 69.111 |
|
- type: mrr_at_100 |
|
value: 69.6 |
|
- type: mrr_at_1000 |
|
value: 69.611 |
|
- type: mrr_at_3 |
|
value: 67.229 |
|
- type: mrr_at_5 |
|
value: 68.214 |
|
- type: ndcg_at_1 |
|
value: 60.801 |
|
- type: ndcg_at_10 |
|
value: 73.128 |
|
- type: ndcg_at_100 |
|
value: 75.614 |
|
- type: ndcg_at_1000 |
|
value: 75.92 |
|
- type: ndcg_at_3 |
|
value: 69.261 |
|
- type: ndcg_at_5 |
|
value: 70.973 |
|
- type: precision_at_1 |
|
value: 60.801 |
|
- type: precision_at_10 |
|
value: 8.662 |
|
- type: precision_at_100 |
|
value: 0.9860000000000001 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 25.149 |
|
- type: precision_at_5 |
|
value: 15.953999999999999 |
|
- type: recall_at_1 |
|
value: 60.643 |
|
- type: recall_at_10 |
|
value: 85.959 |
|
- type: recall_at_100 |
|
value: 97.576 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 75.184 |
|
- type: recall_at_5 |
|
value: 79.32000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB DBPedia |
|
type: dbpedia-entity |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.183 |
|
- type: map_at_10 |
|
value: 23.958 |
|
- type: map_at_100 |
|
value: 34.354 |
|
- type: map_at_1000 |
|
value: 36.442 |
|
- type: map_at_3 |
|
value: 16.345000000000002 |
|
- type: map_at_5 |
|
value: 19.647000000000002 |
|
- type: mrr_at_1 |
|
value: 74.25 |
|
- type: mrr_at_10 |
|
value: 80.976 |
|
- type: mrr_at_100 |
|
value: 81.256 |
|
- type: mrr_at_1000 |
|
value: 81.262 |
|
- type: mrr_at_3 |
|
value: 79.958 |
|
- type: mrr_at_5 |
|
value: 80.37100000000001 |
|
- type: ndcg_at_1 |
|
value: 62.0 |
|
- type: ndcg_at_10 |
|
value: 48.894999999999996 |
|
- type: ndcg_at_100 |
|
value: 53.867 |
|
- type: ndcg_at_1000 |
|
value: 61.304 |
|
- type: ndcg_at_3 |
|
value: 53.688 |
|
- type: ndcg_at_5 |
|
value: 50.900999999999996 |
|
- type: precision_at_1 |
|
value: 74.25 |
|
- type: precision_at_10 |
|
value: 39.525 |
|
- type: precision_at_100 |
|
value: 12.323 |
|
- type: precision_at_1000 |
|
value: 2.539 |
|
- type: precision_at_3 |
|
value: 57.49999999999999 |
|
- type: precision_at_5 |
|
value: 49.1 |
|
- type: recall_at_1 |
|
value: 10.183 |
|
- type: recall_at_10 |
|
value: 29.296 |
|
- type: recall_at_100 |
|
value: 60.394999999999996 |
|
- type: recall_at_1000 |
|
value: 83.12 |
|
- type: recall_at_3 |
|
value: 17.495 |
|
- type: recall_at_5 |
|
value: 22.235 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB DuRetrieval |
|
type: C-MTEB/DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.613999999999997 |
|
- type: map_at_10 |
|
value: 79.77300000000001 |
|
- type: map_at_100 |
|
value: 82.71 |
|
- type: map_at_1000 |
|
value: 82.75 |
|
- type: map_at_3 |
|
value: 55.92700000000001 |
|
- type: map_at_5 |
|
value: 70.085 |
|
- type: mrr_at_1 |
|
value: 90.7 |
|
- type: mrr_at_10 |
|
value: 93.438 |
|
- type: mrr_at_100 |
|
value: 93.504 |
|
- type: mrr_at_1000 |
|
value: 93.50699999999999 |
|
- type: mrr_at_3 |
|
value: 93.125 |
|
- type: mrr_at_5 |
|
value: 93.34 |
|
- type: ndcg_at_1 |
|
value: 90.7 |
|
- type: ndcg_at_10 |
|
value: 87.023 |
|
- type: ndcg_at_100 |
|
value: 90.068 |
|
- type: ndcg_at_1000 |
|
value: 90.43299999999999 |
|
- type: ndcg_at_3 |
|
value: 86.339 |
|
- type: ndcg_at_5 |
|
value: 85.013 |
|
- type: precision_at_1 |
|
value: 90.7 |
|
- type: precision_at_10 |
|
value: 41.339999999999996 |
|
- type: precision_at_100 |
|
value: 4.806 |
|
- type: precision_at_1000 |
|
value: 0.48900000000000005 |
|
- type: precision_at_3 |
|
value: 76.983 |
|
- type: precision_at_5 |
|
value: 64.69 |
|
- type: recall_at_1 |
|
value: 26.613999999999997 |
|
- type: recall_at_10 |
|
value: 87.681 |
|
- type: recall_at_100 |
|
value: 97.44699999999999 |
|
- type: recall_at_1000 |
|
value: 99.348 |
|
- type: recall_at_3 |
|
value: 57.809999999999995 |
|
- type: recall_at_5 |
|
value: 74.258 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB EcomRetrieval |
|
type: C-MTEB/EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.9 |
|
- type: map_at_10 |
|
value: 40.467 |
|
- type: map_at_100 |
|
value: 41.423 |
|
- type: map_at_1000 |
|
value: 41.463 |
|
- type: map_at_3 |
|
value: 37.25 |
|
- type: map_at_5 |
|
value: 39.31 |
|
- type: mrr_at_1 |
|
value: 30.9 |
|
- type: mrr_at_10 |
|
value: 40.467 |
|
- type: mrr_at_100 |
|
value: 41.423 |
|
- type: mrr_at_1000 |
|
value: 41.463 |
|
- type: mrr_at_3 |
|
value: 37.25 |
|
- type: mrr_at_5 |
|
value: 39.31 |
|
- type: ndcg_at_1 |
|
value: 30.9 |
|
- type: ndcg_at_10 |
|
value: 45.957 |
|
- type: ndcg_at_100 |
|
value: 50.735 |
|
- type: ndcg_at_1000 |
|
value: 51.861999999999995 |
|
- type: ndcg_at_3 |
|
value: 39.437 |
|
- type: ndcg_at_5 |
|
value: 43.146 |
|
- type: precision_at_1 |
|
value: 30.9 |
|
- type: precision_at_10 |
|
value: 6.35 |
|
- type: precision_at_100 |
|
value: 0.861 |
|
- type: precision_at_1000 |
|
value: 0.095 |
|
- type: precision_at_3 |
|
value: 15.267 |
|
- type: precision_at_5 |
|
value: 10.96 |
|
- type: recall_at_1 |
|
value: 30.9 |
|
- type: recall_at_10 |
|
value: 63.5 |
|
- type: recall_at_100 |
|
value: 86.1 |
|
- type: recall_at_1000 |
|
value: 95.1 |
|
- type: recall_at_3 |
|
value: 45.800000000000004 |
|
- type: recall_at_5 |
|
value: 54.800000000000004 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB EmotionClassification |
|
type: mteb/emotion |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 49.765 |
|
- type: f1 |
|
value: 45.93242203574485 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB FEVER |
|
type: fever |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 75.138 |
|
- type: map_at_10 |
|
value: 84.21300000000001 |
|
- type: map_at_100 |
|
value: 84.43 |
|
- type: map_at_1000 |
|
value: 84.441 |
|
- type: map_at_3 |
|
value: 83.071 |
|
- type: map_at_5 |
|
value: 83.853 |
|
- type: mrr_at_1 |
|
value: 80.948 |
|
- type: mrr_at_10 |
|
value: 88.175 |
|
- type: mrr_at_100 |
|
value: 88.24 |
|
- type: mrr_at_1000 |
|
value: 88.241 |
|
- type: mrr_at_3 |
|
value: 87.516 |
|
- type: mrr_at_5 |
|
value: 87.997 |
|
- type: ndcg_at_1 |
|
value: 80.948 |
|
- type: ndcg_at_10 |
|
value: 87.84100000000001 |
|
- type: ndcg_at_100 |
|
value: 88.576 |
|
- type: ndcg_at_1000 |
|
value: 88.75699999999999 |
|
- type: ndcg_at_3 |
|
value: 86.176 |
|
- type: ndcg_at_5 |
|
value: 87.214 |
|
- type: precision_at_1 |
|
value: 80.948 |
|
- type: precision_at_10 |
|
value: 10.632 |
|
- type: precision_at_100 |
|
value: 1.123 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 33.193 |
|
- type: precision_at_5 |
|
value: 20.663 |
|
- type: recall_at_1 |
|
value: 75.138 |
|
- type: recall_at_10 |
|
value: 94.89699999999999 |
|
- type: recall_at_100 |
|
value: 97.751 |
|
- type: recall_at_1000 |
|
value: 98.833 |
|
- type: recall_at_3 |
|
value: 90.455 |
|
- type: recall_at_5 |
|
value: 93.085 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB FiQA2018 |
|
type: fiqa |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.45 |
|
- type: map_at_10 |
|
value: 48.596000000000004 |
|
- type: map_at_100 |
|
value: 50.70400000000001 |
|
- type: map_at_1000 |
|
value: 50.83800000000001 |
|
- type: map_at_3 |
|
value: 42.795 |
|
- type: map_at_5 |
|
value: 46.085 |
|
- type: mrr_at_1 |
|
value: 56.172999999999995 |
|
- type: mrr_at_10 |
|
value: 64.35300000000001 |
|
- type: mrr_at_100 |
|
value: 64.947 |
|
- type: mrr_at_1000 |
|
value: 64.967 |
|
- type: mrr_at_3 |
|
value: 62.653999999999996 |
|
- type: mrr_at_5 |
|
value: 63.534 |
|
- type: ndcg_at_1 |
|
value: 56.172999999999995 |
|
- type: ndcg_at_10 |
|
value: 56.593 |
|
- type: ndcg_at_100 |
|
value: 62.942 |
|
- type: ndcg_at_1000 |
|
value: 64.801 |
|
- type: ndcg_at_3 |
|
value: 53.024 |
|
- type: ndcg_at_5 |
|
value: 53.986999999999995 |
|
- type: precision_at_1 |
|
value: 56.172999999999995 |
|
- type: precision_at_10 |
|
value: 15.494 |
|
- type: precision_at_100 |
|
value: 2.222 |
|
- type: precision_at_1000 |
|
value: 0.254 |
|
- type: precision_at_3 |
|
value: 35.185 |
|
- type: precision_at_5 |
|
value: 25.556 |
|
- type: recall_at_1 |
|
value: 29.45 |
|
- type: recall_at_10 |
|
value: 62.882000000000005 |
|
- type: recall_at_100 |
|
value: 85.56099999999999 |
|
- type: recall_at_1000 |
|
value: 96.539 |
|
- type: recall_at_3 |
|
value: 47.911 |
|
- type: recall_at_5 |
|
value: 54.52 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB HotpotQA |
|
type: hotpotqa |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.581 |
|
- type: map_at_10 |
|
value: 68.401 |
|
- type: map_at_100 |
|
value: 69.207 |
|
- type: map_at_1000 |
|
value: 69.25200000000001 |
|
- type: map_at_3 |
|
value: 64.689 |
|
- type: map_at_5 |
|
value: 67.158 |
|
- type: mrr_at_1 |
|
value: 79.163 |
|
- type: mrr_at_10 |
|
value: 85.22999999999999 |
|
- type: mrr_at_100 |
|
value: 85.386 |
|
- type: mrr_at_1000 |
|
value: 85.39099999999999 |
|
- type: mrr_at_3 |
|
value: 84.432 |
|
- type: mrr_at_5 |
|
value: 84.952 |
|
- type: ndcg_at_1 |
|
value: 79.163 |
|
- type: ndcg_at_10 |
|
value: 75.721 |
|
- type: ndcg_at_100 |
|
value: 78.411 |
|
- type: ndcg_at_1000 |
|
value: 79.23599999999999 |
|
- type: ndcg_at_3 |
|
value: 70.68799999999999 |
|
- type: ndcg_at_5 |
|
value: 73.694 |
|
- type: precision_at_1 |
|
value: 79.163 |
|
- type: precision_at_10 |
|
value: 16.134 |
|
- type: precision_at_100 |
|
value: 1.821 |
|
- type: precision_at_1000 |
|
value: 0.193 |
|
- type: precision_at_3 |
|
value: 46.446 |
|
- type: precision_at_5 |
|
value: 30.242 |
|
- type: recall_at_1 |
|
value: 39.581 |
|
- type: recall_at_10 |
|
value: 80.66799999999999 |
|
- type: recall_at_100 |
|
value: 91.033 |
|
- type: recall_at_1000 |
|
value: 96.408 |
|
- type: recall_at_3 |
|
value: 69.669 |
|
- type: recall_at_5 |
|
value: 75.604 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB IFlyTek |
|
type: C-MTEB/IFlyTek-classification |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 45.04809542131589 |
|
- type: f1 |
|
value: 37.01181779071118 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB ImdbClassification |
|
type: mteb/imdb |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 94.78120000000001 |
|
- type: ap |
|
value: 92.52931921594387 |
|
- type: f1 |
|
value: 94.77902110732532 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB JDReview |
|
type: C-MTEB/JDReview-classification |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 85.81613508442777 |
|
- type: ap |
|
value: 52.430320593468394 |
|
- type: f1 |
|
value: 79.95467268178068 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB LCQMC |
|
type: C-MTEB/LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.05801751913393 |
|
- type: cos_sim_spearman |
|
value: 75.47954644971965 |
|
- type: euclidean_pearson |
|
value: 74.27472296759713 |
|
- type: euclidean_spearman |
|
value: 75.47954201369866 |
|
- type: manhattan_pearson |
|
value: 74.30508190186474 |
|
- type: manhattan_spearman |
|
value: 75.51326518159436 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB MMarcoReranking |
|
type: C-MTEB/Mmarco-reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 24.21110921666315 |
|
- type: mrr |
|
value: 22.863492063492064 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB MMarcoRetrieval |
|
type: C-MTEB/MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 61.38400000000001 |
|
- type: map_at_10 |
|
value: 70.895 |
|
- type: map_at_100 |
|
value: 71.314 |
|
- type: map_at_1000 |
|
value: 71.331 |
|
- type: map_at_3 |
|
value: 69.016 |
|
- type: map_at_5 |
|
value: 70.179 |
|
- type: mrr_at_1 |
|
value: 63.481 |
|
- type: mrr_at_10 |
|
value: 71.543 |
|
- type: mrr_at_100 |
|
value: 71.91300000000001 |
|
- type: mrr_at_1000 |
|
value: 71.928 |
|
- type: mrr_at_3 |
|
value: 69.90899999999999 |
|
- type: mrr_at_5 |
|
value: 70.907 |
|
- type: ndcg_at_1 |
|
value: 63.481 |
|
- type: ndcg_at_10 |
|
value: 74.833 |
|
- type: ndcg_at_100 |
|
value: 76.705 |
|
- type: ndcg_at_1000 |
|
value: 77.13600000000001 |
|
- type: ndcg_at_3 |
|
value: 71.236 |
|
- type: ndcg_at_5 |
|
value: 73.199 |
|
- type: precision_at_1 |
|
value: 63.481 |
|
- type: precision_at_10 |
|
value: 9.179 |
|
- type: precision_at_100 |
|
value: 1.011 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 27.044 |
|
- type: precision_at_5 |
|
value: 17.272000000000002 |
|
- type: recall_at_1 |
|
value: 61.38400000000001 |
|
- type: recall_at_10 |
|
value: 86.318 |
|
- type: recall_at_100 |
|
value: 94.786 |
|
- type: recall_at_1000 |
|
value: 98.14500000000001 |
|
- type: recall_at_3 |
|
value: 76.717 |
|
- type: recall_at_5 |
|
value: 81.416 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB MSMARCO |
|
type: msmarco |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.363999999999997 |
|
- type: map_at_10 |
|
value: 36.022 |
|
- type: map_at_100 |
|
value: 37.229 |
|
- type: map_at_1000 |
|
value: 37.274 |
|
- type: map_at_3 |
|
value: 32.131 |
|
- type: map_at_5 |
|
value: 34.391 |
|
- type: mrr_at_1 |
|
value: 24.069 |
|
- type: mrr_at_10 |
|
value: 36.620000000000005 |
|
- type: mrr_at_100 |
|
value: 37.769999999999996 |
|
- type: mrr_at_1000 |
|
value: 37.809 |
|
- type: mrr_at_3 |
|
value: 32.846 |
|
- type: mrr_at_5 |
|
value: 35.02 |
|
- type: ndcg_at_1 |
|
value: 24.069 |
|
- type: ndcg_at_10 |
|
value: 43.056 |
|
- type: ndcg_at_100 |
|
value: 48.754 |
|
- type: ndcg_at_1000 |
|
value: 49.829 |
|
- type: ndcg_at_3 |
|
value: 35.167 |
|
- type: ndcg_at_5 |
|
value: 39.168 |
|
- type: precision_at_1 |
|
value: 24.069 |
|
- type: precision_at_10 |
|
value: 6.762 |
|
- type: precision_at_100 |
|
value: 0.96 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 14.957 |
|
- type: precision_at_5 |
|
value: 11.023 |
|
- type: recall_at_1 |
|
value: 23.363999999999997 |
|
- type: recall_at_10 |
|
value: 64.696 |
|
- type: recall_at_100 |
|
value: 90.795 |
|
- type: recall_at_1000 |
|
value: 98.892 |
|
- type: recall_at_3 |
|
value: 43.247 |
|
- type: recall_at_5 |
|
value: 52.86300000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPDomainClassification (en) |
|
type: mteb/mtop_domain |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 96.11947104423166 |
|
- type: f1 |
|
value: 95.89561841159332 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPDomainClassification (de) |
|
type: mteb/mtop_domain |
|
config: de |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 92.97548605240912 |
