Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results
Inference Endpoints
| pipeline_tag: sentence-similarity | |
| tags: | |
| - text-embedding | |
| - embeddings | |
| - information-retrieval | |
| - beir | |
| - text-classification | |
| - language-model | |
| - text-clustering | |
| - text-semantic-similarity | |
| - text-evaluation | |
| - prompt-retrieval | |
| - text-reranking | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| - transformers | |
| - t5 | |
| - English | |
| - Sentence Similarity | |
| - natural_questions | |
| - ms_marco | |
| - fever | |
| - hotpot_qa | |
| - mteb | |
| language: en | |
| inference: false | |
| license: apache-2.0 | |
| model-index: | |
| - name: final_xl_results | |
| results: | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/amazon_counterfactual | |
| name: MTEB AmazonCounterfactualClassification (en) | |
| config: en | |
| split: test | |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 | |
| metrics: | |
| - type: accuracy | |
| value: 85.08955223880596 | |
| - type: ap | |
| value: 52.66066378722476 | |
| - type: f1 | |
| value: 79.63340218960269 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/amazon_polarity | |
| name: MTEB AmazonPolarityClassification | |
| config: default | |
| split: test | |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 | |
| metrics: | |
| - type: accuracy | |
| value: 86.542 | |
| - type: ap | |
| value: 81.92695193008987 | |
| - type: f1 | |
| value: 86.51466132573681 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/amazon_reviews_multi | |
| name: MTEB AmazonReviewsClassification (en) | |
| config: en | |
| split: test | |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d | |
| metrics: | |
| - type: accuracy | |
| value: 42.964 | |
| - type: f1 | |
| value: 41.43146249774862 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: arguana | |
| name: MTEB ArguAna | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 29.872 | |
| - type: map_at_10 | |
| value: 46.342 | |
| - type: map_at_100 | |
| value: 47.152 | |
| - type: map_at_1000 | |
| value: 47.154 | |
| - type: map_at_3 | |
| value: 41.216 | |
| - type: map_at_5 | |
| value: 44.035999999999994 | |
| - type: mrr_at_1 | |
| value: 30.939 | |
| - type: mrr_at_10 | |
| value: 46.756 | |
| - type: mrr_at_100 | |
| value: 47.573 | |
| - type: mrr_at_1000 | |
| value: 47.575 | |
| - type: mrr_at_3 | |
| value: 41.548 | |
| - type: mrr_at_5 | |
| value: 44.425 | |
| - type: ndcg_at_1 | |
| value: 29.872 | |
| - type: ndcg_at_10 | |
| value: 55.65 | |
| - type: ndcg_at_100 | |
| value: 58.88099999999999 | |
| - type: ndcg_at_1000 | |
| value: 58.951 | |
| - type: ndcg_at_3 | |
| value: 45.0 | |
| - type: ndcg_at_5 | |
| value: 50.09 | |
| - type: precision_at_1 | |
| value: 29.872 | |
| - type: precision_at_10 | |
| value: 8.549 | |
| - type: precision_at_100 | |
| value: 0.991 | |
| - type: precision_at_1000 | |
| value: 0.1 | |
| - type: precision_at_3 | |
| value: 18.658 | |
| - type: precision_at_5 | |
| value: 13.669999999999998 | |
| - type: recall_at_1 | |
| value: 29.872 | |
| - type: recall_at_10 | |
| value: 85.491 | |
| - type: recall_at_100 | |
| value: 99.075 | |
| - type: recall_at_1000 | |
| value: 99.644 | |
| - type: recall_at_3 | |
| value: 55.974000000000004 | |
| - type: recall_at_5 | |
| value: 68.35 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/arxiv-clustering-p2p | |
| name: MTEB ArxivClusteringP2P | |
| config: default | |
