--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu tags: - mteb model-index: - name: udever-bloom-1b1 results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 27.90020553155914 - type: cos_sim_spearman value: 27.980812877007445 - type: euclidean_pearson value: 27.412021502878105 - type: euclidean_spearman value: 27.608320539898134 - type: manhattan_pearson value: 27.493591460276278 - type: manhattan_spearman value: 27.715134644174423 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 35.15277604796132 - type: cos_sim_spearman value: 35.863846005221575 - type: euclidean_pearson value: 37.65681598655078 - type: euclidean_spearman value: 35.50116107334066 - type: manhattan_pearson value: 37.736463166370854 - type: manhattan_spearman value: 35.53412987209704 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 69.9402985074627 - type: ap value: 33.4661141650045 - type: f1 value: 64.31759903129324 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.02783725910065 - type: ap value: 78.25152113775748 - type: f1 value: 64.00236113368896 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.01649175412295 - type: ap value: 21.28416661100625 - type: f1 value: 59.481902269256096 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 58.76873661670234 - type: ap value: 12.828869547428084 - type: f1 value: 47.5200475889544 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 87.191175 - type: ap value: 82.4408783026622 - type: f1 value: 87.16605834054603 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.082 - type: f1 value: 40.54924237159631 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 30.447999999999997 - type: f1 value: 30.0643283775686 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.800000000000004 - type: f1 value: 39.64954112879312 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.686 - type: f1 value: 39.917643425172 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 32.074 - type: f1 value: 31.878305643409334 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.122 - type: f1 value: 37.296210966123446 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 22.262 - type: map_at_10 value: 37.667 - type: map_at_100 value: 38.812999999999995 - type: map_at_1000 value: 38.829 - type: map_at_3 value: 32.421 - type: map_at_5 value: 35.202 - type: mrr_at_1 value: 22.759999999999998 - type: mrr_at_10 value: 37.817 - type: mrr_at_100 value: 38.983000000000004 - type: mrr_at_1000 value: 38.999 - type: mrr_at_3 value: 32.61 - type: mrr_at_5 value: 35.333999999999996 - type: ndcg_at_1 value: 22.262 - type: ndcg_at_10 value: 46.671 - type: ndcg_at_100 value: 51.519999999999996 - type: ndcg_at_1000 value: 51.876999999999995 - type: ndcg_at_3 value: 35.696 - type: ndcg_at_5 value: 40.722 - type: precision_at_1 value: 22.262 - type: precision_at_10 value: 7.575 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.055 - type: precision_at_5 value: 11.479000000000001 - type: recall_at_1 value: 22.262 - type: recall_at_10 value: 75.747 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 45.164 - type: recall_at_5 value: 57.397 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.51799756336072 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 34.44923356952161 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.49540399419566 - type: mrr value: 73.43028624192061 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.67018580352695 - type: cos_sim_spearman value: 84.64530219460785 - type: euclidean_pearson value: 87.10187265189109 - type: euclidean_spearman value: 86.19051812629264 - type: manhattan_pearson value: 86.78890467534343 - type: manhattan_spearman value: 85.60134807514734 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 46.308790362891266 - type: cos_sim_spearman value: 46.22674926863126 - type: euclidean_pearson value: 47.36625172551589 - type: euclidean_spearman value: 47.55854392572494 - type: manhattan_pearson value: 47.3342490976193 - type: manhattan_spearman value: 47.52249648456463 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 42.67223382045929 - type: f1 value: 42.02704262244064 - type: precision value: 41.76166726545405 - type: recall value: 42.67223382045929 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.95289456306405 - type: f1 value: 97.70709516472228 - type: precision value: 97.58602978941964 - type: recall value: 97.95289456306405 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 25.375822653273296 - type: f1 value: 24.105776263207947 - type: precision value: 23.644628498465117 - type: recall value: 25.375822653273296 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.31490258030541 - type: f1 value: 98.24469018781815 - type: precision value: 98.2095839915745 - type: recall value: 98.31490258030541 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.89285714285714 - type: f1 value: 82.84943089389121 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.25261508107809 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 