|
- type: f1 |
|
value: 92.17133696717212 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPDomainClassification (es) |
|
type: mteb/mtop_domain |
|
config: es |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.37224816544364 |
|
- type: f1 |
|
value: 93.19978829237863 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPDomainClassification (fr) |
|
type: mteb/mtop_domain |
|
config: fr |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 91.28719072972127 |
|
- type: f1 |
|
value: 91.28448045979604 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPDomainClassification (hi) |
|
type: mteb/mtop_domain |
|
config: hi |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 88.8131946934385 |
|
- type: f1 |
|
value: 88.27883019362747 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPDomainClassification (th) |
|
type: mteb/mtop_domain |
|
config: th |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 85.52260397830018 |
|
- type: f1 |
|
value: 85.15528226728568 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPIntentClassification (en) |
|
type: mteb/mtop_intent |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 86.10807113543093 |
|
- type: f1 |
|
value: 70.88498219072167 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPIntentClassification (de) |
|
type: mteb/mtop_intent |
|
config: de |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 77.77120315581854 |
|
- type: f1 |
|
value: 57.97153920153224 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPIntentClassification (es) |
|
type: mteb/mtop_intent |
|
config: es |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 79.93995997331554 |
|
- type: f1 |
|
value: 58.839203810064866 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPIntentClassification (fr) |
|
type: mteb/mtop_intent |
|
config: fr |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 77.801440651425 |
|
- type: f1 |
|
value: 58.68009647839332 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPIntentClassification (hi) |
|
type: mteb/mtop_intent |
|
config: hi |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 72.90785227680172 |
|
- type: f1 |
|
value: 49.83760954655788 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MTOPIntentClassification (th) |
|
type: mteb/mtop_intent |
|
config: th |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 73.24050632911391 |
|
- type: f1 |
|
value: 52.0562553541082 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (af) |
|
type: mteb/amazon_massive_intent |
|
config: af |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 66.47948890383321 |
|
- type: f1 |
|
value: 63.334877563135485 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (am) |
|
type: mteb/amazon_massive_intent |
|
config: am |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 44.2871553463349 |
|
- type: f1 |
|
value: 43.17658050605427 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (ar) |
|
type: mteb/amazon_massive_intent |
|
config: ar |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 63.174176193678555 |
|
- type: f1 |
|
value: 59.236659587042425 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (az) |
|
type: mteb/amazon_massive_intent |
|
config: az |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 64.226630800269 |
|
- type: f1 |
|
value: 60.951842696956184 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (bn) |
|
type: mteb/amazon_massive_intent |
|
config: bn |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 64.94283792871555 |
|
- type: f1 |
|
value: 61.40057652844215 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (cy) |
|
type: mteb/amazon_massive_intent |
|
config: cy |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 55.480833893745796 |
|
- type: f1 |
|
value: 52.5298332072816 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (da) |
|
type: mteb/amazon_massive_intent |
|
config: da |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 72.52858103564223 |
|
- type: f1 |
|
value: 69.3770851919204 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (de) |
|
type: mteb/amazon_massive_intent |
|
config: de |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 74.09213180901143 |
|
- type: f1 |
|
value: 71.13518469365879 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (el) |
|
type: mteb/amazon_massive_intent |
|
config: el |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 68.31203765971756 |
|
- type: f1 |
|
value: 66.05906970865144 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (en) |
|
type: mteb/amazon_massive_intent |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 80.57162071284465 |
|
- type: f1 |
|
value: 77.7866172598823 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (es) |
|
type: mteb/amazon_massive_intent |
|
config: es |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 75.09414929388029 |
|
- type: f1 |
|
value: 72.5712594833695 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (fa) |
|
type: mteb/amazon_massive_intent |
|
config: fa |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 72.20914593140553 |
|
- type: f1 |
|
value: 68.90619124909186 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (fi) |
|
type: mteb/amazon_massive_intent |
|
config: fi |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 68.74243443174176 |
|
- type: f1 |
|
value: 64.72743141749955 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (fr) |
|
type: mteb/amazon_massive_intent |
|
config: fr |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 75.11096166778749 |
|
- type: f1 |
|
value: 72.61849933064694 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (he) |
|
type: mteb/amazon_massive_intent |
|
config: he |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 66.22394082044384 |
|
- type: f1 |
|
value: 62.43648797607235 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (hi) |
|
type: mteb/amazon_massive_intent |
|
config: hi |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 69.44855413584399 |
|
- type: f1 |
|
value: 66.56851670913659 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (hu) |
|
type: mteb/amazon_massive_intent |
|
config: hu |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 69.4149293880296 |
|
- type: f1 |
|
value: 66.12960877904776 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (hy) |
|
type: mteb/amazon_massive_intent |
|
config: hy |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 56.916610625420304 |
|
- type: f1 |
|
value: 54.02534600927991 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveIntentClassification (id) |
|
type: mteb/amazon_massive_intent |
|
config: id |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 72.71351714862138 |
|
- type: f1 |
|
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|
type: mteb/amazon_massive_scenario |
|
config: he |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 69.64021519838602 |
|
- type: f1 |
|
value: 68.45118053027653 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (hi) |
|
type: mteb/amazon_massive_scenario |
|
config: hi |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 73.51042367182245 |
|
- type: f1 |
|
value: 72.90013022879003 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (hu) |
|
type: mteb/amazon_massive_scenario |
|
config: hu |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 74.0551445864156 |
|
- type: f1 |
|
value: 73.45871761713292 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (hy) |
|
type: mteb/amazon_massive_scenario |
|
config: hy |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 59.54606590450571 |
|
- type: f1 |
|
value: 57.72711794953869 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (id) |
|
type: mteb/amazon_massive_scenario |
|
config: id |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.40753194351042 |
|
- type: f1 |
|
value: 76.8157455506521 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (is) |
|
type: mteb/amazon_massive_scenario |
|
config: is |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 66.58372562205783 |
|
- type: f1 |
|
value: 65.2654868709758 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (it) |
|
type: mteb/amazon_massive_scenario |
|
config: it |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.39273705447208 |
|
- type: f1 |
|
value: 78.3592956594837 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ja) |
|
type: mteb/amazon_massive_scenario |
|
config: ja |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 79.62004034969739 |
|
- type: f1 |
|
value: 79.78673754501855 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (jv) |
|
type: mteb/amazon_massive_scenario |
|
config: jv |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 64.29051782111634 |
|
- type: f1 |
|
value: 63.12502587609454 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ka) |
|
type: mteb/amazon_massive_scenario |
|
config: ka |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 57.51849361129791 |
|
- type: f1 |
|
value: 56.32320906403241 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (km) |
|
type: mteb/amazon_massive_scenario |
|
config: km |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 52.41761936785474 |
|
- type: f1 |
|
value: 49.113762010098306 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (kn) |
|
type: mteb/amazon_massive_scenario |
|
config: kn |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 58.547410894418284 |
|
- type: f1 |
|
value: 56.87580674198118 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ko) |
|
type: mteb/amazon_massive_scenario |
|
config: ko |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.89038332212507 |
|
- type: f1 |
|
value: 79.09210140529848 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (lv) |
|
type: mteb/amazon_massive_scenario |
|
config: lv |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 63.503698722259585 |
|
- type: f1 |
|
value: 61.45718858568352 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ml) |
|
type: mteb/amazon_massive_scenario |
|
config: ml |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 54.02824478816408 |
|
- type: f1 |
|
value: 52.732738981386504 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (mn) |
|
type: mteb/amazon_massive_scenario |
|
config: mn |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 54.23671822461331 |
|
- type: f1 |
|
value: 52.688080372545286 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ms) |
|
type: mteb/amazon_massive_scenario |
|
config: ms |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 75.5312710154674 |
|
- type: f1 |
|
value: 74.59368478550698 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (my) |
|
type: mteb/amazon_massive_scenario |
|
config: my |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 52.192333557498316 |
|
- type: f1 |
|
value: 50.18302290152229 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (nb) |
|
type: mteb/amazon_massive_scenario |
|
config: nb |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 75.6960322797579 |
|
- type: f1 |
|
value: 75.25331182714856 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (nl) |
|
type: mteb/amazon_massive_scenario |
|
config: nl |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.47679892400808 |
|
- type: f1 |
|
value: 78.24044732352424 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (pl) |
|
type: mteb/amazon_massive_scenario |
|
config: pl |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.36718224613315 |
|
- type: f1 |
|
value: 77.2714452985389 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (pt) |
|
type: mteb/amazon_massive_scenario |
|
config: pt |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.96234028244788 |
|
- type: f1 |
|
value: 78.21282127011372 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ro) |
|
type: mteb/amazon_massive_scenario |
|
config: ro |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 73.19435104236717 |
|
- type: f1 |
|
value: 73.1963711292812 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ru) |
|
type: mteb/amazon_massive_scenario |
|
config: ru |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 80.52118359112306 |
|
- type: f1 |
|
value: 80.4179964390288 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (sl) |
|
type: mteb/amazon_massive_scenario |
|
config: sl |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 73.65837256220577 |
|
- type: f1 |
|
value: 73.07156989634905 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (sq) |
|
type: mteb/amazon_massive_scenario |
|
config: sq |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 64.02824478816409 |
|
- type: f1 |
|
value: 62.972399027713664 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (sv) |
|
type: mteb/amazon_massive_scenario |
|
config: sv |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.87020847343645 |
|
- type: f1 |
|
value: 78.224240866849 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (sw) |
|
type: mteb/amazon_massive_scenario |
|
config: sw |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 64.6570275722932 |
|
- type: f1 |
|
value: 63.274871811412545 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ta) |
|
type: mteb/amazon_massive_scenario |
|
config: ta |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 57.760591795561524 |
|
- type: f1 |
|
value: 56.73711528075771 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (te) |
|
type: mteb/amazon_massive_scenario |
|
config: te |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 57.26967047747142 |
|
- type: f1 |
|
value: 55.74735330863165 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (th) |
|
type: mteb/amazon_massive_scenario |
|
config: th |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 72.46133154001345 |
|
- type: f1 |
|
value: 71.9644168952811 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (tl) |
|
type: mteb/amazon_massive_scenario |
|
config: tl |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 73.70880968392737 |
|
- type: f1 |
|
value: 73.61543141070884 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (tr) |
|
type: mteb/amazon_massive_scenario |
|
config: tr |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 75.0437121721587 |
|
- type: f1 |
|
value: 74.83359868879921 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (ur) |