| split: test | |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d | |
| metrics: | |
| - type: v_measure | |
| value: 42.452729850641276 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/arxiv-clustering-s2s | |
| name: MTEB ArxivClusteringS2S | |
| config: default | |
| split: test | |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 | |
| metrics: | |
| - type: v_measure | |
| value: 32.21141846480423 | |
| - task: | |
| type: Reranking | |
| dataset: | |
| type: mteb/askubuntudupquestions-reranking | |
| name: MTEB AskUbuntuDupQuestions | |
| config: default | |
| split: test | |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 | |
| metrics: | |
| - type: map | |
| value: 65.34710928952622 | |
| - type: mrr | |
| value: 77.61124301983028 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/biosses-sts | |
| name: MTEB BIOSSES | |
| config: default | |
| split: test | |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 84.15312230525639 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/banking77 | |
| name: MTEB Banking77Classification | |
| config: default | |
| split: test | |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 | |
| metrics: | |
| - type: accuracy | |
| value: 82.66233766233766 | |
| - type: f1 | |
| value: 82.04175284777669 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/biorxiv-clustering-p2p | |
| name: MTEB BiorxivClusteringP2P | |
| config: default | |
| split: test | |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 | |
| metrics: | |
| - type: v_measure | |
| value: 37.36697339826455 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/biorxiv-clustering-s2s | |
| name: MTEB BiorxivClusteringS2S | |
| config: default | |
| split: test | |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 | |
| metrics: | |
| - type: v_measure | |
| value: 30.551241447593092 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackAndroidRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 36.797000000000004 | |
| - type: map_at_10 | |
| value: 48.46 | |
| - type: map_at_100 | |
| value: 49.968 | |
| - type: map_at_1000 | |
| value: 50.080000000000005 | |
| - type: map_at_3 | |
| value: 44.71 | |
| - type: map_at_5 | |
| value: 46.592 | |
| - type: mrr_at_1 | |
| value: 45.494 | |
| - type: mrr_at_10 | |
| value: 54.747 | |
| - type: mrr_at_100 | |
| value: 55.43599999999999 | |
| - type: mrr_at_1000 | |
| value: 55.464999999999996 | |
| - type: mrr_at_3 | |
| value: 52.361000000000004 | |
| - type: mrr_at_5 | |
| value: 53.727000000000004 | |
| - type: ndcg_at_1 | |
| value: 45.494 | |
| - type: ndcg_at_10 | |
| value: 54.989 | |
| - type: ndcg_at_100 | |
| value: 60.096000000000004 | |
| - type: ndcg_at_1000 | |
| value: 61.58 | |
| - type: ndcg_at_3 | |
| value: 49.977 | |
| - type: ndcg_at_5 | |
| value: 51.964999999999996 | |
| - type: precision_at_1 | |
| value: 45.494 | |
| - type: precision_at_10 | |
| value: 10.558 | |
| - type: precision_at_100 | |
| value: 1.6049999999999998 | |
| - type: precision_at_1000 | |
| value: 0.203 | |
| - type: precision_at_3 | |
| value: 23.796 | |
| - type: precision_at_5 | |
| value: 16.881 | |
| - type: recall_at_1 | |
| value: 36.797000000000004 | |
| - type: recall_at_10 | |
| value: 66.83 | |
| - type: recall_at_100 | |
| value: 88.34100000000001 | |
| - type: recall_at_1000 | |
| value: 97.202 | |
| - type: recall_at_3 | |
| value: 51.961999999999996 | |
| - type: recall_at_5 | |
