| --- |
| license: bigscience-bloom-rail-1.0 |
| language: |
| - ak |
| - ar |
| - as |
| - bm |
| - bn |
| - ca |
| - code |
| - en |
| - es |
| - eu |
| - fon |
| - fr |
| - gu |
| - hi |
| - id |
| - ig |
| - ki |
| - kn |
| - lg |
| - ln |
| - ml |
| - mr |
| - ne |
| - nso |
| - ny |
| - or |
| - pa |
| - pt |
| - rn |
| - rw |
| - sn |
| - st |
| - sw |
| - ta |
| - te |
| - tn |
| - ts |
| - tum |
| - tw |
| - ur |
| - vi |
| - wo |
| - xh |
| - yo |
| - zh |
| - zhs |
| - zht |
| - zu |
| tags: |
| - mteb |
| model-index: |
| - name: udever-bloom-1b1 |
| results: |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/AFQMC |
| name: MTEB AFQMC |
| config: default |
| split: validation |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 27.90020553155914 |
| - type: cos_sim_spearman |
| value: 27.980812877007445 |
| - type: euclidean_pearson |
| value: 27.412021502878105 |
| - type: euclidean_spearman |
| value: 27.608320539898134 |
| - type: manhattan_pearson |
| value: 27.493591460276278 |
| - type: manhattan_spearman |
| value: 27.715134644174423 |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/ATEC |
| name: MTEB ATEC |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 35.15277604796132 |
| - type: cos_sim_spearman |
| value: 35.863846005221575 |
| - type: euclidean_pearson |
| value: 37.65681598655078 |
| - type: euclidean_spearman |
| value: 35.50116107334066 |
| - type: manhattan_pearson |
| value: 37.736463166370854 |
| - type: manhattan_spearman |
| value: 35.53412987209704 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 69.9402985074627 |
| - type: ap |
| value: 33.4661141650045 |
| - type: f1 |
| value: 64.31759903129324 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (de) |
| config: de |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 66.02783725910065 |
| - type: ap |
| value: 78.25152113775748 |
| - type: f1 |
| value: 64.00236113368896 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en-ext) |
| config: en-ext |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 72.01649175412295 |
| - type: ap |
| value: 21.28416661100625 |
| - type: f1 |
| value: 59.481902269256096 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (ja) |
| config: ja |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 58.76873661670234 |
| - type: ap |
| value: 12.828869547428084 |
| - type: f1 |
| value: 47.5200475889544 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 87.191175 |
| - type: ap |
| value: 82.4408783026622 |
| - type: f1 |
| value: 87.16605834054603 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 41.082 |
| - type: f1 |
| value: 40.54924237159631 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (de) |
| config: de |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 30.447999999999997 |
| - type: f1 |
| value: 30.0643283775686 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (es) |
| config: es |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 40.800000000000004 |
| - type: f1 |
| value: 39.64954112879312 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (fr) |
| config: fr |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 40.686 |
| - type: f1 |
| value: 39.917643425172 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (ja) |
| config: ja |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 32.074 |
| - type: f1 |
| value: 31.878305643409334 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (zh) |
| config: zh |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 38.122 |
| - type: f1 |
| value: 37.296210966123446 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.262 |
| - type: map_at_10 |
| value: 37.667 |
| - type: map_at_100 |
| value: 38.812999999999995 |
| - type: map_at_1000 |
| value: 38.829 |
| - type: map_at_3 |
| value: 32.421 |
| - type: map_at_5 |
| value: 35.202 |
| - type: mrr_at_1 |
| value: 22.759999999999998 |
| - type: mrr_at_10 |
| value: 37.817 |
| - type: mrr_at_100 |
| value: 38.983000000000004 |
| - type: mrr_at_1000 |
| value: 38.999 |
| - type: mrr_at_3 |
| value: 32.61 |
| - type: mrr_at_5 |
| value: 35.333999999999996 |
| - type: ndcg_at_1 |
| value: 22.262 |
| - type: ndcg_at_10 |
| value: 46.671 |
| - type: ndcg_at_100 |
| value: 51.519999999999996 |
| - type: ndcg_at_1000 |
| value: 51.876999999999995 |
| - type: ndcg_at_3 |
| value: 35.696 |
| - type: ndcg_at_5 |
| value: 40.722 |
| - type: precision_at_1 |
| value: 22.262 |
| - type: precision_at_10 |
| value: 7.575 |
| - type: precision_at_100 |
| value: 0.9690000000000001 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 15.055 |
| - type: precision_at_5 |
| value: 11.479000000000001 |
| - type: recall_at_1 |
| value: 22.262 |
| - type: recall_at_10 |
| value: 75.747 |
| - type: recall_at_100 |
| value: 96.871 |
| - type: recall_at_1000 |
| value: 99.57300000000001 |
| - type: recall_at_3 |
| value: 45.164 |
| - type: recall_at_5 |
| value: 57.397 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 44.51799756336072 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 34.44923356952161 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 59.49540399419566 |
| - type: mrr |
| value: 73.43028624192061 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.67018580352695 |
| - type: cos_sim_spearman |
| value: 84.64530219460785 |
| - type: euclidean_pearson |
| value: 87.10187265189109 |
| - type: euclidean_spearman |
| value: 86.19051812629264 |
| - type: manhattan_pearson |
| value: 86.78890467534343 |
| - type: manhattan_spearman |
| value: 85.60134807514734 |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/BQ |
| name: MTEB BQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 46.308790362891266 |
| - type: cos_sim_spearman |
| value: 46.22674926863126 |
| - type: euclidean_pearson |
| value: 47.36625172551589 |
| - type: euclidean_spearman |
| value: 47.55854392572494 |
| - type: manhattan_pearson |
| value: 47.3342490976193 |
| - type: manhattan_spearman |
| value: 47.52249648456463 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/bucc-bitext-mining |
| name: MTEB BUCC (de-en) |
| config: de-en |
| split: test |
| revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| metrics: |
| - type: accuracy |
| value: 42.67223382045929 |
| - type: f1 |
| value: 42.02704262244064 |
| - type: precision |
| value: 41.76166726545405 |
| - type: recall |
| value: 42.67223382045929 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/bucc-bitext-mining |
| name: MTEB BUCC (fr-en) |
| config: fr-en |
| split: test |
| revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| metrics: |
| - type: accuracy |
| value: 97.95289456306405 |
| - type: f1 |
| value: 97.70709516472228 |
| - type: precision |
| value: 97.58602978941964 |
| - type: recall |
| value: 97.95289456306405 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/bucc-bitext-mining |
| name: MTEB BUCC (ru-en) |
| config: ru-en |
| split: test |
| revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| metrics: |
| - type: accuracy |
| value: 25.375822653273296 |
| - type: f1 |
| value: 24.105776263207947 |
| - type: precision |
| value: 23.644628498465117 |
| - type: recall |
| value: 25.375822653273296 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/bucc-bitext-mining |
| name: MTEB BUCC (zh-en) |
| config: zh-en |
| split: test |
| revision: d51519689f32196a32af33b075a01d0e7c51e252 |
| metrics: |
| - type: accuracy |
| value: 98.31490258030541 |
| - type: f1 |
| value: 98.24469018781815 |
| - type: precision |
| value: 98.2095839915745 |
| - type: recall |
| value: 98.31490258030541 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 82.89285714285714 |
| - type: f1 |
| value: 82.84943089389121 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 35.25261508107809 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 30.708512338509653 |
| - task: |
| type: Clustering |
| dataset: |
| type: C-MTEB/CLSClusteringP2P |
| name: MTEB CLSClusteringP2P |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: v_measure |
| value: 35.361295166692464 |
| - task: |
| type: Clustering |
| dataset: |
| type: C-MTEB/CLSClusteringS2S |
| name: MTEB CLSClusteringS2S |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: v_measure |
| value: 37.06879287045825 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/CMedQAv1-reranking |
| name: MTEB CMedQAv1 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map |
| value: 66.06033605600476 |
| - type: mrr |
| value: 70.82825396825396 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/CMedQAv2-reranking |
| name: MTEB CMedQAv2 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map |
| value: 66.9600733219955 |
| - type: mrr |
| value: 72.19742063492063 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 29.526999999999997 |
| - type: map_at_10 |
| value: 38.747 |
| - type: map_at_100 |
| value: 40.172999999999995 |
| - type: map_at_1000 |
| value: 40.311 |
| - type: map_at_3 |
| value: 35.969 |
| - type: map_at_5 |
| value: 37.344 |
| - type: mrr_at_1 |
| value: 36.767 |
| - type: mrr_at_10 |
| value: 45.082 |
| - type: mrr_at_100 |
| value: 45.898 |
| - type: mrr_at_1000 |
| value: 45.958 |
| - type: mrr_at_3 |
| value: 43.085 |
| - type: mrr_at_5 |
| value: 44.044 |
| - type: ndcg_at_1 |
| value: 36.767 |
| - type: ndcg_at_10 |
| value: 44.372 |
| - type: ndcg_at_100 |
| value: 49.908 |
| - type: ndcg_at_1000 |
| value: 52.358000000000004 |
| - type: ndcg_at_3 |
| value: 40.711000000000006 |
| - type: ndcg_at_5 |
| value: 41.914 |
| - type: precision_at_1 |
| value: 36.767 |
| - type: precision_at_10 |
| value: 8.283 |
| - type: precision_at_100 |
| value: 1.3679999999999999 |
| - type: precision_at_1000 |
| value: 0.189 |
| - type: precision_at_3 |
| value: 19.599 |
| - type: precision_at_5 |
| value: 13.505 |
| - type: recall_at_1 |
| value: 29.526999999999997 |
| - type: recall_at_10 |
| value: 54.198 |
| - type: recall_at_100 |
| value: 77.818 |
| - type: recall_at_1000 |
| value: 93.703 |
| - type: recall_at_3 |
| value: 42.122 |
| - type: recall_at_5 |
| value: 46.503 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.646 |
| - type: map_at_10 |
| value: 30.447999999999997 |
| - type: map_at_100 |
| value: 31.417 |
| - type: map_at_1000 |
| value: 31.528 |
| - type: map_at_3 |
| value: 28.168 |
| - type: map_at_5 |
| value: 29.346 |
| - type: mrr_at_1 |
| value: 28.854000000000003 |
| - type: mrr_at_10 |
| value: 35.611 |
| - type: mrr_at_100 |
| value: 36.321 |
| - type: mrr_at_1000 |
| value: 36.378 |
| - type: mrr_at_3 |
| value: 33.726 |
| - type: mrr_at_5 |
| value: 34.745 |
| - type: ndcg_at_1 |
| value: 28.854000000000003 |
| - type: ndcg_at_10 |
| value: 35.052 |
| - type: ndcg_at_100 |
| value: 39.190999999999995 |
| - type: ndcg_at_1000 |
| value: 41.655 |
| - type: ndcg_at_3 |
| value: 31.684 |
| - type: ndcg_at_5 |
| value: 32.998 |
| - type: precision_at_1 |
| value: 28.854000000000003 |
| - type: precision_at_10 |
| value: 6.49 |
| - type: precision_at_100 |
| value: 1.057 |
| - type: precision_at_1000 |
| value: 0.153 |
| - type: precision_at_3 |
| value: 15.244 |
| - type: precision_at_5 |
| value: 10.599 |
| - type: recall_at_1 |
| value: 22.646 |
| - type: recall_at_10 |
| value: 43.482 |
| - type: recall_at_100 |
| value: 61.324 |
| - type: recall_at_1000 |
| value: 77.866 |
| - type: recall_at_3 |
| value: 33.106 |
| - type: recall_at_5 |
| value: 37.124 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 35.061 |
| - type: map_at_10 |
| value: 46.216 |
| - type: map_at_100 |
| value: 47.318 |
| - type: map_at_1000 |
| value: 47.384 |
| - type: map_at_3 |
| value: 43.008 |
| - type: map_at_5 |
| value: 44.79 |
| - type: mrr_at_1 |
| value: 40.251 |
| - type: mrr_at_10 |
| value: 49.677 |
| - type: mrr_at_100 |
| value: 50.39 |
| - type: mrr_at_1000 |
| value: 50.429 |
| - type: mrr_at_3 |
| value: 46.792 |
| - type: mrr_at_5 |
| value: 48.449999999999996 |
| - type: ndcg_at_1 |
| value: 40.251 |
| - type: ndcg_at_10 |
| value: 51.99399999999999 |
| - type: ndcg_at_100 |
| value: 56.418 |
| - type: ndcg_at_1000 |
| value: 57.798 |
| - type: ndcg_at_3 |
| value: 46.192 |
| - type: ndcg_at_5 |
| value: 48.998000000000005 |
| - type: precision_at_1 |
| value: 40.251 |
| - type: precision_at_10 |
| value: 8.469999999999999 |
| - type: precision_at_100 |
| value: 1.159 |
| - type: precision_at_1000 |
| value: 0.133 |
| - type: precision_at_3 |
| value: 20.46 |
| - type: precision_at_5 |
| value: 14.332 |
| - type: recall_at_1 |
| value: 35.061 |
| - type: recall_at_10 |
| value: 65.818 |
| - type: recall_at_100 |
| value: 84.935 |
| - type: recall_at_1000 |
| value: 94.69300000000001 |
| - type: recall_at_3 |
| value: 50.300999999999995 |
| - type: recall_at_5 |
| value: 57.052 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 20.776 |
| - type: map_at_10 |
| value: 27.945999999999998 |
| - type: map_at_100 |
| value: 28.976000000000003 |
| - type: map_at_1000 |
| value: 29.073999999999998 |
| - type: map_at_3 |
| value: 25.673000000000002 |
| - type: map_at_5 |
| value: 26.96 |
| - type: mrr_at_1 |
| value: 22.486 |
| - type: mrr_at_10 |
| value: 29.756 |
| - type: mrr_at_100 |
| value: 30.735 |
| - type: mrr_at_1000 |
| value: 30.81 |
| - type: mrr_at_3 |
| value: 27.571 |
| - type: mrr_at_5 |
| value: 28.808 |
| - type: ndcg_at_1 |
| value: 22.486 |
| - type: ndcg_at_10 |
| value: 32.190000000000005 |
| - type: ndcg_at_100 |
| value: 37.61 |
| - type: ndcg_at_1000 |
| value: 40.116 |
| - type: ndcg_at_3 |
| value: 27.688000000000002 |
| - type: ndcg_at_5 |
| value: 29.87 |
| - type: precision_at_1 |
| value: 22.486 |
| - type: precision_at_10 |
| value: 5.028 |
| - type: precision_at_100 |
| value: 0.818 |
| - type: precision_at_1000 |
| value: 0.107 |
| - type: precision_at_3 |
| value: 11.827 |
| - type: precision_at_5 |
| value: 8.362 |
| - type: recall_at_1 |
| value: 20.776 |
| - type: recall_at_10 |
| value: 43.588 |
| - type: recall_at_100 |
| value: 69.139 |
| - type: recall_at_1000 |
| value: 88.144 |
| - type: recall_at_3 |
| value: 31.411 |
| - type: recall_at_5 |
| value: 36.655 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 12.994 |
| - type: map_at_10 |
| value: 19.747999999999998 |
| - type: map_at_100 |
| value: 20.877000000000002 |
| - type: map_at_1000 |
| value: 21.021 |
| - type: map_at_3 |
| value: 17.473 |
| - type: map_at_5 |
| value: 18.683 |
| - type: mrr_at_1 |
| value: 16.542 |
| - type: mrr_at_10 |
| value: 23.830000000000002 |
| - type: mrr_at_100 |
| value: 24.789 |
| - type: mrr_at_1000 |
| value: 24.877 |
| - type: mrr_at_3 |
| value: 21.476 |
| - type: mrr_at_5 |
| value: 22.838 |
| - type: ndcg_at_1 |
| value: 16.542 |
| - type: ndcg_at_10 |
| value: 24.422 |
| - type: ndcg_at_100 |
| value: 30.011 |
| - type: ndcg_at_1000 |
| value: 33.436 |
| - type: ndcg_at_3 |
| value: 20.061999999999998 |
| - type: ndcg_at_5 |
| value: 22.009999999999998 |
| - type: precision_at_1 |
| value: 16.542 |
| - type: precision_at_10 |
| value: 4.664 |
| - type: precision_at_100 |
| value: 0.876 |
| - type: precision_at_1000 |
| value: 0.132 |
| - type: precision_at_3 |
| value: 9.826 |
| - type: precision_at_5 |
| value: 7.2139999999999995 |
| - type: recall_at_1 |
| value: 12.994 |
| - type: recall_at_10 |
| value: 34.917 |
| - type: recall_at_100 |
| value: 59.455000000000005 |
| - type: recall_at_1000 |
| value: 83.87299999999999 |
| - type: recall_at_3 |
| value: 22.807 |
| - type: recall_at_5 |
| value: 27.773999999999997 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.85 |
| - type: map_at_10 |
| value: 35.285 |
| - type: map_at_100 |