|
type: mteb/amazon_massive_scenario |
|
config: ur |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 67.05110961667788 |
|
- type: f1 |
|
value: 66.25869819274315 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (vi) |
|
type: mteb/amazon_massive_scenario |
|
config: vi |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 75.52118359112306 |
|
- type: f1 |
|
value: 75.92098546052303 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (zh-CN) |
|
type: mteb/amazon_massive_scenario |
|
config: zh-CN |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 79.92938802958977 |
|
- type: f1 |
|
value: 79.79833572573796 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MassiveScenarioClassification (zh-TW) |
|
type: mteb/amazon_massive_scenario |
|
config: zh-TW |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 76.86617350369872 |
|
- type: f1 |
|
value: 77.42645654909516 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB MedicalRetrieval |
|
type: C-MTEB/MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 44.6 |
|
- type: map_at_10 |
|
value: 50.019000000000005 |
|
- type: map_at_100 |
|
value: 50.611 |
|
- type: map_at_1000 |
|
value: 50.67 |
|
- type: map_at_3 |
|
value: 48.699999999999996 |
|
- type: map_at_5 |
|
value: 49.455 |
|
- type: mrr_at_1 |
|
value: 44.800000000000004 |
|
- type: mrr_at_10 |
|
value: 50.119 |
|
- type: mrr_at_100 |
|
value: 50.711 |
|
- type: mrr_at_1000 |
|
value: 50.77 |
|
- type: mrr_at_3 |
|
value: 48.8 |
|
- type: mrr_at_5 |
|
value: 49.555 |
|
- type: ndcg_at_1 |
|
value: 44.6 |
|
- type: ndcg_at_10 |
|
value: 52.754 |
|
- type: ndcg_at_100 |
|
value: 55.935 |
|
- type: ndcg_at_1000 |
|
value: 57.607 |
|
- type: ndcg_at_3 |
|
value: 50.012 |
|
- type: ndcg_at_5 |
|
value: 51.393 |
|
- type: precision_at_1 |
|
value: 44.6 |
|
- type: precision_at_10 |
|
value: 6.140000000000001 |
|
- type: precision_at_100 |
|
value: 0.77 |
|
- type: precision_at_1000 |
|
value: 0.09 |
|
- type: precision_at_3 |
|
value: 17.933 |
|
- type: precision_at_5 |
|
value: 11.44 |
|
- type: recall_at_1 |
|
value: 44.6 |
|
- type: recall_at_10 |
|
value: 61.4 |
|
- type: recall_at_100 |
|
value: 77.0 |
|
- type: recall_at_1000 |
|
value: 90.4 |
|
- type: recall_at_3 |
|
value: 53.800000000000004 |
|
- type: recall_at_5 |
|
value: 57.199999999999996 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB MedrxivClusteringP2P |
|
type: mteb/medrxiv-clustering-p2p |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 38.192667527616315 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB MedrxivClusteringS2S |
|
type: mteb/medrxiv-clustering-s2s |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 37.44738902946689 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB MindSmallReranking |
|
type: mteb/mind_small |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 32.59661273103955 |
|
- type: mrr |
|
value: 33.82024242497473 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB MultilingualSentiment |
|
type: C-MTEB/MultilingualSentiment-classification |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 73.31333333333335 |
|
- type: f1 |
|
value: 73.0873466527602 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB NFCorpus |
|
type: nfcorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.471 |
|
- type: map_at_10 |
|
value: 14.142 |
|
- type: map_at_100 |
|
value: 18.179000000000002 |
|
- type: map_at_1000 |
|
value: 19.772000000000002 |
|
- type: map_at_3 |
|
value: 9.716 |
|
- type: map_at_5 |
|
value: 11.763 |
|
- type: mrr_at_1 |
|
value: 51.393 |
|
- type: mrr_at_10 |
|
value: 58.814 |
|
- type: mrr_at_100 |
|
value: 59.330000000000005 |
|
- type: mrr_at_1000 |
|
value: 59.35 |
|
- type: mrr_at_3 |
|
value: 56.398 |
|
- type: mrr_at_5 |
|
value: 58.038999999999994 |
|
- type: ndcg_at_1 |
|
value: 49.69 |
|
- type: ndcg_at_10 |
|
value: 38.615 |
|
- type: ndcg_at_100 |
|
value: 35.268 |
|
- type: ndcg_at_1000 |
|
value: 43.745 |
|
- type: ndcg_at_3 |
|
value: 43.187 |
|
- type: ndcg_at_5 |
|
value: 41.528999999999996 |
|
- type: precision_at_1 |
|
value: 51.083999999999996 |
|
- type: precision_at_10 |
|
value: 29.474 |
|
- type: precision_at_100 |
|
value: 9.167 |
|
- type: precision_at_1000 |
|
value: 2.2089999999999996 |
|
- type: precision_at_3 |
|
value: 40.351 |
|
- type: precision_at_5 |
|
value: 36.285000000000004 |
|
- type: recall_at_1 |
|
value: 5.471 |
|
- type: recall_at_10 |
|
value: 19.242 |
|
- type: recall_at_100 |
|
value: 37.14 |
|
- type: recall_at_1000 |
|
value: 68.35900000000001 |
|
- type: recall_at_3 |
|
value: 10.896 |
|
- type: recall_at_5 |
|
value: 14.75 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB NQ |
|
type: nq |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.499 |
|
- type: map_at_10 |
|
value: 55.862 |
|
- type: map_at_100 |
|
value: 56.667 |
|
- type: map_at_1000 |
|
value: 56.684999999999995 |
|
- type: map_at_3 |
|
value: 51.534 |
|
- type: map_at_5 |
|
value: 54.2 |
|
- type: mrr_at_1 |
|
value: 44.351 |
|
- type: mrr_at_10 |
|
value: 58.567 |
|
- type: mrr_at_100 |
|
value: 59.099000000000004 |
|
- type: mrr_at_1000 |
|
value: 59.109 |
|
- type: mrr_at_3 |
|
value: 55.218999999999994 |
|
- type: mrr_at_5 |
|
value: 57.391999999999996 |
|
- type: ndcg_at_1 |
|
value: 44.322 |
|
- type: ndcg_at_10 |
|
value: 63.535 |
|
- type: ndcg_at_100 |
|
value: 66.654 |
|
- type: ndcg_at_1000 |
|
value: 66.991 |
|
- type: ndcg_at_3 |
|
value: 55.701 |
|
- type: ndcg_at_5 |
|
value: 60.06700000000001 |
|
- type: precision_at_1 |
|
value: 44.322 |
|
- type: precision_at_10 |
|
value: 10.026 |
|
- type: precision_at_100 |
|
value: 1.18 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 24.865000000000002 |
|
- type: precision_at_5 |
|
value: 17.48 |
|
- type: recall_at_1 |
|
value: 39.499 |
|
- type: recall_at_10 |
|
value: 84.053 |
|
- type: recall_at_100 |
|
value: 97.11 |
|
- type: recall_at_1000 |
|
value: 99.493 |
|
- type: recall_at_3 |
|
value: 64.091 |
|
- type: recall_at_5 |
|
value: 74.063 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
name: MTEB Ocnli |
|
type: C-MTEB/OCNLI |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 61.18029236599891 |
|
- type: cos_sim_ap |
|
value: 64.18398769398412 |
|
- type: cos_sim_f1 |
|
value: 67.96347757046446 |
|
- type: cos_sim_precision |
|
value: 54.4529262086514 |
|
- type: cos_sim_recall |
|
value: 90.3907074973601 |
|
- type: dot_accuracy |
|
value: 61.18029236599891 |
|
- type: dot_ap |
|
value: 64.18393484706077 |
|
- type: dot_f1 |
|
value: 67.96347757046446 |
|
- type: dot_precision |
|
value: 54.4529262086514 |
|
- type: dot_recall |
|
value: 90.3907074973601 |
|
- type: euclidean_accuracy |
|
value: 61.18029236599891 |
|
- type: euclidean_ap |
|
value: 64.18395024821486 |
|
- type: euclidean_f1 |
|
value: 67.96347757046446 |
|
- type: euclidean_precision |
|
value: 54.4529262086514 |
|
- type: euclidean_recall |
|
value: 90.3907074973601 |
|
- type: manhattan_accuracy |
|
value: 61.451001624255554 |
|
- type: manhattan_ap |
|
value: 64.38232708763513 |
|
- type: manhattan_f1 |
|
value: 68.05860805860804 |
|
- type: manhattan_precision |
|
value: 52.10319685922602 |
|
- type: manhattan_recall |
|
value: 98.09926082365365 |
|
- type: max_accuracy |
|
value: 61.451001624255554 |
|
- type: max_ap |
|
value: 64.38232708763513 |
|
- type: max_f1 |
|
value: 68.05860805860804 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB OnlineShopping |
|
type: C-MTEB/OnlineShopping-classification |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 92.19000000000001 |
|
- type: ap |
|
value: 89.73918431886767 |
|
- type: f1 |
|
value: 92.17175032574507 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB PAWSX |
|
type: C-MTEB/PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 15.079320253752224 |
|
- type: cos_sim_spearman |
|
value: 16.813772504404263 |
|
- type: euclidean_pearson |
|
value: 19.476541162041762 |
|
- type: euclidean_spearman |
|
value: 16.813772498098782 |
|
- type: manhattan_pearson |
|
value: 19.497429832915277 |
|
- type: manhattan_spearman |
|
value: 16.869600674180607 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB QBQTC |
|
type: C-MTEB/QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.36139599797913 |
|
- type: cos_sim_spearman |
|
value: 31.80296402851347 |
|
- type: euclidean_pearson |
|
value: 30.10387888252793 |
|
- type: euclidean_spearman |
|
value: 31.80297780103808 |
|
- type: manhattan_pearson |
|
value: 30.86720382849436 |
|
- type: manhattan_spearman |
|
value: 32.70491131366606 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB QuoraRetrieval |
|
type: quora |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.911 |
|
- type: map_at_10 |
|
value: 86.087 |
|
- type: map_at_100 |
|
value: 86.701 |
|
- type: map_at_1000 |
|
value: 86.715 |
|
- type: map_at_3 |
|
value: 83.231 |
|
- type: map_at_5 |
|
value: 85.051 |
|
- type: mrr_at_1 |
|
value: 82.75 |
|
- type: mrr_at_10 |
|
value: 88.759 |
|
- type: mrr_at_100 |
|
value: 88.844 |
|
- type: mrr_at_1000 |
|
value: 88.844 |
|
- type: mrr_at_3 |
|
value: 87.935 |
|
- type: mrr_at_5 |
|
value: 88.504 |
|
- type: ndcg_at_1 |
|
value: 82.75 |
|
- type: ndcg_at_10 |
|
value: 89.605 |
|
- type: ndcg_at_100 |
|
value: 90.664 |
|
- type: ndcg_at_1000 |
|
value: 90.733 |
|
- type: ndcg_at_3 |
|
value: 87.03 |
|
- type: ndcg_at_5 |
|
value: 88.473 |
|
- type: precision_at_1 |
|
value: 82.75 |
|
- type: precision_at_10 |
|
value: 13.575000000000001 |
|
- type: precision_at_100 |
|
value: 1.539 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 38.153 |
|
- type: precision_at_5 |
|
value: 25.008000000000003 |
|
- type: recall_at_1 |
|
value: 71.911 |
|
- type: recall_at_10 |
|
value: 96.261 |
|
- type: recall_at_100 |
|
value: 99.72800000000001 |
|
- type: recall_at_1000 |
|
value: 99.993 |
|
- type: recall_at_3 |
|
value: 88.762 |
|
- type: recall_at_5 |
|
value: 92.949 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB RedditClustering |
|
type: mteb/reddit-clustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 57.711581165572376 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB RedditClusteringP2P |
|
type: mteb/reddit-clustering-p2p |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 66.48938885750297 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB SCIDOCS |
|
type: scidocs |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.7379999999999995 |
|
- type: map_at_10 |
|
value: 9.261 |
|
- type: map_at_100 |
|
value: 11.001 |
|
- type: map_at_1000 |
|
value: 11.262 |
|
- type: map_at_3 |
|
value: 6.816 |
|
- type: map_at_5 |
|
value: 8.0 |
|
- type: mrr_at_1 |
|
value: 18.4 |
|
- type: mrr_at_10 |
|
value: 28.755999999999997 |
|
- type: mrr_at_100 |
|
value: 29.892000000000003 |
|
- type: mrr_at_1000 |
|
value: 29.961 |
|
- type: mrr_at_3 |
|
value: 25.467000000000002 |
|
- type: mrr_at_5 |
|
value: 27.332 |
|
- type: ndcg_at_1 |
|
value: 18.4 |
|
- type: ndcg_at_10 |
|
value: 16.296 |
|
- type: ndcg_at_100 |
|
value: 23.52 |
|
- type: ndcg_at_1000 |
|
value: 28.504 |
|
- type: ndcg_at_3 |
|
value: 15.485 |
|
- type: ndcg_at_5 |
|
value: 13.471 |
|
- type: precision_at_1 |
|
value: 18.4 |
|
- type: precision_at_10 |
|
value: 8.469999999999999 |
|
- type: precision_at_100 |
|
value: 1.8950000000000002 |
|
- type: precision_at_1000 |
|
value: 0.309 |
|
- type: precision_at_3 |
|
value: 14.6 |
|
- type: precision_at_5 |
|
value: 11.84 |
|
- type: recall_at_1 |
|
value: 3.7379999999999995 |
|
- type: recall_at_10 |
|
value: 17.185 |
|
- type: recall_at_100 |
|
value: 38.397 |
|
- type: recall_at_1000 |
|
value: 62.798 |
|
- type: recall_at_3 |
|
value: 8.896999999999998 |
|
- type: recall_at_5 |
|
value: 12.021999999999998 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB SICK-R |
|
type: mteb/sickr-sts |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.43977757480083 |
|
- type: cos_sim_spearman |
|
value: 82.64182475199533 |
|
- type: euclidean_pearson |
|
value: 83.71756009999591 |
|
- type: euclidean_spearman |
|
value: 82.64182331395057 |
|
- type: manhattan_pearson |
|
value: 83.8028936913025 |
|
- type: manhattan_spearman |
|
value: 82.71024597804252 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS12 |
|
type: mteb/sts12-sts |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.85653060698912 |
|
- type: cos_sim_spearman |
|
value: 79.65598885228324 |
|
- type: euclidean_pearson |
|
value: 83.1205137628455 |
|
- type: euclidean_spearman |
|
value: 79.65629387709038 |
|
- type: manhattan_pearson |
|
value: 83.71108853545837 |
|
- type: manhattan_spearman |
|
value: 80.25617619716708 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS13 |
|
type: mteb/sts13-sts |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.22921688565664 |
|
- type: cos_sim_spearman |
|
value: 88.42662103041957 |
|
- type: euclidean_pearson |
|
value: 87.91679798473325 |
|
- type: euclidean_spearman |
|
value: 88.42662103041957 |
|
- type: manhattan_pearson |
|
value: 88.16927537961303 |
|
- type: manhattan_spearman |
|
value: 88.81581680062541 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS14 |
|
type: mteb/sts14-sts |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.77261424554293 |
|
- type: cos_sim_spearman |
|
value: 84.53930146434155 |
|
- type: euclidean_pearson |
|
value: 85.67420491389697 |
|
- type: euclidean_spearman |
|
value: 84.53929771783851 |
|
- type: manhattan_pearson |
|
value: 85.74306784515618 |
|
- type: manhattan_spearman |
|
value: 84.7399304675314 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS15 |
|
type: mteb/sts15-sts |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 89.86138395166455 |
|
- type: cos_sim_spearman |
|
value: 90.42577823022054 |
|
- type: euclidean_pearson |
|
value: 89.8787763797515 |
|
- type: euclidean_spearman |
|
value: 90.42577823022054 |
|
- type: manhattan_pearson |
|
value: 89.9592937492158 |
|
- type: manhattan_spearman |
|
value: 90.63535505335524 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS16 |
|
type: mteb/sts16-sts |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.5176674585941 |
|
- type: cos_sim_spearman |
|
value: 87.6842917085397 |
|
- type: euclidean_pearson |
|
value: 86.70213081520711 |
|
- type: euclidean_spearman |
|