30.708512338509653 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 35.361295166692464 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 37.06879287045825 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 66.06033605600476 - type: mrr value: 70.82825396825396 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 66.9600733219955 - type: mrr value: 72.19742063492063 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.526999999999997 - type: map_at_10 value: 38.747 - type: map_at_100 value: 40.172999999999995 - type: map_at_1000 value: 40.311 - type: map_at_3 value: 35.969 - type: map_at_5 value: 37.344 - type: mrr_at_1 value: 36.767 - type: mrr_at_10 value: 45.082 - type: mrr_at_100 value: 45.898 - type: mrr_at_1000 value: 45.958 - type: mrr_at_3 value: 43.085 - type: mrr_at_5 value: 44.044 - type: ndcg_at_1 value: 36.767 - type: ndcg_at_10 value: 44.372 - type: ndcg_at_100 value: 49.908 - type: ndcg_at_1000 value: 52.358000000000004 - type: ndcg_at_3 value: 40.711000000000006 - type: ndcg_at_5 value: 41.914 - type: precision_at_1 value: 36.767 - type: precision_at_10 value: 8.283 - type: precision_at_100 value: 1.3679999999999999 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 19.599 - type: precision_at_5 value: 13.505 - type: recall_at_1 value: 29.526999999999997 - type: recall_at_10 value: 54.198 - type: recall_at_100 value: 77.818 - type: recall_at_1000 value: 93.703 - type: recall_at_3 value: 42.122 - type: recall_at_5 value: 46.503 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.646 - type: map_at_10 value: 30.447999999999997 - type: map_at_100 value: 31.417 - type: map_at_1000 value: 31.528 - type: map_at_3 value: 28.168 - type: map_at_5 value: 29.346 - type: mrr_at_1 value: 28.854000000000003 - type: mrr_at_10 value: 35.611 - type: mrr_at_100 value: 36.321 - type: mrr_at_1000 value: 36.378 - type: mrr_at_3 value: 33.726 - type: mrr_at_5 value: 34.745 - type: ndcg_at_1 value: 28.854000000000003 - type: ndcg_at_10 value: 35.052 - type: ndcg_at_100 value: 39.190999999999995 - type: ndcg_at_1000 value: 41.655 - type: ndcg_at_3 value: 31.684 - type: ndcg_at_5 value: 32.998 - type: precision_at_1 value: 28.854000000000003 - type: precision_at_10 value: 6.49 - type: precision_at_100 value: 1.057 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 15.244 - type: precision_at_5 value: 10.599 - type: recall_at_1 value: 22.646 - type: recall_at_10 value: 43.482 - type: recall_at_100 value: 61.324 - type: recall_at_1000 value: 77.866 - type: recall_at_3 value: 33.106 - type: recall_at_5 value: 37.124 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.061 - type: map_at_10 value: 46.216 - type: map_at_100 value: 47.318 - type: map_at_1000 value: 47.384 - type: map_at_3 value: 43.008 - type: map_at_5 value: 44.79 - type: mrr_at_1 value: 40.251 - type: mrr_at_10 value: 49.677 - type: mrr_at_100 value: 50.39 - type: mrr_at_1000 value: 50.429 - type: mrr_at_3 value: 46.792 - type: mrr_at_5 value: 48.449999999999996 - type: ndcg_at_1 value: 40.251 - type: ndcg_at_10 value: 51.99399999999999 - type: ndcg_at_100 value: 56.418 - type: ndcg_at_1000 value: 57.798 - type: ndcg_at_3 value: 46.192 - type: ndcg_at_5 value: 48.998000000000005 - type: precision_at_1 value: 40.251 - type: precision_at_10 value: 8.469999999999999 - type: precision_at_100 value: 1.159 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 20.46 - type: precision_at_5 value: 14.332 - type: recall_at_1 value: 35.061 - type: recall_at_10 value: 65.818 - type: recall_at_100 value: 84.935 - type: recall_at_1000 value: 94.69300000000001 - type: recall_at_3 value: 50.300999999999995 - type: recall_at_5 value: 57.052 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.776 - type: map_at_10 value: 27.945999999999998 - type: map_at_100 value: 28.976000000000003 - type: map_at_1000 value: 29.073999999999998 - type: map_at_3 value: 25.673000000000002 - type: map_at_5 value: 26.96 - type: mrr_at_1 value: 22.486 - type: mrr_at_10 value: 29.756 - type: mrr_at_100 value: 30.735 - type: mrr_at_1000 value: 30.81 - type: mrr_at_3 value: 27.571 - type: mrr_at_5 value: 28.808 - type: ndcg_at_1 value: 22.486 - type: ndcg_at_10 value: 32.190000000000005 - type: ndcg_at_100 value: 37.61 - type: ndcg_at_1000 value: 40.116 - type: ndcg_at_3 value: 27.688000000000002 - type: ndcg_at_5 value: 29.87 - type: precision_at_1 value: 22.486 - type: precision_at_10 value: 5.028 - type: precision_at_100 value: 0.818 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 11.827 - type: precision_at_5 value: 8.362 - type: recall_at_1 value: 20.776 - type: recall_at_10 value: 43.588 - type: recall_at_100 value: 69.139 - type: recall_at_1000 value: 88.144 - type: recall_at_3 value: 31.411 - type: recall_at_5 value: 36.655 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 12.994 - type: map_at_10 value: 19.747999999999998 - type: map_at_100 value: 20.877000000000002 - type: map_at_1000 value: 21.021 - type: map_at_3 value: 17.473 - type: map_at_5 