| value: 36.592999999999996 |
| - type: map_at_1000 |
| value: 36.720000000000006 |
| - type: map_at_3 |
| value: 32.183 |
| - type: map_at_5 |
| value: 33.852 |
| - type: mrr_at_1 |
| value: 30.703000000000003 |
| - type: mrr_at_10 |
| value: 40.699000000000005 |
| - type: mrr_at_100 |
| value: 41.598 |
| - type: mrr_at_1000 |
| value: 41.654 |
| - type: mrr_at_3 |
| value: 38.080999999999996 |
| - type: mrr_at_5 |
| value: 39.655 |
| - type: ndcg_at_1 |
| value: 30.703000000000003 |
| - type: ndcg_at_10 |
| value: 41.422 |
| - type: ndcg_at_100 |
| value: 46.998 |
| - type: ndcg_at_1000 |
| value: 49.395 |
| - type: ndcg_at_3 |
| value: 36.353 |
| - type: ndcg_at_5 |
| value: 38.7 |
| - type: precision_at_1 |
| value: 30.703000000000003 |
| - type: precision_at_10 |
| value: 7.757 |
| - type: precision_at_100 |
| value: 1.2349999999999999 |
| - type: precision_at_1000 |
| value: 0.164 |
| - type: precision_at_3 |
| value: 17.613 |
| - type: precision_at_5 |
| value: 12.589 |
| - type: recall_at_1 |
| value: 24.85 |
| - type: recall_at_10 |
| value: 54.19500000000001 |
| - type: recall_at_100 |
| value: 77.697 |
| - type: recall_at_1000 |
| value: 93.35900000000001 |
| - type: recall_at_3 |
| value: 39.739999999999995 |
| - type: recall_at_5 |
| value: 46.03 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 19.844 |
| - type: map_at_10 |
| value: 28.663 |
| - type: map_at_100 |
| value: 30.013 |
| - type: map_at_1000 |
| value: 30.139 |
| - type: map_at_3 |
| value: 25.953 |
| - type: map_at_5 |
| value: 27.425 |
| - type: mrr_at_1 |
| value: 25.457 |
| - type: mrr_at_10 |
| value: 34.266000000000005 |
| - type: mrr_at_100 |
| value: 35.204 |
| - type: mrr_at_1000 |
| value: 35.27 |
| - type: mrr_at_3 |
| value: 31.791999999999998 |
| - type: mrr_at_5 |
| value: 33.213 |
| - type: ndcg_at_1 |
| value: 25.457 |
| - type: ndcg_at_10 |
| value: 34.266000000000005 |
| - type: ndcg_at_100 |
| value: 40.239999999999995 |
| - type: ndcg_at_1000 |
| value: 42.917 |
| - type: ndcg_at_3 |
| value: 29.593999999999998 |
| - type: ndcg_at_5 |
| value: 31.71 |
| - type: precision_at_1 |
| value: 25.457 |
| - type: precision_at_10 |
| value: 6.438000000000001 |
| - type: precision_at_100 |
| value: 1.1159999999999999 |
| - type: precision_at_1000 |
| value: 0.153 |
| - type: precision_at_3 |
| value: 14.46 |
| - type: precision_at_5 |
| value: 10.388 |
| - type: recall_at_1 |
| value: 19.844 |
| - type: recall_at_10 |
| value: 45.787 |
| - type: recall_at_100 |
| value: 71.523 |
| - type: recall_at_1000 |
| value: 89.689 |
| - type: recall_at_3 |
| value: 32.665 |
| - type: recall_at_5 |
| value: 38.292 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 21.601166666666668 |
| - type: map_at_10 |
| value: 29.434166666666666 |
| - type: map_at_100 |
| value: 30.5905 |
| - type: map_at_1000 |
| value: 30.716583333333343 |
| - type: map_at_3 |
| value: 26.962333333333333 |
| - type: map_at_5 |
| value: 28.287250000000004 |
| - type: mrr_at_1 |
| value: 25.84825 |
| - type: mrr_at_10 |
| value: 33.49966666666667 |
| - type: mrr_at_100 |
| value: 34.39425000000001 |
| - type: mrr_at_1000 |
| value: 34.46366666666667 |
| - type: mrr_at_3 |
| value: 31.256 |
| - type: mrr_at_5 |
| value: 32.52016666666667 |
| - type: ndcg_at_1 |
| value: 25.84825 |
| - type: ndcg_at_10 |
| value: 34.2975 |
| - type: ndcg_at_100 |
| value: 39.50983333333333 |
| - type: ndcg_at_1000 |
| value: 42.17958333333333 |
| - type: ndcg_at_3 |
| value: 30.00558333333333 |
| - type: ndcg_at_5 |
| value: 31.931416666666664 |
| - type: precision_at_1 |
| value: 25.84825 |
| - type: precision_at_10 |
| value: 6.075083333333334 |
| - type: precision_at_100 |
| value: 1.0205833333333334 |
| - type: precision_at_1000 |
| value: 0.14425 |
| - type: precision_at_3 |
| value: 13.903249999999998 |
| - type: precision_at_5 |
| value: 9.874999999999998 |
| - type: recall_at_1 |
| value: 21.601166666666668 |
| - type: recall_at_10 |
| value: 44.787333333333336 |
| - type: recall_at_100 |
| value: 67.89450000000001 |
| - type: recall_at_1000 |
| value: 86.62424999999999 |
| - type: recall_at_3 |
| value: 32.66375 |
| - type: recall_at_5 |
| value: 37.71825 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 19.804 |
| - type: map_at_10 |
| value: 25.983 |
| - type: map_at_100 |
| value: 26.956999999999997 |
| - type: map_at_1000 |
| value: 27.067999999999998 |
| - type: map_at_3 |
| value: 23.804 |
| - type: map_at_5 |
| value: 24.978 |
| - type: mrr_at_1 |
| value: 22.853 |
| - type: mrr_at_10 |
| value: 28.974 |
| - type: mrr_at_100 |
| value: 29.855999999999998 |
| - type: mrr_at_1000 |
| value: 29.936 |
| - type: mrr_at_3 |
| value: 26.866 |
| - type: mrr_at_5 |
| value: 28.032 |
| - type: ndcg_at_1 |
| value: 22.853 |
| - type: ndcg_at_10 |
| value: 29.993 |
| - type: ndcg_at_100 |
| value: 34.735 |
| - type: ndcg_at_1000 |
| value: 37.637 |
| - type: ndcg_at_3 |
| value: 25.863000000000003 |
| - type: ndcg_at_5 |
| value: 27.769 |
| - type: precision_at_1 |
| value: 22.853 |
| - type: precision_at_10 |
| value: 4.8469999999999995 |
| - type: precision_at_100 |
| value: 0.779 |
| - type: precision_at_1000 |
| value: 0.11 |
| - type: precision_at_3 |
| value: 11.35 |
| - type: precision_at_5 |
| value: 7.9750000000000005 |
| - type: recall_at_1 |
| value: 19.804 |
| - type: recall_at_10 |
| value: 39.616 |
| - type: recall_at_100 |
| value: 61.06399999999999 |
| - type: recall_at_1000 |
| value: 82.69800000000001 |
| - type: recall_at_3 |
| value: 28.012999999999998 |
| - type: recall_at_5 |
| value: 32.96 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 13.156 |
| - type: map_at_10 |
| value: 18.734 |
| - type: map_at_100 |
| value: 19.721 |
| - type: map_at_1000 |
| value: 19.851 |
| - type: map_at_3 |
| value: 17.057 |
| - type: map_at_5 |
| value: 17.941 |
| - type: mrr_at_1 |
| value: 16.07 |
| - type: mrr_at_10 |
| value: 22.113 |
| - type: mrr_at_100 |
| value: 23.021 |
| - type: mrr_at_1000 |
| value: 23.108 |
| - type: mrr_at_3 |
| value: 20.429 |
| - type: mrr_at_5 |
| value: 21.332 |
| - type: ndcg_at_1 |
| value: 16.07 |
| - type: ndcg_at_10 |
| value: 22.427 |
| - type: ndcg_at_100 |
| value: 27.277 |
| - type: ndcg_at_1000 |
| value: 30.525000000000002 |
| - type: ndcg_at_3 |
| value: 19.374 |
| - type: ndcg_at_5 |
| value: 20.695 |
| - type: precision_at_1 |
| value: 16.07 |
| - type: precision_at_10 |
| value: 4.1259999999999994 |
| - type: precision_at_100 |
| value: 0.769 |
| - type: precision_at_1000 |
| value: 0.122 |
| - type: precision_at_3 |
| value: 9.325999999999999 |
| - type: precision_at_5 |
| value: 6.683 |
| - type: recall_at_1 |
| value: 13.156 |
| - type: recall_at_10 |
| value: 30.223 |
| - type: recall_at_100 |
| value: 52.012 |
| - type: recall_at_1000 |
| value: 75.581 |
| - type: recall_at_3 |
| value: 21.508 |
| - type: recall_at_5 |
| value: 24.975 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.14 |
| - type: map_at_10 |
| value: 28.961 |
| - type: map_at_100 |
| value: 29.996000000000002 |
| - type: map_at_1000 |
| value: 30.112 |
| - type: map_at_3 |
| value: 26.540000000000003 |
| - type: map_at_5 |
| value: 27.916999999999998 |
| - type: mrr_at_1 |
| value: 25.746000000000002 |
| - type: mrr_at_10 |
| value: 32.936 |
| - type: mrr_at_100 |
| value: 33.811 |
| - type: mrr_at_1000 |
| value: 33.887 |
| - type: mrr_at_3 |
| value: 30.55 |
| - type: mrr_at_5 |
| value: 32.08 |
| - type: ndcg_at_1 |
| value: 25.746000000000002 |
| - type: ndcg_at_10 |
| value: 33.536 |
| - type: ndcg_at_100 |
| value: 38.830999999999996 |
| - type: ndcg_at_1000 |
| value: 41.644999999999996 |
| - type: ndcg_at_3 |
| value: 29.004 |
| - type: ndcg_at_5 |
| value: 31.284 |
| - type: precision_at_1 |
| value: 25.746000000000002 |
| - type: precision_at_10 |
| value: 5.569 |
| - type: precision_at_100 |
| value: 0.9259999999999999 |
| - type: precision_at_1000 |
| value: 0.128 |
| - type: precision_at_3 |
| value: 12.748999999999999 |
| - type: precision_at_5 |
| value: 9.216000000000001 |
| - type: recall_at_1 |
| value: 22.14 |
| - type: recall_at_10 |
| value: 43.628 |
| - type: recall_at_100 |
| value: 67.581 |
| - type: recall_at_1000 |
| value: 87.737 |
| - type: recall_at_3 |
| value: 31.579 |
| - type: recall_at_5 |
| value: 37.12 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.384 |
| - type: map_at_10 |
| value: 30.156 |
| - type: map_at_100 |
| value: 31.728 |
| - type: map_at_1000 |
| value: 31.971 |
| - type: map_at_3 |
| value: 27.655 |
| - type: map_at_5 |
| value: 28.965000000000003 |
| - type: mrr_at_1 |
| value: 27.075 |
| - type: mrr_at_10 |
| value: 34.894 |
| - type: mrr_at_100 |
| value: 36.0 |
| - type: mrr_at_1000 |
| value: 36.059000000000005 |
| - type: mrr_at_3 |
| value: 32.708 |
| - type: mrr_at_5 |
| value: 33.893 |
| - type: ndcg_at_1 |
| value: 27.075 |
| - type: ndcg_at_10 |
| value: 35.58 |
| - type: ndcg_at_100 |
| value: 41.597 |
| - type: ndcg_at_1000 |
| value: 44.529999999999994 |
| - type: ndcg_at_3 |
| value: 31.628 |
| - type: ndcg_at_5 |
| value: 33.333 |
| - type: precision_at_1 |
| value: 27.075 |
| - type: precision_at_10 |
| value: 6.9959999999999996 |
| - type: precision_at_100 |
| value: 1.431 |
| - type: precision_at_1000 |
| value: 0.23800000000000002 |
| - type: precision_at_3 |
| value: 15.02 |
| - type: precision_at_5 |
| value: 10.909 |
| - type: recall_at_1 |
| value: 22.384 |
| - type: recall_at_10 |
| value: 45.052 |
| - type: recall_at_100 |
| value: 72.441 |
| - type: recall_at_1000 |
| value: 91.047 |
| - type: recall_at_3 |
| value: 33.617000000000004 |
| - type: recall_at_5 |
| value: 38.171 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 16.032 |
| - type: map_at_10 |
| value: 22.323 |
| - type: map_at_100 |
| value: 23.317 |
| - type: map_at_1000 |
| value: 23.419999999999998 |
| - type: map_at_3 |
| value: 20.064999999999998 |
| - type: map_at_5 |
| value: 21.246000000000002 |
| - type: mrr_at_1 |
| value: 17.375 |
| - type: mrr_at_10 |
| value: 24.157999999999998 |
| - type: mrr_at_100 |
| value: 25.108000000000004 |
| - type: mrr_at_1000 |
| value: 25.197999999999997 |
| - type: mrr_at_3 |
| value: 21.996 |
| - type: mrr_at_5 |
| value: 23.152 |
| - type: ndcg_at_1 |
| value: 17.375 |
| - type: ndcg_at_10 |
| value: 26.316 |
| - type: ndcg_at_100 |
| value: 31.302000000000003 |
| - type: ndcg_at_1000 |
| value: 34.143 |
| - type: ndcg_at_3 |
| value: 21.914 |
| - type: ndcg_at_5 |
| value: 23.896 |
| - type: precision_at_1 |
| value: 17.375 |
| - type: precision_at_10 |
| value: 4.233 |
| - type: precision_at_100 |
| value: 0.713 |
| - type: precision_at_1000 |
| value: 0.10200000000000001 |
| - type: precision_at_3 |
| value: 9.365 |
| - type: precision_at_5 |
| value: 6.728000000000001 |
| - type: recall_at_1 |
| value: 16.032 |
| - type: recall_at_10 |
| value: 36.944 |
| - type: recall_at_100 |
| value: 59.745000000000005 |
| - type: recall_at_1000 |
| value: 81.101 |
| - type: recall_at_3 |
| value: 25.096 |
| - type: recall_at_5 |
| value: 29.963 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 9.656 |
| - type: map_at_10 |
| value: 17.578 |
| - type: map_at_100 |
| value: 19.38 |
| - type: map_at_1000 |
| value: 19.552 |
| - type: map_at_3 |
| value: 14.544 |
| - type: map_at_5 |
| value: 15.914 |
| - type: mrr_at_1 |
| value: 21.041999999999998 |
| - type: mrr_at_10 |
| value: 33.579 |
| - type: mrr_at_100 |
| value: 34.483000000000004 |
| - type: mrr_at_1000 |
| value: 34.526 |
| - type: mrr_at_3 |
| value: 30.0 |
| - type: mrr_at_5 |
| value: 31.813999999999997 |
| - type: ndcg_at_1 |
| value: 21.041999999999998 |
| - type: ndcg_at_10 |
| value: 25.563999999999997 |
| - type: ndcg_at_100 |
| value: 32.714 |
| - type: ndcg_at_1000 |
| value: 35.943000000000005 |
| - type: ndcg_at_3 |
| value: 20.357 |
| - type: ndcg_at_5 |
| value: 21.839 |
| - type: precision_at_1 |
| value: 21.041999999999998 |
| - type: precision_at_10 |
| value: 8.319 |
| - type: precision_at_100 |
| value: 1.593 |
| - type: precision_at_1000 |
| value: 0.219 |
| - type: precision_at_3 |
| value: 15.440000000000001 |
| - type: precision_at_5 |
| value: 11.792 |
| - type: recall_at_1 |
| value: 9.656 |
| - type: recall_at_10 |
| value: 32.023 |
| - type: recall_at_100 |
| value: 56.812 |
| - type: recall_at_1000 |
| value: 75.098 |
| - type: recall_at_3 |
| value: 19.455 |
| - type: recall_at_5 |
| value: 23.68 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/CmedqaRetrieval |
| name: MTEB CmedqaRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 13.084999999999999 |
| - type: map_at_10 |
| value: 19.389 |
| - type: map_at_100 |
| value: 20.761 |
| - type: map_at_1000 |
| value: 20.944 |
| - type: map_at_3 |
| value: 17.273 |
| - type: map_at_5 |
| value: 18.37 |
| - type: mrr_at_1 |
| value: 20.955 |
| - type: mrr_at_10 |
| value: 26.741999999999997 |
| - type: mrr_at_100 |
| value: 27.724 |
| - type: mrr_at_1000 |
| value: 27.819 |
| - type: mrr_at_3 |
| value: 24.881 |
| - type: mrr_at_5 |
| value: 25.833000000000002 |
| - type: ndcg_at_1 |
| value: 20.955 |
| - type: ndcg_at_10 |
| value: 23.905 |
| - type: ndcg_at_100 |
| value: 30.166999999999998 |
| - type: ndcg_at_1000 |
| value: 34.202 |
| - type: ndcg_at_3 |
| value: 20.854 |
| - type: ndcg_at_5 |
| value: 21.918000000000003 |
| - type: precision_at_1 |
| value: 20.955 |
| - type: precision_at_10 |
| value: 5.479 |
| - type: precision_at_100 |
| value: 1.065 |
| - type: precision_at_1000 |
| value: 0.159 |
| - type: precision_at_3 |
| value: 11.960999999999999 |
| - type: precision_at_5 |
| value: 8.647 |
| - type: recall_at_1 |
| value: 13.084999999999999 |
| - type: recall_at_10 |
| value: 30.202 |
| - type: recall_at_100 |
| value: 56.579 |
| - type: recall_at_1000 |
| value: 84.641 |
| - type: recall_at_3 |
| value: 20.751 |
| - type: recall_at_5 |
| value: 24.317 |
| - task: |
| type: PairClassification |
| dataset: |
| type: C-MTEB/CMNLI |
| name: MTEB Cmnli |
| config: default |
| split: validation |
| revision: None |
| metrics: |
| - type: cos_sim_accuracy |
| value: 72.8322309079976 |
| - type: cos_sim_ap |
| value: 81.34356949111096 |
| - type: cos_sim_f1 |
| value: 74.88546438983758 |
| - type: cos_sim_precision |
| value: 67.50516238032664 |
| - type: cos_sim_recall |
| value: 84.07762450315643 |
| - type: dot_accuracy |
| value: 69.28442573662056 |
| - type: dot_ap |
| value: 74.87961278837321 |
| - type: dot_f1 |
| value: 72.20502901353966 |
| - type: dot_precision |
| value: 61.5701797789873 |
| - type: dot_recall |
| value: 87.2808043020809 |
| - type: euclidean_accuracy |
| value: 71.99037883343355 |
| - type: euclidean_ap |
| value: 80.70039825164011 |
| - type: euclidean_f1 |
| value: 74.23149154887813 |
| - type: euclidean_precision |
| value: 64.29794520547945 |
| - type: euclidean_recall |
| value: 87.79518353986438 |
| - type: manhattan_accuracy |
| value: 72.0625375826819 |
| - type: manhattan_ap |
| value: 80.78886354854423 |
| - type: manhattan_f1 |
| value: 74.20842299415924 |
| - type: manhattan_precision |
| value: 66.0525355709595 |
| - type: manhattan_recall |
| value: 84.66214636427402 |
| - type: max_accuracy |
| value: 72.8322309079976 |
| - type: max_ap |
| value: 81.34356949111096 |
| - type: max_f1 |
| value: 74.88546438983758 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/CovidRetrieval |
| name: MTEB CovidRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 54.847 |
| - type: map_at_10 |
| value: 63.736000000000004 |
| - type: map_at_100 |
| value: 64.302 |
| - type: map_at_1000 |
| value: 64.319 |
| - type: map_at_3 |
| value: 61.565000000000005 |
