|
--- |
|
model-index: |
|
- name: stella_en_400M_v5 |
|
results: |
|
- dataset: |
|
config: en |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
split: test |
|
type: mteb/amazon_counterfactual |
|
metrics: |
|
- type: accuracy |
|
value: 92.35820895522387 |
|
- type: ap |
|
value: 70.81322736988783 |
|
- type: ap_weighted |
|
value: 70.81322736988783 |
|
- type: f1 |
|
value: 88.9505466159595 |
|
- type: f1_weighted |
|
value: 92.68630932872613 |
|
- type: main_score |
|
value: 92.35820895522387 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB AmazonPolarityClassification |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
split: test |
|
type: mteb/amazon_polarity |
|
metrics: |
|
- type: accuracy |
|
value: 97.1945 |
|
- type: ap |
|
value: 96.08192192244094 |
|
- type: ap_weighted |
|
value: 96.08192192244094 |
|
- type: f1 |
|
value: 97.1936887167346 |
|
- type: f1_weighted |
|
value: 97.1936887167346 |
|
- type: main_score |
|
value: 97.1945 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: en |
|
name: MTEB AmazonReviewsClassification (en) |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
split: test |
|
type: mteb/amazon_reviews_multi |
|
metrics: |
|
- type: accuracy |
|
value: 59.528000000000006 |
|
- type: f1 |
|
value: 59.21016819840188 |
|
- type: f1_weighted |
|
value: 59.21016819840188 |
|
- type: main_score |
|
value: 59.528000000000006 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB ArguAna |
|
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
|
split: test |
|
type: mteb/arguana |
|
metrics: |
|
- type: main_score |
|
value: 64.24 |
|
- type: map_at_1 |
|
value: 40.398 |
|
- type: map_at_10 |
|
value: 56.215 |
|
- type: map_at_100 |
|
value: 56.833999999999996 |
|
- type: map_at_1000 |
|
value: 56.835 |
|
- type: map_at_20 |
|
value: 56.747 |
|
- type: map_at_3 |
|
value: 52.181 |
|
- type: map_at_5 |
|
value: 54.628 |
|
- type: mrr_at_1 |
|
value: 41.25177809388336 |
|
- type: mrr_at_10 |
|
value: 56.570762491815216 |
|
- type: mrr_at_100 |
|
value: 57.17548614361504 |
|
- type: mrr_at_1000 |
|
value: 57.176650626377466 |
|
- type: mrr_at_20 |
|
value: 57.08916253512566 |
|
- type: mrr_at_3 |
|
value: 52.47747747747754 |
|
- type: mrr_at_5 |
|
value: 54.94547178757718 |
|
- type: nauc_map_at_1000_diff1 |
|
value: 22.408086887100158 |
|
- type: nauc_map_at_1000_max |
|
value: -8.730419096847543 |
|
- type: nauc_map_at_1000_std |
|
value: -17.789262741255737 |
|
- type: nauc_map_at_100_diff1 |
|
value: 22.407371684274025 |
|
- type: nauc_map_at_100_max |
|
value: -8.732263549026266 |
|
- type: nauc_map_at_100_std |
|
value: -17.79550515579994 |
|
- type: nauc_map_at_10_diff1 |
|
value: 21.925005073301246 |
|
- type: nauc_map_at_10_max |
|
value: -8.990323944492134 |
|
- type: nauc_map_at_10_std |
|
value: -18.199246301671458 |
|
- type: nauc_map_at_1_diff1 |
|
value: 26.23276644969203 |
|
- type: nauc_map_at_1_max |
|
value: -12.376511389571245 |
|
- type: nauc_map_at_1_std |
|
value: -18.11411715207284 |
|
- type: nauc_map_at_20_diff1 |
|
value: 22.32455790850922 |
|
- type: nauc_map_at_20_max |
|
value: -8.664671547236034 |
|
- type: nauc_map_at_20_std |
|
value: -17.8290016125137 |
|
- type: nauc_map_at_3_diff1 |
|
value: 22.395462147465064 |
|
- type: nauc_map_at_3_max |
|
value: -8.206580750918844 |
|
- type: nauc_map_at_3_std |
|
value: -17.604490446911484 |
|
- type: nauc_map_at_5_diff1 |
|
value: 21.95307379904799 |
|
- type: nauc_map_at_5_max |
|
value: -8.03958102978443 |
|
- type: nauc_map_at_5_std |
|
value: -17.36578866595004 |
|
- type: nauc_mrr_at_1000_diff1 |
|
value: 20.124236798365587 |
|
- type: nauc_mrr_at_1000_max |
|
value: -9.587376069575898 |
|
- type: nauc_mrr_at_1000_std |
|
value: -17.79191612151833 |
|
- type: nauc_mrr_at_100_diff1 |
|
value: 20.123612603474033 |
|
- type: nauc_mrr_at_100_max |
|
value: -9.589187218607831 |
|
- type: nauc_mrr_at_100_std |
|
value: -17.7981617777748 |
|
- type: nauc_mrr_at_10_diff1 |
|
value: 19.723683875738075 |
|
- type: nauc_mrr_at_10_max |
|
value: -9.774151729178815 |
|
- type: nauc_mrr_at_10_std |
|
value: -18.168668675495162 |
|
- type: nauc_mrr_at_1_diff1 |
|
value: 23.945332059908132 |
|
- type: nauc_mrr_at_1_max |
|
value: -12.260461466152819 |
|
- type: nauc_mrr_at_1_std |
|
value: -18.007194922921148 |
|
- type: nauc_mrr_at_20_diff1 |
|
value: 20.04819461810257 |
|
- type: nauc_mrr_at_20_max |
|
value: -9.518368283588936 |
|
- type: nauc_mrr_at_20_std |
|
value: -17.831608149836136 |
|
- type: nauc_mrr_at_3_diff1 |
|
value: 19.8571785245832 |
|
- type: nauc_mrr_at_3_max |
|
value: -9.464375021240478 |
|
- type: nauc_mrr_at_3_std |
|
value: -17.728533927330453 |
|
- type: nauc_mrr_at_5_diff1 |
|
value: 19.670313652167827 |
|
- type: nauc_mrr_at_5_max |
|
value: -8.966372585728434 |
|
- type: nauc_mrr_at_5_std |
|
value: -17.468955834324817 |
|
- type: nauc_ndcg_at_1000_diff1 |
|
value: 21.863049281767417 |
|
- type: nauc_ndcg_at_1000_max |
|
value: -8.18698520924057 |
|
- type: nauc_ndcg_at_1000_std |
|
value: -17.634483364794804 |
|
- type: nauc_ndcg_at_100_diff1 |
|
value: 21.849924385738586 |
|
- type: nauc_ndcg_at_100_max |
|
value: -8.226437560889345 |
|
- type: nauc_ndcg_at_100_std |
|
value: -17.774648478087002 |
|
- type: nauc_ndcg_at_10_diff1 |
|
value: 19.888395590413573 |
|
- type: nauc_ndcg_at_10_max |
|
value: -8.968706085632382 |
|
- type: nauc_ndcg_at_10_std |
|
value: -19.31386964628115 |
|
- type: nauc_ndcg_at_1_diff1 |
|
value: 26.23276644969203 |
|
- type: nauc_ndcg_at_1_max |
|
value: -12.376511389571245 |
|
- type: nauc_ndcg_at_1_std |
|
value: -18.11411715207284 |
|
- type: nauc_ndcg_at_20_diff1 |
|
value: 21.38413342416933 |
|
- type: nauc_ndcg_at_20_max |
|
value: -7.636238194084164 |
|
- type: nauc_ndcg_at_20_std |
|
value: -17.946390844693028 |
|
- type: nauc_ndcg_at_3_diff1 |
|
value: 21.29169165029195 |
|
- type: nauc_ndcg_at_3_max |
|
value: -6.793840499730093 |
|
- type: nauc_ndcg_at_3_std |
|
value: -17.52359001586737 |
|
- type: nauc_ndcg_at_5_diff1 |
|
value: 20.238297656671364 |
|
- type: nauc_ndcg_at_5_max |
|
value: -6.424992706950072 |
|
- type: nauc_ndcg_at_5_std |
|
value: -17.082391132291356 |
|
- type: nauc_precision_at_1000_diff1 |
|
value: -7.05195108528572 |
|
- type: nauc_precision_at_1000_max |
|
value: 34.439879624882145 |
|
- type: nauc_precision_at_1000_std |
|
value: 68.72436351659353 |
|
- type: nauc_precision_at_100_diff1 |
|
value: -2.769464113932605 |
|
- type: nauc_precision_at_100_max |
|
value: 9.89562961226698 |
|
- type: nauc_precision_at_100_std |
|
value: -0.5880967482224028 |
|
- type: nauc_precision_at_10_diff1 |
|
value: 2.1371544726832323 |
|
- type: nauc_precision_at_10_max |
|
value: -11.93051325147756 |
|
- type: nauc_precision_at_10_std |
|
value: -30.83144187392059 |
|
- type: nauc_precision_at_1_diff1 |
|
value: 26.23276644969203 |
|
- type: nauc_precision_at_1_max |
|
value: -12.376511389571245 |
|
- type: nauc_precision_at_1_std |
|
value: -18.11411715207284 |
|
- type: nauc_precision_at_20_diff1 |
|
value: 3.780146814257504 |
|
- type: nauc_precision_at_20_max |
|
value: 17.06527540214615 |
|
- type: nauc_precision_at_20_std |
|
value: -20.36832563035565 |
|
- type: nauc_precision_at_3_diff1 |
|
value: 17.63894384012077 |
|
- type: nauc_precision_at_3_max |
|
value: -2.0220490624638887 |
|
- type: nauc_precision_at_3_std |
|
value: -17.285601413493918 |
|
- type: nauc_precision_at_5_diff1 |
|
value: 12.557855071944601 |
|
- type: nauc_precision_at_5_max |
|
value: 0.5840236463956658 |
|
- type: nauc_precision_at_5_std |
|
value: -15.827224420217846 |
|
- type: nauc_recall_at_1000_diff1 |
|
value: -7.051951085286463 |
|
- type: nauc_recall_at_1000_max |
|
value: 34.43987962487738 |
|
- type: nauc_recall_at_1000_std |
|
value: 68.724363516591 |
|
- type: nauc_recall_at_100_diff1 |
|
value: -2.769464113930314 |
|
- type: nauc_recall_at_100_max |
|
value: 9.895629612270017 |
|
- type: nauc_recall_at_100_std |
|
value: -0.58809674821745 |
|
- type: nauc_recall_at_10_diff1 |
|
value: 2.1371544726834495 |
|
- type: nauc_recall_at_10_max |
|
value: -11.930513251477253 |
|
- type: nauc_recall_at_10_std |
|
value: -30.83144187392047 |
|
- type: nauc_recall_at_1_diff1 |
|
value: 26.23276644969203 |
|
- type: nauc_recall_at_1_max |
|
value: -12.376511389571245 |
|
- type: nauc_recall_at_1_std |
|
value: -18.11411715207284 |
|
- type: nauc_recall_at_20_diff1 |
|
value: 3.7801468142575922 |
|
- type: nauc_recall_at_20_max |
|
value: 17.0652754021456 |
|
- type: nauc_recall_at_20_std |
|
value: -20.36832563035559 |
|
- type: nauc_recall_at_3_diff1 |
|
value: 17.63894384012074 |
|
- type: nauc_recall_at_3_max |
|
value: -2.02204906246383 |
|
- type: nauc_recall_at_3_std |
|
value: -17.28560141349386 |
|
- type: nauc_recall_at_5_diff1 |
|
value: 12.55785507194463 |
|
- type: nauc_recall_at_5_max |
|
value: 0.5840236463957296 |
|
- type: nauc_recall_at_5_std |
|
value: -15.827224420217856 |
|
- type: ndcg_at_1 |
|
value: 40.398 |
|
- type: ndcg_at_10 |
|
value: 64.24 |
|
- type: ndcg_at_100 |
|
value: 66.631 |
|
- type: ndcg_at_1000 |
|
value: 66.65100000000001 |
|
- type: ndcg_at_20 |
|
value: 66.086 |
|
- type: ndcg_at_3 |
|
value: 55.938 |
|
- type: ndcg_at_5 |
|
value: 60.370000000000005 |
|
- type: precision_at_1 |
|
value: 40.398 |
|
- type: precision_at_10 |
|
value: 8.962 |
|
- type: precision_at_100 |
|
value: 0.9950000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_20 |
|
value: 4.836 |
|
- type: precision_at_3 |
|
value: 22.262 |
|
- type: precision_at_5 |
|
value: 15.519 |
|
- type: recall_at_1 |
|
value: 40.398 |
|
- type: recall_at_10 |
|
value: 89.616 |
|
- type: recall_at_100 |
|
value: 99.502 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_20 |
|
value: 96.72800000000001 |
|
- type: recall_at_3 |
|
value: 66.78500000000001 |
|
- type: recall_at_5 |
|
value: 77.596 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB ArxivClusteringP2P |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
split: test |
|
type: mteb/arxiv-clustering-p2p |
|
metrics: |
|
- type: main_score |
|
value: 55.1564333205451 |
|
- type: v_measure |
|
value: 55.1564333205451 |
|
- type: v_measure_std |
|
value: 14.696883012214512 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB ArxivClusteringS2S |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
split: test |
|
type: mteb/arxiv-clustering-s2s |
|
metrics: |
|
- type: main_score |
|
value: 49.823698316694795 |
|
- type: v_measure |
|
value: 49.823698316694795 |
|
- type: v_measure_std |
|
value: 14.951660654298186 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB AskUbuntuDupQuestions |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
split: test |
|
type: mteb/askubuntudupquestions-reranking |
|
metrics: |
|
- type: main_score |
|
value: 66.15294503553424 |
|
- type: map |
|
value: 66.15294503553424 |
|
- type: mrr |
|
value: 78.53438420612935 |
|
- type: nAUC_map_diff1 |
|
value: 12.569697092717997 |
|
- type: nAUC_map_max |
|
value: 21.50670312412572 |
|
- type: nAUC_map_std |
|
value: 16.943786429229064 |
|
- type: nAUC_mrr_diff1 |
|
value: 15.590272897361238 |
|