| value: 57.940000000000005 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackEnglishRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 32.597 | |
| - type: map_at_10 | |
| value: 43.424 | |
| - type: map_at_100 | |
| value: 44.78 | |
| - type: map_at_1000 | |
| value: 44.913 | |
| - type: map_at_3 | |
| value: 40.315 | |
| - type: map_at_5 | |
| value: 41.987 | |
| - type: mrr_at_1 | |
| value: 40.382 | |
| - type: mrr_at_10 | |
| value: 49.219 | |
| - type: mrr_at_100 | |
| value: 49.895 | |
| - type: mrr_at_1000 | |
| value: 49.936 | |
| - type: mrr_at_3 | |
| value: 46.996 | |
| - type: mrr_at_5 | |
| value: 48.231 | |
| - type: ndcg_at_1 | |
| value: 40.382 | |
| - type: ndcg_at_10 | |
| value: 49.318 | |
| - type: ndcg_at_100 | |
| value: 53.839999999999996 | |
| - type: ndcg_at_1000 | |
| value: 55.82899999999999 | |
| - type: ndcg_at_3 | |
| value: 44.914 | |
| - type: ndcg_at_5 | |
| value: 46.798 | |
| - type: precision_at_1 | |
| value: 40.382 | |
| - type: precision_at_10 | |
| value: 9.274000000000001 | |
| - type: precision_at_100 | |
| value: 1.497 | |
| - type: precision_at_1000 | |
| value: 0.198 | |
| - type: precision_at_3 | |
| value: 21.592 | |
| - type: precision_at_5 | |
| value: 15.159 | |
| - type: recall_at_1 | |
| value: 32.597 | |
| - type: recall_at_10 | |
| value: 59.882000000000005 | |
| - type: recall_at_100 | |
| value: 78.446 | |
| - type: recall_at_1000 | |
| value: 90.88000000000001 | |
| - type: recall_at_3 | |
| value: 46.9 | |
| - type: recall_at_5 | |
| value: 52.222 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackGamingRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 43.8 | |
| - type: map_at_10 | |
| value: 57.293000000000006 | |
| - type: map_at_100 | |
| value: 58.321 | |
| - type: map_at_1000 | |
| value: 58.361 | |
| - type: map_at_3 | |
| value: 53.839999999999996 | |
| - type: map_at_5 | |
| value: 55.838 | |
| - type: mrr_at_1 | |
| value: 49.592000000000006 | |
| - type: mrr_at_10 | |
| value: 60.643 | |
| - type: mrr_at_100 | |
| value: 61.23499999999999 | |
| - type: mrr_at_1000 | |
| value: 61.251999999999995 | |
| - type: mrr_at_3 | |
| value: 58.265 | |
| - type: mrr_at_5 | |
| value: 59.717 | |
| - type: ndcg_at_1 | |
| value: 49.592000000000006 | |
| - type: ndcg_at_10 | |
| value: 63.364 | |
| - type: ndcg_at_100 | |
| value: 67.167 | |
| - type: ndcg_at_1000 | |
| value: 67.867 | |
| - type: ndcg_at_3 | |
| value: 57.912 | |
| - type: ndcg_at_5 | |
| value: 60.697 | |
| - type: precision_at_1 | |
| value: 49.592000000000006 | |
| - type: precision_at_10 | |
| value: 10.088 | |
| - type: precision_at_100 | |
| value: 1.2930000000000001 | |
| - type: precision_at_1000 | |
| value: 0.13899999999999998 | |
| - type: precision_at_3 | |
| value: 25.789 | |
| - type: precision_at_5 | |
| value: 17.541999999999998 | |
| - type: recall_at_1 | |
| value: 43.8 | |
| - type: recall_at_10 | |
| value: 77.635 | |
| - type: recall_at_100 | |
| value: 93.748 | |
| - type: recall_at_1000 | |
| value: 98.468 | |
| - type: recall_at_3 | |
| value: 63.223 | |
| - type: recall_at_5 | |
| value: 70.122 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackGisRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 27.721 | |
| - type: map_at_10 | |
| value: 35.626999999999995 | |
| - type: map_at_100 | |
| value: 36.719 | |
| - type: map_at_1000 | |
| value: 36.8 | |
| - type: map_at_3 | |
| value: 32.781 | |
| - type: map_at_5 | |
| value: 34.333999999999996 | |
| - type: mrr_at_1 | |