| - type: map_at_5 |
| value: 62.671 |
| - type: mrr_at_1 |
| value: 54.900000000000006 |
| - type: mrr_at_10 |
| value: 63.744 |
| - type: mrr_at_100 |
| value: 64.287 |
| - type: mrr_at_1000 |
| value: 64.30399999999999 |
| - type: mrr_at_3 |
| value: 61.590999999999994 |
| - type: mrr_at_5 |
| value: 62.724000000000004 |
| - type: ndcg_at_1 |
| value: 55.005 |
| - type: ndcg_at_10 |
| value: 68.142 |
| - type: ndcg_at_100 |
| value: 70.95 |
| - type: ndcg_at_1000 |
| value: 71.40100000000001 |
| - type: ndcg_at_3 |
| value: 63.641999999999996 |
| - type: ndcg_at_5 |
| value: 65.62599999999999 |
| - type: precision_at_1 |
| value: 55.005 |
| - type: precision_at_10 |
| value: 8.272 |
| - type: precision_at_100 |
| value: 0.963 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 23.288 |
| - type: precision_at_5 |
| value: 14.963000000000001 |
| - type: recall_at_1 |
| value: 54.847 |
| - type: recall_at_10 |
| value: 81.955 |
| - type: recall_at_100 |
| value: 95.258 |
| - type: recall_at_1000 |
| value: 98.84100000000001 |
| - type: recall_at_3 |
| value: 69.547 |
| - type: recall_at_5 |
| value: 74.315 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 7.2620000000000005 |
| - type: map_at_10 |
| value: 15.196000000000002 |
| - type: map_at_100 |
| value: 19.454 |
| - type: map_at_1000 |
| value: 20.445 |
| - type: map_at_3 |
| value: 11.532 |
| - type: map_at_5 |
| value: 13.053999999999998 |
| - type: mrr_at_1 |
| value: 57.49999999999999 |
| - type: mrr_at_10 |
| value: 66.661 |
| - type: mrr_at_100 |
| value: 67.086 |
| - type: mrr_at_1000 |
| value: 67.105 |
| - type: mrr_at_3 |
| value: 64.625 |
| - type: mrr_at_5 |
| value: 65.962 |
| - type: ndcg_at_1 |
| value: 46.125 |
| - type: ndcg_at_10 |
| value: 32.609 |
| - type: ndcg_at_100 |
| value: 34.611999999999995 |
| - type: ndcg_at_1000 |
| value: 40.836 |
| - type: ndcg_at_3 |
| value: 37.513000000000005 |
| - type: ndcg_at_5 |
| value: 34.699999999999996 |
| - type: precision_at_1 |
| value: 57.49999999999999 |
| - type: precision_at_10 |
| value: 24.975 |
| - type: precision_at_100 |
| value: 6.9830000000000005 |
| - type: precision_at_1000 |
| value: 1.505 |
| - type: precision_at_3 |
| value: 40.75 |
| - type: precision_at_5 |
| value: 33.2 |
| - type: recall_at_1 |
| value: 7.2620000000000005 |
| - type: recall_at_10 |
| value: 20.341 |
| - type: recall_at_100 |
| value: 38.690999999999995 |
| - type: recall_at_1000 |
| value: 58.879000000000005 |
| - type: recall_at_3 |
| value: 12.997 |
| - type: recall_at_5 |
| value: 15.628 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/DuRetrieval |
| name: MTEB DuRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 20.86 |
| - type: map_at_10 |
| value: 62.28 |
| - type: map_at_100 |
| value: 65.794 |
| - type: map_at_1000 |
| value: 65.903 |
| - type: map_at_3 |
| value: 42.616 |
| - type: map_at_5 |
| value: 53.225 |
| - type: mrr_at_1 |
| value: 76.75 |
| - type: mrr_at_10 |
| value: 83.387 |
| - type: mrr_at_100 |
| value: 83.524 |
| - type: mrr_at_1000 |
| value: 83.531 |
| - type: mrr_at_3 |
| value: 82.592 |
| - type: mrr_at_5 |
| value: 83.07900000000001 |
| - type: ndcg_at_1 |
| value: 76.75 |
| - type: ndcg_at_10 |
| value: 72.83500000000001 |
| - type: ndcg_at_100 |
| value: 77.839 |
| - type: ndcg_at_1000 |
| value: 78.976 |
| - type: ndcg_at_3 |
| value: 70.977 |
| - type: ndcg_at_5 |
| value: 69.419 |
| - type: precision_at_1 |
| value: 76.75 |
| - type: precision_at_10 |
| value: 35.825 |
| - type: precision_at_100 |
| value: 4.507 |
| - type: precision_at_1000 |
| value: 0.47800000000000004 |
| - type: precision_at_3 |
| value: 63.733 |
| - type: precision_at_5 |
| value: 53.44 |
| - type: recall_at_1 |
| value: 20.86 |
| - type: recall_at_10 |
| value: 75.115 |
| - type: recall_at_100 |
| value: 90.47699999999999 |
| - type: recall_at_1000 |
| value: 96.304 |
| - type: recall_at_3 |
| value: 45.976 |
| - type: recall_at_5 |
| value: 59.971 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/EcomRetrieval |
| name: MTEB EcomRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 37.8 |
| - type: map_at_10 |
| value: 47.154 |
| - type: map_at_100 |
| value: 48.012 |
| - type: map_at_1000 |
| value: 48.044 |
| - type: map_at_3 |
| value: 44.667 |
| - type: map_at_5 |
| value: 45.992 |
| - type: mrr_at_1 |
| value: 37.8 |
| - type: mrr_at_10 |
| value: 47.154 |
| - type: mrr_at_100 |
| value: 48.012 |
| - type: mrr_at_1000 |
| value: 48.044 |
| - type: mrr_at_3 |
| value: 44.667 |
| - type: mrr_at_5 |
| value: 45.992 |
| - type: ndcg_at_1 |
| value: 37.8 |
| - type: ndcg_at_10 |
| value: 52.025 |
| - type: ndcg_at_100 |
| value: 56.275 |
| - type: ndcg_at_1000 |
| value: 57.174 |
| - type: ndcg_at_3 |
| value: 46.861999999999995 |
| - type: ndcg_at_5 |
| value: 49.229 |
| - type: precision_at_1 |
| value: 37.8 |
| - type: precision_at_10 |
| value: 6.75 |
| - type: precision_at_100 |
| value: 0.8750000000000001 |
| - type: precision_at_1000 |
| value: 0.095 |
| - type: precision_at_3 |
| value: 17.732999999999997 |
| - type: precision_at_5 |
| value: 11.78 |
| - type: recall_at_1 |
| value: 37.8 |
| - type: recall_at_10 |
| value: 67.5 |
| - type: recall_at_100 |
| value: 87.5 |
| - type: recall_at_1000 |
| value: 94.69999999999999 |
| - type: recall_at_3 |
| value: 53.2 |
| - type: recall_at_5 |
| value: 58.9 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 46.845 |
| - type: f1 |
| value: 42.70952656074019 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 50.058 |
| - type: map_at_10 |
| value: 61.295 |
| - type: map_at_100 |
| value: 61.82 |
| - type: map_at_1000 |
| value: 61.843 |
| - type: map_at_3 |
| value: 58.957 |
| - type: map_at_5 |
| value: 60.467999999999996 |
| - type: mrr_at_1 |
| value: 54.05 |
| - type: mrr_at_10 |
| value: 65.52900000000001 |
| - type: mrr_at_100 |
| value: 65.984 |
| - type: mrr_at_1000 |
| value: 65.999 |
| - type: mrr_at_3 |
| value: 63.286 |
| - type: mrr_at_5 |
| value: 64.777 |
| - type: ndcg_at_1 |
| value: 54.05 |
| - type: ndcg_at_10 |
| value: 67.216 |
| - type: ndcg_at_100 |
| value: 69.594 |
| - type: ndcg_at_1000 |
| value: 70.13000000000001 |
| - type: ndcg_at_3 |
| value: 62.778999999999996 |
| - type: ndcg_at_5 |
| value: 65.36 |
| - type: precision_at_1 |
| value: 54.05 |
| - type: precision_at_10 |
| value: 8.924 |
| - type: precision_at_100 |
| value: 1.019 |
| - type: precision_at_1000 |
| value: 0.108 |
| - type: precision_at_3 |
| value: 25.218 |
| - type: precision_at_5 |
| value: 16.547 |
| - type: recall_at_1 |
| value: 50.058 |
| - type: recall_at_10 |
| value: 81.39699999999999 |
| - type: recall_at_100 |
| value: 92.022 |
| - type: recall_at_1000 |
| value: 95.877 |
| - type: recall_at_3 |
| value: 69.485 |
| - type: recall_at_5 |
| value: 75.833 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 15.078 |
| - type: map_at_10 |
| value: 24.162 |
| - type: map_at_100 |
| value: 25.818 |
| - type: map_at_1000 |
| value: 26.009 |
| - type: map_at_3 |
| value: 20.706 |
| - type: map_at_5 |
| value: 22.542 |
| - type: mrr_at_1 |
| value: 30.709999999999997 |
| - type: mrr_at_10 |
| value: 38.828 |
| - type: mrr_at_100 |
| value: 39.794000000000004 |
| - type: mrr_at_1000 |
| value: 39.843 |
| - type: mrr_at_3 |
| value: 36.163000000000004 |
| - type: mrr_at_5 |
| value: 37.783 |
| - type: ndcg_at_1 |
| value: 30.709999999999997 |
| - type: ndcg_at_10 |
| value: 31.290000000000003 |
| - type: ndcg_at_100 |
| value: 38.051 |
| - type: ndcg_at_1000 |
| value: 41.487 |
| - type: ndcg_at_3 |
| value: 27.578999999999997 |
| - type: ndcg_at_5 |
| value: 28.799000000000003 |
| - type: precision_at_1 |
| value: 30.709999999999997 |
| - type: precision_at_10 |
| value: 8.92 |
| - type: precision_at_100 |
| value: 1.5599999999999998 |
| - type: precision_at_1000 |
| value: 0.219 |
| - type: precision_at_3 |
| value: 18.416 |
| - type: precision_at_5 |
| value: 13.827 |
| - type: recall_at_1 |
| value: 15.078 |
| - type: recall_at_10 |
| value: 37.631 |
| - type: recall_at_100 |
| value: 63.603 |
| - type: recall_at_1000 |
| value: 84.121 |
| - type: recall_at_3 |
| value: 24.438 |
| - type: recall_at_5 |
| value: 29.929 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 31.202 |
| - type: map_at_10 |
| value: 42.653 |
| - type: map_at_100 |
| value: 43.411 |
| - type: map_at_1000 |
| value: 43.479 |
| - type: map_at_3 |
| value: 40.244 |
| - type: map_at_5 |
| value: 41.736000000000004 |
| - type: mrr_at_1 |
| value: 62.404 |
| - type: mrr_at_10 |
| value: 69.43599999999999 |
| - type: mrr_at_100 |
| value: 69.788 |
| - type: mrr_at_1000 |
| value: 69.809 |
| - type: mrr_at_3 |
| value: 68.12700000000001 |
| - type: mrr_at_5 |
| value: 68.961 |
| - type: ndcg_at_1 |
| value: 62.404 |
| - type: ndcg_at_10 |
| value: 51.665000000000006 |
| - type: ndcg_at_100 |
| value: 54.623 |
| - type: ndcg_at_1000 |
| value: 56.154 |
| - type: ndcg_at_3 |
| value: 47.861 |
| - type: ndcg_at_5 |
| value: 49.968 |
| - type: precision_at_1 |
| value: 62.404 |
| - type: precision_at_10 |
| value: 10.57 |
| - type: precision_at_100 |
| value: 1.2890000000000001 |
| - type: precision_at_1000 |
| value: 0.149 |
| - type: precision_at_3 |
| value: 29.624 |
| - type: precision_at_5 |
| value: 19.441 |
| - type: recall_at_1 |
| value: 31.202 |
| - type: recall_at_10 |
| value: 52.849000000000004 |
| - type: recall_at_100 |
| value: 64.47 |
| - type: recall_at_1000 |
| value: 74.74 |
| - type: recall_at_3 |
| value: 44.436 |
| - type: recall_at_5 |
| value: 48.602000000000004 |
| - task: |
| type: Classification |
| dataset: |
| type: C-MTEB/IFlyTek-classification |
| name: MTEB IFlyTek |
| config: default |
| split: validation |
| revision: None |
| metrics: |
| - type: accuracy |
| value: 43.51673720661793 |
| - type: f1 |
| value: 35.81126468608715 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 74.446 |
| - type: ap |
| value: 68.71359666500074 |
| - type: f1 |
| value: 74.32080431056023 |
| - task: |
| type: Classification |
| dataset: |
| type: C-MTEB/JDReview-classification |
| name: MTEB JDReview |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: accuracy |
| value: 81.08818011257036 |
| - type: ap |
| value: 43.68599141287235 |
| - type: f1 |
| value: 74.37787266346157 |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/LCQMC |
| name: MTEB LCQMC |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 65.9116523539515 |
| - type: cos_sim_spearman |
| value: 72.79966865646485 |
| - type: euclidean_pearson |
| value: 71.4995885009818 |
| - type: euclidean_spearman |
| value: 72.91799793240196 |
| - type: manhattan_pearson |
| value: 71.83065174544116 |
| - type: manhattan_spearman |
| value: 73.22568775268935 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/MMarcoRetrieval |
| name: MTEB MMarcoRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 61.79900000000001 |
| - type: map_at_10 |
| value: 70.814 |
| - type: map_at_100 |
| value: 71.22500000000001 |
| - type: map_at_1000 |
| value: 71.243 |
| - type: map_at_3 |
| value: 68.795 |
| - type: map_at_5 |
| value: 70.12 |
| - type: mrr_at_1 |
| value: 63.910999999999994 |
| - type: mrr_at_10 |
| value: 71.437 |
| - type: mrr_at_100 |
| value: 71.807 |
| - type: mrr_at_1000 |
| value: 71.82300000000001 |
| - type: mrr_at_3 |
| value: 69.65599999999999 |
| - type: mrr_at_5 |
| value: 70.821 |
| - type: ndcg_at_1 |
| value: 63.910999999999994 |
| - type: ndcg_at_10 |
| value: 74.664 |
| - type: ndcg_at_100 |
| value: 76.545 |
| - type: ndcg_at_1000 |
| value: 77.00099999999999 |
| - type: ndcg_at_3 |
| value: 70.838 |
| - type: ndcg_at_5 |
| value: 73.076 |
| - type: precision_at_1 |
| value: 63.910999999999994 |
| - type: precision_at_10 |
| value: 9.139999999999999 |
| - type: precision_at_100 |
| value: 1.008 |
| - type: precision_at_1000 |
| value: 0.105 |
| - type: precision_at_3 |
| value: 26.729000000000003 |
| - type: precision_at_5 |
| value: 17.232 |
| - type: recall_at_1 |
| value: 61.79900000000001 |
| - type: recall_at_10 |
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| config: default |
| split: dev |
| revision: None |
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| - type: mrr_at_1000 |
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| - type: ndcg_at_100 |
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| - type: ndcg_at_1000 |
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| - type: ndcg_at_3 |
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| - type: precision_at_1000 |
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| - type: precision_at_3 |
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| - type: precision_at_5 |
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| - type: recall_at_100 |
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| - type: recall_at_1000 |
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| - task: |
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| - type: accuracy |
| value: 47.9119031607263 |
| - type: f1 |
| value: 45.96730030717468 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (sw) |
| config: sw |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 51.20040349697377 |
| - type: f1 |
| value: 49.113423730259214 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ta) |
| config: ta |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 61.8392737054472 |
| - type: f1 |
| value: 61.65834459536364 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (te) |
| config: te |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 59.791526563550775 |
| - type: f1 |
| value: 58.2891677685128 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (th) |
| config: th |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 41.62071284465366 |
| - type: f1 |
| value: 39.591525429243575 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (tl) |
| config: tl |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 50.46738399462004 |
| - type: f1 |
| value: 49.50612154409957 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (tr) |
| config: tr |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 43.41291190316072 |
| - type: f1 |
| value: 43.85070302174815 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (ur) |
| config: ur |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 60.15131136516476 |
| - type: f1 |
| value: 59.260012738676316 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (vi) |
| config: vi |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 68.98789509078682 |
| - type: f1 |
| value: 69.86968024553558 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (zh-CN) |
| config: zh-CN |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 74.72091459314055 |
| - type: f1 |
| value: 74.69866015852224 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (zh-TW) |
| config: zh-TW |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 71.7014122394082 |
| - type: f1 |
| value: 72.66856729607628 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/MedicalRetrieval |
| name: MTEB MedicalRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 35.8 |
| - type: map_at_10 |
| value: 40.949999999999996 |
| - type: map_at_100 |
| value: 41.455999999999996 |
| - type: map_at_1000 |
| value: 41.52 |
| - type: map_at_3 |
| value: 40.033 |
| - type: map_at_5 |
| value: 40.493 |
| - type: mrr_at_1 |
| value: 35.9 |
| - type: mrr_at_10 |
| value: 41.0 |
| - type: mrr_at_100 |
| value: 41.506 |
| - type: mrr_at_1000 |
| value: 41.57 |
| - type: mrr_at_3 |
| value: 40.083 |
| - type: mrr_at_5 |
| value: 40.543 |
| - type: ndcg_at_1 |
| value: 35.8 |