- type: nAUC_mrr_max |
|
value: 34.96072022474653 |
|
- type: nAUC_mrr_std |
|
value: 21.649217605241045 |
|
task: |
|
type: Reranking |
|
- dataset: |
|
config: default |
|
name: MTEB BIOSSES |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
split: test |
|
type: mteb/biosses-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 85.7824546319275 |
|
- type: cosine_spearman |
|
value: 83.29587385660628 |
|
- type: euclidean_pearson |
|
value: 84.58764190565167 |
|
- type: euclidean_spearman |
|
value: 83.30069324352772 |
|
- type: main_score |
|
value: 83.29587385660628 |
|
- type: manhattan_pearson |
|
value: 84.95996839947179 |
|
- type: manhattan_spearman |
|
value: 83.87480271054358 |
|
- type: pearson |
|
value: 85.7824546319275 |
|
- type: spearman |
|
value: 83.29587385660628 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB Banking77Classification |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
split: test |
|
type: mteb/banking77 |
|
metrics: |
|
- type: accuracy |
|
value: 89.30194805194806 |
|
- type: f1 |
|
value: 89.26182507266391 |
|
- type: f1_weighted |
|
value: 89.26182507266391 |
|
- type: main_score |
|
value: 89.30194805194806 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB BiorxivClusteringP2P |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
split: test |
|
type: mteb/biorxiv-clustering-p2p |
|
metrics: |
|
- type: main_score |
|
value: 50.67972171889736 |
|
- type: v_measure |
|
value: 50.67972171889736 |
|
- type: v_measure_std |
|
value: 0.7687409980036303 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB BiorxivClusteringS2S |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
split: test |
|
type: mteb/biorxiv-clustering-s2s |
|
metrics: |
|
- type: main_score |
|
value: 45.80539715556144 |
|
- type: v_measure |
|
value: 45.80539715556144 |
|
- type: v_measure_std |
|
value: 0.9601346216579142 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB CQADupstackRetrieval |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
split: test |
|
type: mteb/cqadupstack |
|
metrics: |
|
- type: main_score |
|
value: 44.361250000000005 |
|
- type: map_at_1 |
|
value: 28.304499999999997 |
|
- type: map_at_10 |
|
value: 38.54841666666666 |
|
- type: map_at_100 |
|
value: 39.83141666666667 |
|
- type: map_at_1000 |
|
value: 39.944750000000006 |
|
- type: map_at_20 |
|
value: 39.25341666666667 |
|
- type: map_at_3 |
|
value: 35.406749999999995 |
|
- type: map_at_5 |
|
value: 37.15558333333333 |
|
- type: mrr_at_1 |
|
value: 34.09077232860122 |
|
- type: mrr_at_10 |
|
value: 43.15445393211421 |
|
- type: mrr_at_100 |
|
value: 43.98645286848257 |
|
- type: mrr_at_1000 |
|
value: 44.037631313469404 |
|
- type: mrr_at_20 |
|
value: 43.64045813249614 |
|
- type: mrr_at_3 |
|
value: 40.674138648480486 |
|
- type: mrr_at_5 |
|
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task: |
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type: Retrieval |
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- dataset: |
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config: default |
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name: MTEB ClimateFEVER |
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revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
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split: test |
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value: 43.779 |
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value: 43.525999999999996 |
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value: 50.138000000000005 |
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value: 52.991 |
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value: 46.083 |
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value: 38.002 |
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|
value: 39.842 |
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value: 43.779 |
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value: 13.205 |
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value: 2.051 |
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value: 21.368000000000002 |
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value: 48.754 |
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- type: recall_at_100 |
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value: 70.97200000000001 |
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value: 55.884 |
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- type: recall_at_3 |
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- type: recall_at_5 |
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value: 40.784 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB DBPedia |
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
|
split: test |
|
type: mteb/dbpedia |
|
metrics: |
|
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|
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value: 15.656 |
|
- type: map_at_5 |
|
value: 18.945999999999998 |
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- type: mrr_at_1 |
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value: 75.75 |
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- type: mrr_at_10 |
|
value: 82.16279761904761 |
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- type: mrr_at_100 |
|
value: 82.48445635330299 |
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- type: mrr_at_1000 |
|
value: 82.4870246719901 |
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- type: mrr_at_20 |
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value: 82.36203632968338 |
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- type: mrr_at_3 |
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value: 82.02916666666667 |
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|
value: -4.556638938723358 |
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- type: nauc_recall_at_20_max |
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- type: nauc_recall_at_20_std |
|
value: -3.0740743798103676 |
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|
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|
- type: nauc_recall_at_3_max |
|
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|
- type: nauc_recall_at_3_std |
|
value: -18.27746460743442 |
|
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|
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|
- type: nauc_recall_at_5_max |
|
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|
- type: nauc_recall_at_5_std |
|
value: -14.441820321214108 |
|
- type: ndcg_at_1 |
|
value: 63.87500000000001 |
|
- type: ndcg_at_10 |
|
value: 49.884 |
|
- type: ndcg_at_100 |
|
value: 54.738 |
|
- type: ndcg_at_1000 |
|
value: 61.635 |
|
- type: ndcg_at_20 |
|
value: 48.894999999999996 |
|
- type: ndcg_at_3 |
|
value: 54.287 |
|
- type: ndcg_at_5 |
|
value: 52.40899999999999 |
|
- type: precision_at_1 |
|
value: 75.75 |
|
- type: precision_at_10 |
|
value: 40.9 |
|
- type: precision_at_100 |
|
value: 13.139999999999999 |
|
- type: precision_at_1000 |
|
value: 2.533 |
|
- type: precision_at_20 |
|
value: 30.8 |
|
- type: precision_at_3 |
|
value: 57.667 |
|
- type: precision_at_5 |
|
value: 51.05 |
|
- type: recall_at_1 |
|
value: 9.913 |
|
- type: recall_at_10 |
|
value: 28.591 |
|
- type: recall_at_100 |
|
value: 61.017999999999994 |
|
- type: recall_at_1000 |
|
value: 83.383 |
|
- type: recall_at_20 |
|
value: 37.834 |
|
- type: recall_at_3 |
|
value: 17.049 |
|
- type: recall_at_5 |
|
value: 21.685 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB EmotionClassification |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
split: test |
|
type: mteb/emotion |
|
metrics: |
|
- type: accuracy |
|
value: 78.77499999999999 |
|
- type: f1 |
|
value: 73.74058240799386 |
|
- type: f1_weighted |
|
value: 79.78804377638227 |
|
- type: main_score |
|
value: 78.77499999999999 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB FEVER |
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
|
split: test |
|
type: mteb/fever |
|
metrics: |
|
- type: main_score |
|
value: 90.986 |
|
- type: map_at_1 |
|
value: 81.601 |
|
- type: map_at_10 |
|
value: 88.242 |
|
- type: map_at_100 |
|
value: 88.46000000000001 |
|
- type: map_at_1000 |
|
value: 88.472 |
|
- type: map_at_20 |
|
value: 88.375 |
|
- type: map_at_3 |
|
value: 87.237 |
|
- type: map_at_5 |
|
value: 87.85300000000001 |
|
- type: mrr_at_1 |
|
value: 87.81878187818782 |
|
- type: mrr_at_10 |
|
value: 92.20301196786335 |
|
- type: mrr_at_100 |
|
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|
- type: mrr_at_1000 |
|
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|
- type: mrr_at_20 |
|
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|
- type: mrr_at_3 |
|
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|
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|
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB FiQA2018 |
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
|
split: test |
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type: mteb/fiqa |
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- type: recall_at_5 |
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value: 53.313 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB HotpotQA |
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
|
split: test |
|
type: mteb/hotpotqa |
|
metrics: |
|
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task: |
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type: Retrieval |
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- dataset: |
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config: default |
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name: MTEB ImdbClassification |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
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split: test |
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type: mteb/imdb |
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task: |
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type: Classification |
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config: default |
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name: MTEB MSMARCO |
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revision: c5a29a104738b98a9e76336939199e264163d4a0 |
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split: dev |
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- type: ndcg_at_10 |
|
value: 43.692 |
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- type: ndcg_at_100 |
|
value: 49.211 |
|
- type: ndcg_at_1000 |
|
value: 50.244 |
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- type: ndcg_at_20 |
|
value: 46.278000000000006 |
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value: 35.719 |
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value: 39.652 |
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|
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|
value: 6.857 |
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- type: precision_at_100 |
|
value: 0.9610000000000001 |
|
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|