| value: 29.604999999999997 | |
| - type: mrr_at_10 | |
| value: 37.564 | |
| - type: mrr_at_100 | |
| value: 38.505 | |
| - type: mrr_at_1000 | |
| value: 38.565 | |
| - type: mrr_at_3 | |
| value: 34.727000000000004 | |
| - type: mrr_at_5 | |
| value: 36.207 | |
| - type: ndcg_at_1 | |
| value: 29.604999999999997 | |
| - type: ndcg_at_10 | |
| value: 40.575 | |
| - type: ndcg_at_100 | |
| value: 45.613 | |
| - type: ndcg_at_1000 | |
| value: 47.676 | |
| - type: ndcg_at_3 | |
| value: 34.811 | |
| - type: ndcg_at_5 | |
| value: 37.491 | |
| - type: precision_at_1 | |
| value: 29.604999999999997 | |
| - type: precision_at_10 | |
| value: 6.1690000000000005 | |
| - type: precision_at_100 | |
| value: 0.906 | |
| - type: precision_at_1000 | |
| value: 0.11199999999999999 | |
| - type: precision_at_3 | |
| value: 14.237 | |
| - type: precision_at_5 | |
| value: 10.056 | |
| - type: recall_at_1 | |
| value: 27.721 | |
| - type: recall_at_10 | |
| value: 54.041 | |
| - type: recall_at_100 | |
| value: 76.62299999999999 | |
| - type: recall_at_1000 | |
| value: 92.134 | |
| - type: recall_at_3 | |
| value: 38.582 | |
| - type: recall_at_5 | |
| value: 44.989000000000004 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackMathematicaRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 16.553 | |
| - type: map_at_10 | |
| value: 25.384 | |
| - type: map_at_100 | |
| value: 26.655 | |
| - type: map_at_1000 | |
| value: 26.778000000000002 | |
| - type: map_at_3 | |
| value: 22.733 | |
| - type: map_at_5 | |
| value: 24.119 | |
| - type: mrr_at_1 | |
| value: 20.149 | |
| - type: mrr_at_10 | |
| value: 29.705 | |
| - type: mrr_at_100 | |
| value: 30.672 | |
| - type: mrr_at_1000 | |
| value: 30.737 | |
| - type: mrr_at_3 | |
| value: 27.032 | |
| - type: mrr_at_5 | |
| value: 28.369 | |
| - type: ndcg_at_1 | |
| value: 20.149 | |
| - type: ndcg_at_10 | |
| value: 30.843999999999998 | |
| - type: ndcg_at_100 | |
| value: 36.716 | |
| - type: ndcg_at_1000 | |
| value: 39.495000000000005 | |
| - type: ndcg_at_3 | |
| value: 25.918999999999997 | |
| - type: ndcg_at_5 | |
| value: 27.992 | |
| - type: precision_at_1 | |
| value: 20.149 | |
| - type: precision_at_10 | |
| value: 5.858 | |
| - type: precision_at_100 | |
| value: 1.009 | |
| - type: precision_at_1000 | |
| value: 0.13799999999999998 | |
| - type: precision_at_3 | |
| value: 12.645000000000001 | |
| - type: precision_at_5 | |
| value: 9.179 | |
| - type: recall_at_1 | |
| value: 16.553 | |
| - type: recall_at_10 | |
| value: 43.136 | |
| - type: recall_at_100 | |
| value: 68.562 | |
| - type: recall_at_1000 | |
| value: 88.208 | |
| - type: recall_at_3 | |
| value: 29.493000000000002 | |
| - type: recall_at_5 | |
| value: 34.751 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackPhysicsRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 28.000999999999998 | |
| - type: map_at_10 | |
| value: 39.004 | |
| - type: map_at_100 | |
| value: 40.461999999999996 | |
| - type: map_at_1000 | |
| value: 40.566 | |
| - type: map_at_3 | |
| value: 35.805 | |
| - type: map_at_5 | |
| value: 37.672 | |
| - type: mrr_at_1 | |
| value: 33.782000000000004 | |
| - type: mrr_at_10 | |
| value: 44.702 | |
| - type: mrr_at_100 | |
| value: 45.528 | |
| - type: mrr_at_1000 | |
| value: 45.576 | |
| - type: mrr_at_3 | |
| value: 42.14 | |
| - type: mrr_at_5 | |
| value: 43.651 | |
| - type: ndcg_at_1 | |
| value: 33.782000000000004 | |
| - type: ndcg_at_10 | |
| value: 45.275999999999996 | |