| - type: ndcg_at_10 |
| value: 43.269000000000005 |
| - type: ndcg_at_100 |
| value: 45.974 |
| - type: ndcg_at_1000 |
| value: 47.969 |
| - type: ndcg_at_3 |
| value: 41.339999999999996 |
| - type: ndcg_at_5 |
| value: 42.167 |
| - type: precision_at_1 |
| value: 35.8 |
| - type: precision_at_10 |
| value: 5.050000000000001 |
| - type: precision_at_100 |
| value: 0.637 |
| - type: precision_at_1000 |
| value: 0.08 |
| - type: precision_at_3 |
| value: 15.033 |
| - type: precision_at_5 |
| value: 9.42 |
| - type: recall_at_1 |
| value: 35.8 |
| - type: recall_at_10 |
| value: 50.5 |
| - type: recall_at_100 |
| value: 63.7 |
| - type: recall_at_1000 |
| value: 80.0 |
| - type: recall_at_3 |
| value: 45.1 |
| - type: recall_at_5 |
| value: 47.099999999999994 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 29.43291218491871 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 28.87018200800912 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.51003589330728 |
| - type: mrr |
| value: 31.57412386045135 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/Mmarco-reranking |
| name: MTEB MMarcoReranking |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map |
| value: 26.136250989818222 |
| - type: mrr |
| value: 25.00753968253968 |
| - task: |
| type: Classification |
| dataset: |
| type: C-MTEB/MultilingualSentiment-classification |
| name: MTEB MultilingualSentiment |
| config: default |
| split: validation |
| revision: None |
| metrics: |
| - type: accuracy |
| value: 66.32999999999998 |
| - type: f1 |
| value: 66.2828795526323 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.369 |
| - type: map_at_10 |
| value: 11.04 |
| - type: map_at_100 |
| value: 13.850000000000001 |
| - type: map_at_1000 |
| value: 15.290000000000001 |
| - type: map_at_3 |
| value: 8.014000000000001 |
| - type: map_at_5 |
| value: 9.4 |
| - type: mrr_at_1 |
| value: 39.938 |
| - type: mrr_at_10 |
| value: 49.043 |
| - type: mrr_at_100 |
| value: 49.775000000000006 |
| - type: mrr_at_1000 |
| value: 49.803999999999995 |
| - type: mrr_at_3 |
| value: 47.007 |
| - type: mrr_at_5 |
| value: 48.137 |
| - type: ndcg_at_1 |
| value: 37.461 |
| - type: ndcg_at_10 |
| value: 30.703000000000003 |
| - type: ndcg_at_100 |
| value: 28.686 |
| - type: ndcg_at_1000 |
| value: 37.809 |
| - type: ndcg_at_3 |
| value: 35.697 |
| - type: ndcg_at_5 |
| value: 33.428000000000004 |
| - type: precision_at_1 |
| value: 39.628 |
| - type: precision_at_10 |
| value: 23.250999999999998 |
| - type: precision_at_100 |
| value: 7.553999999999999 |
| - type: precision_at_1000 |
| value: 2.077 |
| - type: precision_at_3 |
| value: 34.159 |
| - type: precision_at_5 |
| value: 29.164 |
| - type: recall_at_1 |
| value: 4.369 |
| - type: recall_at_10 |
| value: 15.024000000000001 |
| - type: recall_at_100 |
| value: 30.642999999999997 |
| - type: recall_at_1000 |
| value: 62.537 |
| - type: recall_at_3 |
| value: 9.504999999999999 |
| - type: recall_at_5 |
| value: 11.89 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.161 |
| - type: map_at_10 |
| value: 39.126 |
| - type: map_at_100 |
| value: 40.201 |
| - type: map_at_1000 |
| value: 40.247 |
| - type: map_at_3 |
| value: 35.169 |
| - type: map_at_5 |
| value: 37.403 |
| - type: mrr_at_1 |
| value: 29.403000000000002 |
| - type: mrr_at_10 |
| value: 41.644999999999996 |
| - type: mrr_at_100 |
| value: 42.503 |
| - type: mrr_at_1000 |
| value: 42.535000000000004 |
| - type: mrr_at_3 |
| value: 38.321 |
| - type: mrr_at_5 |
| value: 40.265 |
| - type: ndcg_at_1 |
| value: 29.403000000000002 |
| - type: ndcg_at_10 |
| value: 46.155 |
| - type: ndcg_at_100 |
| value: 50.869 |
| - type: ndcg_at_1000 |
| value: 52.004 |
| - type: ndcg_at_3 |
| value: 38.65 |
| - type: ndcg_at_5 |
| value: 42.400999999999996 |
| - type: precision_at_1 |
| value: 29.403000000000002 |
| - type: precision_at_10 |
| value: 7.743 |
| - type: precision_at_100 |
| value: 1.0410000000000001 |
| - type: precision_at_1000 |
| value: 0.11499999999999999 |
| - type: precision_at_3 |
| value: 17.623 |
| - type: precision_at_5 |
| value: 12.764000000000001 |
| - type: recall_at_1 |
| value: 26.161 |
| - type: recall_at_10 |
| value: 65.155 |
| - type: recall_at_100 |
| value: 85.885 |
| - type: recall_at_1000 |
| value: 94.443 |
| - type: recall_at_3 |
| value: 45.592 |
| - type: recall_at_5 |
| value: 54.234 |
| - task: |
| type: PairClassification |
| dataset: |
| type: C-MTEB/OCNLI |
| name: MTEB Ocnli |
| config: default |
| split: validation |
| revision: None |
| metrics: |
| - type: cos_sim_accuracy |
| value: 65.34921494315105 |
| - type: cos_sim_ap |
| value: 68.58191894316523 |
| - type: cos_sim_f1 |
| value: 70.47294418406477 |
| - type: cos_sim_precision |
| value: 59.07142857142858 |
| - type: cos_sim_recall |
| value: 87.32840549102428 |
| - type: dot_accuracy |
| value: 61.93827828911749 |
| - type: dot_ap |
| value: 64.19230712895958 |
| - type: dot_f1 |
| value: 68.30769230769232 |
| - type: dot_precision |
| value: 53.72050816696915 |
| - type: dot_recall |
| value: 93.76979936642027 |
| - type: euclidean_accuracy |
| value: 67.0817541959935 |
| - type: euclidean_ap |
| value: 69.17499163875786 |
| - type: euclidean_f1 |
| value: 71.67630057803468 |
| - type: euclidean_precision |
| value: 61.904761904761905 |
| - type: euclidean_recall |
| value: 85.11087645195353 |
| - type: manhattan_accuracy |
| value: 67.19003789929616 |
| - type: manhattan_ap |
| value: 69.72684682556992 |
| - type: manhattan_f1 |
| value: 71.25396106835673 |
| - type: manhattan_precision |
| value: 62.361331220285265 |
| - type: manhattan_recall |
| value: 83.10454065469905 |
| - type: max_accuracy |
| value: 67.19003789929616 |
| - type: max_ap |
| value: 69.72684682556992 |
| - type: max_f1 |
| value: 71.67630057803468 |
| - task: |
| type: Classification |
| dataset: |
| type: C-MTEB/OnlineShopping-classification |
| name: MTEB OnlineShopping |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: accuracy |
| value: 88.35000000000001 |
| - type: ap |
| value: 85.45377991151882 |
| - type: f1 |
| value: 88.33274122313945 |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/PAWSX |
| name: MTEB PAWSX |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 13.700131726042631 |
| - type: cos_sim_spearman |
| value: 15.663851577320184 |
| - type: euclidean_pearson |
| value: 17.869909454798112 |
| - type: euclidean_spearman |
| value: 16.09518673735175 |
| - type: manhattan_pearson |
| value: 18.030818366917593 |
| - type: manhattan_spearman |
| value: 16.34096397687474 |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/QBQTC |
| name: MTEB QBQTC |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.200343733562946 |
| - type: cos_sim_spearman |
| value: 32.645434631834966 |
| - type: euclidean_pearson |
| value: 32.612030669583234 |
| - type: euclidean_spearman |
| value: 34.67603837485763 |
| - type: manhattan_pearson |
| value: 32.6673080122766 |
| - type: manhattan_spearman |
| value: 34.8163622783733 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 69.321 |
| - type: map_at_10 |
| value: 83.07 |
| - type: map_at_100 |
| value: 83.737 |
| - type: map_at_1000 |
| value: 83.758 |
| - type: map_at_3 |
| value: 80.12700000000001 |
| - type: map_at_5 |
| value: 81.97 |
| - type: mrr_at_1 |
| value: 79.74 |
| - type: mrr_at_10 |
| value: 86.22 |
| - type: mrr_at_100 |
| value: 86.345 |
| - type: mrr_at_1000 |
| value: 86.347 |
| - type: mrr_at_3 |
| value: 85.172 |
| - type: mrr_at_5 |
| value: 85.89099999999999 |
| - type: ndcg_at_1 |
| value: 79.77 |
| - type: ndcg_at_10 |
| value: 87.01299999999999 |
| - type: ndcg_at_100 |
| value: 88.382 |
| - type: ndcg_at_1000 |
| value: 88.53 |
| - type: ndcg_at_3 |
| value: 84.04 |
| - type: ndcg_at_5 |
| value: 85.68 |
| - type: precision_at_1 |
| value: 79.77 |
| - type: precision_at_10 |
| value: 13.211999999999998 |
| - type: precision_at_100 |
| value: 1.52 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 36.730000000000004 |
| - type: precision_at_5 |
| value: 24.21 |
| - type: recall_at_1 |
| value: 69.321 |
| - type: recall_at_10 |
| value: 94.521 |
| - type: recall_at_100 |
| value: 99.258 |
| - type: recall_at_1000 |
| value: 99.97200000000001 |
| - type: recall_at_3 |
| value: 85.97200000000001 |
| - type: recall_at_5 |
| value: 90.589 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 44.51751457277441 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 53.60727449352775 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.058 |
| - type: map_at_10 |
| value: 9.995999999999999 |
| - type: map_at_100 |
| value: 11.738 |
| - type: map_at_1000 |
| value: 11.999 |
| - type: map_at_3 |
| value: 7.353999999999999 |
| - type: map_at_5 |
| value: 8.68 |
| - type: mrr_at_1 |
| value: 20.0 |
| - type: mrr_at_10 |
| value: 30.244 |
| - type: mrr_at_100 |
| value: 31.378 |
| - type: mrr_at_1000 |
| value: 31.445 |
| - type: mrr_at_3 |
| value: 26.933 |
| - type: mrr_at_5 |
| value: 28.748 |
| - type: ndcg_at_1 |
| value: 20.0 |
| - type: ndcg_at_10 |
| value: 17.235 |
| - type: ndcg_at_100 |
| value: 24.241 |
| - type: ndcg_at_1000 |
| value: 29.253 |
| - type: ndcg_at_3 |
| value: 16.542 |
| - type: ndcg_at_5 |
| value: 14.386 |
| - type: precision_at_1 |
| value: 20.0 |
| - type: precision_at_10 |
| value: 8.9 |
| - type: precision_at_100 |
| value: 1.8929999999999998 |
| - type: precision_at_1000 |
| value: 0.31 |
| - type: precision_at_3 |
| value: 15.567 |
| - type: precision_at_5 |
| value: 12.620000000000001 |
| - type: recall_at_1 |
| value: 4.058 |
| - type: recall_at_10 |
| value: 18.062 |
| - type: recall_at_100 |
| value: 38.440000000000005 |
| - type: recall_at_1000 |
| value: 63.044999999999995 |
| - type: recall_at_3 |
| value: 9.493 |
| - type: recall_at_5 |
| value: 12.842 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.36702895231333 |
| - type: cos_sim_spearman |
| value: 79.91790376084445 |
| - type: euclidean_pearson |
| value: 81.58989754571684 |
| - type: euclidean_spearman |
| value: 79.43876559435684 |
| - type: manhattan_pearson |
| value: 81.5041355053572 |
| - type: manhattan_spearman |
| value: 79.35411927652234 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.77166067512005 |
| - type: cos_sim_spearman |
| value: 75.7961015562481 |
| - type: euclidean_pearson |
| value: 82.03845114943047 |
| - type: euclidean_spearman |
| value: 78.75422268992615 |
| - type: manhattan_pearson |
| value: 82.11841609875198 |
| - type: manhattan_spearman |
| value: 78.79349601386468 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.28403658061106 |
| - type: cos_sim_spearman |
| value: 83.61682237930194 |
| - type: euclidean_pearson |
| value: 84.50220149144553 |
| - type: euclidean_spearman |
| value: 85.01944483089126 |
| - type: manhattan_pearson |
| value: 84.5526583345216 |
| - type: manhattan_spearman |
| value: 85.06290695547032 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.66893263127082 |
| - type: cos_sim_spearman |
| value: 78.73277873007592 |
| - type: euclidean_pearson |
| value: 80.78325001462842 |
| - type: euclidean_spearman |
| value: 79.1692321029638 |
| - type: manhattan_pearson |
| value: 80.82812137898084 |
| - type: manhattan_spearman |
| value: 79.23433932409523 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.6046231732945 |
| - type: cos_sim_spearman |
| value: 86.41326579037185 |
| - type: euclidean_pearson |
| value: 85.85739124012164 |
| - type: euclidean_spearman |
| value: 86.54285701350923 |
| - type: manhattan_pearson |
| value: 85.78835254765399 |
| - type: manhattan_spearman |
| value: 86.45431641050791 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.97881854103466 |
| - type: cos_sim_spearman |
| value: 84.50343997301495 |
| - type: euclidean_pearson |
| value: 82.83306004280789 |
| - type: euclidean_spearman |
| value: 83.2801802732528 |
| - type: manhattan_pearson |
| value: 82.73250604776496 |
| - type: manhattan_spearman |
| value: 83.12452727964241 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (ko-ko) |
| config: ko-ko |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 61.59564206989664 |
| - type: cos_sim_spearman |
| value: 61.88740058576333 |
| - type: euclidean_pearson |
| value: 60.23297902405152 |
| - type: euclidean_spearman |
| value: 60.21120786234968 |
| - type: manhattan_pearson |
| value: 60.48897723321176 |
| - type: manhattan_spearman |
| value: 60.44230460138873 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (ar-ar) |
| config: ar-ar |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 80.44912821552151 |
| - type: cos_sim_spearman |
| value: 81.13348443154915 |
| - type: euclidean_pearson |
| value: 81.09038308120358 |
| - type: euclidean_spearman |
| value: 80.5609874348409 |
| - type: manhattan_pearson |
| value: 81.13776188970186 |
| - type: manhattan_spearman |
| value: 80.5900946438308 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-ar) |
| config: en-ar |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 78.72913217243624 |
| - type: cos_sim_spearman |
| value: 79.63696165091363 |
| - type: euclidean_pearson |
| value: 73.19989464436063 |
| - type: euclidean_spearman |
| value: 73.54600704085456 |
| - type: manhattan_pearson |
| value: 72.86702738433412 |
| - type: manhattan_spearman |
| value: 72.90617504239171 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-de) |
| config: en-de |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 50.732677791011525 |
| - type: cos_sim_spearman |
| value: 52.523598781843916 |
| - type: euclidean_pearson |
| value: 49.35416337421446 |
| - type: euclidean_spearman |
| value: 51.33696662867874 |
| - type: manhattan_pearson |
| value: 50.506295752592145 |
| - type: manhattan_spearman |
| value: 52.62915407476881 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-en) |
| config: en-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 89.36491555020613 |
| - type: cos_sim_spearman |
| value: 89.9454102616469 |
| - type: euclidean_pearson |
| value: 88.86298725696331 |
| - type: euclidean_spearman |
| value: 88.65552919486326 |
| - type: manhattan_pearson |
| value: 88.92114540797368 |
| - type: manhattan_spearman |
| value: 88.70527010857221 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-tr) |
| config: en-tr |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 8.714024392790805 |
| - type: cos_sim_spearman |
| value: 4.749252746175972 |
| - type: euclidean_pearson |
| value: 10.22053449467633 |
| - type: euclidean_spearman |
| value: 9.037870998258068 |
| - type: manhattan_pearson |
| value: 12.0555115545086 |
| - type: manhattan_spearman |
| value: 10.63527037732596 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (es-en) |
| config: es-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.02829923391249 |
| - type: cos_sim_spearman |
| value: 85.4083636563418 |
| - type: euclidean_pearson |
| value: 80.36151292795275 |
| - type: euclidean_spearman |
| value: 80.77292573694929 |
| - type: manhattan_pearson |
| value: 80.6693169692864 |
| - type: manhattan_spearman |
| value: 81.14159565166888 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (es-es) |
| config: es-es |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.99900583005198 |
| - type: cos_sim_spearman |
| value: 87.3279898301188 |
| - type: euclidean_pearson |
| value: 86.87787294488236 |
| - type: euclidean_spearman |
| value: 85.53646010337043 |
| - type: manhattan_pearson |
| value: 86.9509718845318 |
| - type: manhattan_spearman |
| value: 85.71691660800931 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (fr-en) |