value: 0.105 |
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|
value: 3.968 |
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- type: precision_at_3 |
|
value: 15.181000000000001 |
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- type: precision_at_5 |
|
value: 11.117 |
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- type: recall_at_1 |
|
value: 23.751 |
|
- type: recall_at_10 |
|
value: 65.64 |
|
- type: recall_at_100 |
|
value: 90.967 |
|
- type: recall_at_1000 |
|
value: 98.738 |
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- type: recall_at_20 |
|
value: 75.639 |
|
- type: recall_at_3 |
|
value: 43.927 |
|
- type: recall_at_5 |
|
value: 53.366 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: en |
|
name: MTEB MTOPDomainClassification (en) |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
split: test |
|
type: mteb/mtop_domain |
|
metrics: |
|
- type: accuracy |
|
value: 98.82580939352485 |
|
- type: f1 |
|
value: 98.75201754333801 |
|
- type: f1_weighted |
|
value: 98.82795205108245 |
|
- type: main_score |
|
value: 98.82580939352485 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: en |
|
name: MTEB MTOPIntentClassification (en) |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
split: test |
|
type: mteb/mtop_intent |
|
metrics: |
|
- type: accuracy |
|
value: 92.29822161422709 |
|
- type: f1 |
|
value: 77.75210224871594 |
|
- type: f1_weighted |
|
value: 93.58661422540348 |
|
- type: main_score |
|
value: 92.29822161422709 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: en |
|
name: MTEB MassiveIntentClassification (en) |
|
revision: 4672e20407010da34463acc759c162ca9734bca6 |
|
split: test |
|
type: mteb/amazon_massive_intent |
|
metrics: |
|
- type: accuracy |
|
value: 85.17484868863484 |
|
- type: f1 |
|
value: 81.94484244487094 |
|
- type: f1_weighted |
|
value: 85.21022593423332 |
|
- type: main_score |
|
value: 85.17484868863484 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: en |
|
name: MTEB MassiveScenarioClassification (en) |
|
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 |
|
split: test |
|
type: mteb/amazon_massive_scenario |
|
metrics: |
|
- type: accuracy |
|
value: 89.61667787491594 |
|
- type: f1 |
|
value: 89.02701927621264 |
|
- type: f1_weighted |
|
value: 89.56306982022801 |
|
- type: main_score |
|
value: 89.61667787491594 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB MedrxivClusteringP2P |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
split: test |
|
type: mteb/medrxiv-clustering-p2p |
|
metrics: |
|
- type: main_score |
|
value: 46.318282423948574 |
|
- type: v_measure |
|
value: 46.318282423948574 |
|
- type: v_measure_std |
|
value: 0.9729055662461538 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB MedrxivClusteringS2S |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
split: test |
|
type: mteb/medrxiv-clustering-s2s |
|
metrics: |
|
- type: main_score |
|
value: 44.29033625273981 |
|
- type: v_measure |
|
value: 44.29033625273981 |
|
- type: v_measure_std |
|
value: 1.0596383629128594 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB MindSmallReranking |
|
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 |
|
split: test |
|
type: mteb/mind_small |
|
metrics: |
|
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|
value: 33.0526129239962 |
|
- type: map |
|
value: 33.0526129239962 |
|
- type: mrr |
|
value: 34.29260046890935 |
|
- type: nAUC_map_diff1 |
|
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|
- type: nAUC_map_max |
|
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|
- type: nAUC_map_std |
|
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|
- type: nAUC_mrr_diff1 |
|
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|
- type: nAUC_mrr_max |
|
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|
- type: nAUC_mrr_std |
|
value: 0.2719172965777737 |
|
task: |
|
type: Reranking |
|
- dataset: |
|
config: default |
|
name: MTEB NFCorpus |
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
|
split: test |
|
type: mteb/nfcorpus |
|
metrics: |
|
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|
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|
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|
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|
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|
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|
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|
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|
- type: map_at_1000 |
|
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|
- type: map_at_20 |
|
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|
- type: map_at_3 |
|
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|
- type: map_at_5 |
|
value: 13.415 |
|
- type: mrr_at_1 |
|
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|
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|
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|
- type: mrr_at_100 |
|
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|
- type: mrr_at_1000 |
|
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|
- type: mrr_at_20 |
|
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- type: mrr_at_3 |
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
- type: precision_at_3 |
|
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|
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|
- type: recall_at_1 |
|
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|
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task: |
|
type: Retrieval |
|
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config: default |
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name: MTEB NQ |
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
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split: test |
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type: mteb/nq |
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB QuoraRetrieval |
|
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
|
split: test |
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type: mteb/quora |
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- type: ndcg_at_10 |
|
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- type: ndcg_at_100 |
|
value: 90.606 |
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- type: ndcg_at_1000 |
|
value: 90.676 |
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- type: ndcg_at_20 |
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value: 90.132 |
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- type: ndcg_at_3 |
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value: 86.88 |
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value: 13.604 |
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- type: precision_at_100 |
|
value: 1.539 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_20 |
|
value: 7.188 |
|
- type: precision_at_3 |
|
value: 38.083 |
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- type: precision_at_5 |
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value: 25.018 |
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- type: recall_at_1 |
|
value: 71.819 |
|
- type: recall_at_10 |
|
value: 96.34700000000001 |
|
- type: recall_at_100 |
|
value: 99.715 |
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- type: recall_at_1000 |
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value: 99.995 |
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- type: recall_at_20 |
|
value: 98.073 |
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- type: recall_at_3 |
|
value: 88.57300000000001 |
|
- type: recall_at_5 |
|
value: 92.908 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB RedditClustering |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
split: test |
|
type: mteb/reddit-clustering |
|
metrics: |
|
- type: main_score |
|
value: 71.18966762070158 |
|
- type: v_measure |
|
value: 71.18966762070158 |
|
- type: v_measure_std |
|
value: 2.7498969054457048 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB RedditClusteringP2P |
|
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
|
split: test |
|
type: mteb/reddit-clustering-p2p |
|
metrics: |
|
- type: main_score |
|
value: 74.42014716862516 |
|
- type: v_measure |
|
value: 74.42014716862516 |
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- type: v_measure_std |
|
value: 9.909739891410648 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB SCIDOCS |
|
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
|
split: test |
|
type: mteb/scidocs |
|
metrics: |
|
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|
value: 25.041999999999998 |
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- type: map_at_1 |
|
value: 5.893000000000001 |
|
- type: map_at_10 |
|
value: 15.260000000000002 |
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- type: map_at_100 |
|
value: 18.084 |
|
- type: map_at_1000 |
|
value: 18.467 |
|
- type: map_at_20 |
|
value: 16.675 |
|
- type: map_at_3 |
|
value: 10.526 |
|
- type: map_at_5 |
|
value: 12.775 |
|
- type: mrr_at_1 |
|
value: 28.999999999999996 |
|
- type: mrr_at_10 |
|
value: 41.03575396825395 |
|
- type: mrr_at_100 |
|
value: 42.136771862785835 |
|
- type: mrr_at_1000 |
|
value: 42.16698555415099 |
|
- type: mrr_at_20 |
|
value: 41.707493696104315 |
|
- type: mrr_at_3 |
|
value: 37.34999999999998 |
|
- type: mrr_at_5 |
|
value: 39.59999999999995 |
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- type: nauc_map_at_1000_diff1 |
|
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|
- type: nauc_map_at_1000_max |
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- type: nauc_map_at_1000_std |
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|
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|
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- type: nauc_map_at_100_std |
|
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- type: nauc_map_at_10_max |
|
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- type: nauc_map_at_10_std |
|
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- type: nauc_map_at_1_diff1 |
|
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|
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|
- type: nauc_map_at_1_std |
|
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- type: nauc_map_at_20_diff1 |
|
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|
- type: nauc_map_at_20_max |
|
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- type: nauc_map_at_20_std |
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- type: nauc_map_at_3_diff1 |
|
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- type: nauc_map_at_3_max |
|
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- type: nauc_map_at_3_std |
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- type: nauc_map_at_5_max |
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- type: nauc_mrr_at_10_std |
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|
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|
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|