value: 18.683 - type: mrr_at_1 value: 16.542 - type: mrr_at_10 value: 23.830000000000002 - type: mrr_at_100 value: 24.789 - type: mrr_at_1000 value: 24.877 - type: mrr_at_3 value: 21.476 - type: mrr_at_5 value: 22.838 - type: ndcg_at_1 value: 16.542 - type: ndcg_at_10 value: 24.422 - type: ndcg_at_100 value: 30.011 - type: ndcg_at_1000 value: 33.436 - type: ndcg_at_3 value: 20.061999999999998 - type: ndcg_at_5 value: 22.009999999999998 - type: precision_at_1 value: 16.542 - type: precision_at_10 value: 4.664 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 9.826 - type: precision_at_5 value: 7.2139999999999995 - type: recall_at_1 value: 12.994 - type: recall_at_10 value: 34.917 - type: recall_at_100 value: 59.455000000000005 - type: recall_at_1000 value: 83.87299999999999 - type: recall_at_3 value: 22.807 - type: recall_at_5 value: 27.773999999999997 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.85 - type: map_at_10 value: 35.285 - type: map_at_100 value: 36.592999999999996 - type: map_at_1000 value: 36.720000000000006 - type: map_at_3 value: 32.183 - type: map_at_5 value: 33.852 - type: mrr_at_1 value: 30.703000000000003 - type: mrr_at_10 value: 40.699000000000005 - type: mrr_at_100 value: 41.598 - type: mrr_at_1000 value: 41.654 - type: mrr_at_3 value: 38.080999999999996 - type: mrr_at_5 value: 39.655 - type: ndcg_at_1 value: 30.703000000000003 - type: ndcg_at_10 value: 41.422 - type: ndcg_at_100 value: 46.998 - type: ndcg_at_1000 value: 49.395 - type: ndcg_at_3 value: 36.353 - type: ndcg_at_5 value: 38.7 - type: precision_at_1 value: 30.703000000000003 - type: precision_at_10 value: 7.757 - type: precision_at_100 value: 1.2349999999999999 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.613 - type: precision_at_5 value: 12.589 - type: recall_at_1 value: 24.85 - type: recall_at_10 value: 54.19500000000001 - type: recall_at_100 value: 77.697 - type: recall_at_1000 value: 93.35900000000001 - type: recall_at_3 value: 39.739999999999995 - type: recall_at_5 value: 46.03 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.844 - type: map_at_10 value: 28.663 - type: map_at_100 value: 30.013 - type: map_at_1000 value: 30.139 - type: map_at_3 value: 25.953 - type: map_at_5 value: 27.425 - type: mrr_at_1 value: 25.457 - type: mrr_at_10 value: 34.266000000000005 - type: mrr_at_100 value: 35.204 - type: mrr_at_1000 value: 35.27 - type: mrr_at_3 value: 31.791999999999998 - type: mrr_at_5 value: 33.213 - type: ndcg_at_1 value: 25.457 - type: ndcg_at_10 value: 34.266000000000005 - type: ndcg_at_100 value: 40.239999999999995 - type: ndcg_at_1000 value: 42.917 - type: ndcg_at_3 value: 29.593999999999998 - type: ndcg_at_5 value: 31.71 - type: precision_at_1 value: 25.457 - type: precision_at_10 value: 6.438000000000001 - type: precision_at_100 value: 1.1159999999999999 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 14.46 - type: precision_at_5 value: 10.388 - type: recall_at_1 value: 19.844 - type: recall_at_10 value: 45.787 - type: recall_at_100 value: 71.523 - type: recall_at_1000 value: 89.689 - type: recall_at_3 value: 32.665 - type: recall_at_5 value: 38.292 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.601166666666668 - type: map_at_10 value: 29.434166666666666 - type: map_at_100 value: 30.5905 - type: map_at_1000 value: 30.716583333333343 - type: map_at_3 value: 26.962333333333333 - type: map_at_5 value: 28.287250000000004 - type: mrr_at_1 value: 25.84825 - type: mrr_at_10 value: 33.49966666666667 - type: mrr_at_100 value: 34.39425000000001 - type: mrr_at_1000 value: 34.46366666666667 - type: mrr_at_3 value: 31.256 - type: mrr_at_5 value: 32.52016666666667 - type: ndcg_at_1 value: 25.84825 - type: ndcg_at_10 value: 34.2975 - type: ndcg_at_100 value: 39.50983333333333 - type: ndcg_at_1000 value: 42.17958333333333 - type: ndcg_at_3 value: 30.00558333333333 - type: ndcg_at_5 value: 31.931416666666664 - type: precision_at_1 value: 25.84825 - type: precision_at_10 value: 6.075083333333334 - type: precision_at_100 value: 1.0205833333333334 - type: precision_at_1000 value: 0.14425 - type: precision_at_3 value: 13.903249999999998 - type: precision_at_5 value: 9.874999999999998 - type: recall_at_1 value: 21.601166666666668 - type: recall_at_10 value: 44.787333333333336 - type: recall_at_100 value: 67.89450000000001 - type: recall_at_1000 value: 86.62424999999999 - type: recall_at_3 value: 32.66375 - type: recall_at_5 value: 37.71825 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.804 - type: map_at_10 value: 25.983 - type: map_at_100 value: 26.956999999999997 - type: map_at_1000 value: 27.067999999999998 - type: map_at_3 value: 23.804 - type: map_at_5 value: 24.978 - type: mrr_at_1 value: 22.853 - type: mrr_at_10 value: 28.974 - type: mrr_at_100 value: 29.855999999999998 - type: mrr_at_1000 value: 29.936 - type: mrr_at_3 value: 26.866 - type: mrr_at_5 value: 28.032 - type: ndcg_at_1 value: 22.853 - type: ndcg_at_10 value: 29.993 - type: ndcg_at_100 value: 34.735 - type: ndcg_at_1000 