| config: fr-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.46126526473 |
| - type: cos_sim_spearman |
| value: 83.95970248728918 |
| - type: euclidean_pearson |
| value: 81.73140443111127 |
| - type: euclidean_spearman |
| value: 81.74150374966206 |
| - type: manhattan_pearson |
| value: 81.86557893665228 |
| - type: manhattan_spearman |
| value: 82.09645552492371 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (it-en) |
| config: it-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 46.49174934231959 |
| - type: cos_sim_spearman |
| value: 45.61787630214591 |
| - type: euclidean_pearson |
| value: 49.99290765454166 |
| - type: euclidean_spearman |
| value: 49.69936044179364 |
| - type: manhattan_pearson |
| value: 51.3375093082487 |
| - type: manhattan_spearman |
| value: 51.28438118049182 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (nl-en) |
| config: nl-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 48.29554395534795 |
| - type: cos_sim_spearman |
| value: 46.68726750723354 |
| - type: euclidean_pearson |
| value: 47.17222230888035 |
| - type: euclidean_spearman |
| value: 45.92754616369105 |
| - type: manhattan_pearson |
| value: 47.75493126673596 |
| - type: manhattan_spearman |
| value: 46.20677181839115 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (en) |
| config: en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 66.3630120343016 |
| - type: cos_sim_spearman |
| value: 65.81094140725656 |
| - type: euclidean_pearson |
| value: 67.90672012385122 |
| - type: euclidean_spearman |
| value: 67.81659181369037 |
| - type: manhattan_pearson |
| value: 68.0253831292356 |
| - type: manhattan_spearman |
| value: 67.6187327404364 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de) |
| config: de |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 29.18452426712489 |
| - type: cos_sim_spearman |
| value: 37.51420703956064 |
| - type: euclidean_pearson |
| value: 28.026224447990934 |
| - type: euclidean_spearman |
| value: 38.80123640343127 |
| - type: manhattan_pearson |
| value: 28.71522521219943 |
| - type: manhattan_spearman |
| value: 39.336233734574066 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (es) |
| config: es |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 56.859180417788316 |
| - type: cos_sim_spearman |
| value: 59.78915219131012 |
| - type: euclidean_pearson |
| value: 62.96361204638708 |
| - type: euclidean_spearman |
| value: 61.17669127090527 |
| - type: manhattan_pearson |
| value: 63.76244034298364 |
| - type: manhattan_spearman |
| value: 61.86264089685531 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (pl) |
| config: pl |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 16.606738041913964 |
| - type: cos_sim_spearman |
| value: 27.979167349378507 |
| - type: euclidean_pearson |
| value: 9.681469291321502 |
| - type: euclidean_spearman |
| value: 28.088375191612652 |
| - type: manhattan_pearson |
| value: 10.511180494241913 |
| - type: manhattan_spearman |
| value: 28.551302212661085 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (tr) |
| config: tr |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 25.299512638088835 |
| - type: cos_sim_spearman |
| value: 42.32704160389304 |
| - type: euclidean_pearson |
| value: 38.695432241220615 |
| - type: euclidean_spearman |
| value: 42.64456376476522 |
| - type: manhattan_pearson |
| value: 39.85979335053606 |
| - type: manhattan_spearman |
| value: 42.769358737309716 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (ar) |
| config: ar |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 47.92303842321097 |
| - type: cos_sim_spearman |
| value: 55.000760154318996 |
| - type: euclidean_pearson |
| value: 54.09534510237817 |
| - type: euclidean_spearman |
| value: 56.174584414116055 |
| - type: manhattan_pearson |
| value: 56.361913198454616 |
| - type: manhattan_spearman |
| value: 58.34526441198397 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (ru) |
| config: ru |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 31.742856551594826 |
| - type: cos_sim_spearman |
| value: 43.13787302806463 |
| - type: euclidean_pearson |
| value: 31.905579993088136 |
| - type: euclidean_spearman |
| value: 39.885035201343186 |
| - type: manhattan_pearson |
| value: 32.43242118943698 |
| - type: manhattan_spearman |
| value: 40.11107248799126 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (zh) |
| config: zh |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 47.44633750616152 |
| - type: cos_sim_spearman |
| value: 54.083033284097816 |
| - type: euclidean_pearson |
| value: 51.444658791680155 |
| - type: euclidean_spearman |
| value: 53.1381741726486 |
| - type: manhattan_pearson |
| value: 56.75523385117588 |
| - type: manhattan_spearman |
| value: 58.34517911003165 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (fr) |
| config: fr |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 79.36983311049038 |
| - type: cos_sim_spearman |
| value: 81.25208121596035 |
| - type: euclidean_pearson |
| value: 79.0841246591628 |
| - type: euclidean_spearman |
| value: 79.63170247237287 |
| - type: manhattan_pearson |
| value: 79.76857988012227 |
| - type: manhattan_spearman |
| value: 80.19933344030764 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de-en) |
| config: de-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 50.08537255290631 |
| - type: cos_sim_spearman |
| value: 51.6560951182032 |
| - type: euclidean_pearson |
| value: 56.245817211229856 |
| - type: euclidean_spearman |
| value: 57.84579505485162 |
| - type: manhattan_pearson |
| value: 57.178628792860394 |
| - type: manhattan_spearman |
| value: 58.868316567418965 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (es-en) |
| config: es-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 69.32518691946098 |
| - type: cos_sim_spearman |
| value: 73.58536905137812 |
| - type: euclidean_pearson |
| value: 73.3593301595928 |
| - type: euclidean_spearman |
| value: 74.72443890443692 |
| - type: manhattan_pearson |
| value: 73.89491090838783 |
| - type: manhattan_spearman |
| value: 75.01810348241496 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (it) |
| config: it |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 65.63185657261381 |
| - type: cos_sim_spearman |
| value: 68.8680524426534 |
| - type: euclidean_pearson |
| value: 65.8069214967351 |
| - type: euclidean_spearman |
| value: 67.58006300921988 |
| - type: manhattan_pearson |
| value: 66.42691541820066 |
| - type: manhattan_spearman |
| value: 68.20501753012334 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (pl-en) |
| config: pl-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 63.5746658293195 |
| - type: cos_sim_spearman |
| value: 60.766781234511114 |
| - type: euclidean_pearson |
| value: 63.87934914483433 |
| - type: euclidean_spearman |
| value: 57.609930019070575 |
| - type: manhattan_pearson |
| value: 66.02268099209732 |
| - type: manhattan_spearman |
| value: 60.27189531789914 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (zh-en) |
| config: zh-en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 66.00715694009531 |
| - type: cos_sim_spearman |
| value: 65.00759157082473 |
| - type: euclidean_pearson |
| value: 46.532834841771916 |
| - type: euclidean_spearman |
| value: 45.726258106671516 |
| - type: manhattan_pearson |
| value: 67.32238041001737 |
| - type: manhattan_spearman |
| value: 66.143420656417 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (es-it) |
| config: es-it |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 62.65123838155666 |
| - type: cos_sim_spearman |
| value: 67.8261281384735 |
| - type: euclidean_pearson |
| value: 63.477912220562025 |
| - type: euclidean_spearman |
| value: 65.51430407718927 |
| - type: manhattan_pearson |
| value: 61.935191484002964 |
| - type: manhattan_spearman |
| value: 63.836661905551374 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de-fr) |
| config: de-fr |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 38.397676312074786 |
| - type: cos_sim_spearman |
| value: 39.66141773675305 |
| - type: euclidean_pearson |
| value: 32.78160515193193 |
| - type: euclidean_spearman |
| value: 33.754398073832384 |
| - type: manhattan_pearson |
| value: 31.542566989070103 |
| - type: manhattan_spearman |
| value: 31.84555978703678 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (de-pl) |
| config: de-pl |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 16.134054972017115 |
| - type: cos_sim_spearman |
| value: 26.113399767684193 |
| - type: euclidean_pearson |
| value: 24.956029896964587 |
| - type: euclidean_spearman |
| value: 26.513723113179346 |
| - type: manhattan_pearson |
| value: 27.504346443344712 |
| - type: manhattan_spearman |
| value: 35.382424921072094 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (fr-pl) |
| config: fr-pl |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 74.63601297425362 |
| - type: cos_sim_spearman |
| value: 84.51542547285167 |
| - type: euclidean_pearson |
| value: 72.60877043745072 |
| - type: euclidean_spearman |
| value: 73.24670207647144 |
| - type: manhattan_pearson |
| value: 69.30655335948613 |
| - type: manhattan_spearman |
| value: 73.24670207647144 |
| - task: |
| type: STS |
| dataset: |
| type: C-MTEB/STSB |
| name: MTEB STSB |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: cos_sim_pearson |
| value: 79.4028184159866 |
| - type: cos_sim_spearman |
| value: 79.53464687577328 |
| - type: euclidean_pearson |
| value: 79.25913610578554 |
| - type: euclidean_spearman |
| value: 79.55288323830753 |
| - type: manhattan_pearson |
| value: 79.44759977916512 |
| - type: manhattan_spearman |
| value: 79.71927216173198 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.07398235741444 |
| - type: cos_sim_spearman |
| value: 85.78865814488006 |
| - type: euclidean_pearson |
| value: 83.2824378418878 |
| - type: euclidean_spearman |
| value: 83.36258201307002 |
| - type: manhattan_pearson |
| value: 83.22221949643878 |
| - type: manhattan_spearman |
| value: 83.27892691688584 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 78.1122816381465 |
| - type: mrr |
| value: 93.44523849425809 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 51.132999999999996 |
| - type: map_at_10 |
| value: 60.672000000000004 |
| - type: map_at_100 |
| value: 61.504000000000005 |
| - type: map_at_1000 |
| value: 61.526 |
| - type: map_at_3 |
| value: 57.536 |
| - type: map_at_5 |
| value: 59.362 |
| - type: mrr_at_1 |
| value: 53.667 |
| - type: mrr_at_10 |
| value: 61.980000000000004 |
| - type: mrr_at_100 |
| value: 62.633 |
| - type: mrr_at_1000 |
| value: 62.653000000000006 |
| - type: mrr_at_3 |
| value: 59.721999999999994 |
| - type: mrr_at_5 |
| value: 60.789 |
| - type: ndcg_at_1 |
| value: 53.667 |
| - type: ndcg_at_10 |
| value: 65.42099999999999 |
| - type: ndcg_at_100 |
| value: 68.884 |
| - type: ndcg_at_1000 |
| value: 69.494 |
| - type: ndcg_at_3 |
| value: 60.007 |
| - type: ndcg_at_5 |
| value: 62.487 |
| - type: precision_at_1 |
| value: 53.667 |
| - type: precision_at_10 |
| value: 8.833 |
| - type: precision_at_100 |
| value: 1.0699999999999998 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 23.222 |
| - type: precision_at_5 |
| value: 15.667 |
| - type: recall_at_1 |
| value: 51.132999999999996 |
| - type: recall_at_10 |
| value: 78.989 |
| - type: recall_at_100 |
| value: 94.167 |
| - type: recall_at_1000 |
| value: 99.0 |
| - type: recall_at_3 |
| value: 64.328 |
| - type: recall_at_5 |
| value: 70.35 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.78910891089109 |
| - type: cos_sim_ap |
| value: 94.58344155979994 |
| - type: cos_sim_f1 |
| value: 89.2354124748491 |
| - type: cos_sim_precision |
| value: 89.77732793522267 |
| - type: cos_sim_recall |
| value: 88.7 |
| - type: dot_accuracy |
| value: 99.74158415841585 |
| - type: dot_ap |
| value: 92.08599680108772 |
| - type: dot_f1 |
| value: 87.00846192135391 |
| - type: dot_precision |
| value: 86.62041625371654 |
| - type: dot_recall |
| value: 87.4 |
| - type: euclidean_accuracy |
| value: 99.78316831683168 |
| - type: euclidean_ap |
| value: 94.57715670055748 |
| - type: euclidean_f1 |
| value: 88.98765432098766 |
| - type: euclidean_precision |
| value: 87.90243902439025 |
| - type: euclidean_recall |
| value: 90.10000000000001 |
| - type: manhattan_accuracy |
| value: 99.78811881188119 |
| - type: manhattan_ap |
| value: 94.73016642953513 |
| - type: manhattan_f1 |
| value: 89.3326838772528 |
| - type: manhattan_precision |
| value: 87.08452041785375 |
| - type: manhattan_recall |
| value: 91.7 |
| - type: max_accuracy |
| value: 99.78910891089109 |
| - type: max_ap |
| value: 94.73016642953513 |
| - type: max_f1 |
| value: 89.3326838772528 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 57.11358892084413 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 31.914375833951354 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 48.9994487557691 |
| - type: mrr |
| value: 49.78547290128173 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.19567881069216 |
| - type: cos_sim_spearman |
| value: 31.098791519646298 |
| - type: dot_pearson |
| value: 30.61141391110544 |
| - type: dot_spearman |
| value: 30.995416064312153 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/T2Reranking |
| name: MTEB T2Reranking |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map |
| value: 65.9449793956858 |
| - type: mrr |
| value: 75.83074738584217 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/T2Retrieval |
| name: MTEB T2Retrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.186999999999998 |
| - type: map_at_10 |
| value: 63.007000000000005 |
| - type: map_at_100 |
| value: 66.956 |
| - type: map_at_1000 |
| value: 67.087 |
| - type: map_at_3 |
| value: 44.769999999999996 |
| - type: map_at_5 |
| value: 54.629000000000005 |
| - type: mrr_at_1 |
| value: 81.22500000000001 |
| - type: mrr_at_10 |
| value: 85.383 |
| - type: mrr_at_100 |
| value: 85.555 |
| - type: mrr_at_1000 |
| value: 85.564 |
| - type: mrr_at_3 |
| value: 84.587 |
| - type: mrr_at_5 |
| value: 85.105 |
| - type: ndcg_at_1 |
| value: 81.22500000000001 |
| - type: ndcg_at_10 |
| value: 72.81 |
| - type: ndcg_at_100 |
| value: 78.108 |
| - type: ndcg_at_1000 |
| value: 79.477 |
| - type: ndcg_at_3 |
| value: 75.36 |
| - type: ndcg_at_5 |
| value: 73.19099999999999 |
| - type: precision_at_1 |
| value: 81.22500000000001 |
| - type: precision_at_10 |
| value: 36.419000000000004 |
| - type: precision_at_100 |
| value: 4.6850000000000005 |
| - type: precision_at_1000 |
| value: 0.502 |
| - type: precision_at_3 |
| value: 66.125 |
| - type: precision_at_5 |
| value: 54.824 |
| - type: recall_at_1 |
| value: 23.186999999999998 |
| - type: recall_at_10 |
| value: 71.568 |
| - type: recall_at_100 |
| value: 88.32799999999999 |
| - type: recall_at_1000 |
| value: 95.256 |
| - type: recall_at_3 |
| value: 47.04 |
| - type: recall_at_5 |
| value: 59.16400000000001 |
| - task: |
| type: Classification |
| dataset: |
| type: C-MTEB/TNews-classification |
| name: MTEB TNews |
| config: default |
| split: validation |
| revision: None |
| metrics: |
| - type: accuracy |
| value: 46.08 |
| - type: f1 |
| value: 44.576714769815986 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.23600000000000002 |
| - type: map_at_10 |
| value: 2.01 |
| - type: map_at_100 |
| value: 11.237 |
| - type: map_at_1000 |
| value: 26.241999999999997 |
| - type: map_at_3 |
| value: 0.705 |
| - type: map_at_5 |
| value: 1.134 |