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- type: nauc_recall_at_1_std |
|
value: 8.19886078055046 |
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|
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|
- type: nauc_recall_at_20_max |
|
value: 28.611698183338202 |
|
- type: nauc_recall_at_20_std |
|
value: 26.69018660149911 |
|
- type: nauc_recall_at_3_diff1 |
|
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|
- type: nauc_recall_at_3_max |
|
value: 29.269835821380465 |
|
- type: nauc_recall_at_3_std |
|
value: 14.104419605998094 |
|
- type: nauc_recall_at_5_diff1 |
|
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|
- type: nauc_recall_at_5_max |
|
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|
- type: nauc_recall_at_5_std |
|
value: 17.758048545573825 |
|
- type: ndcg_at_1 |
|
value: 28.999999999999996 |
|
- type: ndcg_at_10 |
|
value: 25.041999999999998 |
|
- type: ndcg_at_100 |
|
value: 35.045 |
|
- type: ndcg_at_1000 |
|
value: 40.803 |
|
- type: ndcg_at_20 |
|
value: 28.584 |
|
- type: ndcg_at_3 |
|
value: 23.249 |
|
- type: ndcg_at_5 |
|
value: 20.533 |
|
- type: precision_at_1 |
|
value: 28.999999999999996 |
|
- type: precision_at_10 |
|
value: 13.120000000000001 |
|
- type: precision_at_100 |
|
value: 2.7470000000000003 |
|
- type: precision_at_1000 |
|
value: 0.41200000000000003 |
|
- type: precision_at_20 |
|
value: 8.584999999999999 |
|
- type: precision_at_3 |
|
value: 21.633 |
|
- type: precision_at_5 |
|
value: 18.099999999999998 |
|
- type: recall_at_1 |
|
value: 5.893000000000001 |
|
- type: recall_at_10 |
|
value: 26.567 |
|
- type: recall_at_100 |
|
value: 55.800000000000004 |
|
- type: recall_at_1000 |
|
value: 83.608 |
|
- type: recall_at_20 |
|
value: 34.86 |
|
- type: recall_at_3 |
|
value: 13.153 |
|
- type: recall_at_5 |
|
value: 18.323 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB SICK-R |
|
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
|
split: test |
|
type: mteb/sickr-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 86.57284584320382 |
|
- type: cosine_spearman |
|
value: 82.20531642680812 |
|
- type: euclidean_pearson |
|
value: 83.94261758556554 |
|
- type: euclidean_spearman |
|
value: 82.20721497738559 |
|
- type: main_score |
|
value: 82.20531642680812 |
|
- type: manhattan_pearson |
|
value: 84.15902154703083 |
|
- type: manhattan_spearman |
|
value: 82.19506027155957 |
|
- type: pearson |
|
value: 86.57284584320382 |
|
- type: spearman |
|
value: 82.20531642680812 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS12 |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
split: test |
|
type: mteb/sts12-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 86.28047602146931 |
|
- type: cosine_spearman |
|
value: 79.51504881448884 |
|
- type: euclidean_pearson |
|
value: 83.10545189967856 |
|
- type: euclidean_spearman |
|
value: 79.50586960492797 |
|
- type: main_score |
|
value: 79.51504881448884 |
|
- type: manhattan_pearson |
|
value: 83.44244457500889 |
|
- type: manhattan_spearman |
|
value: 79.730303339846 |
|
- type: pearson |
|
value: 86.28047602146931 |
|
- type: spearman |
|
value: 79.51504881448884 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS13 |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
split: test |
|
type: mteb/sts13-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 88.74723553048702 |
|
- type: cosine_spearman |
|
value: 89.18936052329725 |
|
- type: euclidean_pearson |
|
value: 88.90400878928668 |
|
- type: euclidean_spearman |
|
value: 89.19174821431281 |
|
- type: main_score |
|
value: 89.18936052329725 |
|
- type: manhattan_pearson |
|
value: 88.81504628424054 |
|
- type: manhattan_spearman |
|
value: 89.18063294142597 |
|
- type: pearson |
|
value: 88.74723553048702 |
|
- type: spearman |
|
value: 89.18936052329725 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS14 |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
split: test |
|
type: mteb/sts14-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 86.45403437836023 |
|
- type: cosine_spearman |
|
value: 85.14654611519086 |
|
- type: euclidean_pearson |
|
value: 85.87509624462743 |
|
- type: euclidean_spearman |
|
value: 85.1391108856681 |
|
- type: main_score |
|
value: 85.14654611519086 |
|
- type: manhattan_pearson |
|
value: 85.96635794953866 |
|
- type: manhattan_spearman |
|
value: 85.3271371527667 |
|
- type: pearson |
|
value: 86.45403437836023 |
|
- type: spearman |
|
value: 85.14654611519086 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS15 |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
split: test |
|
type: mteb/sts15-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 87.84742260009705 |
|
- type: cosine_spearman |
|
value: 89.10215217191254 |
|
- type: euclidean_pearson |
|
value: 88.97393286325477 |
|
- type: euclidean_spearman |
|
value: 89.1014105509662 |
|
- type: main_score |
|
value: 89.10215217191254 |
|
- type: manhattan_pearson |
|
value: 89.31698781090151 |
|
- type: manhattan_spearman |
|
value: 89.53000001764433 |
|
- type: pearson |
|
value: 87.84742260009705 |
|
- type: spearman |
|
value: 89.10215217191254 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STS16 |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
split: test |
|
type: mteb/sts16-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 85.22397535461835 |
|
- type: cosine_spearman |
|
value: 87.14066355879785 |
|
- type: euclidean_pearson |
|
value: 86.31393364087295 |
|
- type: euclidean_spearman |
|
value: 87.14018892702765 |
|
- type: main_score |
|
value: 87.14066355879785 |
|
- type: manhattan_pearson |
|
value: 86.36366855248434 |
|
- type: manhattan_spearman |
|
value: 87.20858630423012 |
|
- type: pearson |
|
value: 85.22397535461835 |
|
- type: spearman |
|
value: 87.14066355879785 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: en-en |
|
name: MTEB STS17 (en-en) |
|
revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
|
split: test |
|
type: mteb/sts17-crosslingual-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 90.66131612061355 |
|
- type: cosine_spearman |
|
value: 90.97082650129164 |
|
- type: euclidean_pearson |
|
value: 90.98181906744969 |
|
- type: euclidean_spearman |
|
value: 90.99008476850047 |
|
- type: main_score |
|
value: 90.97082650129164 |
|
- type: manhattan_pearson |
|
value: 90.75245040709021 |
|
- type: manhattan_spearman |
|
value: 90.6199877691265 |
|
- type: pearson |
|
value: 90.66131612061355 |
|
- type: spearman |
|
value: 90.97082650129164 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: en |
|
name: MTEB STS22 (en) |
|
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
|
split: test |
|
type: mteb/sts22-crosslingual-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 67.270656447085 |
|
- type: cosine_spearman |
|
value: 67.82870469746828 |
|
- type: euclidean_pearson |
|
value: 69.03857775285664 |
|
- type: euclidean_spearman |
|
value: 67.74455108773341 |
|
- type: main_score |
|
value: 67.82870469746828 |
|
- type: manhattan_pearson |
|
value: 69.25304172245812 |
|
- type: manhattan_spearman |
|
value: 68.00987097916055 |
|
- type: pearson |
|
value: 67.270656447085 |
|
- type: spearman |
|
value: 67.82870469746828 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB STSBenchmark |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
split: test |
|
type: mteb/stsbenchmark-sts |
|
metrics: |
|
- type: cosine_pearson |
|
value: 87.17245205384889 |
|
- type: cosine_spearman |
|
value: 87.7360146030987 |
|
- type: euclidean_pearson |
|
value: 87.48919412794656 |
|
- type: euclidean_spearman |
|
value: 87.7312047878383 |
|
- type: main_score |
|
value: 87.7360146030987 |
|
- type: manhattan_pearson |
|
value: 87.61476224354806 |
|
- type: manhattan_spearman |
|
value: 87.95220889254693 |
|
- type: pearson |
|
value: 87.17245205384889 |
|
- type: spearman |
|
value: 87.7360146030987 |
|
task: |
|
type: STS |
|
- dataset: |
|
config: default |
|
name: MTEB SciDocsRR |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
split: test |
|
type: mteb/scidocs-reranking |
|
metrics: |
|
- type: main_score |
|
value: 88.43547871921146 |
|
- type: map |
|
value: 88.43547871921146 |
|
- type: mrr |
|
value: 96.5564473652709 |
|
- type: nAUC_map_diff1 |
|
value: -13.66029392579231 |
|
- type: nAUC_map_max |
|
value: 50.325613574053506 |
|
- type: nAUC_map_std |
|
value: 60.02986231275796 |
|
- type: nAUC_mrr_diff1 |
|
value: 23.83821476411125 |
|
- type: nAUC_mrr_max |
|
value: 86.72643311769906 |
|
- type: nAUC_mrr_std |
|
value: 72.12741063469213 |
|
task: |
|
type: Reranking |
|
- dataset: |
|
config: default |
|
name: MTEB SciFact |
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
|
split: test |
|
type: mteb/scifact |
|
metrics: |
|
- type: main_score |
|
value: 78.233 |
|
- type: map_at_1 |
|
value: 61.49400000000001 |
|
- type: map_at_10 |
|
value: 73.30600000000001 |
|
- type: map_at_100 |
|
value: 73.719 |
|
- type: map_at_1000 |
|
value: 73.724 |
|
- type: map_at_20 |
|
value: 73.611 |
|
- type: map_at_3 |
|
value: 70.626 |
|
- type: map_at_5 |
|
value: 72.417 |
|
- type: mrr_at_1 |
|
value: 64.66666666666666 |
|
- type: mrr_at_10 |
|
value: 74.30357142857143 |
|
- type: mrr_at_100 |
|
value: 74.56950898079988 |
|
- type: mrr_at_1000 |
|
value: 74.57295833098681 |
|
- type: mrr_at_20 |
|
value: 74.46165223665226 |
|
- type: mrr_at_3 |
|
value: 72.3888888888889 |
|
- type: mrr_at_5 |
|
value: 73.60555555555557 |
|
- type: nauc_map_at_1000_diff1 |
|
value: 76.51524604780636 |
|
- type: nauc_map_at_1000_max |
|
value: 53.48521938401881 |
|
- type: nauc_map_at_1000_std |
|
value: -7.347799382158861 |
|
- type: nauc_map_at_100_diff1 |
|
value: 76.5122888096236 |
|
- type: nauc_map_at_100_max |
|
value: 53.49221847471618 |
|
- type: nauc_map_at_100_std |
|
value: -7.329683735681086 |
|
- type: nauc_map_at_10_diff1 |
|
value: 76.30928630674504 |
|
- type: nauc_map_at_10_max |
|
value: 53.00102977185941 |
|
- type: nauc_map_at_10_std |
|
value: -7.7467740085108705 |
|
- type: nauc_map_at_1_diff1 |
|
value: 79.54189281784247 |
|
- type: nauc_map_at_1_max |
|
value: 46.630071622109526 |
|
- type: nauc_map_at_1_std |
|
value: -14.395943134644112 |
|
- type: nauc_map_at_20_diff1 |
|
value: 76.41604361947962 |
|
- type: nauc_map_at_20_max |
|
value: 53.578883876146875 |
|
- type: nauc_map_at_20_std |
|
value: -7.403103451288041 |
|
- type: nauc_map_at_3_diff1 |
|
value: 76.25911617571941 |
|
- type: nauc_map_at_3_max |
|
value: 49.140287380513605 |
|
- type: nauc_map_at_3_std |
|
value: -11.35992449218983 |
|
- type: nauc_map_at_5_diff1 |
|
value: 76.35122077770336 |
|
- type: nauc_map_at_5_max |
|
value: 52.1744367901208 |
|
- type: nauc_map_at_5_std |
|
value: -7.85753955055384 |
|
- type: nauc_mrr_at_1000_diff1 |
|
value: 76.97223309515867 |
|
- type: nauc_mrr_at_1000_max |
|
value: 57.263787498613326 |
|
- type: nauc_mrr_at_1000_std |
|
value: -4.884090708840035 |
|
- type: nauc_mrr_at_100_diff1 |
|
value: 76.97312970894603 |
|
- type: nauc_mrr_at_100_max |
|
value: 57.26850730446478 |
|
- type: nauc_mrr_at_100_std |
|
value: -4.875200894216617 |