value: 87.6842917085397 |
|
- type: manhattan_pearson |
|
value: 86.83702628983627 |
|
- type: manhattan_spearman |
|
value: 87.87791000374443 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (ko-ko) |
|
type: mteb/sts17-crosslingual-sts |
|
config: ko-ko |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.86395454805867 |
|
- type: cos_sim_spearman |
|
value: 83.69454595252267 |
|
- type: euclidean_pearson |
|
value: 83.04743892608313 |
|
- type: euclidean_spearman |
|
value: 83.69454026433006 |
|
- type: manhattan_pearson |
|
value: 83.4032095553322 |
|
- type: manhattan_spearman |
|
value: 84.11527379013802 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (ar-ar) |
|
type: mteb/sts17-crosslingual-sts |
|
config: ar-ar |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.80249894729546 |
|
- type: cos_sim_spearman |
|
value: 81.87004960533409 |
|
- type: euclidean_pearson |
|
value: 80.0392760044179 |
|
- type: euclidean_spearman |
|
value: 81.87004960533409 |
|
- type: manhattan_pearson |
|
value: 80.38096542355912 |
|
- type: manhattan_spearman |
|
value: 82.40774679630341 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (en-ar) |
|
type: mteb/sts17-crosslingual-sts |
|
config: en-ar |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.6158201787172 |
|
- type: cos_sim_spearman |
|
value: 77.934651044009 |
|
- type: euclidean_pearson |
|
value: 77.7874683895269 |
|
- type: euclidean_spearman |
|
value: 77.934651044009 |
|
- type: manhattan_pearson |
|
value: 78.36151849193052 |
|
- type: manhattan_spearman |
|
value: 78.52439586349938 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (en-de) |
|
type: mteb/sts17-crosslingual-sts |
|
config: en-de |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.04363311392207 |
|
- type: cos_sim_spearman |
|
value: 87.30483659369973 |
|
- type: euclidean_pearson |
|
value: 87.62634489502616 |
|
- type: euclidean_spearman |
|
value: 87.30483659369973 |
|
- type: manhattan_pearson |
|
value: 88.02340837141445 |
|
- type: manhattan_spearman |
|
value: 87.55012003294 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (en-en) |
|
type: mteb/sts17-crosslingual-sts |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 91.69172851958248 |
|
- type: cos_sim_spearman |
|
value: 91.7546879482416 |
|
- type: euclidean_pearson |
|
value: 91.84843039183963 |
|
- type: euclidean_spearman |
|
value: 91.7546879482416 |
|
- type: manhattan_pearson |
|
value: 91.72325753804357 |
|
- type: manhattan_spearman |
|
value: 91.55330259513397 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (en-tr) |
|
type: mteb/sts17-crosslingual-sts |
|
config: en-tr |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 73.95572901084864 |
|
- type: cos_sim_spearman |
|
value: 72.56217821552626 |
|
- type: euclidean_pearson |
|
value: 74.24242980323574 |
|
- type: euclidean_spearman |
|
value: 72.56217821552626 |
|
- type: manhattan_pearson |
|
value: 74.57473362519922 |
|
- type: manhattan_spearman |
|
value: 72.76048826648497 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (es-en) |
|
type: mteb/sts17-crosslingual-sts |
|
config: es-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.93329396008296 |
|
- type: cos_sim_spearman |
|
value: 88.2406635486219 |
|
- type: euclidean_pearson |
|
value: 87.49687343908533 |
|
- type: euclidean_spearman |
|
value: 88.2406635486219 |
|
- type: manhattan_pearson |
|
value: 88.14088309231084 |
|
- type: manhattan_spearman |
|
value: 88.93314020908534 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (es-es) |
|
type: mteb/sts17-crosslingual-sts |
|
config: es-es |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.70124451546057 |
|
- type: cos_sim_spearman |
|
value: 87.45988160052252 |
|
- type: euclidean_pearson |
|
value: 88.44395505247728 |
|
- type: euclidean_spearman |
|
value: 87.45988160052252 |
|
- type: manhattan_pearson |
|
value: 88.69269783495425 |
|
- type: manhattan_spearman |
|
value: 87.65383425621 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (fr-en) |
|
type: mteb/sts17-crosslingual-sts |
|
config: fr-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.64109149761346 |
|
- type: cos_sim_spearman |
|
value: 88.06459637689733 |
|
- type: euclidean_pearson |
|
value: 88.02313315797703 |
|
- type: euclidean_spearman |
|
value: 88.06459637689733 |
|
- type: manhattan_pearson |
|
value: 88.28328539133253 |
|
- type: manhattan_spearman |
|
value: 88.06605708379142 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (it-en) |
|
type: mteb/sts17-crosslingual-sts |
|
config: it-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.9040028177525 |
|
- type: cos_sim_spearman |
|
value: 89.68152202933464 |
|
- type: euclidean_pearson |
|
value: 89.23684469601253 |
|
- type: euclidean_spearman |
|
value: 89.68152202933464 |
|
- type: manhattan_pearson |
|
value: 89.59504307277454 |
|
- type: manhattan_spearman |
|
value: 89.88060100313582 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS17 (nl-en) |
|
type: mteb/sts17-crosslingual-sts |
|
config: nl-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.69891585325125 |
|
- type: cos_sim_spearman |
|
value: 88.25252785071736 |
|
- type: euclidean_pearson |
|
value: 87.99932873748662 |
|
- type: euclidean_spearman |
|
value: 88.25252785071736 |
|
- type: manhattan_pearson |
|
value: 88.26959683009446 |
|
- type: manhattan_spearman |
|
value: 88.32583227300715 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (en) |
|
type: mteb/sts22-crosslingual-sts |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 67.53235909794135 |
|
- type: cos_sim_spearman |
|
value: 66.97521740529574 |
|
- type: euclidean_pearson |
|
value: 68.19502223613912 |
|
- type: euclidean_spearman |
|
value: 66.97521740529574 |
|
- type: manhattan_pearson |
|
value: 68.39070714774539 |
|
- type: manhattan_spearman |
|
value: 67.1072812364868 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (de) |
|
type: mteb/sts22-crosslingual-sts |
|
config: de |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 43.715742021204775 |
|
- type: cos_sim_spearman |
|
value: 49.12255971271453 |
|
- type: euclidean_pearson |
|
value: 40.76848562610837 |
|
- type: euclidean_spearman |
|
value: 49.12255971271453 |
|
- type: manhattan_pearson |
|
value: 40.92204625614112 |
|
- type: manhattan_spearman |
|
value: 49.23333793661129 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (es) |
|
type: mteb/sts22-crosslingual-sts |
|
config: es |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 63.35268345563588 |
|
- type: cos_sim_spearman |
|
value: 66.99661626042061 |
|
- type: euclidean_pearson |
|
value: 65.85589122857066 |
|
- type: euclidean_spearman |
|
value: 66.99661626042061 |
|
- type: manhattan_pearson |
|
value: 66.78454301512294 |
|
- type: manhattan_spearman |
|
value: 67.17570330149233 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (pl) |
|
type: mteb/sts22-crosslingual-sts |
|
config: pl |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 33.36599908204445 |
|
- type: cos_sim_spearman |
|
value: 39.20768331939503 |
|
- type: euclidean_pearson |
|
value: 22.16066769530468 |
|
- type: euclidean_spearman |
|
value: 39.20768331939503 |
|
- type: manhattan_pearson |
|
value: 22.386053195546022 |
|
- type: manhattan_spearman |
|
value: 39.70172817465986 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (tr) |
|
type: mteb/sts22-crosslingual-sts |
|
config: tr |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 63.06813956986753 |
|
- type: cos_sim_spearman |
|
value: 68.72065117995668 |
|
- type: euclidean_pearson |
|
value: 66.97373456344194 |
|
- type: euclidean_spearman |
|
value: 68.72065117995668 |
|
- type: manhattan_pearson |
|
value: 67.34907265771595 |
|
- type: manhattan_spearman |
|
value: 68.73705769957843 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (ar) |
|
type: mteb/sts22-crosslingual-sts |
|
config: ar |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 47.17664865207108 |
|
- type: cos_sim_spearman |
|
value: 54.115568323148864 |
|
- type: euclidean_pearson |
|
value: 48.56418162879182 |
|
- type: euclidean_spearman |
|
value: 54.115568323148864 |
|
- type: manhattan_pearson |
|
value: 48.85951643453165 |
|
- type: manhattan_spearman |
|
value: 54.13599784169052 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (ru) |
|
type: mteb/sts22-crosslingual-sts |
|
config: ru |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 55.87514136275987 |
|
- type: cos_sim_spearman |
|
value: 60.82923573674973 |
|
- type: euclidean_pearson |
|
value: 53.724183308215615 |
|
- type: euclidean_spearman |
|
value: 60.82923573674973 |
|
- type: manhattan_pearson |
|
value: 53.954305573102445 |
|
- type: manhattan_spearman |
|
value: 60.957483900644526 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (zh) |
|
type: mteb/sts22-crosslingual-sts |
|
config: zh |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 59.55001413648593 |
|
- type: cos_sim_spearman |
|
value: 63.395777040381276 |
|
- type: euclidean_pearson |
|
value: 59.869972550293305 |
|
- type: euclidean_spearman |
|
value: 63.395777040381276 |
|
- type: manhattan_pearson |
|
value: 61.16195496847885 |
|
- type: manhattan_spearman |
|
value: 63.41968682525581 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (fr) |
|
type: mteb/sts22-crosslingual-sts |
|
config: fr |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.13334972675852 |
|
- type: cos_sim_spearman |
|
value: 79.86263136371802 |
|
- type: euclidean_pearson |
|
value: 78.2433603592541 |
|
- type: euclidean_spearman |
|
value: 79.86263136371802 |
|
- type: manhattan_pearson |
|
value: 78.87337106318412 |
|
- type: manhattan_spearman |
|
value: 80.31230584758441 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (de-en) |
|
type: mteb/sts22-crosslingual-sts |
|
config: de-en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 63.559700748242356 |
|
- type: cos_sim_spearman |
|
value: 60.92342109509558 |
|
- type: euclidean_pearson |
|
value: 66.07256437521119 |
|
- type: euclidean_spearman |
|
value: 60.92342109509558 |
|
- type: manhattan_pearson |
|
value: 67.72769744612663 |
|
- type: manhattan_spearman |
|
value: 59.64714507774168 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (es-en) |
|
type: mteb/sts22-crosslingual-sts |
|
config: es-en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 73.93491616145891 |
|
- type: cos_sim_spearman |
|
value: 75.84242594400156 |
|
- type: euclidean_pearson |
|
value: 74.87279745626121 |
|
- type: euclidean_spearman |
|
value: 75.84242594400156 |
|
- type: manhattan_pearson |
|
value: 76.47764144677505 |
|
- type: manhattan_spearman |
|
value: 77.08411157845183 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (it) |
|
type: mteb/sts22-crosslingual-sts |
|
config: it |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 72.75624124540954 |
|
- type: cos_sim_spearman |
|
value: 75.8667941654703 |
|
- type: euclidean_pearson |
|
value: 73.74314588451925 |
|
- type: euclidean_spearman |
|
value: 75.8667941654703 |
|
- type: manhattan_pearson |
|
value: 73.99641425871518 |
|
- type: manhattan_spearman |
|
value: 76.1982840205817 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (pl-en) |
|
type: mteb/sts22-crosslingual-sts |
|
config: pl-en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 75.20898141298767 |
|
- type: cos_sim_spearman |
|
value: 73.18060375331436 |
|
- type: euclidean_pearson |
|
value: 75.44489280944619 |
|
- type: euclidean_spearman |
|
value: 73.18060375331436 |
|
- type: manhattan_pearson |
|
value: 75.65451039552286 |
|
- type: manhattan_spearman |
|
value: 72.97744006123156 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (zh-en) |
|
type: mteb/sts22-crosslingual-sts |
|
config: zh-en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 72.04278252247816 |
|
- type: cos_sim_spearman |
|
value: 71.8846446821539 |
|
- type: euclidean_pearson |
|
value: 73.16043307050612 |
|
- type: euclidean_spearman |
|
value: 71.8846446821539 |
|
- type: manhattan_pearson |
|
value: 74.76905116839777 |
|
- type: manhattan_spearman |
|
value: 72.66237093518471 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (es-it) |
|
type: mteb/sts22-crosslingual-sts |
|
config: es-it |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.71033173838558 |
|
- type: cos_sim_spearman |
|
value: 75.043122881885 |
|
- type: euclidean_pearson |
|
value: 72.77579680345087 |
|
- type: euclidean_spearman |
|
value: 75.043122881885 |
|
- type: manhattan_pearson |
|
value: 72.99901534854922 |
|
- type: manhattan_spearman |
|
value: 75.15418335015957 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (de-fr) |
|
type: mteb/sts22-crosslingual-sts |
|
config: de-fr |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 55.75733447190482 |
|
- type: cos_sim_spearman |
|
value: 61.38968334176681 |
|
- type: euclidean_pearson |
|
value: 55.479231520643744 |
|
- type: euclidean_spearman |
|
value: 61.38968334176681 |
|
- type: manhattan_pearson |
|
value: 56.05230571465244 |
|
- type: manhattan_spearman |
|
value: 62.69383054007398 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (de-pl) |
|
type: mteb/sts22-crosslingual-sts |
|
config: de-pl |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 41.72244325050302 |
|
- type: cos_sim_spearman |
|
value: 54.47476909084119 |
|
- type: euclidean_pearson |
|
value: 43.94629756436873 |
|
- type: euclidean_spearman |
|
value: 54.47476909084119 |
|
- type: manhattan_pearson |
|
value: 46.36533046394657 |
|
- type: manhattan_spearman |
|
value: 54.87509243633636 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STS22 (fr-pl) |
|
type: mteb/sts22-crosslingual-sts |
|
config: fr-pl |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 70.75183711835146 |
|
- type: cos_sim_spearman |
|
value: 84.51542547285167 |
|
- type: euclidean_pearson |
|
value: 71.84188960126669 |
|
- type: euclidean_spearman |
|
value: 84.51542547285167 |
|
- type: manhattan_pearson |
|
value: 73.94847166379994 |
|
- type: manhattan_spearman |
|
value: 84.51542547285167 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STSB |
|
type: C-MTEB/STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.78690149086131 |
|
- type: cos_sim_spearman |
|
value: 81.81202616916873 |
|