value: 37.637 - type: ndcg_at_3 value: 25.863000000000003 - type: ndcg_at_5 value: 27.769 - type: precision_at_1 value: 22.853 - type: precision_at_10 value: 4.8469999999999995 - type: precision_at_100 value: 0.779 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 11.35 - type: precision_at_5 value: 7.9750000000000005 - type: recall_at_1 value: 19.804 - type: recall_at_10 value: 39.616 - type: recall_at_100 value: 61.06399999999999 - type: recall_at_1000 value: 82.69800000000001 - type: recall_at_3 value: 28.012999999999998 - type: recall_at_5 value: 32.96 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.156 - type: map_at_10 value: 18.734 - type: map_at_100 value: 19.721 - type: map_at_1000 value: 19.851 - type: map_at_3 value: 17.057 - type: map_at_5 value: 17.941 - type: mrr_at_1 value: 16.07 - type: mrr_at_10 value: 22.113 - type: mrr_at_100 value: 23.021 - type: mrr_at_1000 value: 23.108 - type: mrr_at_3 value: 20.429 - type: mrr_at_5 value: 21.332 - type: ndcg_at_1 value: 16.07 - type: ndcg_at_10 value: 22.427 - type: ndcg_at_100 value: 27.277 - type: ndcg_at_1000 value: 30.525000000000002 - type: ndcg_at_3 value: 19.374 - type: ndcg_at_5 value: 20.695 - type: precision_at_1 value: 16.07 - type: precision_at_10 value: 4.1259999999999994 - type: precision_at_100 value: 0.769 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 9.325999999999999 - type: precision_at_5 value: 6.683 - type: recall_at_1 value: 13.156 - type: recall_at_10 value: 30.223 - type: recall_at_100 value: 52.012 - type: recall_at_1000 value: 75.581 - type: recall_at_3 value: 21.508 - type: recall_at_5 value: 24.975 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.14 - type: map_at_10 value: 28.961 - type: map_at_100 value: 29.996000000000002 - type: map_at_1000 value: 30.112 - type: map_at_3 value: 26.540000000000003 - type: map_at_5 value: 27.916999999999998 - type: mrr_at_1 value: 25.746000000000002 - type: mrr_at_10 value: 32.936 - type: mrr_at_100 value: 33.811 - type: mrr_at_1000 value: 33.887 - type: mrr_at_3 value: 30.55 - type: mrr_at_5 value: 32.08 - type: ndcg_at_1 value: 25.746000000000002 - type: ndcg_at_10 value: 33.536 - type: ndcg_at_100 value: 38.830999999999996 - type: ndcg_at_1000 value: 41.644999999999996 - type: ndcg_at_3 value: 29.004 - type: ndcg_at_5 value: 31.284 - type: precision_at_1 value: 25.746000000000002 - type: precision_at_10 value: 5.569 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 12.748999999999999 - type: precision_at_5 value: 9.216000000000001 - type: recall_at_1 value: 22.14 - type: recall_at_10 value: 43.628 - type: recall_at_100 value: 67.581 - type: recall_at_1000 value: 87.737 - type: recall_at_3 value: 31.579 - type: recall_at_5 value: 37.12 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.384 - type: map_at_10 value: 30.156 - type: map_at_100 value: 31.728 - type: map_at_1000 value: 31.971 - type: map_at_3 value: 27.655 - type: map_at_5 value: 28.965000000000003 - type: mrr_at_1 value: 27.075 - type: mrr_at_10 value: 34.894 - type: mrr_at_100 value: 36.0 - type: mrr_at_1000 value: 36.059000000000005 - type: mrr_at_3 value: 32.708 - type: mrr_at_5 value: 33.893 - type: ndcg_at_1 value: 27.075 - type: ndcg_at_10 value: 35.58 - type: ndcg_at_100 value: 41.597 - type: ndcg_at_1000 value: 44.529999999999994 - type: ndcg_at_3 value: 31.628 - type: ndcg_at_5 value: 33.333 - type: precision_at_1 value: 27.075 - type: precision_at_10 value: 6.9959999999999996 - type: precision_at_100 value: 1.431 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_3 value: 15.02 - type: precision_at_5 value: 10.909 - type: recall_at_1 value: 22.384 - type: recall_at_10 value: 45.052 - type: recall_at_100 value: 72.441 - type: recall_at_1000 value: 91.047 - type: recall_at_3 value: 33.617000000000004 - type: recall_at_5 value: 38.171 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.032 - type: map_at_10 value: 22.323 - type: map_at_100 value: 23.317 - type: map_at_1000 value: 23.419999999999998 - type: map_at_3 value: 20.064999999999998 - type: map_at_5 value: 21.246000000000002 - type: mrr_at_1 value: 17.375 - type: mrr_at_10 value: 24.157999999999998 - type: mrr_at_100 value: 25.108000000000004 - type: mrr_at_1000 value: 25.197999999999997 - type: mrr_at_3 value: 21.996 - type: mrr_at_5 value: 23.152 - type: ndcg_at_1 value: 17.375 - type: ndcg_at_10 value: 26.316 - type: ndcg_at_100 value: 31.302000000000003 - type: ndcg_at_1000 value: 34.143 - type: ndcg_at_3 value: 21.914 - type: ndcg_at_5 value: 23.896 - type: precision_at_1 value: 17.375 - type: precision_at_10 value: 4.233 - type: precision_at_100 value: 0.713 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 9.365 - type: precision_at_5 value: 6.728000000000001 - type: recall_at_1 value: 16.032 - type: recall_at_10 value: 36.944 - type: recall_at_100 value: 59.745000000000005 - type: recall_at_1000 value: 81.101 - type: recall_at_3 value: 25.096 - type: recall_at_5 value: 29.963 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.656 - type: map_at_10 value: 17.578 - type: map_at_100 value: 19.38 - type: map_at_1000 value: 19.552 - type: map_at_3 value: 14.544 - type: map_at_5 value: 15.914 - type: mrr_at_1 value: 21.041999999999998 - type: mrr_at_10 value: 33.579 - type: mrr_at_100 value: 34.483000000000004 - type: mrr_at_1000 value: 34.526 - type: mrr_at_3 value: 30.0 - type: mrr_at_5 value: 31.813999999999997 - type: ndcg_at_1 value: 21.041999999999998 - type: ndcg_at_10 value: 25.563999999999997 - type: ndcg_at_100 value: 32.714 - type: ndcg_at_1000 value: 35.943000000000005 - type: ndcg_at_3 value: 20.357 - type: ndcg_at_5 value: 21.839 - type: precision_at_1 value: 21.041999999999998 - type: precision_at_10 value: 8.319 - type: precision_at_100 value: 1.593 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 15.440000000000001 - type: precision_at_5 value: 11.792 - type: recall_at_1 value: 9.656 - type: recall_at_10 value: 32.023 - type: recall_at_100 value: 56.812 - type: recall_at_1000 value: 75.098 - type: recall_at_3 value: 19.455 - type: recall_at_5 value: 23.68 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 13.084999999999999 - type: map_at_10 value: 19.389 - type: map_at_100 value: 20.761 - type: map_at_1000 value: 20.944 - type: map_at_3 value: 17.273 - type: map_at_5 value: 18.37 - type: mrr_at_1 value: 20.955 - type: mrr_at_10 value: 26.741999999999997 - type: mrr_at_100 value: 27.724 - type: mrr_at_1000 value: 27.819 - type: mrr_at_3 value: 24.881 - type: mrr_at_5 value: 25.833000000000002 - type: ndcg_at_1 value: 20.955 - type: ndcg_at_10 value: 23.905 - type: ndcg_at_100 value: 30.166999999999998 - type: ndcg_at_1000 value: 34.202 - type: ndcg_at_3 value: 20.854 - type: ndcg_at_5 value: 21.918000000000003 - type: precision_at_1 value: 20.955 - type: precision_at_10 value: 5.479 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 11.960999999999999 - type: precision_at_5 value: 8.647 - type: recall_at_1 value: 13.084999999999999 - type: recall_at_10 value: 30.202 - type: recall_at_100 value: 56.579 - type: recall_at_1000 value: 84.641 - type: recall_at_3 value: 20.751 - type: recall_at_5 value: 24.317 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 72.8322309079976 - type: cos_sim_ap value: 81.34356949111096 - type: cos_sim_f1 value: 74.88546438983758 - type: cos_sim_precision value: 67.50516238032664 - type: cos_sim_recall value: 84.07762450315643 - type: dot_accuracy value: 69.28442573662056 - type: dot_ap value: 74.87961278837321 - type: dot_f1 value: 72.20502901353966 - type: dot_precision value: 61.5701797789873 - type: dot_recall value: 87.2808043020809 - type: euclidean_accuracy value: 71.99037883343355 - type: euclidean_ap value: 80.70039825164011 - type: euclidean_f1 value: 74.23149154887813 - type: euclidean_precision value: 64.29794520547945 - type: euclidean_recall value: 87.79518353986438 - type: manhattan_accuracy value: 72.0625375826819 - type: manhattan_ap value: 80.78886354854423 - type: manhattan_f1 value: 74.20842299415924 - type: manhattan_precision value: 66.0525355709595 - type: manhattan_recall value: 84.66214636427402 - type: max_accuracy value: 72.8322309079976 - type: max_ap value: 81.34356949111096 - type: max_f1 value: 74.88546438983758 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 54.847 - type: map_at_10 value: 63.736000000000004 - type: map_at_100 value: 64.302 - type: map_at_1000 value: 64.319 - type: map_at_3 value: 61.565000000000005 - type: map_at_5 value: 62.671 - type: mrr_at_1 value: 54.900000000000006 - type: mrr_at_10 value: 63.744 - type: mrr_at_100 value: 64.287 - type: mrr_at_1000 value: 64.30399999999999 - type: mrr_at_3 value: 61.590999999999994 - type: mrr_at_5 value: 62.724000000000004 - type: ndcg_at_1 value: 55.005 - type: ndcg_at_10 value: 68.142 - type: ndcg_at_100 value: 70.95 - type: ndcg_at_1000 value: 71.40100000000001 - type: ndcg_at_3 value: 63.641999999999996 - type: ndcg_at_5 value: 65.62599999999999 - type: precision_at_1 value: 55.005 - type: precision_at_10 value: 8.272 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.288 - type: precision_at_5 value: 14.963000000000001 - type: recall_at_1 value: 54.847 - type: recall_at_10 value: 81.955 - type: recall_at_100 value: 95.258 - type: recall_at_1000 value: 98.84100000000001 - type: recall_at_3 value: 69.547 - type: recall_at_5 value: 74.315 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 7.2620000000000005 - type: map_at_10 value: 15.196000000000002 - type: map_at_100 value: 19.454 - type: map_at_1000 value: 20.445 - type: map_at_3 value: 11.532 - type: map_at_5 value: 13.053999999999998 - type: mrr_at_1 value: 57.49999999999999 - type: mrr_at_10 value: 66.661 - type: mrr_at_100 value: 67.086 - type: mrr_at_1000 value: 67.105 - type: mrr_at_3 value: 64.625 - type: mrr_at_5 value: 65.962 - type: ndcg_at_1 value: 46.125 - type: ndcg_at_10 value: 32.609 - type: ndcg_at_100 value: 34.611999999999995 - type: ndcg_at_1000 value: 40.836 - type: ndcg_at_3 