|
- type: nauc_mrr_at_10_diff1 |
|
value: 76.65927674223613 |
|
- type: nauc_mrr_at_10_max |
|
value: 57.30979763941454 |
|
- type: nauc_mrr_at_10_std |
|
value: -4.863331094022142 |
|
- type: nauc_mrr_at_1_diff1 |
|
value: 80.0454932568644 |
|
- type: nauc_mrr_at_1_max |
|
value: 56.76038421319305 |
|
- type: nauc_mrr_at_1_std |
|
value: -4.101939392632653 |
|
- type: nauc_mrr_at_20_diff1 |
|
value: 76.87237970440503 |
|
- type: nauc_mrr_at_20_max |
|
value: 57.33843605225869 |
|
- type: nauc_mrr_at_20_std |
|
value: -4.96248984417978 |
|
- type: nauc_mrr_at_3_diff1 |
|
value: 76.74130186666727 |
|
- type: nauc_mrr_at_3_max |
|
value: 56.19313244846155 |
|
- type: nauc_mrr_at_3_std |
|
value: -5.684365934009136 |
|
- type: nauc_mrr_at_5_diff1 |
|
value: 76.66406918799962 |
|
- type: nauc_mrr_at_5_max |
|
value: 57.56110093228628 |
|
- type: nauc_mrr_at_5_std |
|
value: -3.7464413085588073 |
|
- type: nauc_ndcg_at_1000_diff1 |
|
value: 76.19194173971773 |
|
- type: nauc_ndcg_at_1000_max |
|
value: 55.57464600170693 |
|
- type: nauc_ndcg_at_1000_std |
|
value: -6.0761689532372625 |
|
- type: nauc_ndcg_at_100_diff1 |
|
value: 76.14631273843654 |
|
- type: nauc_ndcg_at_100_max |
|
value: 55.72246565373382 |
|
- type: nauc_ndcg_at_100_std |
|
value: -5.595160698860595 |
|
- type: nauc_ndcg_at_10_diff1 |
|
value: 75.0108223611192 |
|
- type: nauc_ndcg_at_10_max |
|
value: 55.27894212877493 |
|
- type: nauc_ndcg_at_10_std |
|
value: -6.968331740214591 |
|
- type: nauc_ndcg_at_1_diff1 |
|
value: 80.0454932568644 |
|
- type: nauc_ndcg_at_1_max |
|
value: 56.76038421319305 |
|
- type: nauc_ndcg_at_1_std |
|
value: -4.101939392632653 |
|
- type: nauc_ndcg_at_20_diff1 |
|
value: 75.54887755702472 |
|
- type: nauc_ndcg_at_20_max |
|
value: 56.406879417251496 |
|
- type: nauc_ndcg_at_20_std |
|
value: -6.495231061329629 |
|
- type: nauc_ndcg_at_3_diff1 |
|
value: 75.03620356688509 |
|
- type: nauc_ndcg_at_3_max |
|
value: 52.147381077773424 |
|
- type: nauc_ndcg_at_3_std |
|
value: -8.448005688956199 |
|
- type: nauc_ndcg_at_5_diff1 |
|
value: 75.1195898074229 |
|
- type: nauc_ndcg_at_5_max |
|
value: 54.2321033861173 |
|
- type: nauc_ndcg_at_5_std |
|
value: -5.882690780895338 |
|
- type: nauc_precision_at_1000_diff1 |
|
value: -28.081979732100532 |
|
- type: nauc_precision_at_1000_max |
|
value: 35.055348014832916 |
|
- type: nauc_precision_at_1000_std |
|
value: 59.61280468927384 |
|
- type: nauc_precision_at_100_diff1 |
|
value: -25.112740730587458 |
|
- type: nauc_precision_at_100_max |
|
value: 38.26331300116496 |
|
- type: nauc_precision_at_100_std |
|
value: 62.46316222328831 |
|
- type: nauc_precision_at_10_diff1 |
|
value: -2.6766206473658833 |
|
- type: nauc_precision_at_10_max |
|
value: 45.95321867204845 |
|
- type: nauc_precision_at_10_std |
|
value: 45.07212468670564 |
|
- type: nauc_precision_at_1_diff1 |
|
value: 80.0454932568644 |
|
- type: nauc_precision_at_1_max |
|
value: 56.76038421319305 |
|
- type: nauc_precision_at_1_std |
|
value: -4.101939392632653 |
|
- type: nauc_precision_at_20_diff1 |
|
value: -10.698911116738385 |
|
- type: nauc_precision_at_20_max |
|
value: 43.467275950182994 |
|
- type: nauc_precision_at_20_std |
|
value: 48.00467321991766 |
|
- type: nauc_precision_at_3_diff1 |
|
value: 33.6344708541193 |
|
- type: nauc_precision_at_3_max |
|
value: 49.309242331670504 |
|
- type: nauc_precision_at_3_std |
|
value: 21.02940391379915 |
|
- type: nauc_precision_at_5_diff1 |
|
value: 13.560415600596318 |
|
- type: nauc_precision_at_5_max |
|
value: 48.918726500100085 |
|
- type: nauc_precision_at_5_std |
|
value: 39.940930429172184 |
|
- type: nauc_recall_at_1000_diff1 |
|
value: .nan |
|
- type: nauc_recall_at_1000_max |
|
value: .nan |
|
- type: nauc_recall_at_1000_std |
|
value: .nan |
|
- type: nauc_recall_at_100_diff1 |
|
value: 70.82166199813196 |
|
- type: nauc_recall_at_100_max |
|
value: 76.6106442577042 |
|
- type: nauc_recall_at_100_std |
|
value: 66.47992530345513 |
|
- type: nauc_recall_at_10_diff1 |
|
value: 62.68908885556092 |
|
- type: nauc_recall_at_10_max |
|
value: 58.14262437741839 |
|
- type: nauc_recall_at_10_std |
|
value: -12.946717875063369 |
|
- type: nauc_recall_at_1_diff1 |
|
value: 79.54189281784247 |
|
- type: nauc_recall_at_1_max |
|
value: 46.630071622109526 |
|
- type: nauc_recall_at_1_std |
|
value: -14.395943134644112 |
|
- type: nauc_recall_at_20_diff1 |
|
value: 65.79470497876567 |
|
- type: nauc_recall_at_20_max |
|
value: 71.68308183488456 |
|
- type: nauc_recall_at_20_std |
|
value: -12.556850697268453 |
|
- type: nauc_recall_at_3_diff1 |
|
value: 68.3240211318129 |
|
- type: nauc_recall_at_3_max |
|
value: 45.05998217275036 |
|
- type: nauc_recall_at_3_std |
|
value: -14.23179772593869 |
|
- type: nauc_recall_at_5_diff1 |
|
value: 67.53366869904056 |
|
- type: nauc_recall_at_5_max |
|
value: 53.57935627081027 |
|
- type: nauc_recall_at_5_std |
|
value: -3.3271112904853393 |
|
- type: ndcg_at_1 |
|
value: 64.667 |
|
- type: ndcg_at_10 |
|
value: 78.233 |
|
- type: ndcg_at_100 |
|
value: 79.806 |
|
- type: ndcg_at_1000 |
|
value: 79.92099999999999 |
|
- type: ndcg_at_20 |
|
value: 79.006 |
|
- type: ndcg_at_3 |
|
value: 74.018 |
|
- type: ndcg_at_5 |
|
value: 76.334 |
|
- type: precision_at_1 |
|
value: 64.667 |
|
- type: precision_at_10 |
|
value: 10.4 |
|
- type: precision_at_100 |
|
value: 1.1199999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_20 |
|
value: 5.383 |
|
- type: precision_at_3 |
|
value: 29.444 |
|
- type: precision_at_5 |
|
value: 19.467000000000002 |
|
- type: recall_at_1 |
|
value: 61.49400000000001 |
|
- type: recall_at_10 |
|
value: 92.156 |
|
- type: recall_at_100 |
|
value: 99.167 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_20 |
|
value: 94.833 |
|
- type: recall_at_3 |
|
value: 80.833 |
|
- type: recall_at_5 |
|
value: 86.6 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB SprintDuplicateQuestions |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
split: test |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 99.8039603960396 |
|
- type: cosine_accuracy_threshold |
|
value: 84.54211950302124 |
|
- type: cosine_ap |
|
value: 95.59056372734358 |
|
- type: cosine_f1 |
|
value: 90.1394422310757 |
|
- type: cosine_f1_threshold |
|
value: 84.54211950302124 |
|
- type: cosine_precision |
|
value: 89.78174603174604 |
|
- type: cosine_recall |
|
value: 90.5 |
|
- type: dot_accuracy |
|
value: 99.80594059405941 |
|
- type: dot_accuracy_threshold |
|
value: 85.57180166244507 |
|
- type: dot_ap |
|
value: 95.53453431914399 |
|
- type: dot_f1 |
|
value: 90.10442565887618 |
|
- type: dot_f1_threshold |
|
value: 84.59715843200684 |
|
- type: dot_precision |
|
value: 89.61424332344214 |
|
- type: dot_recall |
|
value: 90.60000000000001 |
|
- type: euclidean_accuracy |
|
value: 99.8039603960396 |
|
- type: euclidean_accuracy_threshold |
|
value: 53.253382444381714 |
|
- type: euclidean_ap |
|
value: 95.5850992402159 |
|
- type: euclidean_f1 |
|
value: 90.09457441513192 |
|
- type: euclidean_f1_threshold |
|
value: 55.725520849227905 |
|
- type: euclidean_precision |
|
value: 89.69276511397423 |
|
- type: euclidean_recall |
|
value: 90.5 |
|
- type: main_score |
|
value: 95.7485189884476 |
|
- type: manhattan_accuracy |
|
value: 99.81485148514851 |
|
- type: manhattan_accuracy_threshold |
|
value: 3491.29638671875 |
|
- type: manhattan_ap |
|
value: 95.7485189884476 |
|
- type: manhattan_f1 |
|
value: 90.464048954615 |
|
- type: manhattan_f1_threshold |
|
value: 3491.29638671875 |
|
- type: manhattan_precision |
|
value: 92.2996878251821 |
|
- type: manhattan_recall |
|
value: 88.7 |
|
- type: max_ap |
|
value: 95.7485189884476 |
|
- type: max_f1 |
|
value: 90.464048954615 |
|
- type: max_precision |
|
value: 92.2996878251821 |
|
- type: max_recall |
|
value: 90.60000000000001 |
|
- type: similarity_accuracy |
|
value: 99.8039603960396 |
|
- type: similarity_accuracy_threshold |
|
value: 84.54211950302124 |
|
- type: similarity_ap |
|
value: 95.59056372734358 |
|
- type: similarity_f1 |
|
value: 90.1394422310757 |
|
- type: similarity_f1_threshold |
|
value: 84.54211950302124 |
|
- type: similarity_precision |
|
value: 89.78174603174604 |
|
- type: similarity_recall |
|
value: 90.5 |
|
task: |
|
type: PairClassification |
|
- dataset: |
|
config: default |
|
name: MTEB StackExchangeClustering |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
split: test |
|
type: mteb/stackexchange-clustering |
|
metrics: |
|
- type: main_score |
|
value: 78.49205191950675 |
|
- type: v_measure |
|
value: 78.49205191950675 |
|
- type: v_measure_std |
|
value: 2.84869550699959 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB StackExchangeClusteringP2P |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
split: test |
|
type: mteb/stackexchange-clustering-p2p |
|
metrics: |
|
- type: main_score |
|
value: 48.90421736513028 |
|
- type: v_measure |
|
value: 48.90421736513028 |
|
- type: v_measure_std |
|
value: 1.6875865714471023 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB StackOverflowDupQuestions |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
split: test |
|
type: mteb/stackoverflowdupquestions-reranking |
|
metrics: |
|
- type: main_score |
|
value: 52.9874730481696 |
|
- type: map |
|
value: 52.9874730481696 |
|
- type: mrr |
|
value: 53.85867604617604 |
|
- type: nAUC_map_diff1 |
|
value: 39.633429293407616 |
|
- type: nAUC_map_max |
|
value: 10.236807988858546 |
|
- type: nAUC_map_std |
|
value: 10.276522217929674 |
|
- type: nAUC_mrr_diff1 |
|
value: 40.0543079218377 |
|
- type: nAUC_mrr_max |
|
value: 10.96209807382042 |
|
- type: nAUC_mrr_std |
|
value: 10.524400196109918 |
|
task: |
|
type: Reranking |
|
- dataset: |
|
config: default |
|
name: MTEB SummEval |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
split: test |
|
type: mteb/summeval |
|
metrics: |
|
- type: cosine_pearson |
|
value: 30.727801109114232 |
|
- type: cosine_spearman |
|
value: 31.66058223980157 |
|
- type: dot_pearson |
|
value: 30.78818248622866 |
|
- type: dot_spearman |
|
value: 31.525158776890265 |
|
- type: main_score |
|
value: 31.66058223980157 |
|
- type: pearson |
|
value: 30.727801109114232 |
|
- type: spearman |
|
value: 31.66058223980157 |
|
task: |
|
type: Summarization |
|
- dataset: |
|
config: default |
|
name: MTEB TRECCOVID |
|
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
|
split: test |
|
type: mteb/trec-covid |
|
metrics: |
|
- type: main_score |
|
value: 85.206 |
|
- type: map_at_1 |
|
value: 0.246 |
|
- type: map_at_10 |
|
value: 2.1950000000000003 |
|
- type: map_at_100 |
|
value: 14.179 |
|
- type: map_at_1000 |
|
value: 35.037 |
|
- type: map_at_20 |
|
value: 4.143 |
|
- type: map_at_3 |
|
value: 0.7100000000000001 |
|
- type: map_at_5 |
|
value: 1.135 |
|
- type: mrr_at_1 |
|
value: 94.0 |
|
- type: mrr_at_10 |
|
value: 96.66666666666666 |
|
- type: mrr_at_100 |
|
value: 96.66666666666666 |
|
- type: mrr_at_1000 |
|
value: 96.66666666666666 |
|
- type: mrr_at_20 |
|
value: 96.66666666666666 |
|
- type: mrr_at_3 |
|
value: 96.66666666666666 |
|
- type: mrr_at_5 |
|
value: 96.66666666666666 |
|