- type: euclidean_pearson |
|
value: 80.98792254251062 |
|
- type: euclidean_spearman |
|
value: 81.81202616916873 |
|
- type: manhattan_pearson |
|
value: 81.46953021346732 |
|
- type: manhattan_spearman |
|
value: 82.34259562492315 |
|
- task: |
|
type: STS |
|
dataset: |
|
name: MTEB STSBenchmark |
|
type: mteb/stsbenchmark-sts |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.68273341294419 |
|
- type: cos_sim_spearman |
|
value: 88.59927164210958 |
|
- type: euclidean_pearson |
|
value: 88.10745681818025 |
|
- type: euclidean_spearman |
|
value: 88.59927164210958 |
|
- type: manhattan_pearson |
|
value: 88.25166703784649 |
|
- type: manhattan_spearman |
|
value: 88.85343247873482 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB SciDocsRR |
|
type: mteb/scidocs-reranking |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 86.3340463345719 |
|
- type: mrr |
|
value: 96.5182611506141 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB SciFact |
|
type: scifact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 60.967000000000006 |
|
- type: map_at_10 |
|
value: 71.873 |
|
- type: map_at_100 |
|
value: 72.271 |
|
- type: map_at_1000 |
|
value: 72.292 |
|
- type: map_at_3 |
|
value: 69.006 |
|
- type: map_at_5 |
|
value: 70.856 |
|
- type: mrr_at_1 |
|
value: 63.666999999999994 |
|
- type: mrr_at_10 |
|
value: 72.929 |
|
- type: mrr_at_100 |
|
value: 73.26 |
|
- type: mrr_at_1000 |
|
value: 73.282 |
|
- type: mrr_at_3 |
|
value: 71.111 |
|
- type: mrr_at_5 |
|
value: 72.328 |
|
- type: ndcg_at_1 |
|
value: 63.666999999999994 |
|
- type: ndcg_at_10 |
|
value: 76.414 |
|
- type: ndcg_at_100 |
|
value: 78.152 |
|
- type: ndcg_at_1000 |
|
value: 78.604 |
|
- type: ndcg_at_3 |
|
value: 71.841 |
|
- type: ndcg_at_5 |
|
value: 74.435 |
|
- type: precision_at_1 |
|
value: 63.666999999999994 |
|
- type: precision_at_10 |
|
value: 10.067 |
|
- type: precision_at_100 |
|
value: 1.097 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 27.667 |
|
- type: precision_at_5 |
|
value: 18.467 |
|
- type: recall_at_1 |
|
value: 60.967000000000006 |
|
- type: recall_at_10 |
|
value: 88.922 |
|
- type: recall_at_100 |
|
value: 96.667 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 77.228 |
|
- type: recall_at_5 |
|
value: 83.428 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
name: MTEB SprintDuplicateQuestions |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.82277227722773 |
|
- type: cos_sim_ap |
|
value: 95.66279851444406 |
|
- type: cos_sim_f1 |
|
value: 90.9367088607595 |
|
- type: cos_sim_precision |
|
value: 92.1025641025641 |
|
- type: cos_sim_recall |
|
value: 89.8 |
|
- type: dot_accuracy |
|
value: 99.82277227722773 |
|
- type: dot_ap |
|
value: 95.66279851444406 |
|
- type: dot_f1 |
|
value: 90.9367088607595 |
|
- type: dot_precision |
|
value: 92.1025641025641 |
|
- type: dot_recall |
|
value: 89.8 |
|
- type: euclidean_accuracy |
|
value: 99.82277227722773 |
|
- type: euclidean_ap |
|
value: 95.66279851444406 |
|
- type: euclidean_f1 |
|
value: 90.9367088607595 |
|
- type: euclidean_precision |
|
value: 92.1025641025641 |
|
- type: euclidean_recall |
|
value: 89.8 |
|
- type: manhattan_accuracy |
|
value: 99.82673267326733 |
|
- type: manhattan_ap |
|
value: 95.86094873177069 |
|
- type: manhattan_f1 |
|
value: 91.26788357178096 |
|
- type: manhattan_precision |
|
value: 90.06815968841285 |
|
- type: manhattan_recall |
|
value: 92.5 |
|
- type: max_accuracy |
|
value: 99.82673267326733 |
|
- type: max_ap |
|
value: 95.86094873177069 |
|
- type: max_f1 |
|
value: 91.26788357178096 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB StackExchangeClustering |
|
type: mteb/stackexchange-clustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 73.09533925852372 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB StackExchangeClusteringP2P |
|
type: mteb/stackexchange-clustering-p2p |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 45.90745648090035 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB StackOverflowDupQuestions |
|
type: mteb/stackoverflowdupquestions-reranking |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 54.91147686504404 |
|
- type: mrr |
|
value: 56.03900082760377 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
name: MTEB SummEval |
|
type: mteb/summeval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.46908662038217 |
|
- type: cos_sim_spearman |
|
value: 31.40325730367437 |
|
- type: dot_pearson |
|
value: 31.469083969291894 |
|
- type: dot_spearman |
|
value: 31.40325730367437 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
name: MTEB T2Reranking |
|
type: C-MTEB/T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 66.90300783402137 |
|
- type: mrr |
|
value: 77.06451972574179 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB T2Retrieval |
|
type: C-MTEB/T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.82 |
|
- type: map_at_10 |
|
value: 72.32300000000001 |
|
- type: map_at_100 |
|
value: 76.198 |
|
- type: map_at_1000 |
|
value: 76.281 |
|
- type: map_at_3 |
|
value: 50.719 |
|
- type: map_at_5 |
|
value: 62.326 |
|
- type: mrr_at_1 |
|
value: 86.599 |
|
- type: mrr_at_10 |
|
value: 89.751 |
|
- type: mrr_at_100 |
|
value: 89.876 |
|
- type: mrr_at_1000 |
|
value: 89.88000000000001 |
|
- type: mrr_at_3 |
|
value: 89.151 |
|
- type: mrr_at_5 |
|
value: 89.519 |
|
- type: ndcg_at_1 |
|
value: 86.599 |
|
- type: ndcg_at_10 |
|
value: 80.676 |
|
- type: ndcg_at_100 |
|
value: 85.03 |
|
- type: ndcg_at_1000 |
|
value: 85.854 |
|
- type: ndcg_at_3 |
|
value: 82.057 |
|
- type: ndcg_at_5 |
|
value: 80.537 |
|
- type: precision_at_1 |
|
value: 86.599 |
|
- type: precision_at_10 |
|
value: 40.373 |
|
- type: precision_at_100 |
|
value: 4.95 |
|
- type: precision_at_1000 |
|
value: 0.514 |
|
- type: precision_at_3 |
|
value: 71.918 |
|
- type: precision_at_5 |
|
value: 60.246 |
|
- type: recall_at_1 |
|
value: 25.82 |
|
- type: recall_at_10 |
|
value: 79.905 |
|
- type: recall_at_100 |
|
value: 93.88499999999999 |
|
- type: recall_at_1000 |
|
value: 98.073 |
|
- type: recall_at_3 |
|
value: 52.623 |
|
- type: recall_at_5 |
|
value: 66.233 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB TNews |
|
type: C-MTEB/TNews-classification |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 47.050000000000004 |
|
- type: f1 |
|
value: 45.704071498353294 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB TRECCOVID |
|
type: trec-covid |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.243 |
|
- type: map_at_10 |
|
value: 2.278 |
|
- type: map_at_100 |
|
value: 14.221 |
|
- type: map_at_1000 |
|
value: 33.474 |
|
- type: map_at_3 |
|
value: 0.7270000000000001 |
|
- type: map_at_5 |
|
value: 1.183 |
|
- type: mrr_at_1 |
|
value: 94.0 |
|
- type: mrr_at_10 |
|
value: 97.0 |
|
- type: mrr_at_100 |
|
value: 97.0 |
|
- type: mrr_at_1000 |
|
value: 97.0 |
|
- type: mrr_at_3 |
|
value: 97.0 |
|
- type: mrr_at_5 |
|
value: 97.0 |
|
- type: ndcg_at_1 |
|
value: 90.0 |
|
- type: ndcg_at_10 |
|
value: 87.249 |
|
- type: ndcg_at_100 |
|
value: 67.876 |
|
- type: ndcg_at_1000 |
|
value: 59.205 |
|
- type: ndcg_at_3 |
|
value: 90.12299999999999 |
|
- type: ndcg_at_5 |
|
value: 89.126 |
|
- type: precision_at_1 |
|
value: 94.0 |
|
- type: precision_at_10 |
|
value: 90.8 |
|
- type: precision_at_100 |
|
value: 69.28 |
|
- type: precision_at_1000 |
|
value: 25.85 |
|
- type: precision_at_3 |
|
value: 94.667 |
|
- type: precision_at_5 |
|
value: 92.80000000000001 |
|
- type: recall_at_1 |
|
value: 0.243 |
|
- type: recall_at_10 |
|
value: 2.392 |
|
- type: recall_at_100 |
|
value: 16.982 |
|
- type: recall_at_1000 |
|
value: 55.214 |
|
- type: recall_at_3 |
|
value: 0.745 |
|
- type: recall_at_5 |
|
value: 1.2229999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (sqi-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: sqi-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 70.5 |
|
- type: f1 |
|
value: 67.05501804646966 |
|
- type: precision |
|
value: 65.73261904761904 |
|
- type: recall |
|
value: 70.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (fry-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: fry-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 75.14450867052022 |
|
- type: f1 |
|
value: 70.98265895953759 |
|
- type: precision |
|
value: 69.26782273603082 |
|
- type: recall |
|
value: 75.14450867052022 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (kur-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: kur-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 33.170731707317074 |
|
- type: f1 |
|
value: 29.92876500193573 |
|
- type: precision |
|
value: 28.669145894755648 |
|
- type: recall |
|
value: 33.170731707317074 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tur-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tur-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.5 |
|
- type: f1 |
|
value: 94.13333333333333 |
|
- type: precision |
|
value: 93.46666666666667 |
|
- type: recall |
|
value: 95.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (deu-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: deu-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 99.6 |
|
- type: f1 |
|
value: 99.46666666666665 |
|
- type: precision |
|
value: 99.4 |
|
- type: recall |
|
value: 99.6 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (nld-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: nld-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 97.2 |
|
- type: f1 |
|
value: 96.39999999999999 |
|
- type: precision |
|
value: 96.0 |
|
- type: recall |
|
value: 97.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ron-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ron-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 94.5 |
|
- type: f1 |
|
value: 92.99666666666667 |
|
- type: precision |
|
value: 92.31666666666666 |
|
- type: recall |
|
value: 94.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ang-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ang-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 85.82089552238806 |
|
- type: f1 |
|
value: 81.59203980099502 |
|
- type: precision |
|
value: 79.60199004975124 |
|
- type: recall |
|
value: 85.82089552238806 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ido-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ido-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 79.5 |
|
- type: f1 |
|
value: 75.11246031746032 |
|
- type: precision |
|
value: 73.38734126984127 |
|
- type: recall |
|
value: 79.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (jav-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: jav-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 44.390243902439025 |
|
- type: f1 |
|
value: 38.48896631823461 |
|
- type: precision |
|
value: 36.57220286488579 |
|
- type: recall |
|
value: 44.390243902439025 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (isl-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: isl-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 90.2 |
|
- type: f1 |
|
value: 87.57333333333334 |
|
- type: precision |
|
value: 86.34166666666665 |
|
- type: recall |
|
value: 90.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (slv-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: slv-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 88.82138517618469 |
|
- type: f1 |
|
value: 85.98651854423423 |
|
- type: precision |
|
value: 84.79257073424753 |
|
- type: recall |
|
value: 88.82138517618469 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (cym-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: cym-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 77.04347826086956 |
|
- type: f1 |
|
value: 72.32108147606868 |
|
- type: precision |
|
value: 70.37207357859532 |
|
- type: recall |
|
value: 77.04347826086956 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (kaz-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: kaz-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 53.04347826086957 |
|
- type: f1 |
|
value: 46.88868184955141 |
|
- type: precision |
|
value: 44.71730105643149 |
|
- type: recall |
|
value: 53.04347826086957 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (est-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: est-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 68.0 |
|
- type: f1 |
|
value: 62.891813186813195 |
|
- type: precision |
|
value: 61.037906162464985 |
|
- type: recall |
|
value: 68.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (heb-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: heb-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 86.3 |
|
- type: f1 |
|
value: 82.82000000000001 |
|
- type: precision |
|
value: 81.25690476190475 |
|
- type: recall |
|
value: 86.3 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (gla-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: gla-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 68.87816646562122 |
|
- type: f1 |
|
value: 63.53054933272062 |
|
- type: precision |
|
value: 61.47807816331196 |
|
- type: recall |
|
value: 68.87816646562122 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (mar-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: mar-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 74.4 |
|
- type: f1 |
|
value: 68.99388888888889 |
|
- type: precision |
|
value: 66.81035714285713 |
|
- type: recall |
|
value: 74.4 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (lat-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: lat-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 90.5 |
|
- type: f1 |
|
value: 87.93666666666667 |
|
- type: precision |
|
value: 86.825 |
|
- type: recall |
|
value: 90.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (bel-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: bel-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 90.7 |
|
- type: f1 |
|
value: 88.09 |
|
- type: precision |
|
value: 86.85833333333333 |
|
- type: recall |
|
value: 90.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (pms-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: pms-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 67.61904761904762 |
|
- type: f1 |
|
value: 62.30239247214037 |
|
- type: precision |
|
value: 60.340702947845806 |
|