| - type: mrr_at_1 |
| value: 92.0 |
| - type: mrr_at_10 |
| value: 95.667 |
| - type: mrr_at_100 |
| value: 95.667 |
| - type: mrr_at_1000 |
| value: 95.667 |
| - type: mrr_at_3 |
| value: 95.667 |
| - type: mrr_at_5 |
| value: 95.667 |
| - type: ndcg_at_1 |
| value: 88.0 |
| - type: ndcg_at_10 |
| value: 80.028 |
| - type: ndcg_at_100 |
| value: 58.557 |
| - type: ndcg_at_1000 |
| value: 51.108 |
| - type: ndcg_at_3 |
| value: 86.235 |
| - type: ndcg_at_5 |
| value: 83.776 |
| - type: precision_at_1 |
| value: 92.0 |
| - type: precision_at_10 |
| value: 83.6 |
| - type: precision_at_100 |
| value: 59.9 |
| - type: precision_at_1000 |
| value: 22.556 |
| - type: precision_at_3 |
| value: 92.667 |
| - type: precision_at_5 |
| value: 89.60000000000001 |
| - type: recall_at_1 |
| value: 0.23600000000000002 |
| - type: recall_at_10 |
| value: 2.164 |
| - type: recall_at_100 |
| value: 14.268 |
| - type: recall_at_1000 |
| value: 47.993 |
| - type: recall_at_3 |
| value: 0.728 |
| - type: recall_at_5 |
| value: 1.18 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (sqi-eng) |
| config: sqi-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 16.0 |
| - type: f1 |
| value: 12.072197229668266 |
| - type: precision |
| value: 11.07125213426268 |
| - type: recall |
| value: 16.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (fry-eng) |
| config: fry-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 31.79190751445087 |
| - type: f1 |
| value: 25.33993944398569 |
| - type: precision |
| value: 23.462449892587426 |
| - type: recall |
| value: 31.79190751445087 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (kur-eng) |
| config: kur-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 14.390243902439023 |
| - type: f1 |
| value: 10.647146321087272 |
| - type: precision |
| value: 9.753700307679768 |
| - type: recall |
| value: 14.390243902439023 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tur-eng) |
| config: tur-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 7.8 |
| - type: f1 |
| value: 5.087296515623526 |
| - type: precision |
| value: 4.543963123070674 |
| - type: recall |
| value: 7.8 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (deu-eng) |
| config: deu-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 58.5 |
| - type: f1 |
| value: 53.26571428571428 |
| - type: precision |
| value: 51.32397398353281 |
| - type: recall |
| value: 58.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (nld-eng) |
| config: nld-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 29.5 |
| - type: f1 |
| value: 25.14837668933257 |
| - type: precision |
| value: 23.949224030449837 |
| - type: recall |
| value: 29.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ron-eng) |
| config: ron-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 28.7 |
| - type: f1 |
| value: 23.196045369663018 |
| - type: precision |
| value: 21.502155293536873 |
| - type: recall |
| value: 28.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ang-eng) |
| config: ang-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 27.611940298507463 |
| - type: f1 |
| value: 19.431414356787492 |
| - type: precision |
| value: 17.160948504232085 |
| - type: recall |
| value: 27.611940298507463 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ido-eng) |
| config: ido-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 46.0 |
| - type: f1 |
| value: 39.146820760938404 |
| - type: precision |
| value: 36.89055652165172 |
| - type: recall |
| value: 46.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (jav-eng) |
| config: jav-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 23.414634146341466 |
| - type: f1 |
| value: 18.60234074868221 |
| - type: precision |
| value: 17.310239781020474 |
| - type: recall |
| value: 23.414634146341466 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (isl-eng) |
| config: isl-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 7.3 |
| - type: f1 |
| value: 5.456411432480631 |
| - type: precision |
| value: 5.073425278627456 |
| - type: recall |
| value: 7.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (slv-eng) |
| config: slv-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 10.814094775212636 |
| - type: f1 |
| value: 8.096556306772158 |
| - type: precision |
| value: 7.501928709802902 |
| - type: recall |
| value: 10.814094775212636 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (cym-eng) |
| config: cym-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 11.304347826086957 |
| - type: f1 |
| value: 7.766717493033283 |
| - type: precision |
| value: 6.980930791147511 |
| - type: recall |
| value: 11.304347826086957 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (kaz-eng) |
| config: kaz-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.260869565217392 |
| - type: f1 |
| value: 4.695624631925284 |
| - type: precision |
| value: 4.520242639508398 |
| - type: recall |
| value: 6.260869565217392 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (est-eng) |
| config: est-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.9 |
| - type: f1 |
| value: 4.467212205066257 |
| - type: precision |
| value: 4.004142723685108 |
| - type: recall |
| value: 6.9 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (heb-eng) |
| config: heb-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 1.0999999999999999 |
| - type: f1 |
| value: 0.6945869191049914 |
| - type: precision |
| value: 0.6078431372549019 |
| - type: recall |
| value: 1.0999999999999999 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (gla-eng) |
| config: gla-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 4.583835946924005 |
| - type: f1 |
| value: 2.9858475730729075 |
| - type: precision |
| value: 2.665996515212438 |
| - type: recall |
| value: 4.583835946924005 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (mar-eng) |
| config: mar-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 59.199999999999996 |
| - type: f1 |
| value: 52.67345238095238 |
| - type: precision |
| value: 50.13575757575758 |
| - type: recall |
| value: 59.199999999999996 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (lat-eng) |
| config: lat-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 35.0 |
| - type: f1 |
| value: 27.648653013653007 |
| - type: precision |
| value: 25.534839833369244 |
| - type: recall |
| value: 35.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (bel-eng) |
| config: bel-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 13.100000000000001 |
| - type: f1 |
| value: 9.62336638477808 |
| - type: precision |
| value: 8.875194920058407 |
| - type: recall |
| value: 13.100000000000001 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (pms-eng) |
| config: pms-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 32.95238095238095 |
| - type: f1 |
| value: 27.600581429152854 |
| - type: precision |
| value: 26.078624096473064 |
| - type: recall |
| value: 32.95238095238095 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (gle-eng) |
| config: gle-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.5 |
| - type: f1 |
| value: 3.9595645184317045 |
| - type: precision |
| value: 3.5893378968989453 |
| - type: recall |
| value: 6.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (pes-eng) |
| config: pes-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 17.8 |
| - type: f1 |
| value: 13.508124743694003 |
| - type: precision |
| value: 12.24545634920635 |
| - type: recall |
| value: 17.8 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (nob-eng) |
| config: nob-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 21.7 |
| - type: f1 |
| value: 17.67074499610417 |
| - type: precision |
| value: 16.47070885787265 |
| - type: recall |
| value: 21.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (bul-eng) |
| config: bul-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 19.3 |
| - type: f1 |
| value: 14.249803276788573 |
| - type: precision |
| value: 12.916981621996223 |
| - type: recall |
| value: 19.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (cbk-eng) |
| config: cbk-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 67.2 |
| - type: f1 |
| value: 61.03507936507936 |
| - type: precision |
| value: 58.69699346405229 |
| - type: recall |
| value: 67.2 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (hun-eng) |
| config: hun-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.5 |
| - type: f1 |
| value: 4.295097572176196 |
| - type: precision |
| value: 3.809609027256814 |
| - type: recall |
| value: 6.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (uig-eng) |
| config: uig-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 2.8000000000000003 |
| - type: f1 |
| value: 1.678577135635959 |
| - type: precision |
| value: 1.455966810966811 |
| - type: recall |
| value: 2.8000000000000003 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (rus-eng) |
| config: rus-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 47.9 |
| - type: f1 |
| value: 40.26661017143776 |
| - type: precision |
| value: 37.680778943278945 |
| - type: recall |
| value: 47.9 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (spa-eng) |
| config: spa-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 97.0 |
| - type: f1 |
| value: 96.05 |
| - type: precision |
| value: 95.58333333333334 |
| - type: recall |
| value: 97.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (hye-eng) |
| config: hye-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 0.9433962264150944 |
| - type: f1 |
| value: 0.6457074216068709 |
| - type: precision |
| value: 0.6068362258275373 |
| - type: recall |
| value: 0.9433962264150944 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tel-eng) |
| config: tel-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 74.78632478632478 |
| - type: f1 |
| value: 69.05372405372405 |
| - type: precision |
| value: 66.82336182336182 |
| - type: recall |
| value: 74.78632478632478 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (afr-eng) |
| config: afr-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 19.2 |
| - type: f1 |
| value: 14.54460169057995 |
| - type: precision |
| value: 13.265236397589335 |
| - type: recall |
| value: 19.2 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (mon-eng) |
| config: mon-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.8181818181818175 |
| - type: f1 |
| value: 4.78808236251355 |
| - type: precision |
| value: 4.4579691142191145 |
| - type: recall |
| value: 6.8181818181818175 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (arz-eng) |
| config: arz-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 72.53668763102725 |
| - type: f1 |
| value: 66.00978336827393 |
| - type: precision |
| value: 63.21104122990915 |
| - type: recall |
| value: 72.53668763102725 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (hrv-eng) |
| config: hrv-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 12.7 |
| - type: f1 |
| value: 9.731576351893512 |
| - type: precision |
| value: 8.986658245110663 |
| - type: recall |
| value: 12.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (nov-eng) |
| config: nov-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 57.19844357976653 |
| - type: f1 |
| value: 49.138410227904394 |
| - type: precision |
| value: 45.88197146562906 |
| - type: recall |
| value: 57.19844357976653 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (gsw-eng) |
| config: gsw-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 28.205128205128204 |
| - type: f1 |
| value: 21.863766936230704 |
| - type: precision |
| value: 20.212164378831048 |
| - type: recall |
| value: 28.205128205128204 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (nds-eng) |
| config: nds-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 23.3 |
| - type: f1 |
| value: 17.75959261382939 |
| - type: precision |
| value: 16.18907864830205 |
| - type: recall |
| value: 23.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ukr-eng) |
| config: ukr-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 19.1 |
| - type: f1 |
| value: 14.320618913993744 |
| - type: precision |
| value: 12.980748202777615 |
| - type: recall |
| value: 19.1 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (uzb-eng) |
| config: uzb-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 8.411214953271028 |
| - type: f1 |
| value: 5.152309182683014 |
| - type: precision |
| value: 4.456214003721122 |
| - type: recall |
| value: 8.411214953271028 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (lit-eng) |
| config: lit-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.7 |
| - type: f1 |
| value: 4.833930504764646 |
| - type: precision |
| value: 4.475394510103751 |
| - type: recall |
| value: 6.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ina-eng) |
| config: ina-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 79.4 |
| - type: f1 |
| value: 74.59166666666667 |
| - type: precision |
| value: 72.59928571428571 |
| - type: recall |
| value: 79.4 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (lfn-eng) |
| config: lfn-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 47.8 |
| - type: f1 |
| value: 41.944877899877895 |
| - type: precision |
| value: 39.87211701696996 |
| - type: recall |
| value: 47.8 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (zsm-eng) |
| config: zsm-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 85.0 |
| - type: f1 |
| value: 81.47666666666666 |
| - type: precision |
| value: 79.95909090909092 |
| - type: recall |
| value: 85.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ita-eng) |
| config: ita-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 62.6 |
| - type: f1 |
| value: 55.96755336167101 |
| - type: precision |
| value: 53.49577131202131 |
| - type: recall |
| value: 62.6 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (cmn-eng) |
| config: cmn-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 95.3 |
| - type: f1 |
| value: 93.96666666666668 |
| - type: precision |
| value: 93.33333333333333 |
| - type: recall |
| value: 95.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (lvs-eng) |
| config: lvs-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 7.7 |
| - type: f1 |
| value: 5.534253062728994 |
| - type: precision |
| value: 4.985756669800788 |
| - type: recall |
| value: 7.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (glg-eng) |
| config: glg-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 80.5 |
| - type: f1 |
| value: 75.91705128205129 |
| - type: precision |
| value: 73.96261904761904 |
| - type: recall |
| value: 80.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ceb-eng) |
| config: ceb-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 10.333333333333334 |
| - type: f1 |
| value: 7.753678057001793 |
| - type: precision |
| value: 7.207614225986279 |
| - type: recall |
| value: 10.333333333333334 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (bre-eng) |
| config: bre-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 8.6 |
| - type: f1 |
| value: 5.345683110450071 |
| - type: precision |
| value: 4.569931461907268 |
| - type: recall |
| value: 8.6 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ben-eng) |
| config: ben-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 82.8 |
| - type: f1 |
| value: 78.75999999999999 |
| - type: precision |
| value: 76.97666666666666 |
| - type: recall |
| value: 82.8 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (swg-eng) |
| config: swg-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 26.785714285714285 |