- type: nauc_map_at_1000_diff1 |
|
value: -4.6264497624527525 |
|
- type: nauc_map_at_1000_max |
|
value: 44.594457564749355 |
|
- type: nauc_map_at_1000_std |
|
value: 73.17642341400133 |
|
- type: nauc_map_at_100_diff1 |
|
value: 23.451335157405726 |
|
- type: nauc_map_at_100_max |
|
value: 25.426398857299525 |
|
- type: nauc_map_at_100_std |
|
value: 64.07416694472633 |
|
- type: nauc_map_at_10_diff1 |
|
value: 46.57568738568346 |
|
- type: nauc_map_at_10_max |
|
value: 9.693233249079238 |
|
- type: nauc_map_at_10_std |
|
value: 28.549530265164357 |
|
- type: nauc_map_at_1_diff1 |
|
value: 53.48238396620123 |
|
- type: nauc_map_at_1_max |
|
value: 0.33476619393733076 |
|
- type: nauc_map_at_1_std |
|
value: 8.906362219128463 |
|
- type: nauc_map_at_20_diff1 |
|
value: 39.40719602207749 |
|
- type: nauc_map_at_20_max |
|
value: 9.635915072074045 |
|
- type: nauc_map_at_20_std |
|
value: 35.15634791346394 |
|
- type: nauc_map_at_3_diff1 |
|
value: 53.11784737840137 |
|
- type: nauc_map_at_3_max |
|
value: 3.059682761072153 |
|
- type: nauc_map_at_3_std |
|
value: 21.310633086556617 |
|
- type: nauc_map_at_5_diff1 |
|
value: 49.91570701185436 |
|
- type: nauc_map_at_5_max |
|
value: 8.045082896244576 |
|
- type: nauc_map_at_5_std |
|
value: 20.597686235051647 |
|
- type: nauc_mrr_at_1000_diff1 |
|
value: 41.98412698412726 |
|
- type: nauc_mrr_at_1000_max |
|
value: 78.24463118580779 |
|
- type: nauc_mrr_at_1000_std |
|
value: 0.30812324930028195 |
|
- type: nauc_mrr_at_100_diff1 |
|
value: 41.98412698412726 |
|
- type: nauc_mrr_at_100_max |
|
value: 78.24463118580779 |
|
- type: nauc_mrr_at_100_std |
|
value: 0.30812324930028195 |
|
- type: nauc_mrr_at_10_diff1 |
|
value: 41.98412698412726 |
|
- type: nauc_mrr_at_10_max |
|
value: 78.24463118580779 |
|
- type: nauc_mrr_at_10_std |
|
value: 0.30812324930028195 |
|
- type: nauc_mrr_at_1_diff1 |
|
value: 38.62433862433873 |
|
- type: nauc_mrr_at_1_max |
|
value: 80.78120136943666 |
|
- type: nauc_mrr_at_1_std |
|
value: -10.768751945222197 |
|
- type: nauc_mrr_at_20_diff1 |
|
value: 41.98412698412726 |
|
- type: nauc_mrr_at_20_max |
|
value: 78.24463118580779 |
|
- type: nauc_mrr_at_20_std |
|
value: 0.30812324930028195 |
|
- type: nauc_mrr_at_3_diff1 |
|
value: 41.98412698412726 |
|
- type: nauc_mrr_at_3_max |
|
value: 78.24463118580779 |
|
- type: nauc_mrr_at_3_std |
|
value: 0.30812324930028195 |
|
- type: nauc_mrr_at_5_diff1 |
|
value: 41.98412698412726 |
|
- type: nauc_mrr_at_5_max |
|
value: 78.24463118580779 |
|
- type: nauc_mrr_at_5_std |
|
value: 0.30812324930028195 |
|
- type: nauc_ndcg_at_1000_diff1 |
|
value: 0.5174948602880207 |
|
- type: nauc_ndcg_at_1000_max |
|
value: 48.60686602077053 |
|
- type: nauc_ndcg_at_1000_std |
|
value: 75.72456343175277 |
|
- type: nauc_ndcg_at_100_diff1 |
|
value: -20.747252137999254 |
|
- type: nauc_ndcg_at_100_max |
|
value: 49.985132618254994 |
|
- type: nauc_ndcg_at_100_std |
|
value: 61.096383293836574 |
|
- type: nauc_ndcg_at_10_diff1 |
|
value: 6.791377920463332 |
|
- type: nauc_ndcg_at_10_max |
|
value: 57.50019332833286 |
|
- type: nauc_ndcg_at_10_std |
|
value: 49.201028841219426 |
|
- type: nauc_ndcg_at_1_diff1 |
|
value: 54.92683440362145 |
|
- type: nauc_ndcg_at_1_max |
|
value: 83.8667228129276 |
|
- type: nauc_ndcg_at_1_std |
|
value: 1.6738604063586122 |
|
- type: nauc_ndcg_at_20_diff1 |
|
value: -5.1948699196314925 |
|
- type: nauc_ndcg_at_20_max |
|
value: 54.483087684806556 |
|
- type: nauc_ndcg_at_20_std |
|
value: 50.54823818118781 |
|
- type: nauc_ndcg_at_3_diff1 |
|
value: 26.267246500164372 |
|
- type: nauc_ndcg_at_3_max |
|
value: 63.0173212926611 |
|
- type: nauc_ndcg_at_3_std |
|
value: 41.025597406368256 |
|
- type: nauc_ndcg_at_5_diff1 |
|
value: 16.910185454343036 |
|
- type: nauc_ndcg_at_5_max |
|
value: 60.9328683868778 |
|
- type: nauc_ndcg_at_5_std |
|
value: 36.70169905857712 |
|
- type: nauc_precision_at_1000_diff1 |
|
value: -46.374447765983525 |
|
- type: nauc_precision_at_1000_max |
|
value: 35.36052337813863 |
|
- type: nauc_precision_at_1000_std |
|
value: 14.219220668161018 |
|
- type: nauc_precision_at_100_diff1 |
|
value: -29.7838083657744 |
|
- type: nauc_precision_at_100_max |
|
value: 43.93589400385112 |
|
- type: nauc_precision_at_100_std |
|
value: 55.425045718579945 |
|
- type: nauc_precision_at_10_diff1 |
|
value: -12.016613405227687 |
|
- type: nauc_precision_at_10_max |
|
value: 57.79924427743131 |
|
- type: nauc_precision_at_10_std |
|
value: 49.022036703550675 |
|
- type: nauc_precision_at_1_diff1 |
|
value: 38.62433862433873 |
|
- type: nauc_precision_at_1_max |
|
value: 80.78120136943666 |
|
- type: nauc_precision_at_1_std |
|
value: -10.768751945222197 |
|
- type: nauc_precision_at_20_diff1 |
|
value: -23.95633847880195 |
|
- type: nauc_precision_at_20_max |
|
value: 48.34715917258276 |
|
- type: nauc_precision_at_20_std |
|
value: 48.82198285255887 |
|
- type: nauc_precision_at_3_diff1 |
|
value: 6.871296905858807 |
|
- type: nauc_precision_at_3_max |
|
value: 70.54805793285054 |
|
- type: nauc_precision_at_3_std |
|
value: 44.65108624094803 |
|
- type: nauc_precision_at_5_diff1 |
|
value: -9.074932448759695 |
|
- type: nauc_precision_at_5_max |
|
value: 67.41284242437573 |
|
- type: nauc_precision_at_5_std |
|
value: 23.876891983919577 |
|
- type: nauc_recall_at_1000_diff1 |
|
value: 8.142288830293255 |
|
- type: nauc_recall_at_1000_max |
|
value: 38.85182826835104 |
|
- type: nauc_recall_at_1000_std |
|
value: 68.60783819217335 |
|
- type: nauc_recall_at_100_diff1 |
|
value: 34.262914076287466 |
|
- type: nauc_recall_at_100_max |
|
value: 12.87009658528838 |
|
- type: nauc_recall_at_100_std |
|
value: 56.21330603762995 |
|
- type: nauc_recall_at_10_diff1 |
|
value: 49.33830945338758 |
|
- type: nauc_recall_at_10_max |
|
value: 0.3539875530671406 |
|
- type: nauc_recall_at_10_std |
|
value: 26.85864465557644 |
|
- type: nauc_recall_at_1_diff1 |
|
value: 53.48238396620123 |
|
- type: nauc_recall_at_1_max |
|
value: 0.33476619393733076 |
|
- type: nauc_recall_at_1_std |
|
value: 8.906362219128463 |
|
- type: nauc_recall_at_20_diff1 |
|
value: 44.21928181266254 |
|
- type: nauc_recall_at_20_max |
|
value: -0.9198356057088594 |
|
- type: nauc_recall_at_20_std |
|
value: 31.484376992896784 |
|
- type: nauc_recall_at_3_diff1 |
|
value: 53.038093080990876 |
|
- type: nauc_recall_at_3_max |
|
value: -1.4170895916973003 |
|
- type: nauc_recall_at_3_std |
|
value: 21.890202855574497 |
|
- type: nauc_recall_at_5_diff1 |
|
value: 49.39742214825278 |
|
- type: nauc_recall_at_5_max |
|
value: 2.8412267611894517 |
|
- type: nauc_recall_at_5_std |
|
value: 18.01598921859512 |
|
- type: ndcg_at_1 |
|
value: 91.0 |
|
- type: ndcg_at_10 |
|
value: 85.206 |
|
- type: ndcg_at_100 |
|
value: 67.29 |
|
- type: ndcg_at_1000 |
|
value: 60.584 |
|
- type: ndcg_at_20 |
|
value: 82.321 |
|
- type: ndcg_at_3 |
|
value: 88.642 |
|
- type: ndcg_at_5 |
|
value: 87.063 |
|
- type: precision_at_1 |
|
value: 94.0 |
|
- type: precision_at_10 |
|
value: 89.8 |
|
- type: precision_at_100 |
|
value: 69.78 |
|
- type: precision_at_1000 |
|
value: 26.738 |
|
- type: precision_at_20 |
|
value: 87.2 |
|
- type: precision_at_3 |
|
value: 92.0 |
|
- type: precision_at_5 |
|
value: 90.8 |
|
- type: recall_at_1 |
|
value: 0.246 |
|
- type: recall_at_10 |
|
value: 2.344 |
|
- type: recall_at_100 |
|
value: 16.962 |
|
- type: recall_at_1000 |
|
value: 57.325 |
|
- type: recall_at_20 |
|
value: 4.517 |
|
- type: recall_at_3 |
|
value: 0.731 |
|
- type: recall_at_5 |
|
value: 1.1780000000000002 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB Touche2020 |
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
|
split: test |
|
type: mteb/touche2020 |
|
metrics: |
|
- type: main_score |
|
value: 31.455 |
|
- type: map_at_1 |
|
value: 2.9739999999999998 |
|
- type: map_at_10 |
|
value: 12.183 |
|
- type: map_at_100 |
|
value: 18.772 |
|
- type: map_at_1000 |
|
value: 20.415 |
|
- type: map_at_20 |
|
value: 14.451 |
|
- type: map_at_3 |
|
value: 6.507000000000001 |
|
- type: map_at_5 |
|
value: 8.66 |
|
- type: mrr_at_1 |
|
value: 40.816326530612244 |
|
- type: mrr_at_10 |
|
value: 57.70975056689341 |
|
- type: mrr_at_100 |
|
value: 58.18379126542391 |
|
- type: mrr_at_1000 |
|
value: 58.18379126542391 |
|
- type: mrr_at_20 |
|
value: 57.85552316164561 |
|
- type: mrr_at_3 |
|
value: 54.08163265306123 |
|
- type: mrr_at_5 |
|
value: 56.42857142857143 |
|
- type: nauc_map_at_1000_diff1 |
|
value: 3.1567471051481437 |
|
- type: nauc_map_at_1000_max |
|
value: -1.5882060729791523 |
|
- type: nauc_map_at_1000_std |
|
value: 18.69622198722074 |
|
- type: nauc_map_at_100_diff1 |
|
value: 3.3449677678147536 |
|
- type: nauc_map_at_100_max |
|
value: -2.8928606866168405 |
|
- type: nauc_map_at_100_std |
|
value: 15.789984947653412 |
|
- type: nauc_map_at_10_diff1 |
|
value: 2.9696743570444264 |
|
- type: nauc_map_at_10_max |
|
value: -9.096749212011876 |
|
- type: nauc_map_at_10_std |
|
value: -5.38545817258353 |
|
- type: nauc_map_at_1_diff1 |
|
value: 20.680780404542546 |
|
- type: nauc_map_at_1_max |
|
value: -7.04722927447817 |
|
- type: nauc_map_at_1_std |
|
value: -7.062494733973898 |
|
- type: nauc_map_at_20_diff1 |
|
value: 4.070437790119271 |
|
- type: nauc_map_at_20_max |
|
value: -4.84491434686032 |
|
- type: nauc_map_at_20_std |
|
value: 0.5846341109021014 |
|
- type: nauc_map_at_3_diff1 |
|
value: 11.9634978045925 |
|
- type: nauc_map_at_3_max |
|
value: -8.27834591046608 |
|
- type: nauc_map_at_3_std |
|
value: -8.687615453381065 |
|
- type: nauc_map_at_5_diff1 |
|
value: 0.9195191526009436 |
|
- type: nauc_map_at_5_max |
|
value: -1.673813362719489 |
|
- type: nauc_map_at_5_std |
|
value: -6.67549753473631 |
|
- type: nauc_mrr_at_1000_diff1 |
|
value: 19.877993208719573 |
|
- type: nauc_mrr_at_1000_max |
|
value: -10.37776706406218 |
|
- type: nauc_mrr_at_1000_std |
|
value: 7.132169578056367 |
|
- type: nauc_mrr_at_100_diff1 |
|
value: 19.877993208719573 |
|
- type: nauc_mrr_at_100_max |
|
value: -10.37776706406218 |
|
- type: nauc_mrr_at_100_std |
|
value: 7.132169578056367 |
|
- type: nauc_mrr_at_10_diff1 |
|
value: 20.414285568401457 |
|
- type: nauc_mrr_at_10_max |
|
value: -9.677800295687861 |
|
- type: nauc_mrr_at_10_std |
|
value: 8.001103690180859 |
|
- type: nauc_mrr_at_1_diff1 |
|
value: 22.393284073955723 |
|
- type: nauc_mrr_at_1_max |
|
value: -5.889370191243167 |
|
- type: nauc_mrr_at_1_std |
|
value: -1.5183536173658247 |
|
- type: nauc_mrr_at_20_diff1 |
|
value: 20.455564720604055 |
|
- type: nauc_mrr_at_20_max |
|
value: -10.230642830103074 |
|
- type: nauc_mrr_at_20_std |
|
value: 7.863582453266621 |
|
- type: nauc_mrr_at_3_diff1 |
|
value: 17.554895390732618 |
|
- type: nauc_mrr_at_3_max |
|
value: -15.618463505555052 |
|
- type: nauc_mrr_at_3_std |
|
value: 5.913231577966864 |
|
- type: nauc_mrr_at_5_diff1 |
|
value: 18.393678507779914 |
|
- type: nauc_mrr_at_5_max |
|
value: -11.903593353147762 |
|
- type: nauc_mrr_at_5_std |
|
value: 7.580745996262831 |
|
- type: nauc_ndcg_at_1000_diff1 |
|
value: 13.746937095530473 |
|
- type: nauc_ndcg_at_1000_max |
|
value: -0.9319249687895838 |
|