- type: recall |
|
value: 67.61904761904762 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (gle-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: gle-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 77.9 |
|
- type: f1 |
|
value: 73.81285714285714 |
|
- type: precision |
|
value: 72.21570818070818 |
|
- type: recall |
|
value: 77.9 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (pes-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: pes-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 91.8 |
|
- type: f1 |
|
value: 89.66666666666667 |
|
- type: precision |
|
value: 88.66666666666666 |
|
- type: recall |
|
value: 91.8 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (nob-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: nob-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 97.6 |
|
- type: f1 |
|
value: 96.85666666666665 |
|
- type: precision |
|
value: 96.50833333333333 |
|
- type: recall |
|
value: 97.6 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (bul-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: bul-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.39999999999999 |
|
- type: f1 |
|
value: 93.98333333333333 |
|
- type: precision |
|
value: 93.30000000000001 |
|
- type: recall |
|
value: 95.39999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (cbk-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: cbk-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 85.0 |
|
- type: f1 |
|
value: 81.31538461538462 |
|
- type: precision |
|
value: 79.70666666666666 |
|
- type: recall |
|
value: 85.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (hun-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: hun-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 91.60000000000001 |
|
- type: f1 |
|
value: 89.81888888888888 |
|
- type: precision |
|
value: 89.08583333333333 |
|
- type: recall |
|
value: 91.60000000000001 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (uig-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: uig-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 44.3 |
|
- type: f1 |
|
value: 38.8623088023088 |
|
- type: precision |
|
value: 37.03755623461505 |
|
- type: recall |
|
value: 44.3 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (rus-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: rus-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.19999999999999 |
|
- type: f1 |
|
value: 93.75 |
|
- type: precision |
|
value: 93.05 |
|
- type: recall |
|
value: 95.19999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (spa-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: spa-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 99.1 |
|
- type: f1 |
|
value: 98.8 |
|
- type: precision |
|
value: 98.65 |
|
- type: recall |
|
value: 99.1 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (hye-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: hye-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 69.6765498652291 |
|
- type: f1 |
|
value: 63.991785393402644 |
|
- type: precision |
|
value: 61.7343729944808 |
|
- type: recall |
|
value: 69.6765498652291 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tel-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tel-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 50.0 |
|
- type: f1 |
|
value: 42.79341029341029 |
|
- type: precision |
|
value: 40.25098358431692 |
|
- type: recall |
|
value: 50.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (afr-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: afr-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 89.7 |
|
- type: f1 |
|
value: 87.19023809523809 |
|
- type: precision |
|
value: 86.12595238095237 |
|
- type: recall |
|
value: 89.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (mon-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: mon-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 42.72727272727273 |
|
- type: f1 |
|
value: 37.78789518562245 |
|
- type: precision |
|
value: 36.24208471267295 |
|
- type: recall |
|
value: 42.72727272727273 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (arz-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: arz-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 75.26205450733752 |
|
- type: f1 |
|
value: 70.72842833849123 |
|
- type: precision |
|
value: 68.93256464011182 |
|
- type: recall |
|
value: 75.26205450733752 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (hrv-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: hrv-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.19999999999999 |
|
- type: f1 |
|
value: 93.96666666666668 |
|
- type: precision |
|
value: 93.42 |
|
- type: recall |
|
value: 95.19999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (nov-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: nov-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 76.26459143968872 |
|
- type: f1 |
|
value: 72.40190419178747 |
|
- type: precision |
|
value: 70.84954604409856 |
|
- type: recall |
|
value: 76.26459143968872 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (gsw-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: gsw-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 59.82905982905983 |
|
- type: f1 |
|
value: 52.2100122100122 |
|
- type: precision |
|
value: 49.52516619183286 |
|
- type: recall |
|
value: 59.82905982905983 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (nds-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: nds-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 81.69999999999999 |
|
- type: f1 |
|
value: 77.41714285714286 |
|
- type: precision |
|
value: 75.64833333333334 |
|
- type: recall |
|
value: 81.69999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ukr-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ukr-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.5 |
|
- type: f1 |
|
value: 94.45 |
|
- type: precision |
|
value: 93.93333333333334 |
|
- type: recall |
|
value: 95.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (uzb-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: uzb-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 58.41121495327103 |
|
- type: f1 |
|
value: 52.73495974430554 |
|
- type: precision |
|
value: 50.717067200712066 |
|
- type: recall |
|
value: 58.41121495327103 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (lit-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: lit-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 73.3 |
|
- type: f1 |
|
value: 69.20371794871795 |
|
- type: precision |
|
value: 67.6597557997558 |
|
- type: recall |
|
value: 73.3 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ina-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ina-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 96.5 |
|
- type: f1 |
|
value: 95.51666666666667 |
|
- type: precision |
|
value: 95.05 |
|
- type: recall |
|
value: 96.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (lfn-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: lfn-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 78.4 |
|
- type: f1 |
|
value: 73.88856643356644 |
|
- type: precision |
|
value: 72.01373015873016 |
|
- type: recall |
|
value: 78.4 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (zsm-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: zsm-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.3 |
|
- type: f1 |
|
value: 94.09666666666668 |
|
- type: precision |
|
value: 93.53333333333332 |
|
- type: recall |
|
value: 95.3 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ita-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ita-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 93.7 |
|
- type: f1 |
|
value: 91.94 |
|
- type: precision |
|
value: 91.10833333333333 |
|
- type: recall |
|
value: 93.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (cmn-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: cmn-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 96.8 |
|
- type: f1 |
|
value: 95.89999999999999 |
|
- type: precision |
|
value: 95.46666666666668 |
|
- type: recall |
|
value: 96.8 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (lvs-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: lvs-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 70.5 |
|
- type: f1 |
|
value: 66.00635642135641 |
|
- type: precision |
|
value: 64.36345238095238 |
|
- type: recall |
|
value: 70.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (glg-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: glg-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 92.4 |
|
- type: f1 |
|
value: 90.44388888888889 |
|
- type: precision |
|
value: 89.5767857142857 |
|
- type: recall |
|
value: 92.4 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ceb-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ceb-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 48.0 |
|
- type: f1 |
|
value: 43.15372775372776 |
|
- type: precision |
|
value: 41.53152510162313 |
|
- type: recall |
|
value: 48.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (bre-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: bre-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 16.7 |
|
- type: f1 |
|
value: 14.198431372549017 |
|
- type: precision |
|
value: 13.411765873015872 |
|
- type: recall |
|
value: 16.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ben-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ben-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 85.7 |
|
- type: f1 |
|
value: 81.81666666666666 |
|
- type: precision |
|
value: 80.10833333333332 |
|
- type: recall |
|
value: 85.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (swg-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: swg-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 69.64285714285714 |
|
- type: f1 |
|
value: 64.745670995671 |
|
- type: precision |
|
value: 62.916666666666664 |
|
- type: recall |
|
value: 69.64285714285714 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (arq-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: arq-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 54.665203073545555 |
|
- type: f1 |
|
value: 48.55366630916923 |
|
- type: precision |
|
value: 46.35683318998357 |
|
- type: recall |
|
value: 54.665203073545555 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (kab-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: kab-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 4.8 |
|
- type: f1 |
|
value: 3.808587223587223 |
|
- type: precision |
|
value: 3.5653174603174604 |
|
- type: recall |
|
value: 4.8 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (fra-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: fra-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 96.6 |
|
- type: f1 |
|
value: 95.77333333333333 |
|
- type: precision |
|
value: 95.39166666666667 |
|
- type: recall |
|
value: 96.6 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (por-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: por-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.39999999999999 |
|
- type: f1 |
|
value: 94.44 |
|
- type: precision |
|
value: 93.975 |
|
- type: recall |
|
value: 95.39999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tat-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tat-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 42.0 |
|
- type: f1 |
|
value: 37.024908424908425 |
|
- type: precision |
|
value: 35.365992063492065 |
|
- type: recall |
|
value: 42.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (oci-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: oci-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 66.7 |
|
- type: f1 |
|
value: 62.20460835058661 |
|
- type: precision |
|
value: 60.590134587634594 |
|
- type: recall |
|
value: 66.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (pol-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: pol-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 97.3 |
|
- type: f1 |
|
value: 96.46666666666667 |
|
- type: precision |
|
value: 96.06666666666668 |
|
- type: recall |
|
value: 97.3 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (war-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: war-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 47.3 |
|
- type: f1 |
|
value: 41.96905408317173 |
|
- type: precision |
|
value: 40.18741402116402 |
|
- type: recall |
|
value: 47.3 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (aze-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: aze-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 80.2 |
|
- type: f1 |
|
value: 76.22690476190476 |
|
- type: precision |
|
value: 74.63539682539682 |
|
- type: recall |
|
value: 80.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (vie-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: vie-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 96.0 |
|
- type: f1 |
|
value: 94.83333333333333 |
|
- type: precision |
|
value: 94.26666666666668 |
|
- type: recall |
|
value: 96.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (nno-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: nno-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 89.7 |
|
- type: f1 |
|
value: 87.24333333333334 |
|
- type: precision |
|
value: 86.17 |
|
- type: recall |
|
value: 89.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (cha-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: cha-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 50.36496350364964 |
|
- type: f1 |
|
value: 44.795520780922246 |
|
- type: precision |
|
value: 43.09002433090024 |
|
- type: recall |
|
value: 50.36496350364964 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (mhr-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: mhr-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 18.8 |
|
- type: f1 |
|
value: 16.242864357864356 |
|
- type: precision |
|
value: 15.466596638655464 |
|
- type: recall |
|
value: 18.8 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (dan-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: dan-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.19999999999999 |
|
- type: f1 |
|
value: 93.92333333333333 |
|
- type: precision |
|
value: 93.30833333333332 |
|
- type: recall |
|