| - type: f1 |
| value: 21.62627551020408 |
| - type: precision |
| value: 20.17219387755102 |
| - type: recall |
| value: 26.785714285714285 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (arq-eng) |
| config: arq-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 32.93084522502745 |
| - type: f1 |
| value: 26.281513627941628 |
| - type: precision |
| value: 24.05050619189897 |
| - type: recall |
| value: 32.93084522502745 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (kab-eng) |
| config: kab-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 2.1 |
| - type: f1 |
| value: 1.144678201129814 |
| - type: precision |
| value: 1.0228433014856975 |
| - type: recall |
| value: 2.1 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (fra-eng) |
| config: fra-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 94.3 |
| - type: f1 |
| value: 92.77000000000001 |
| - type: precision |
| value: 92.09166666666667 |
| - type: recall |
| value: 94.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (por-eng) |
| config: por-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 94.1 |
| - type: f1 |
| value: 92.51666666666667 |
| - type: precision |
| value: 91.75 |
| - type: recall |
| value: 94.1 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tat-eng) |
| config: tat-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 4.1000000000000005 |
| - type: f1 |
| value: 2.856566814643248 |
| - type: precision |
| value: 2.6200368188362506 |
| - type: recall |
| value: 4.1000000000000005 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (oci-eng) |
| config: oci-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 45.9 |
| - type: f1 |
| value: 39.02207792207792 |
| - type: precision |
| value: 36.524158064158065 |
| - type: recall |
| value: 45.9 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (pol-eng) |
| config: pol-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 13.4 |
| - type: f1 |
| value: 9.61091517529598 |
| - type: precision |
| value: 8.755127233877234 |
| - type: recall |
| value: 13.4 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (war-eng) |
| config: war-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 11.1 |
| - type: f1 |
| value: 8.068379205189386 |
| - type: precision |
| value: 7.400827352459544 |
| - type: recall |
| value: 11.1 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (aze-eng) |
| config: aze-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 8.9 |
| - type: f1 |
| value: 6.632376174517077 |
| - type: precision |
| value: 6.07114926880766 |
| - type: recall |
| value: 8.9 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (vie-eng) |
| config: vie-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 95.8 |
| - type: f1 |
| value: 94.57333333333334 |
| - type: precision |
| value: 93.99166666666667 |
| - type: recall |
| value: 95.8 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (nno-eng) |
| config: nno-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 16.6 |
| - type: f1 |
| value: 13.328940031174618 |
| - type: precision |
| value: 12.47204179664362 |
| - type: recall |
| value: 16.6 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (cha-eng) |
| config: cha-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 29.927007299270077 |
| - type: f1 |
| value: 22.899432278994322 |
| - type: precision |
| value: 20.917701519891303 |
| - type: recall |
| value: 29.927007299270077 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (mhr-eng) |
| config: mhr-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 3.5000000000000004 |
| - type: f1 |
| value: 2.3809722674927083 |
| - type: precision |
| value: 2.1368238705738705 |
| - type: recall |
| value: 3.5000000000000004 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (dan-eng) |
| config: dan-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 21.6 |
| - type: f1 |
| value: 17.54705304666238 |
| - type: precision |
| value: 16.40586970344022 |
| - type: recall |
| value: 21.6 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ell-eng) |
| config: ell-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 3.5999999999999996 |
| - type: f1 |
| value: 2.3374438522182763 |
| - type: precision |
| value: 2.099034070054354 |
| - type: recall |
| value: 3.5999999999999996 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (amh-eng) |
| config: amh-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 1.7857142857142856 |
| - type: f1 |
| value: 0.12056962540054328 |
| - type: precision |
| value: 0.0628414244485673 |
| - type: recall |
| value: 1.7857142857142856 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (pam-eng) |
| config: pam-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 7.3999999999999995 |
| - type: f1 |
| value: 5.677284679983816 |
| - type: precision |
| value: 5.314304945764335 |
| - type: recall |
| value: 7.3999999999999995 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (hsb-eng) |
| config: hsb-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 13.043478260869565 |
| - type: f1 |
| value: 9.776306477806768 |
| - type: precision |
| value: 9.09389484497104 |
| - type: recall |
| value: 13.043478260869565 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (srp-eng) |
| config: srp-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 12.3 |
| - type: f1 |
| value: 8.757454269574472 |
| - type: precision |
| value: 7.882868657107786 |
| - type: recall |
| value: 12.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (epo-eng) |
| config: epo-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 28.9 |
| - type: f1 |
| value: 23.108557220070377 |
| - type: precision |
| value: 21.35433328562513 |
| - type: recall |
| value: 28.9 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (kzj-eng) |
| config: kzj-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.4 |
| - type: f1 |
| value: 4.781499273475174 |
| - type: precision |
| value: 4.4496040053464565 |
| - type: recall |
| value: 6.4 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (awa-eng) |
| config: awa-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 51.94805194805194 |
| - type: f1 |
| value: 45.658020784071205 |
| - type: precision |
| value: 43.54163933709388 |
| - type: recall |
| value: 51.94805194805194 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (fao-eng) |
| config: fao-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 14.50381679389313 |
| - type: f1 |
| value: 9.416337348733041 |
| - type: precision |
| value: 8.17070085031468 |
| - type: recall |
| value: 14.50381679389313 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (mal-eng) |
| config: mal-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 88.79184861717613 |
| - type: f1 |
| value: 85.56040756914118 |
| - type: precision |
| value: 84.08539543910723 |
| - type: recall |
| value: 88.79184861717613 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ile-eng) |
| config: ile-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 62.5 |
| - type: f1 |
| value: 56.0802331002331 |
| - type: precision |
| value: 53.613788230739445 |
| - type: recall |
| value: 62.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (bos-eng) |
| config: bos-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 16.101694915254235 |
| - type: f1 |
| value: 11.927172795816864 |
| - type: precision |
| value: 10.939011968423735 |
| - type: recall |
| value: 16.101694915254235 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (cor-eng) |
| config: cor-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 5.5 |
| - type: f1 |
| value: 3.1258727724517197 |
| - type: precision |
| value: 2.679506580565404 |
| - type: recall |
| value: 5.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (cat-eng) |
| config: cat-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 87.6 |
| - type: f1 |
| value: 84.53666666666666 |
| - type: precision |
| value: 83.125 |
| - type: recall |
| value: 87.6 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (eus-eng) |
| config: eus-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 65.7 |
| - type: f1 |
| value: 59.64428571428571 |
| - type: precision |
| value: 57.30171568627451 |
| - type: recall |
| value: 65.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (yue-eng) |
| config: yue-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 84.7 |
| - type: f1 |
| value: 81.34523809523809 |
| - type: precision |
| value: 79.82777777777778 |
| - type: recall |
| value: 84.7 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (swe-eng) |
| config: swe-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 18.6 |
| - type: f1 |
| value: 14.93884103295868 |
| - type: precision |
| value: 14.059478087803882 |
| - type: recall |
| value: 18.6 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (dtp-eng) |
| config: dtp-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 5.5 |
| - type: f1 |
| value: 3.815842342611909 |
| - type: precision |
| value: 3.565130046415928 |
| - type: recall |
| value: 5.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (kat-eng) |
| config: kat-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 1.2064343163538873 |
| - type: f1 |
| value: 0.9147778048582338 |
| - type: precision |
| value: 0.8441848589301671 |
| - type: recall |
| value: 1.2064343163538873 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (jpn-eng) |
| config: jpn-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 71.3 |
| - type: f1 |
| value: 65.97350649350648 |
| - type: precision |
| value: 63.85277777777777 |
| - type: recall |
| value: 71.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (csb-eng) |
| config: csb-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 13.043478260869565 |
| - type: f1 |
| value: 9.043759194508343 |
| - type: precision |
| value: 8.097993164155737 |
| - type: recall |
| value: 13.043478260869565 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (xho-eng) |
| config: xho-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 11.267605633802818 |
| - type: f1 |
| value: 8.30172606520348 |
| - type: precision |
| value: 7.737059013603729 |
| - type: recall |
| value: 11.267605633802818 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (orv-eng) |
| config: orv-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 5.029940119760479 |
| - type: f1 |
| value: 3.07264903262435 |
| - type: precision |
| value: 2.7633481831401783 |
| - type: recall |
| value: 5.029940119760479 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ind-eng) |
| config: ind-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 90.60000000000001 |
| - type: f1 |
| value: 88.29666666666667 |
| - type: precision |
| value: 87.21666666666667 |
| - type: recall |
| value: 90.60000000000001 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tuk-eng) |
| config: tuk-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 7.389162561576355 |
| - type: f1 |
| value: 5.142049156827481 |
| - type: precision |
| value: 4.756506859714838 |
| - type: recall |
| value: 7.389162561576355 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (max-eng) |
| config: max-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 44.36619718309859 |
| - type: f1 |
| value: 39.378676538811256 |
| - type: precision |
| value: 37.71007182068377 |
| - type: recall |
| value: 44.36619718309859 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (swh-eng) |
| config: swh-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 21.794871794871796 |
| - type: f1 |
| value: 16.314588577641768 |
| - type: precision |
| value: 14.962288221599962 |
| - type: recall |
| value: 21.794871794871796 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (hin-eng) |
| config: hin-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 93.5 |
| - type: f1 |
| value: 91.53333333333333 |
| - type: precision |
| value: 90.58333333333333 |
| - type: recall |
| value: 93.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (dsb-eng) |
| config: dsb-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 12.526096033402922 |
| - type: f1 |
| value: 9.57488704957882 |
| - type: precision |
| value: 8.943001322776725 |
| - type: recall |
| value: 12.526096033402922 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ber-eng) |
| config: ber-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 6.9 |
| - type: f1 |
| value: 4.5770099528158 |
| - type: precision |
| value: 4.166915172638407 |
| - type: recall |
| value: 6.9 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tam-eng) |
| config: tam-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 81.75895765472313 |
| - type: f1 |
| value: 77.29641693811075 |
| - type: precision |
| value: 75.3528773072747 |
| - type: recall |
| value: 81.75895765472313 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (slk-eng) |
| config: slk-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 11.0 |
| - type: f1 |
| value: 8.522094712720397 |
| - type: precision |
| value: 7.883076528738328 |
| - type: recall |
| value: 11.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tgl-eng) |
| config: tgl-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 11.3 |
| - type: f1 |
| value: 8.626190704312432 |
| - type: precision |
| value: 7.994434420637179 |
| - type: recall |
| value: 11.3 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ast-eng) |
| config: ast-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 74.01574803149606 |
| - type: f1 |
| value: 68.16272965879266 |
| - type: precision |
| value: 65.99737532808399 |
| - type: recall |
| value: 74.01574803149606 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (mkd-eng) |
| config: mkd-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 9.0 |
| - type: f1 |
| value: 6.189958106409719 |
| - type: precision |
| value: 5.445330404889228 |
| - type: recall |
| value: 9.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (khm-eng) |
| config: khm-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 0.2770083102493075 |
| - type: f1 |
| value: 0.011664800298618888 |
| - type: precision |
| value: 0.005957856811560036 |
| - type: recall |
| value: 0.2770083102493075 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ces-eng) |
| config: ces-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 8.799999999999999 |
| - type: f1 |
| value: 5.636139438882621 |
| - type: precision |
| value: 4.993972914553003 |
| - type: recall |
| value: 8.799999999999999 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tzl-eng) |
| config: tzl-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 37.5 |
| - type: f1 |
| value: 31.31118881118881 |
| - type: precision |
| value: 29.439102564102566 |
| - type: recall |
| value: 37.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (urd-eng) |
| config: urd-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 74.5 |
| - type: f1 |
| value: 68.96380952380953 |
| - type: precision |
| value: 66.67968253968255 |
| - type: recall |
| value: 74.5 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (ara-eng) |
| config: ara-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 89.0 |
| - type: f1 |
| value: 86.42523809523809 |
| - type: precision |
| value: 85.28333333333332 |
| - type: recall |
| value: 89.0 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (kor-eng) |
| config: kor-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 17.2 |
| - type: f1 |
| value: 12.555081585081584 |
| - type: precision |
| value: 11.292745310245309 |
| - type: recall |
| value: 17.2 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (yid-eng) |