- type: nauc_ndcg_at_1000_std |
|
value: 38.56328031451904 |
|
- type: nauc_ndcg_at_100_diff1 |
|
value: 13.854865944415895 |
|
- type: nauc_ndcg_at_100_max |
|
value: -7.142142012591404 |
|
- type: nauc_ndcg_at_100_std |
|
value: 35.61341954818848 |
|
- type: nauc_ndcg_at_10_diff1 |
|
value: 9.010144273248759 |
|
- type: nauc_ndcg_at_10_max |
|
value: -15.320014897424574 |
|
- type: nauc_ndcg_at_10_std |
|
value: 2.84883880489144 |
|
- type: nauc_ndcg_at_1_diff1 |
|
value: 20.939533945592967 |
|
- type: nauc_ndcg_at_1_max |
|
value: -6.387319972188946 |
|
- type: nauc_ndcg_at_1_std |
|
value: -0.5258673122126726 |
|
- type: nauc_ndcg_at_20_diff1 |
|
value: 14.660827309009496 |
|
- type: nauc_ndcg_at_20_max |
|
value: -13.476196120145994 |
|
- type: nauc_ndcg_at_20_std |
|
value: 8.22391881710838 |
|
- type: nauc_ndcg_at_3_diff1 |
|
value: 13.429985227235935 |
|
- type: nauc_ndcg_at_3_max |
|
value: -14.904544592570247 |
|
- type: nauc_ndcg_at_3_std |
|
value: 1.599779998183342 |
|
- type: nauc_ndcg_at_5_diff1 |
|
value: 8.085466231900622 |
|
- type: nauc_ndcg_at_5_max |
|
value: -9.09591969526831 |
|
- type: nauc_ndcg_at_5_std |
|
value: 3.5794092637248505 |
|
- type: nauc_precision_at_1000_diff1 |
|
value: -9.31941215946743 |
|
- type: nauc_precision_at_1000_max |
|
value: 31.52913520470716 |
|
- type: nauc_precision_at_1000_std |
|
value: 22.720784312185856 |
|
- type: nauc_precision_at_100_diff1 |
|
value: 8.958548406995279 |
|
- type: nauc_precision_at_100_max |
|
value: 15.100597910674104 |
|
- type: nauc_precision_at_100_std |
|
value: 71.04548238175113 |
|
- type: nauc_precision_at_10_diff1 |
|
value: 12.4698194690008 |
|
- type: nauc_precision_at_10_max |
|
value: -15.84870544871496 |
|
- type: nauc_precision_at_10_std |
|
value: 7.575297622501928 |
|
- type: nauc_precision_at_1_diff1 |
|
value: 22.393284073955723 |
|
- type: nauc_precision_at_1_max |
|
value: -5.889370191243167 |
|
- type: nauc_precision_at_1_std |
|
value: -1.5183536173658247 |
|
- type: nauc_precision_at_20_diff1 |
|
value: 15.393505718138758 |
|
- type: nauc_precision_at_20_max |
|
value: -3.70684298539384 |
|
- type: nauc_precision_at_20_std |
|
value: 29.426137824970304 |
|
- type: nauc_precision_at_3_diff1 |
|
value: 9.997768085465394 |
|
- type: nauc_precision_at_3_max |
|
value: -17.12224314347674 |
|
- type: nauc_precision_at_3_std |
|
value: -1.343018166772313 |
|
- type: nauc_precision_at_5_diff1 |
|
value: 3.8936997437913554 |
|
- type: nauc_precision_at_5_max |
|
value: -5.689104289687632 |
|
- type: nauc_precision_at_5_std |
|
value: 3.181098051304285 |
|
- type: nauc_recall_at_1000_diff1 |
|
value: 9.908303508158387 |
|
- type: nauc_recall_at_1000_max |
|
value: 6.174506592699848 |
|
- type: nauc_recall_at_1000_std |
|
value: 77.41931114780012 |
|
- type: nauc_recall_at_100_diff1 |
|
value: 10.286839241876192 |
|
- type: nauc_recall_at_100_max |
|
value: -6.6138697026666815 |
|
- type: nauc_recall_at_100_std |
|
value: 49.608313692633224 |
|
- type: nauc_recall_at_10_diff1 |
|
value: 2.215545846659851 |
|
- type: nauc_recall_at_10_max |
|
value: -17.83025802478445 |
|
- type: nauc_recall_at_10_std |
|
value: -3.3784768673705465 |
|
- type: nauc_recall_at_1_diff1 |
|
value: 20.680780404542546 |
|
- type: nauc_recall_at_1_max |
|
value: -7.04722927447817 |
|
- type: nauc_recall_at_1_std |
|
value: -7.062494733973898 |
|
- type: nauc_recall_at_20_diff1 |
|
value: 6.974410239251615 |
|
- type: nauc_recall_at_20_max |
|
value: -14.161147924731646 |
|
- type: nauc_recall_at_20_std |
|
value: 9.328412057721454 |
|
- type: nauc_recall_at_3_diff1 |
|
value: 7.904589805754212 |
|
- type: nauc_recall_at_3_max |
|
value: -12.1912388648593 |
|
- type: nauc_recall_at_3_std |
|
value: -9.221542013385555 |
|
- type: nauc_recall_at_5_diff1 |
|
value: -3.2604132752706914 |
|
- type: nauc_recall_at_5_max |
|
value: -6.886351441658915 |
|
- type: nauc_recall_at_5_std |
|
value: -7.014252851712789 |
|
- type: ndcg_at_1 |
|
value: 39.796 |
|
- type: ndcg_at_10 |
|
value: 31.455 |
|
- type: ndcg_at_100 |
|
value: 42.388999999999996 |
|
- type: ndcg_at_1000 |
|
value: 53.556000000000004 |
|
- type: ndcg_at_20 |
|
value: 30.808000000000003 |
|
- type: ndcg_at_3 |
|
value: 35.831 |
|
- type: ndcg_at_5 |
|
value: 32.845 |
|
- type: precision_at_1 |
|
value: 40.816 |
|
- type: precision_at_10 |
|
value: 27.143 |
|
- type: precision_at_100 |
|
value: 8.449 |
|
- type: precision_at_1000 |
|
value: 1.6179999999999999 |
|
- type: precision_at_20 |
|
value: 19.387999999999998 |
|
- type: precision_at_3 |
|
value: 35.374 |
|
- type: precision_at_5 |
|
value: 31.019999999999996 |
|
- type: recall_at_1 |
|
value: 2.9739999999999998 |
|
- type: recall_at_10 |
|
value: 19.39 |
|
- type: recall_at_100 |
|
value: 51.636 |
|
- type: recall_at_1000 |
|
value: 86.99900000000001 |
|
- type: recall_at_20 |
|
value: 26.478 |
|
- type: recall_at_3 |
|
value: 7.703 |
|
- type: recall_at_5 |
|
value: 11.42 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB ToxicConversationsClassification |
|
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
|
split: test |
|
type: mteb/toxic_conversations_50k |
|
metrics: |
|
- type: accuracy |
|
value: 86.9384765625 |
|
- type: ap |
|
value: 31.737513704141552 |
|
- type: ap_weighted |
|
value: 31.737513704141552 |
|
- type: f1 |
|
value: 71.5490757306975 |
|
- type: f1_weighted |
|
value: 89.14632533489856 |
|
- type: main_score |
|
value: 86.9384765625 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB TweetSentimentExtractionClassification |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
split: test |
|
type: mteb/tweet_sentiment_extraction |
|
metrics: |
|
- type: accuracy |
|
value: 73.57668364459535 |
|
- type: f1 |
|
value: 73.90467103648074 |
|
- type: f1_weighted |
|
value: 73.42158415034704 |
|
- type: main_score |
|
value: 73.57668364459535 |
|
task: |
|
type: Classification |
|
- dataset: |
|
config: default |
|
name: MTEB TwentyNewsgroupsClustering |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
split: test |
|
type: mteb/twentynewsgroups-clustering |
|
metrics: |
|
- type: main_score |
|
value: 58.574148097494685 |
|
- type: v_measure |
|
value: 58.574148097494685 |
|
- type: v_measure_std |
|
value: 0.9443161637490822 |
|
task: |
|
type: Clustering |
|
- dataset: |
|
config: default |
|
name: MTEB TwitterSemEval2015 |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
split: test |
|
type: mteb/twittersemeval2015-pairclassification |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 88.1385229778864 |
|
- type: cosine_accuracy_threshold |
|
value: 83.86307954788208 |
|
- type: cosine_ap |
|
value: 80.17965893449055 |
|
- type: cosine_f1 |
|
value: 73.0614300100705 |
|
- type: cosine_f1_threshold |
|
value: 80.7942807674408 |
|
- type: cosine_precision |
|
value: 69.8603755416466 |
|
- type: cosine_recall |
|
value: 76.56992084432717 |
|
- type: dot_accuracy |
|
value: 88.2100494724921 |
|
- type: dot_accuracy_threshold |
|
value: 83.84793996810913 |
|
- type: dot_ap |
|
value: 80.18603932881858 |
|
- type: dot_f1 |
|
value: 73.07643714466204 |
|
- type: dot_f1_threshold |
|
value: 80.87586164474487 |
|
- type: dot_precision |
|
value: 70.10909090909091 |
|
- type: dot_recall |
|
value: 76.3060686015831 |
|
- type: euclidean_accuracy |
|
value: 88.1385229778864 |
|
- type: euclidean_accuracy_threshold |
|
value: 56.77661895751953 |
|
- type: euclidean_ap |
|
value: 80.1784070881624 |
|
- type: euclidean_f1 |
|
value: 73.04830369529574 |
|
- type: euclidean_f1_threshold |
|
value: 61.91838979721069 |
|
- type: euclidean_precision |
|
value: 69.96859144720948 |
|
- type: euclidean_recall |
|
value: 76.41160949868075 |
|
- type: main_score |
|
value: 80.18603932881858 |
|
- type: manhattan_accuracy |
|
value: 88.0431543184121 |
|
- type: manhattan_accuracy_threshold |
|
value: 3755.6137084960938 |
|
- type: manhattan_ap |
|
value: 79.98270453664578 |
|
- type: manhattan_f1 |
|
value: 72.68242015061023 |
|
- type: manhattan_f1_threshold |
|
value: 3892.494583129883 |
|
- type: manhattan_precision |
|
value: 71.54907975460122 |
|
- type: manhattan_recall |
|
value: 73.85224274406332 |
|
- type: max_ap |
|
value: 80.18603932881858 |
|
- type: max_f1 |
|
value: 73.07643714466204 |
|
- type: max_precision |
|
value: 71.54907975460122 |
|
- type: max_recall |
|
value: 76.56992084432717 |
|
- type: similarity_accuracy |
|
value: 88.1385229778864 |
|
- type: similarity_accuracy_threshold |
|
value: 83.86307954788208 |
|
- type: similarity_ap |
|
value: 80.17965893449055 |
|
- type: similarity_f1 |
|
value: 73.0614300100705 |
|
- type: similarity_f1_threshold |
|
value: 80.7942807674408 |
|
- type: similarity_precision |
|
value: 69.8603755416466 |
|
- type: similarity_recall |
|
value: 76.56992084432717 |
|
task: |
|
type: PairClassification |
|
- dataset: |
|
config: default |
|
name: MTEB TwitterURLCorpus |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
split: test |
|
type: mteb/twitterurlcorpus-pairclassification |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 89.7892653393876 |
|
- type: cosine_accuracy_threshold |
|
value: 79.69566583633423 |
|
- type: cosine_ap |
|
value: 87.4579867302024 |
|
- type: cosine_f1 |
|
value: 79.91620843152658 |
|
- type: cosine_f1_threshold |
|
value: 78.53609323501587 |
|
- type: cosine_precision |
|
value: 77.7155329210622 |
|
- type: cosine_recall |
|
value: 82.24514936864799 |
|
- type: dot_accuracy |
|
value: 89.78732487289945 |
|
- type: dot_accuracy_threshold |
|
value: 80.05315661430359 |
|
- type: dot_ap |
|
value: 87.44916182456272 |
|
- type: dot_f1 |
|
value: 79.90419878751591 |
|
- type: dot_f1_threshold |
|
value: 78.57890725135803 |
|
- type: dot_precision |
|
value: 77.73409057812728 |
|
- type: dot_recall |
|
value: 82.19895287958116 |
|
- type: euclidean_accuracy |
|
value: 89.78538440641131 |
|
- type: euclidean_accuracy_threshold |
|
value: 62.29925751686096 |
|
- type: euclidean_ap |
|
value: 87.45904868911386 |
|
- type: euclidean_f1 |
|
value: 79.93127404474657 |
|
- type: euclidean_f1_threshold |
|
value: 65.61101078987122 |
|
- type: euclidean_precision |
|
value: 77.62060210373595 |
|
- type: euclidean_recall |
|
value: 82.38373883584848 |
|
- type: main_score |
|
value: 87.46554314325058 |
|
- type: manhattan_accuracy |
|
value: 89.76597974152986 |
|
- type: manhattan_accuracy_threshold |
|
value: 3988.5299682617188 |
|
- type: manhattan_ap |
|
value: 87.46554314325058 |
|
- type: manhattan_f1 |
|
value: 79.97181740645973 |
|
- type: manhattan_f1_threshold |
|
value: 4235.905838012695 |
|
- type: manhattan_precision |
|
value: 77.13713427283783 |
|
- type: manhattan_recall |
|
value: 83.02279026793964 |
|
- type: max_ap |
|
value: 87.46554314325058 |
|
- type: max_f1 |
|
value: 79.97181740645973 |
|
- type: max_precision |
|
value: 77.73409057812728 |
|
- type: max_recall |
|
value: 83.02279026793964 |
|
- type: similarity_accuracy |
|
value: 89.7892653393876 |
|
- type: similarity_accuracy_threshold |
|
value: 79.69566583633423 |
|
- type: similarity_ap |
|
value: 87.4579867302024 |
|
- type: similarity_f1 |
|
value: 79.91620843152658 |
|