value: 95.19999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ell-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ell-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 93.4 |
|
- type: f1 |
|
value: 91.42333333333333 |
|
- type: precision |
|
value: 90.50833333333334 |
|
- type: recall |
|
value: 93.4 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (amh-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: amh-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 26.190476190476193 |
|
- type: f1 |
|
value: 22.05208151636723 |
|
- type: precision |
|
value: 21.09292328042328 |
|
- type: recall |
|
value: 26.190476190476193 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (pam-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: pam-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 17.2 |
|
- type: f1 |
|
value: 14.021009731460952 |
|
- type: precision |
|
value: 13.1389886698243 |
|
- type: recall |
|
value: 17.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (hsb-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: hsb-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 78.67494824016563 |
|
- type: f1 |
|
value: 74.24430641821947 |
|
- type: precision |
|
value: 72.50747642051991 |
|
- type: recall |
|
value: 78.67494824016563 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (srp-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: srp-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 94.19999999999999 |
|
- type: f1 |
|
value: 92.54 |
|
- type: precision |
|
value: 91.75833333333334 |
|
- type: recall |
|
value: 94.19999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (epo-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: epo-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 90.2 |
|
- type: f1 |
|
value: 87.78666666666666 |
|
- type: precision |
|
value: 86.69833333333334 |
|
- type: recall |
|
value: 90.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (kzj-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: kzj-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 14.7 |
|
- type: f1 |
|
value: 12.19206214842218 |
|
- type: precision |
|
value: 11.526261904761904 |
|
- type: recall |
|
value: 14.7 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (awa-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: awa-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 73.16017316017316 |
|
- type: f1 |
|
value: 67.44858316286889 |
|
- type: precision |
|
value: 65.23809523809523 |
|
- type: recall |
|
value: 73.16017316017316 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (fao-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: fao-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 75.19083969465649 |
|
- type: f1 |
|
value: 70.33078880407125 |
|
- type: precision |
|
value: 68.3969465648855 |
|
- type: recall |
|
value: 75.19083969465649 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (mal-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: mal-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 62.154294032023294 |
|
- type: f1 |
|
value: 55.86030821838681 |
|
- type: precision |
|
value: 53.53509623160277 |
|
- type: recall |
|
value: 62.154294032023294 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ile-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ile-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 86.8 |
|
- type: f1 |
|
value: 83.9652380952381 |
|
- type: precision |
|
value: 82.84242424242424 |
|
- type: recall |
|
value: 86.8 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (bos-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: bos-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 93.50282485875707 |
|
- type: f1 |
|
value: 91.54425612052731 |
|
- type: precision |
|
value: 90.65442561205272 |
|
- type: recall |
|
value: 93.50282485875707 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (cor-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: cor-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 11.4 |
|
- type: f1 |
|
value: 9.189775870222714 |
|
- type: precision |
|
value: 8.66189886502811 |
|
- type: recall |
|
value: 11.4 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (cat-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: cat-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 93.4 |
|
- type: f1 |
|
value: 91.88666666666666 |
|
- type: precision |
|
value: 91.21444444444444 |
|
- type: recall |
|
value: 93.4 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (eus-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: eus-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 46.0 |
|
- type: f1 |
|
value: 40.51069226095542 |
|
- type: precision |
|
value: 38.57804926010808 |
|
- type: recall |
|
value: 46.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (yue-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: yue-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 91.0 |
|
- type: f1 |
|
value: 89.11333333333333 |
|
- type: precision |
|
value: 88.27000000000001 |
|
- type: recall |
|
value: 91.0 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (swe-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: swe-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 94.39999999999999 |
|
- type: f1 |
|
value: 92.95 |
|
- type: precision |
|
value: 92.27000000000001 |
|
- type: recall |
|
value: 94.39999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (dtp-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: dtp-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 14.2 |
|
- type: f1 |
|
value: 11.73701698770113 |
|
- type: precision |
|
value: 11.079207014736676 |
|
- type: recall |
|
value: 14.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (kat-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: kat-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 65.14745308310992 |
|
- type: f1 |
|
value: 59.665707393589415 |
|
- type: precision |
|
value: 57.560853653346946 |
|
- type: recall |
|
value: 65.14745308310992 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (jpn-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: jpn-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.39999999999999 |
|
- type: f1 |
|
value: 94.0 |
|
- type: precision |
|
value: 93.33333333333333 |
|
- type: recall |
|
value: 95.39999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (csb-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: csb-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 69.56521739130434 |
|
- type: f1 |
|
value: 62.92490118577074 |
|
- type: precision |
|
value: 60.27009222661397 |
|
- type: recall |
|
value: 69.56521739130434 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (xho-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: xho-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 40.140845070422536 |
|
- type: f1 |
|
value: 35.96411804158283 |
|
- type: precision |
|
value: 34.89075869357559 |
|
- type: recall |
|
value: 40.140845070422536 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (orv-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: orv-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 65.86826347305389 |
|
- type: f1 |
|
value: 59.646248628284546 |
|
- type: precision |
|
value: 57.22982606216139 |
|
- type: recall |
|
value: 65.86826347305389 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ind-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ind-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 94.89999999999999 |
|
- type: f1 |
|
value: 93.48333333333333 |
|
- type: precision |
|
value: 92.83666666666667 |
|
- type: recall |
|
value: 94.89999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tuk-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tuk-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 47.783251231527096 |
|
- type: f1 |
|
value: 42.006447302013804 |
|
- type: precision |
|
value: 40.12747105111637 |
|
- type: recall |
|
value: 47.783251231527096 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (max-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: max-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 69.71830985915493 |
|
- type: f1 |
|
value: 64.80266212660578 |
|
- type: precision |
|
value: 63.08098591549296 |
|
- type: recall |
|
value: 69.71830985915493 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (swh-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: swh-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 67.94871794871796 |
|
- type: f1 |
|
value: 61.59912309912309 |
|
- type: precision |
|
value: 59.17338217338218 |
|
- type: recall |
|
value: 67.94871794871796 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (hin-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: hin-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 96.39999999999999 |
|
- type: f1 |
|
value: 95.28333333333335 |
|
- type: precision |
|
value: 94.75 |
|
- type: recall |
|
value: 96.39999999999999 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (dsb-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: dsb-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 70.14613778705638 |
|
- type: f1 |
|
value: 65.4349338900487 |
|
- type: precision |
|
value: 63.57599255302805 |
|
- type: recall |
|
value: 70.14613778705638 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ber-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ber-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 9.2 |
|
- type: f1 |
|
value: 7.622184434339607 |
|
- type: precision |
|
value: 7.287048159682417 |
|
- type: recall |
|
value: 9.2 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tam-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tam-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 77.85016286644951 |
|
- type: f1 |
|
value: 72.83387622149837 |
|
- type: precision |
|
value: 70.58450959102424 |
|
- type: recall |
|
value: 77.85016286644951 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (slk-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: slk-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 90.8 |
|
- type: f1 |
|
value: 88.84333333333333 |
|
- type: precision |
|
value: 87.96666666666665 |
|
- type: recall |
|
value: 90.8 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tgl-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tgl-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 94.6 |
|
- type: f1 |
|
value: 93.14 |
|
- type: precision |
|
value: 92.49833333333333 |
|
- type: recall |
|
value: 94.6 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ast-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ast-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 84.25196850393701 |
|
- type: f1 |
|
value: 80.94488188976378 |
|
- type: precision |
|
value: 79.65879265091863 |
|
- type: recall |
|
value: 84.25196850393701 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (mkd-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: mkd-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 89.5 |
|
- type: f1 |
|
value: 86.89666666666666 |
|
- type: precision |
|
value: 85.7 |
|
- type: recall |
|
value: 89.5 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (khm-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: khm-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 42.797783933518005 |
|
- type: f1 |
|
value: 37.30617360155193 |
|
- type: precision |
|
value: 35.34933825792552 |
|
- type: recall |
|
value: 42.797783933518005 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ces-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ces-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 96.1 |
|
- type: f1 |
|
value: 94.93333333333332 |
|
- type: precision |
|
value: 94.38333333333333 |
|
- type: recall |
|
value: 96.1 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tzl-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tzl-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 54.807692307692314 |
|
- type: f1 |
|
value: 49.506903353057204 |
|
- type: precision |
|
value: 47.54807692307693 |
|
- type: recall |
|
value: 54.807692307692314 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (urd-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: urd-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 87.1 |
|
- type: f1 |
|
value: 83.61857142857143 |
|
- type: precision |
|
value: 81.975 |
|
- type: recall |
|
value: 87.1 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (ara-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: ara-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 91.10000000000001 |
|
- type: f1 |
|
value: 88.76333333333332 |
|
- type: precision |
|
value: 87.67 |
|
- type: recall |
|
value: 91.10000000000001 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (kor-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: kor-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 93.10000000000001 |
|
- type: f1 |
|
value: 91.28999999999999 |
|
- type: precision |
|
value: 90.44500000000001 |
|
- type: recall |
|
value: 93.10000000000001 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (yid-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: yid-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 39.97641509433962 |
|
- type: f1 |
|
value: 33.12271889998028 |
|
- type: precision |
|
value: 30.95185381542554 |
|
- type: recall |
|
value: 39.97641509433962 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (fin-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: fin-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 92.60000000000001 |
|
- type: f1 |
|
value: 90.69 |
|
- type: precision |
|
value: 89.84500000000001 |
|
- type: recall |
|
value: 92.60000000000001 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (tha-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: tha-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 95.07299270072993 |
|
- type: f1 |
|
value: 93.64355231143554 |
|
- type: precision |
|
value: 92.94403892944038 |
|
- type: recall |
|
value: 95.07299270072993 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
name: MTEB Tatoeba (wuu-eng) |
|
type: mteb/tatoeba-bitext-mining |
|
config: wuu-eng |
|
split: test |
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
|
metrics: |
|
- type: accuracy |
|
value: 91.9 |
|
- type: f1 |