| config: yid-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 0.3537735849056604 |
| - type: f1 |
| value: 0.12010530448397783 |
| - type: precision |
| value: 0.11902214818132154 |
| - type: recall |
| value: 0.3537735849056604 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (fin-eng) |
| config: fin-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 5.8999999999999995 |
| - type: f1 |
| value: 4.26942162679512 |
| - type: precision |
| value: 3.967144120536608 |
| - type: recall |
| value: 5.8999999999999995 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (tha-eng) |
| config: tha-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 2.737226277372263 |
| - type: f1 |
| value: 1.64474042578532 |
| - type: precision |
| value: 1.567547886228932 |
| - type: recall |
| value: 2.737226277372263 |
| - task: |
| type: BitextMining |
| dataset: |
| type: mteb/tatoeba-bitext-mining |
| name: MTEB Tatoeba (wuu-eng) |
| config: wuu-eng |
| split: test |
| revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 |
| metrics: |
| - type: accuracy |
| value: 84.89999999999999 |
| - type: f1 |
| value: 81.17555555555555 |
| - type: precision |
| value: 79.56416666666667 |
| - type: recall |
| value: 84.89999999999999 |
| - task: |
| type: Clustering |
| dataset: |
| type: C-MTEB/ThuNewsClusteringP2P |
| name: MTEB ThuNewsClusteringP2P |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: v_measure |
| value: 48.90675612551149 |
| - task: |
| type: Clustering |
| dataset: |
| type: C-MTEB/ThuNewsClusteringS2S |
| name: MTEB ThuNewsClusteringS2S |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: v_measure |
| value: 48.33955538054993 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 2.604 |
| - type: map_at_10 |
| value: 10.005 |
| - type: map_at_100 |
| value: 15.626999999999999 |
| - type: map_at_1000 |
| value: 16.974 |
| - type: map_at_3 |
| value: 5.333 |
| - type: map_at_5 |
| value: 7.031999999999999 |
| - type: mrr_at_1 |
| value: 30.612000000000002 |
| - type: mrr_at_10 |
| value: 45.324999999999996 |
| - type: mrr_at_100 |
| value: 46.261 |
| - type: mrr_at_1000 |
| value: 46.275 |
| - type: mrr_at_3 |
| value: 41.156 |
| - type: mrr_at_5 |
| value: 43.401 |
| - type: ndcg_at_1 |
| value: 28.571 |
| - type: ndcg_at_10 |
| value: 24.917 |
| - type: ndcg_at_100 |
| value: 35.304 |
| - type: ndcg_at_1000 |
| value: 45.973000000000006 |
| - type: ndcg_at_3 |
| value: 25.813000000000002 |
| - type: ndcg_at_5 |
| value: 24.627 |
| - type: precision_at_1 |
| value: 30.612000000000002 |
| - type: precision_at_10 |
| value: 23.061 |
| - type: precision_at_100 |
| value: 7.327 |
| - type: precision_at_1000 |
| value: 1.443 |
| - type: precision_at_3 |
| value: 27.211000000000002 |
| - type: precision_at_5 |
| value: 24.898 |
| - type: recall_at_1 |
| value: 2.604 |
| - type: recall_at_10 |
| value: 16.459 |
| - type: recall_at_100 |
| value: 45.344 |
| - type: recall_at_1000 |
| value: 77.437 |
| - type: recall_at_3 |
| value: 6.349 |
| - type: recall_at_5 |
| value: 9.487 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 72.01180000000001 |
| - type: ap |
| value: 14.626345366340157 |
| - type: f1 |
| value: 55.341805198526096 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 61.51103565365025 |
| - type: f1 |
| value: 61.90767326783032 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 39.80161553107969 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 84.32377659891517 |
| - type: cos_sim_ap |
| value: 69.1354481874608 |
| - type: cos_sim_f1 |
| value: 64.52149133222514 |
| - type: cos_sim_precision |
| value: 58.65716753022453 |
| - type: cos_sim_recall |
| value: 71.68865435356201 |
| - type: dot_accuracy |
| value: 82.82172021219527 |
| - type: dot_ap |
| value: 64.00853575391538 |
| - type: dot_f1 |
| value: 60.32341223341926 |
| - type: dot_precision |
| value: 54.25801011804384 |
| - type: dot_recall |
| value: 67.9155672823219 |
| - type: euclidean_accuracy |
| value: 84.1151576563152 |
| - type: euclidean_ap |
| value: 67.83576623331122 |
| - type: euclidean_f1 |
| value: 63.15157338457842 |
| - type: euclidean_precision |
| value: 57.95855379188713 |
| - type: euclidean_recall |
| value: 69.36675461741424 |
| - type: manhattan_accuracy |
| value: 84.09727603266377 |
| - type: manhattan_ap |
| value: 67.82849173216036 |
| - type: manhattan_f1 |
| value: 63.34376956793989 |
| - type: manhattan_precision |
| value: 60.28605482717521 |
| - type: manhattan_recall |
| value: 66.72823218997361 |
| - type: max_accuracy |
| value: 84.32377659891517 |
| - type: max_ap |
| value: 69.1354481874608 |
| - type: max_f1 |
| value: 64.52149133222514 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.90053168781775 |
| - type: cos_sim_ap |
| value: 85.61513175543742 |
| - type: cos_sim_f1 |
| value: 78.12614999632001 |
| - type: cos_sim_precision |
| value: 74.82729451571973 |
| - type: cos_sim_recall |
| value: 81.72928857406838 |
| - type: dot_accuracy |
| value: 88.3086894089339 |
| - type: dot_ap |
| value: 83.12888443163673 |
| - type: dot_f1 |
| value: 77.2718948023882 |
| - type: dot_precision |
| value: 73.69524208761266 |
| - type: dot_recall |
| value: 81.21342777948875 |
| - type: euclidean_accuracy |
| value: 88.51825978965343 |
| - type: euclidean_ap |
| value: 84.99220411819988 |
| - type: euclidean_f1 |
| value: 77.30590577305905 |
| - type: euclidean_precision |
| value: 74.16183335691045 |
| - type: euclidean_recall |
| value: 80.72836464428703 |
| - type: manhattan_accuracy |
| value: 88.54542632048744 |
| - type: manhattan_ap |
| value: 84.98068073894048 |
| - type: manhattan_f1 |
| value: 77.28853696440466 |
| - type: manhattan_precision |
| value: 74.39806240205158 |
| - type: manhattan_recall |
| value: 80.41268863566368 |
| - type: max_accuracy |
| value: 88.90053168781775 |
| - type: max_ap |
| value: 85.61513175543742 |
| - type: max_f1 |
| value: 78.12614999632001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: C-MTEB/VideoRetrieval |
| name: MTEB VideoRetrieval |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 41.8 |
| - type: map_at_10 |
| value: 51.413 |
| - type: map_at_100 |
| value: 52.127 |
| - type: map_at_1000 |
| value: 52.168000000000006 |
| - type: map_at_3 |
| value: 49.25 |
| - type: map_at_5 |
| value: 50.425 |
| - type: mrr_at_1 |
| value: 41.699999999999996 |
| - type: mrr_at_10 |
| value: 51.363 |
| - type: mrr_at_100 |
| value: 52.077 |
| - type: mrr_at_1000 |
| value: 52.117999999999995 |
| - type: mrr_at_3 |
| value: 49.2 |
| - type: mrr_at_5 |
| value: 50.375 |
| - type: ndcg_at_1 |
| value: 41.8 |
| - type: ndcg_at_10 |
| value: 56.071000000000005 |
| - type: ndcg_at_100 |
| value: 59.58599999999999 |
| - type: ndcg_at_1000 |
| value: 60.718 |
| - type: ndcg_at_3 |
| value: 51.605999999999995 |
| - type: ndcg_at_5 |
| value: 53.714 |
| - type: precision_at_1 |
| value: 41.8 |
| - type: precision_at_10 |
| value: 7.07 |
| - type: precision_at_100 |
| value: 0.873 |
| - type: precision_at_1000 |
| value: 0.096 |
| - type: precision_at_3 |
| value: 19.467000000000002 |
| - type: precision_at_5 |
| value: 12.7 |
| - type: recall_at_1 |
| value: 41.8 |
| - type: recall_at_10 |
| value: 70.7 |
| - type: recall_at_100 |
| value: 87.3 |
| - type: recall_at_1000 |
| value: 96.39999999999999 |
| - type: recall_at_3 |
| value: 58.4 |
| - type: recall_at_5 |
| value: 63.5 |
| - task: |
| type: Classification |
| dataset: |
| type: C-MTEB/waimai-classification |
| name: MTEB Waimai |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: accuracy |
| value: 82.67 |
| - type: ap |
| value: 63.20621490084175 |
| - type: f1 |
| value: 80.81778523320692 |
| --- |
| |
| # Model Card for udever-bloom |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| `udever-bloom-1b1` is finetuned from [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) via [BitFit](https://aclanthology.org/2022.acl-short.1/) on MS MARCO Passage Ranking, SNLI and MultiNLI data. |
| It is a universal embedding model across tasks, natural and programming languages. |
| (From the technical view, `udever` is merely with some minor improvements to `sgpt-bloom`) |
|
|
| <div align=center><img width="338" height="259" src="https://user-images.githubusercontent.com/26690193/277643721-cdb7f227-cae5-40e1-b6e1-a201bde00339.png" /></div> |
|
|
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** Alibaba Group |
| - **Model type:** Transformer-based Language Model (decoder-only) |
| - **Language(s) (NLP):** Multiple; see [bloom training data](https://huggingface.co/bigscience/bloom-1b1#training-data) |
| - **Finetuned from model :** [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) |
|
|
| ### Model Sources |
|
|
| <!-- Provide the basic links for the model. --> |
|
|
| - **Repository:** [github.com/izhx/uni-rep](https://github.com/izhx/uni-rep) |
| - **Paper :** [Language Models are Universal Embedders](https://arxiv.org/pdf/2310.08232.pdf) |
| - **Training Date :** 2023-06 |
|
|
|
|
|
|
| ## How to Get Started with the Model |
|
|
| Use the code below to get started with the model. |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, BloomModel |
| |
| tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-1b1') |
| model = BloomModel.from_pretrained('izhx/udever-bloom-1b1') |
| |
| boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]' |
| eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod]) |
| |
| if tokenizer.padding_side != 'left': |
| print('!!!', tokenizer.padding_side) |
| tokenizer.padding_side = 'left' |
| |
| |
| def encode(texts: list, is_query: bool = True, max_length=300): |
| bos = boq if is_query else bod |
| eos_id = eoq_id if is_query else eod_id |
| texts = [bos + t for t in texts] |
| encoding = tokenizer( |
| texts, truncation=True, max_length=max_length - 1, padding=True |
| ) |
| for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']): |
| ids.append(eos_id) |
| mask.append(1) |
| inputs = tokenizer.pad(encoding, return_tensors='pt') |
| with torch.inference_mode(): |
| outputs = model(**inputs) |
| embeds = outputs.last_hidden_state[:, -1] |
| return embeds |
| |
| encode(['I am Bert', 'You are Elmo']) |
| |
| ``` |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
|
|
| - MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86) |
| - SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz) |
| |
| |
| ### Training Procedure |
| |
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
| |
| #### Preprocessing |
| |
| MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86). |
| Negatives for SNLI and MultiNLI are randomly sampled. |
|
|
|
|
| #### Training Hyperparameters |
|
|
| - **Training regime:** tf32, BitFit |
| - **Batch size:** 1024 |
| - **Epochs:** 3 |
| - **Optimizer:** AdamW |
| - **Learning rate:** 1e-4 |
| - **Scheduler:** constant with warmup. |
| - **Warmup:** 0.25 epoch |
|
|
|
|
| ## Evaluation |
|
|
| ### Table 1: Massive Text Embedding Benchmark [MTEB](https://huggingface.co/spaces/mteb/leaderboard) |
|
|
| | MTEB | Avg. | Class. | Clust. | PairClass. | Rerank. | Retr. | STS | Summ. | |
| |-----------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------| |
| | #Datasets ➡️ | 56 | 12 | 11 | 3 | 4 | 15 | 10 | 1 | |
| || |
| | bge-large-en-v1.5 | **64.23** | **75.97** | 46.08| **87.12** | **60.03** | **54.29** | 83.11| 31.61 | |
| | bge-base-en-v1.5 | 63.55| 75.53| 45.77| 86.55| 58.86| 53.25| 82.4| 31.07 | |
| | gte-large | 63.13| 73.33| **46.84** | 85| 59.13| 52.22| **83.35** | 31.66 | |
| | gte-base | 62.39| 73.01| 46.2| 84.57| 58.61| 51.14| 82.3| 31.17 | |
| | e5-large-v2 | 62.25| 75.24| 44.49| 86.03| 56.61| 50.56| 82.05| 30.19 | |
| | instructor-xl | 61.79| 73.12| 44.74| 86.62| 57.29| 49.26| 83.06| 32.32 | |
| | instructor-large | 61.59| 73.86| 45.29| 85.89| 57.54| 47.57| 83.15| 31.84 | |
| | e5-base-v2 | 61.5 | 73.84| 43.8| 85.73| 55.91| 50.29| 81.05| 30.28 | |
| | e5-large | 61.42| 73.14| 43.33| 85.94| 56.53| 49.99| 82.06| 30.97 | |
| | text-embedding-ada-002 (OpenAI API) | 60.99| 70.93| 45.9 | 84.89| 56.32| 49.25| 80.97| 30.8 | |
| | e5-base | 60.44| 72.63| 42.11| 85.09| 55.7 | 48.75| 80.96| 31.01 | |
| | SGPT-5.8B-msmarco | 58.93| 68.13| 40.34| 82 | 56.56| 50.25| 78.1 | 31.46 | |
| | sgpt-bloom-7b1-msmarco | 57.59| 66.19| 38.93| 81.9 | 55.65| 48.22| 77.74| **33.6** | |
| || |
| | Udever-bloom-560m | 55.80| 68.04| 36.89| 81.05| 52.60| 41.19| 79.93| 32.06 | |
| | Udever-bloom-1b1 | 58.28| 70.18| 39.11| 83.11| 54.28| 45.27| 81.52| 31.10 | |
| | Udever-bloom-3b | 59.86| 71.91| 40.74| 84.06| 54.90| 47.67| 82.37| 30.62 | |
| | Udever-bloom-7b1 | 60.63 | 72.13| 40.81| 85.40| 55.91| 49.34| 83.01| 30.97 | |
|
|
|
|
| ### Table 2: [CodeSearchNet](https://github.com/github/CodeSearchNet) |
|
|
| | CodeSearchNet | Go | Ruby | Python | Java | JS | PHP | Avg. | |
| |-|-|-|-|-|-|-|-| |
| | CodeBERT | 69.3 | 70.6 | 84.0 | 86.8 | 74.8 | 70.6 | 76.0 | |
| | GraphCodeBERT | 84.1 | 73.2 | 87.9 | 75.7 | 71.1 | 72.5 | 77.4 | |
| | cpt-code S | **97.7** | **86.3** | 99.8 | 94.0 | 86.0 | 96.7 | 93.4 | |
| | cpt-code M | 97.5 | 85.5 | **99.9** | **94.4** | **86.5** | **97.2** | **93.5** | |
| | sgpt-bloom-7b1-msmarco | 76.79 | 69.25 | 95.68 | 77.93 | 70.35 | 73.45 | 77.24 | |
| || |
| | Udever-bloom-560m | 75.38 | 66.67 | 96.23 | 78.99 | 69.39 | 73.69 | 76.73 | |
| | Udever-bloom-1b1 | 78.76 | 72.85 | 97.67 | 82.77 | 74.38 | 78.97 | 80.90 | |
| | Udever-bloom-3b | 80.63 | 75.40 | 98.02 | 83.88 | 76.18 | 79.67 | 82.29 | |
| | Udever-bloom-7b1 | 79.37 | 76.59 | 98.38 | 84.68 | 77.49 | 80.03 | 82.76 | |
|
|
|
|
| ### Table 3: Chinese multi-domain retrieval [Multi-cpr](https://dl.acm.org/doi/10.1145/3477495.3531736) |
|
|
| | | | |E-commerce | | Entertainment video | | Medical | | |
| |--|--|--|--|--|--|--|--|--| |
| | Model | Train | Backbone | MRR@10 | Recall@1k | MRR@10 | Recall@1k | MRR@10 | Recall@1k | |
| || |
| | BM25 | - | - | 0.225 | 0.815 | 0.225 | 0.780 | 0.187 | 0.482 | |
| | Doc2Query | - | - | 0.239 | 0.826 | 0.238 | 0.794 | 0.210 | 0.505 | |
| | DPR-1 | In-Domain | BERT | 0.270 | 0.921 | 0.254 | 0.934 | 0.327 | 0.747 | |
| | DPR-2 | In-Domain | BERT-CT | 0.289 | **0.926** | 0.263 | **0.935** | 0.339 | **0.769** | |
| | text-embedding-ada-002 | General | GPT | 0.183 | 0.825 | 0.159 | 0.786 | 0.245 | 0.593 | |
| | sgpt-bloom-7b1-msmarco | General | BLOOM | 0.242 | 0.840 | 0.227 | 0.829 | 0.311 | 0.675 | |
| || |
| | Udever-bloom-560m | General | BLOOM | 0.156 | 0.802 | 0.149 | 0.749 | 0.245 | 0.571 | |
| | Udever-bloom-1b1 | General | BLOOM | 0.244 | 0.863 | 0.208 | 0.815 | 0.241 | 0.557 | |
| | Udever-bloom-3b | General | BLOOM | 0.267 | 0.871 | 0.228 | 0.836 | 0.288 | 0.619 | |
| | Udever-bloom-7b1 | General | BLOOM | **0.296** | 0.889 | **0.267** | 0.907 | **0.343** | 0.705 | |
|
|
| #### More results refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 3. |
|
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|
|
|
|
| ## Technical Specifications |
|
|
| ### Model Architecture and Objective |
|
|
| - Model: [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1). |
| - Objective: Constrastive loss with hard negatives (refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 2.2). |
|
|
|
|
| ### Compute Infrastructure |
|
|
| - Nvidia A100 SXM4 80GB. |
| - torch 2.0.0, transformers 4.29.2. |
|
|
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```BibTeX |
| @article{zhang2023language, |
| title={Language Models are Universal Embedders}, |
| author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min}, |
| journal={arXiv preprint arXiv:2310.08232}, |
| year={2023} |
| } |
| ``` |
|
|