| - type: ndcg_at_100 | |
| value: 50.888 | |
| - type: ndcg_at_1000 | |
| value: 52.879 | |
| - type: ndcg_at_3 | |
| value: 40.191 | |
| - type: ndcg_at_5 | |
| value: 42.731 | |
| - type: precision_at_1 | |
| value: 33.782000000000004 | |
| - type: precision_at_10 | |
| value: 8.200000000000001 | |
| - type: precision_at_100 | |
| value: 1.287 | |
| - type: precision_at_1000 | |
| value: 0.16199999999999998 | |
| - type: precision_at_3 | |
| value: 19.185 | |
| - type: precision_at_5 | |
| value: 13.667000000000002 | |
| - type: recall_at_1 | |
| value: 28.000999999999998 | |
| - type: recall_at_10 | |
| value: 58.131 | |
| - type: recall_at_100 | |
| value: 80.869 | |
| - type: recall_at_1000 | |
| value: 93.931 | |
| - type: recall_at_3 | |
| value: 44.161 | |
| - type: recall_at_5 | |
| value: 50.592000000000006 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackProgrammersRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 28.047 | |
| - type: map_at_10 | |
| value: 38.596000000000004 | |
| - type: map_at_100 | |
| value: 40.116 | |
| - type: map_at_1000 | |
| value: 40.232 | |
| - type: map_at_3 | |
| value: 35.205 | |
| - type: map_at_5 | |
| value: 37.076 | |
| - type: mrr_at_1 | |
| value: 34.932 | |
| - type: mrr_at_10 | |
| value: 44.496 | |
| - type: mrr_at_100 | |
| value: 45.47 | |
| - type: mrr_at_1000 | |
| value: 45.519999999999996 | |
| - type: mrr_at_3 | |
| value: 41.743 | |
| - type: mrr_at_5 | |
| value: 43.352000000000004 | |
| - type: ndcg_at_1 | |
| value: 34.932 | |
| - type: ndcg_at_10 | |
| value: 44.901 | |
| - type: ndcg_at_100 | |
| value: 50.788999999999994 | |
| - type: ndcg_at_1000 | |
| value: 52.867 | |
| - type: ndcg_at_3 | |
| value: 39.449 | |
| - type: ndcg_at_5 | |
| value: 41.929 | |
| - type: precision_at_1 | |
| value: 34.932 | |
| - type: precision_at_10 | |
| value: 8.311 | |
| - type: precision_at_100 | |
| value: 1.3050000000000002 | |
| - type: precision_at_1000 | |
| value: 0.166 | |
| - type: precision_at_3 | |
| value: 18.836 | |
| - type: precision_at_5 | |
| value: 13.447000000000001 | |
| - type: recall_at_1 | |
| value: 28.047 | |
| - type: recall_at_10 | |
| value: 57.717 | |
| - type: recall_at_100 | |
| value: 82.182 | |
| - type: recall_at_1000 | |
| value: 95.82000000000001 | |
| - type: recall_at_3 | |
| value: 42.448 | |
| - type: recall_at_5 | |
| value: 49.071 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 27.861250000000005 | |
| - type: map_at_10 | |
| value: 37.529583333333335 | |
| - type: map_at_100 | |
| value: 38.7915 | |
| - type: map_at_1000 | |
| value: 38.90558333333335 | |
| - type: map_at_3 | |
| value: 34.57333333333333 | |
| - type: map_at_5 | |
| value: 36.187166666666656 | |
| - type: mrr_at_1 | |
| value: 32.88291666666666 | |
| - type: mrr_at_10 | |
| value: 41.79750000000001 | |
| - type: mrr_at_100 | |
| value: 42.63183333333333 | |
| - type: mrr_at_1000 | |
| value: 42.68483333333333 | |
| - type: mrr_at_3 | |
| value: 39.313750000000006 | |
| - type: mrr_at_5 | |
| value: 40.70483333333333 | |
| - type: ndcg_at_1 | |
| value: 32.88291666666666 | |
| - type: ndcg_at_10 | |
| value: 43.09408333333333 | |
| - type: ndcg_at_100 | |
| value: 48.22158333333333 | |
| - type: ndcg_at_1000 | |
| value: 50.358000000000004 | |
| - type: ndcg_at_3 | |
| value: 38.129583333333336 | |
| - type: ndcg_at_5 | |
| value: 40.39266666666666 | |
| - type: precision_at_1 | |
| value: 32.88291666666666 | |
| - type: precision_at_10 | |