- type: similarity_f1_threshold |
|
value: 78.53609323501587 |
|
- type: similarity_precision |
|
value: 77.7155329210622 |
|
- type: similarity_recall |
|
value: 82.24514936864799 |
|
task: |
|
type: PairClassification |
|
tags: |
|
- mteb |
|
- sentence-transformers |
|
- transformers |
|
- sentence-similarity |
|
license: mit |
|
--- |
|
|
|
|
|
|
|
# Updates |
|
|
|
New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md. |
|
|
|
You can also find these models on my [homepage](https://huggingface.co/infgrad). |
|
|
|
# Introduction |
|
|
|
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for |
|
their contributions! |
|
|
|
**We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** |
|
|
|
Prompt of s2p task(e.g. retrieve task): |
|
|
|
```text |
|
Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query} |
|
``` |
|
|
|
Prompt of s2s task(e.g. semantic textual similarity task): |
|
|
|
```text |
|
Instruct: Retrieve semantically similar text.\nQuery: {query} |
|
``` |
|
|
|
The models are finally trained by [MRL](https://arxiv.org/abs/2205.13147), so they have multiple dimensions: 512, 768, |
|
1024, 2048, 4096, 6144 and 8192. |
|
|
|
The higher the dimension, the better the performance. |
|
**Generally speaking, 1024d is good enough.** The MTEB score of 1024d is only 0.001 lower than 8192d. |
|
|
|
# Model directory structure |
|
|
|
The model directory structure is very simple, it is a standard SentenceTransformer directory **with a series |
|
of `2_Dense_{dims}` |
|
folders**, where `dims` represents the final vector dimension. |
|
|
|
For example, the `2_Dense_256` folder stores Linear weights that convert vector dimensions to 256 dimensions. |
|
Please refer to the following chapters for specific instructions on how to use them. |
|
|
|
# Usage |
|
|
|
You can use `SentenceTransformers` or `transformers` library to encode text. |
|
|
|
## Sentence Transformers |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively. |
|
# They are defined in `config_sentence_transformers.json` |
|
query_prompt_name = "s2p_query" |
|
queries = [ |
|
"What are some ways to reduce stress?", |
|
"What are the benefits of drinking green tea?", |
|
] |
|
# docs do not need any prompts |
|
docs = [ |
|
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.", |
|
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.", |
|
] |
|
|
|
# ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` ! |
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# on gpu |
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model = SentenceTransformer("dunzhang/stella_en_400M_v5", trust_remote_code=True).cuda() |
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# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU. |
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# model = SentenceTransformer( |
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# "dunzhang/stella_en_400M_v5", |
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# trust_remote_code=True, |
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# device="cpu", |
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# config_kwargs={"use_memory_efficient_attention": False, "unpad_inputs": False} |
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# ) |
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query_embeddings = model.encode(queries, prompt_name=query_prompt_name) |
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doc_embeddings = model.encode(docs) |
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print(query_embeddings.shape, doc_embeddings.shape) |
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# (2, 1024) (2, 1024) |
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|
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similarities = model.similarity(query_embeddings, doc_embeddings) |
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print(similarities) |
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# tensor([[0.8398, 0.2990], |
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# [0.3282, 0.8095]]) |
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``` |
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|
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## Transformers |
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|
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```python |
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import os |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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from sklearn.preprocessing import normalize |
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|
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query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: " |
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queries = [ |
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"What are some ways to reduce stress?", |
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"What are the benefits of drinking green tea?", |
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] |
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queries = [query_prompt + query for query in queries] |
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# docs do not need any prompts |
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docs = [ |
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"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.", |
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"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.", |
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] |
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|
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# The path of your model after cloning it |
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model_dir = "{Your MODEL_PATH}" |
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|
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vector_dim = 1024 |
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vector_linear_directory = f"2_Dense_{vector_dim}" |
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model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval() |
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# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU. |
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# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True,use_memory_efficient_attention=False,unpad_inputs=False).cuda().eval() |
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) |
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vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim) |
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vector_linear_dict = { |
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k.replace("linear.", ""): v for k, v in |
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torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items() |
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} |
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vector_linear.load_state_dict(vector_linear_dict) |
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vector_linear.cuda() |
|
|
|
# Embed the queries |
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with torch.no_grad(): |
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input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt") |
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input_data = {k: v.cuda() for k, v in input_data.items()} |
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attention_mask = input_data["attention_mask"] |
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last_hidden_state = model(**input_data)[0] |
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last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
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query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
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query_vectors = normalize(vector_linear(query_vectors).cpu().numpy()) |
|
|
|
# Embed the documents |
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with torch.no_grad(): |
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input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt") |
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input_data = {k: v.cuda() for k, v in input_data.items()} |
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attention_mask = input_data["attention_mask"] |
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last_hidden_state = model(**input_data)[0] |
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last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
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docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
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docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy()) |
|
|
|
print(query_vectors.shape, docs_vectors.shape) |
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# (2, 1024) (2, 1024) |
|
|
|
similarities = query_vectors @ docs_vectors.T |
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print(similarities) |
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# [[0.8397531 0.29900077] |
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# [0.32818374 0.80954516]] |
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``` |
|
|
|
### infinity_emb |
|
|
|
Usage via [infinity, MIT Licensed](https://github.com/michaelfeil/infinity). |
|
|
|
```bash |
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docker run \ |
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--gpus all -p "7997":"7997" \ |
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michaelf34/infinity:0.0.69 \ |
|
v2 --model-id dunzhang/stella_en_400M_v5 --revision "refs/pr/24" --dtype bfloat16 --batch-size 16 --device cuda --engine torch --port 7997 --no-bettertransformer |
|
``` |
|
|
|
# FAQ |
|
|
|
Q: The details of training? |
|
|
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A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper) |
|
|
|
Q: How to choose a suitable prompt for my own task? |
|
|
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A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training |
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data. |
|
|
|
Q: How to reproduce MTEB results? |
|
|
|
A: Please use evaluation scripts in `Alibaba-NLP/gte-Qwen2-1.5B-instruct` or `intfloat/e5-mistral-7b-instruct` |
|
|
|
Q: Why each dimension has a linear weight? |
|
|
|
A: MRL has multiple training methods, we choose this method which has the best performance. |
|
|
|
Q: What is the sequence length of models? |
|
|
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A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the |
|
model is trained on datasets of 512 length. This may be an optimization term. |
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|
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If you have any questions, please start a discussion on community. |