value: 37.513000000000005 - type: ndcg_at_5 value: 34.699999999999996 - type: precision_at_1 value: 57.49999999999999 - type: precision_at_10 value: 24.975 - type: precision_at_100 value: 6.9830000000000005 - type: precision_at_1000 value: 1.505 - type: precision_at_3 value: 40.75 - type: precision_at_5 value: 33.2 - type: recall_at_1 value: 7.2620000000000005 - type: recall_at_10 value: 20.341 - type: recall_at_100 value: 38.690999999999995 - type: recall_at_1000 value: 58.879000000000005 - type: recall_at_3 value: 12.997 - type: recall_at_5 value: 15.628 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 20.86 - type: map_at_10 value: 62.28 - type: map_at_100 value: 65.794 - type: map_at_1000 value: 65.903 - type: map_at_3 value: 42.616 - type: map_at_5 value: 53.225 - type: mrr_at_1 value: 76.75 - type: mrr_at_10 value: 83.387 - type: mrr_at_100 value: 83.524 - type: mrr_at_1000 value: 83.531 - type: mrr_at_3 value: 82.592 - type: mrr_at_5 value: 83.07900000000001 - type: ndcg_at_1 value: 76.75 - type: ndcg_at_10 value: 72.83500000000001 - type: ndcg_at_100 value: 77.839 - type: ndcg_at_1000 value: 78.976 - type: ndcg_at_3 value: 70.977 - type: ndcg_at_5 value: 69.419 - type: precision_at_1 value: 76.75 - type: precision_at_10 value: 35.825 - type: precision_at_100 value: 4.507 - type: precision_at_1000 value: 0.47800000000000004 - type: precision_at_3 value: 63.733 - type: precision_at_5 value: 53.44 - type: recall_at_1 value: 20.86 - type: recall_at_10 value: 75.115 - type: recall_at_100 value: 90.47699999999999 - type: recall_at_1000 value: 96.304 - type: recall_at_3 value: 45.976 - type: recall_at_5 value: 59.971 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 37.8 - type: map_at_10 value: 47.154 - type: map_at_100 value: 48.012 - type: map_at_1000 value: 48.044 - type: map_at_3 value: 44.667 - type: map_at_5 value: 45.992 - type: mrr_at_1 value: 37.8 - type: mrr_at_10 value: 47.154 - type: mrr_at_100 value: 48.012 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 44.667 - type: mrr_at_5 value: 45.992 - type: ndcg_at_1 value: 37.8 - type: ndcg_at_10 value: 52.025 - type: ndcg_at_100 value: 56.275 - type: ndcg_at_1000 value: 57.174 - type: ndcg_at_3 value: 46.861999999999995 - type: ndcg_at_5 value: 49.229 - type: precision_at_1 value: 37.8 - type: precision_at_10 value: 6.75 - type: precision_at_100 value: 0.8750000000000001 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 17.732999999999997 - type: precision_at_5 value: 11.78 - type: recall_at_1 value: 37.8 - type: recall_at_10 value: 67.5 - type: recall_at_100 value: 87.5 - type: recall_at_1000 value: 94.69999999999999 - type: recall_at_3 value: 53.2 - type: recall_at_5 value: 58.9 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.845 - type: f1 value: 42.70952656074019 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 50.058 - type: map_at_10 value: 61.295 - type: map_at_100 value: 61.82 - type: map_at_1000 value: 61.843 - type: map_at_3 value: 58.957 - type: map_at_5 value: 60.467999999999996 - type: mrr_at_1 value: 54.05 - type: mrr_at_10 value: 65.52900000000001 - type: mrr_at_100 value: 65.984 - type: mrr_at_1000 value: 65.999 - type: mrr_at_3 value: 63.286 - type: mrr_at_5 value: 64.777 - type: ndcg_at_1 value: 54.05 - type: ndcg_at_10 value: 67.216 - type: ndcg_at_100 value: 69.594 - type: ndcg_at_1000 value: 70.13000000000001 - type: ndcg_at_3 value: 62.778999999999996 - type: ndcg_at_5 value: 65.36 - type: precision_at_1 value: 54.05 - type: precision_at_10 value: 8.924 - type: precision_at_100 value: 1.019 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 25.218 - type: precision_at_5 value: 16.547 - type: recall_at_1 value: 50.058 - type: recall_at_10 value: 81.39699999999999 - type: recall_at_100 value: 92.022 - type: recall_at_1000 value: 95.877 - type: recall_at_3 value: 69.485 - type: recall_at_5 value: 75.833 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 15.078 - type: map_at_10 value: 24.162 - type: map_at_100 value: 25.818 - type: map_at_1000 value: 26.009 - type: map_at_3 value: 20.706 - type: map_at_5 value: 22.542 - type: mrr_at_1 value: 30.709999999999997 - type: mrr_at_10 value: 38.828 - type: mrr_at_100 value: 39.794000000000004 - type: mrr_at_1000 value: 39.843 - type: mrr_at_3 value: 36.163000000000004 - type: mrr_at_5 value: 37.783 - type: ndcg_at_1 value: 30.709999999999997 - type: ndcg_at_10 value: 31.290000000000003 - type: ndcg_at_100 value: 38.051 - type: ndcg_at_1000 value: 41.487 - type: ndcg_at_3 value: 27.578999999999997 - type: ndcg_at_5 value: 28.799000000000003 - type: precision_at_1 value: 30.709999999999997 - type: precision_at_10 value: 8.92 - type: precision_at_100 value: 1.5599999999999998 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 18.416 - type: precision_at_5 value: 13.827 - type: recall_at_1 value: 15.078 - type: recall_at_10 value: 37.631 - type: recall_at_100 value: 63.603 - type: recall_at_1000 value: 84.121 - type: recall_at_3 value: 24.438 - type: recall_at_5 value: 29.929 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 31.202 - type: map_at_10 value: 42.653 - type: map_at_100 value: 43.411 - type: map_at_1000 value: 43.479 - type: map_at_3 value: 40.244 - type: map_at_5 value: 41.736000000000004 - type: mrr_at_1 value: 62.404 - type: mrr_at_10 value: 69.43599999999999 - type: mrr_at_100 value: 69.788 - type: mrr_at_1000 value: 69.809 - type: mrr_at_3 value: 68.12700000000001 - type: mrr_at_5 value: 68.961 - type: ndcg_at_1 value: 62.404 - type: ndcg_at_10 value: 51.665000000000006 - type: ndcg_at_100 value: 54.623 - type: ndcg_at_1000 value: 56.154 - type: ndcg_at_3 value: 47.861 - type: ndcg_at_5 value: 49.968 - type: precision_at_1 value: 62.404 - type: precision_at_10 value: 10.57 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 29.624 - type: precision_at_5 value: 19.441 - type: recall_at_1 value: 31.202 - type: recall_at_10 value: 52.849000000000004 - type: recall_at_100 value: 64.47 - type: recall_at_1000 value: 74.74 - type: recall_at_3 value: 44.436 - type: recall_at_5 value: 48.602000000000004 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - 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type: precision_at_5 value: 24.21 - type: recall_at_1 value: 69.321 - type: recall_at_10 value: 94.521 - type: recall_at_100 value: 99.258 - type: recall_at_1000 value: 99.97200000000001 - type: recall_at_3 value: 85.97200000000001 - type: recall_at_5 value: 90.589 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 44.51751457277441 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 53.60727449352775 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.058 - type: map_at_10 value: 9.995999999999999 - type: map_at_100 value: 11.738 - type: map_at_1000 value: 11.999 - type: map_at_3 value: 7.353999999999999 - type: map_at_5 value: 8.68 - type: mrr_at_1 value: 20.0 - type: mrr_at_10 value: 30.244 - type: mrr_at_100 value: 31.378 - type: mrr_at_1000 value: 31.445 - type: mrr_at_3 value: 26.933 - type: mrr_at_5 value: 28.748 - type: ndcg_at_1 value: 20.0 - type: ndcg_at_10 value: 17.235 - type: ndcg_at_100 value: 24.241 - type: ndcg_at_1000 value: 29.253 - type: ndcg_at_3 value: 16.542 - type: ndcg_at_5 value: 14.386 - type: precision_at_1 value: 20.0 - type: precision_at_10 value: 8.9 - type: precision_at_100 value: 1.8929999999999998 - type: precision_at_1000 value: 0.31 - type: precision_at_3 value: 15.567 - type: precision_at_5 value: 12.620000000000001 - type: recall_at_1 value: 4.058 - type: recall_at_10 value: 18.062 - type: recall_at_100 value: 38.440000000000005 - type: recall_at_1000 value: 63.044999999999995 - type: recall_at_3 value: 9.493 - type: recall_at_5 value: 12.842 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - 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type: precision_at_5 value: 12.7 - type: recall_at_1 value: 41.8 - type: recall_at_10 value: 70.7 - type: recall_at_100 value: 87.3 - type: recall_at_1000 value: 96.39999999999999 - type: recall_at_3 value: 58.4 - type: recall_at_5 value: 63.5 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 82.67 - type: ap value: 63.20621490084175 - type: f1 value: 80.81778523320692 --- # Model Card for udever-bloom `udever-bloom-1b1` is finetuned from [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) via [BitFit](https://aclanthology.org/2022.acl-short.1/) on MS MARCO Passage Ranking, SNLI and MultiNLI data. It is a universal embedding model across tasks, natural and programming languages. (From the technical view, `udever` is merely with some minor improvements to `sgpt-bloom`)
## Model Details ### Model Description - **Developed by:** Alibaba Group - **Model type:** Transformer-based Language Model (decoder-only) - **Language(s) (NLP):** Multiple; see [bloom training data](https://huggingface.co/bigscience/bloom-1b1#training-data) - **Finetuned from model :** [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) ### Model Sources - **Repository:** [github.com/izhx/uni-rep](https://github.com/izhx/uni-rep) - **Paper :** [Language Models are Universal Embedders](https://arxiv.org/pdf/2310.08232.pdf) - **Training Date :** 2023-06 ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch from transformers import AutoTokenizer, BloomModel tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-1b1') model = BloomModel.from_pretrained('izhx/udever-bloom-1b1') boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]' eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod]) if tokenizer.padding_side != 'left': print('!!!', tokenizer.padding_side) tokenizer.padding_side = 'left' def encode(texts: list, is_query: bool = True, max_length=300): bos = boq if is_query else bod eos_id = eoq_id if is_query else eod_id texts = [bos + t for t in texts] encoding = tokenizer( texts, truncation=True, max_length=max_length - 1, padding=True ) for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']): ids.append(eos_id) mask.append(1) inputs = tokenizer.pad(encoding, return_tensors='pt') with torch.inference_mode(): outputs = model(**inputs) embeds = outputs.last_hidden_state[:, -1] return embeds encode(['I am Bert', 'You are Elmo']) ``` ## Training Details ### Training Data - MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86) - SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz) ### Training Procedure #### Preprocessing MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86). Negatives for SNLI and MultiNLI are randomly sampled. #### Training Hyperparameters - **Training regime:** tf32, BitFit - **Batch size:** 1024 - **Epochs:** 3 - **Optimizer:** AdamW - **Learning rate:** 1e-4 - **Scheduler:** constant with warmup. - **Warmup:** 0.25 epoch ## Evaluation ### Table 1: Massive Text Embedding Benchmark [MTEB](https://huggingface.co/spaces/mteb/leaderboard) | MTEB | Avg. | Class. | Clust. | PairClass. | Rerank. | Retr. | STS | Summ. | |-----------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------| | #Datasets ➡️ | 56 | 12 | 11 | 3 | 4 | 15 | 10 | 1 | || | bge-large-en-v1.5 | **64.23** | **75.97** | 46.08| **87.12** | **60.03** | **54.29** | 83.11| 31.61 | | bge-base-en-v1.5 | 63.55| 75.53| 45.77| 86.55| 58.86| 53.25| 82.4| 31.07 | | gte-large | 63.13| 73.33| **46.84** | 85| 59.13| 52.22| **83.35** | 31.66 | | gte-base | 62.39| 73.01| 46.2| 84.57| 58.61| 51.14| 82.3| 31.17 | | e5-large-v2 | 62.25| 75.24| 44.49| 86.03| 56.61| 50.56| 82.05| 30.19 | | instructor-xl | 61.79| 73.12| 44.74| 86.62| 57.29| 49.26| 83.06| 32.32 | | instructor-large | 61.59| 73.86| 45.29| 85.89| 57.54| 47.57| 83.15| 31.84 | | e5-base-v2 | 61.5 | 73.84| 43.8| 85.73| 55.91| 50.29| 81.05| 30.28 | | e5-large | 61.42| 73.14| 43.33| 85.94| 56.53| 49.99| 82.06| 30.97 | | text-embedding-ada-002 (OpenAI API) | 60.99| 70.93| 45.9 | 84.89| 56.32| 49.25| 80.97| 30.8 | | e5-base | 60.44| 72.63| 42.11| 85.09| 55.7 | 48.75| 80.96| 31.01 | | SGPT-5.8B-msmarco | 58.93| 68.13| 40.34| 82 | 56.56| 50.25| 78.1 | 31.46 | | sgpt-bloom-7b1-msmarco | 57.59| 66.19| 38.93| 81.9 | 55.65| 48.22| 77.74| **33.6** | || | Udever-bloom-560m | 55.80| 68.04| 36.89| 81.05| 52.60| 41.19| 79.93| 32.06 | | Udever-bloom-1b1 | 58.28| 70.18| 39.11| 83.11| 54.28| 45.27| 81.52| 31.10 | | Udever-bloom-3b | 59.86| 71.91| 40.74| 84.06| 54.90| 47.67| 82.37| 30.62 | | Udever-bloom-7b1 | 60.63 | 72.13| 40.81| 85.40| 55.91| 49.34| 83.01| 30.97 | ### Table 2: [CodeSearchNet](https://github.com/github/CodeSearchNet) | CodeSearchNet | Go | Ruby | Python | Java | JS | PHP | Avg. | |-|-|-|-|-|-|-|-| | CodeBERT | 69.3 | 70.6 | 84.0 | 86.8 | 74.8 | 70.6 | 76.0 | | GraphCodeBERT | 84.1 | 73.2 | 87.9 | 75.7 | 71.1 | 72.5 | 77.4 | | cpt-code S | **97.7** | **86.3** | 99.8 | 94.0 | 86.0 | 96.7 | 93.4 | | cpt-code M | 97.5 | 85.5 | **99.9** | **94.4** | **86.5** | **97.2** | **93.5** | | sgpt-bloom-7b1-msmarco | 76.79 | 69.25 | 95.68 | 77.93 | 70.35 | 73.45 | 77.24 | || | Udever-bloom-560m | 75.38 | 66.67 | 96.23 | 78.99 | 69.39 | 73.69 | 76.73 | | Udever-bloom-1b1 | 78.76 | 72.85 | 97.67 | 82.77 | 74.38 | 78.97 | 80.90 | | Udever-bloom-3b | 80.63 | 75.40 | 98.02 | 83.88 | 76.18 | 79.67 | 82.29 | | Udever-bloom-7b1 | 79.37 | 76.59 | 98.38 | 84.68 | 77.49 | 80.03 | 82.76 | ### Table 3: Chinese multi-domain retrieval [Multi-cpr](https://dl.acm.org/doi/10.1145/3477495.3531736) | | | |E-commerce | | Entertainment video | | Medical | | |--|--|--|--|--|--|--|--|--| | Model | Train | Backbone | MRR@10 | Recall@1k | MRR@10 | Recall@1k | MRR@10 | Recall@1k | || | BM25 | - | - | 0.225 | 0.815 | 0.225 | 0.780 | 0.187 | 0.482 | | Doc2Query | - | - | 0.239 | 0.826 | 0.238 | 0.794 | 0.210 | 0.505 | | DPR-1 | In-Domain | BERT | 0.270 | 0.921 | 0.254 | 0.934 | 0.327 | 0.747 | | DPR-2 | In-Domain | BERT-CT | 0.289 | **0.926** | 0.263 | **0.935** | 0.339 | **0.769** | | text-embedding-ada-002 | General | GPT | 0.183 | 0.825 | 0.159 | 0.786 | 0.245 | 0.593 | | sgpt-bloom-7b1-msmarco | General | BLOOM | 0.242 | 0.840 | 0.227 | 0.829 | 0.311 | 0.675 | || | Udever-bloom-560m | General | BLOOM | 0.156 | 0.802 | 0.149 | 0.749 | 0.245 | 0.571 | | Udever-bloom-1b1 | General | BLOOM | 0.244 | 0.863 | 0.208 | 0.815 | 0.241 | 0.557 | | Udever-bloom-3b | General | BLOOM | 0.267 | 0.871 | 0.228 | 0.836 | 0.288 | 0.619 | | Udever-bloom-7b1 | General | BLOOM | **0.296** | 0.889 | **0.267** | 0.907 | **0.343** | 0.705 | #### More results refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 3. ## Technical Specifications ### Model Architecture and Objective - Model: [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1). - Objective: Constrastive loss with hard negatives (refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 2.2). ### Compute Infrastructure - Nvidia A100 SXM4 80GB. - torch 2.0.0, transformers 4.29.2. ## Citation **BibTeX:** ```BibTeX @article{zhang2023language, title={Language Models are Universal Embedders}, author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min}, journal={arXiv preprint arXiv:2310.08232}, year={2023} } ```