|
value: 89.61333333333333 |
|
- type: precision |
|
value: 88.53333333333333 |
|
- type: recall |
|
value: 91.9 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB ThuNewsClusteringP2P |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 64.68478289806511 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB ThuNewsClusteringS2S |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 57.53010296184097 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB Touche2020 |
|
type: webis-touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.519 |
|
- type: map_at_10 |
|
value: 10.31 |
|
- type: map_at_100 |
|
value: 16.027 |
|
- type: map_at_1000 |
|
value: 17.827 |
|
- type: map_at_3 |
|
value: 5.721 |
|
- type: map_at_5 |
|
value: 7.7829999999999995 |
|
- type: mrr_at_1 |
|
value: 34.694 |
|
- type: mrr_at_10 |
|
value: 52.642999999999994 |
|
- type: mrr_at_100 |
|
value: 53.366 |
|
- type: mrr_at_1000 |
|
value: 53.366 |
|
- type: mrr_at_3 |
|
value: 48.638999999999996 |
|
- type: mrr_at_5 |
|
value: 50.578 |
|
- type: ndcg_at_1 |
|
value: 31.633 |
|
- type: ndcg_at_10 |
|
value: 26.394000000000002 |
|
- type: ndcg_at_100 |
|
value: 36.41 |
|
- type: ndcg_at_1000 |
|
value: 49.206 |
|
- type: ndcg_at_3 |
|
value: 31.694 |
|
- type: ndcg_at_5 |
|
value: 29.529 |
|
- type: precision_at_1 |
|
value: 34.694 |
|
- type: precision_at_10 |
|
value: 23.469 |
|
- type: precision_at_100 |
|
value: 7.286 |
|
- type: precision_at_1000 |
|
value: 1.5610000000000002 |
|
- type: precision_at_3 |
|
value: 34.014 |
|
- type: precision_at_5 |
|
value: 29.796 |
|
- type: recall_at_1 |
|
value: 2.519 |
|
- type: recall_at_10 |
|
value: 17.091 |
|
- type: recall_at_100 |
|
value: 45.429 |
|
- type: recall_at_1000 |
|
value: 84.621 |
|
- type: recall_at_3 |
|
value: 7.208 |
|
- type: recall_at_5 |
|
value: 10.523 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB ToxicConversationsClassification |
|
type: mteb/toxic_conversations_50k |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 69.58659999999999 |
|
- type: ap |
|
value: 14.735696532619 |
|
- type: f1 |
|
value: 54.23517220069903 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB TweetSentimentExtractionClassification |
|
type: mteb/tweet_sentiment_extraction |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 63.723825693265425 |
|
- type: f1 |
|
value: 64.02405729449103 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
name: MTEB TwentyNewsgroupsClustering |
|
type: mteb/twentynewsgroups-clustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 54.310161547491006 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
name: MTEB TwitterSemEval2015 |
|
type: mteb/twittersemeval2015-pairclassification |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.77630088812064 |
|
- type: cos_sim_ap |
|
value: 81.61725457333809 |
|
- type: cos_sim_f1 |
|
value: 74.91373801916932 |
|
- type: cos_sim_precision |
|
value: 72.63940520446097 |
|
- type: cos_sim_recall |
|
value: 77.33509234828496 |
|
- type: dot_accuracy |
|
value: 88.77630088812064 |
|
- type: dot_ap |
|
value: 81.61725317476251 |
|
- type: dot_f1 |
|
value: 74.91373801916932 |
|
- type: dot_precision |
|
value: 72.63940520446097 |
|
- type: dot_recall |
|
value: 77.33509234828496 |
|
- type: euclidean_accuracy |
|
value: 88.77630088812064 |
|
- type: euclidean_ap |
|
value: 81.61724596869566 |
|
- type: euclidean_f1 |
|
value: 74.91373801916932 |
|
- type: euclidean_precision |
|
value: 72.63940520446097 |
|
- type: euclidean_recall |
|
value: 77.33509234828496 |
|
- type: manhattan_accuracy |
|
value: 88.67497168742922 |
|
- type: manhattan_ap |
|
value: 81.430251048948 |
|
- type: manhattan_f1 |
|
value: 74.79593118171543 |
|
- type: manhattan_precision |
|
value: 71.3635274382938 |
|
- type: manhattan_recall |
|
value: 78.57519788918206 |
|
- type: max_accuracy |
|
value: 88.77630088812064 |
|
- type: max_ap |
|
value: 81.61725457333809 |
|
- type: max_f1 |
|
value: 74.91373801916932 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
name: MTEB TwitterURLCorpus |
|
type: mteb/twitterurlcorpus-pairclassification |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 89.85136026700819 |
|
- type: cos_sim_ap |
|
value: 87.74656687446567 |
|
- type: cos_sim_f1 |
|
value: 80.3221673073403 |
|
- type: cos_sim_precision |
|
value: 76.56871640957633 |
|
- type: cos_sim_recall |
|
value: 84.46258084385587 |
|
- type: dot_accuracy |
|
value: 89.85136026700819 |
|
- type: dot_ap |
|
value: 87.74656471395072 |
|
- type: dot_f1 |
|
value: 80.3221673073403 |
|
- type: dot_precision |
|
value: 76.56871640957633 |
|
- type: dot_recall |
|
value: 84.46258084385587 |
|
- type: euclidean_accuracy |
|
value: 89.85136026700819 |
|
- type: euclidean_ap |
|
value: 87.74656885754466 |
|
- type: euclidean_f1 |
|
value: 80.3221673073403 |
|
- type: euclidean_precision |
|
value: 76.56871640957633 |
|
- type: euclidean_recall |
|
value: 84.46258084385587 |
|
- type: manhattan_accuracy |
|
value: 89.86300306593705 |
|
- type: manhattan_ap |
|
value: 87.78807479093082 |
|
- type: manhattan_f1 |
|
value: 80.31663429471911 |
|
- type: manhattan_precision |
|
value: 76.63472970137772 |
|
- type: manhattan_recall |
|
value: 84.3701878657222 |
|
- type: max_accuracy |
|
value: 89.86300306593705 |
|
- type: max_ap |
|
value: 87.78807479093082 |
|
- type: max_f1 |
|
value: 80.3221673073403 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB VideoRetrieval |
|
type: C-MTEB/VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.4 |
|
- type: map_at_10 |
|
value: 40.961999999999996 |
|
- type: map_at_100 |
|
value: 41.660000000000004 |
|
- type: map_at_1000 |
|
value: 41.721000000000004 |
|
- type: map_at_3 |
|
value: 38.550000000000004 |
|
- type: map_at_5 |
|
value: 40.06 |
|
- type: mrr_at_1 |
|
value: 32.4 |
|
- type: mrr_at_10 |
|
value: 40.961999999999996 |
|
- type: mrr_at_100 |
|
value: 41.660000000000004 |
|
- type: mrr_at_1000 |
|
value: 41.721000000000004 |
|
- type: mrr_at_3 |
|
value: 38.550000000000004 |
|
- type: mrr_at_5 |
|
value: 40.06 |
|
- type: ndcg_at_1 |
|
value: 32.4 |
|
- type: ndcg_at_10 |
|
value: 45.388 |
|
- type: ndcg_at_100 |
|
value: 49.012 |
|
- type: ndcg_at_1000 |
|
value: 50.659 |
|
- type: ndcg_at_3 |
|
value: 40.47 |
|
- type: ndcg_at_5 |
|
value: 43.232 |
|
- type: precision_at_1 |
|
value: 32.4 |
|
- type: precision_at_10 |
|
value: 5.94 |
|
- type: precision_at_100 |
|
value: 0.769 |
|
- type: precision_at_1000 |
|
value: 0.09 |
|
- type: precision_at_3 |
|
value: 15.333 |
|
- type: precision_at_5 |
|
value: 10.56 |
|
- type: recall_at_1 |
|
value: 32.4 |
|
- type: recall_at_10 |
|
value: 59.4 |
|
- type: recall_at_100 |
|
value: 76.9 |
|
- type: recall_at_1000 |
|
value: 90.0 |
|
- type: recall_at_3 |
|
value: 46.0 |
|
- type: recall_at_5 |
|
value: 52.800000000000004 |
|
- task: |
|
type: Classification |
|
dataset: |
|
name: MTEB Waimai |
|
type: C-MTEB/waimai-classification |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 86.94000000000001 |
|
- type: ap |
|
value: 70.57373468481975 |
|
- type: f1 |
|
value: 85.26264784928323 |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: AI2 Reasoning Challenge (25-Shot) |
|
type: ai2_arc |
|
config: ARC-Challenge |
|
split: test |
|
args: |
|
num_few_shot: 25 |
|
metrics: |
|
- type: acc_norm |
|
value: 29.61 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: HellaSwag (10-Shot) |
|
type: hellaswag |
|
split: validation |
|
args: |
|
num_few_shot: 10 |
|
metrics: |
|
- type: acc_norm |
|
value: 27.05 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: MMLU (5-Shot) |
|
type: cais/mmlu |
|
config: all |
|
split: test |
|
args: |
|
num_few_shot: 5 |
|
metrics: |
|
- type: acc |
|
value: 23.12 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: TruthfulQA (0-shot) |
|
type: truthful_qa |
|
config: multiple_choice |
|
split: validation |
|
args: |
|
num_few_shot: 0 |
|
metrics: |
|
- type: mc2 |
|
value: 47.53 |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: Winogrande (5-shot) |
|
type: winogrande |
|
config: winogrande_xl |
|
split: validation |
|
args: |
|
num_few_shot: 5 |
|
metrics: |
|
- type: acc |
|
value: 51.93 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: GSM8k (5-shot) |
|
type: gsm8k |
|
config: main |
|
split: test |
|
args: |
|
num_few_shot: 5 |
|
metrics: |
|
- type: acc |
|
value: 0.0 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct |
|
name: Open LLM Leaderboard |
|
--- |
|
|
|
## E5-mistral-7b-instruct |
|
|
|
[Improving Text Embeddings with Large Language Models](https://arxiv.org/pdf/2401.00368.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 |
|
|
|
This model has 32 layers and the embedding size is 4096. |
|
|
|
## Usage |
|
|
|
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
|
|
|
### Sentence Transformers |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
model = SentenceTransformer("intfloat/e5-mistral-7b-instruct") |
|
# In case you want to reduce the maximum sequence length: |
|
model.max_seq_length = 4096 |
|
|
|
queries = [ |
|
"how much protein should a female eat", |
|
"summit define", |
|
] |
|
documents = [ |
|
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
|
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
|
] |
|
|
|
query_embeddings = model.encode(queries, prompt_name="web_search_query") |
|
document_embeddings = model.encode(documents) |
|
|
|
scores = (query_embeddings @ document_embeddings.T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
Have a look at [config_sentence_transformers.json](config_sentence_transformers.json) for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `summarization_query`. Additionally, check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for prompts we used for evaluation. You can use these via e.g. `model.encode(queries, prompt="Instruct: Given a claim, find documents that refute the claim\nQuery: ")`. |
|
|
|
|
|
### Transformers |
|
|
|
```python |
|
import torch |
|
import torch.nn.functional as F |
|
|
|
from torch import Tensor |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
|
|
def last_token_pool(last_hidden_states: Tensor, |
|
attention_mask: Tensor) -> Tensor: |
|
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) |
|
if left_padding: |
|
return last_hidden_states[:, -1] |
|
else: |
|
sequence_lengths = attention_mask.sum(dim=1) - 1 |
|
batch_size = last_hidden_states.shape[0] |
|
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] |
|
|
|
|
|
def get_detailed_instruct(task_description: str, query: str) -> str: |
|
return f'Instruct: {task_description}\nQuery: {query}' |
|
|
|
|
|
# Each query must come with a one-sentence instruction that describes the task |
|
task = 'Given a web search query, retrieve relevant passages that answer the query' |
|
queries = [ |
|
get_detailed_instruct(task, 'how much protein should a female eat'), |
|
get_detailed_instruct(task, 'summit define') |
|
] |
|
# No need to add instruction for retrieval documents |
|
documents = [ |
|
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
|
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
|
] |
|
input_texts = queries + documents |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct') |
|
model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct') |
|
|
|
max_length = 4096 |
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
|
|
|
# normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
## Supported Languages |
|
|
|
This model is initialized from [Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1) |
|
and fine-tuned on a mixture of multilingual datasets. |
|
As a result, it has some multilingual capability. |
|
However, since Mistral-7B-v0.1 is mainly trained on English data, we recommend using this model for English only. |
|
For multilingual use cases, please refer to [multilingual-e5-large](https://huggingface.co./intfloat/multilingual-e5-large). |
|
|
|
## MTEB Benchmark Evaluation |
|
|
|
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
|
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
|
|
|
## FAQ |
|
|
|
**1. Do I need to add instructions to the query?** |
|
|
|
Yes, this is how the model is trained, otherwise you will see a performance degradation. |
|
The task definition should be a one-sentence instruction that describes the task. |
|
This is a way to customize text embeddings for different scenarios through natural language instructions. |
|
|
|
Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation. |
|
|
|
On the other hand, there is no need to add instructions to the document side. |
|
|
|
**2. Why are my reproduced results slightly different from reported in the model card?** |
|
|
|
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
|
|
|
**3. Where are the LoRA-only weights?** |
|
|
|
You can find the LoRA-only weights at [https://huggingface.co./intfloat/e5-mistral-7b-instruct/tree/main/lora](https://huggingface.co./intfloat/e5-mistral-7b-instruct/tree/main/lora). |
|
|
|
## Citation |
|
|
|
If you find our paper or models helpful, please consider cite as follows: |
|
|
|
```bibtex |
|
@article{wang2023improving, |
|
title={Improving Text Embeddings with Large Language Models}, |
|
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, |
|
journal={arXiv preprint arXiv:2401.00368}, |
|
year={2023} |
|
} |
|
|
|
@article{wang2022text, |
|
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, |
|
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, |
|
journal={arXiv preprint arXiv:2212.03533}, |
|
year={2022} |
|
} |
|
``` |
|
|
|
## Limitations |
|
|
|
Using this model for inputs longer than 4096 tokens is not recommended. |
|
|
|
This model's multilingual capability is still inferior to [multilingual-e5-large](https://huggingface.co./intfloat/multilingual-e5-large) for some cases. |
|
|
|
|
|
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard) |
|
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_intfloat__e5-mistral-7b-instruct) |
|
|
|
| Metric |Value| |
|
|---------------------------------|----:| |
|
|Avg. |29.87| |
|
|AI2 Reasoning Challenge (25-Shot)|29.61| |
|
|HellaSwag (10-Shot) |27.05| |
|
|MMLU (5-Shot) |23.12| |
|
|TruthfulQA (0-shot) |47.53| |
|
|Winogrande (5-shot) |51.93| |
|
|GSM8k (5-shot) | 0.00| |
|
|
|
|