| value: 7.5584999999999996 | |
| - type: precision_at_100 | |
| value: 1.1903333333333332 | |
| - type: precision_at_1000 | |
| value: 0.15658333333333332 | |
| - type: precision_at_3 | |
| value: 17.495916666666666 | |
| - type: precision_at_5 | |
| value: 12.373833333333332 | |
| - type: recall_at_1 | |
| value: 27.861250000000005 | |
| - type: recall_at_10 | |
| value: 55.215916666666665 | |
| - type: recall_at_100 | |
| value: 77.392 | |
| - type: recall_at_1000 | |
| value: 92.04908333333334 | |
| - type: recall_at_3 | |
| value: 41.37475 | |
| - type: recall_at_5 | |
| value: 47.22908333333333 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackStatsRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 25.064999999999998 | |
| - type: map_at_10 | |
| value: 31.635999999999996 | |
| - type: map_at_100 | |
| value: 32.596000000000004 | |
| - type: map_at_1000 | |
| value: 32.695 | |
| - type: map_at_3 | |
| value: 29.612 | |
| - type: map_at_5 | |
| value: 30.768 | |
| - type: mrr_at_1 | |
| value: 28.528 | |
| - type: mrr_at_10 | |
| value: 34.717 | |
| - type: mrr_at_100 | |
| value: 35.558 | |
| - type: mrr_at_1000 | |
| value: 35.626000000000005 | |
| - type: mrr_at_3 | |
| value: 32.745000000000005 | |
| - type: mrr_at_5 | |
| value: 33.819 | |
| - type: ndcg_at_1 | |
| value: 28.528 | |
| - type: ndcg_at_10 | |
| value: 35.647 | |
| - type: ndcg_at_100 | |
| value: 40.207 | |
| - type: ndcg_at_1000 | |
| value: 42.695 | |
| - type: ndcg_at_3 | |
| value: 31.878 | |
| - type: ndcg_at_5 | |
| value: 33.634 | |
| - type: precision_at_1 | |
| value: 28.528 | |
| - type: precision_at_10 | |
| value: 5.46 | |
| - type: precision_at_100 | |
| value: 0.84 | |
| - type: precision_at_1000 | |
| value: 0.11399999999999999 | |
| - type: precision_at_3 | |
| value: 13.547999999999998 | |
| - type: precision_at_5 | |
| value: 9.325 | |
| - type: recall_at_1 | |
| value: 25.064999999999998 | |
| - type: recall_at_10 | |
| value: 45.096000000000004 | |
| - type: recall_at_100 | |
| value: 65.658 | |
| - type: recall_at_1000 | |
| value: 84.128 | |
| - type: recall_at_3 | |
| value: 34.337 | |
| - type: recall_at_5 | |
| value: 38.849000000000004 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackTexRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 17.276 | |
| - type: map_at_10 | |
| value: 24.535 | |
| - type: map_at_100 | |
| value: 25.655 | |
| - type: map_at_1000 | |
| value: 25.782 | |
| - type: map_at_3 | |
| value: 22.228 | |
| - type: map_at_5 | |
| value: 23.612 | |
| - type: mrr_at_1 | |
| value: 21.266 | |
| - type: mrr_at_10 | |
| value: 28.474 | |
| - type: mrr_at_100 | |
| value: 29.398000000000003 | |
| - type: mrr_at_1000 | |
| value: 29.482000000000003 | |
| - type: mrr_at_3 | |
| value: 26.245 | |
| - type: mrr_at_5 | |
| value: 27.624 | |
| - type: ndcg_at_1 | |
| value: 21.266 | |
| - type: ndcg_at_10 | |
| value: 29.087000000000003 | |
| - type: ndcg_at_100 | |
| value: 34.374 | |
| - type: ndcg_at_1000 | |
| value: 37.433 | |
| - type: ndcg_at_3 | |
| value: 25.040000000000003 | |
| - type: ndcg_at_5 | |
| value: 27.116 | |
| - type: precision_at_1 | |
| value: 21.266 | |
| - type: precision_at_10 | |
| value: 5.258 | |
| - type: precision_at_100 | |
| value: 0.9299999999999999 | |
| - type: precision_at_1000 | |
| value: 0.13699999999999998 | |
| - type: precision_at_3 | |
| value: 11.849 | |
| - type: precision_at_5 | |
| value: 8.699 | |
| - type: recall_at_1 | |
| value: 17.276 | |
| - type: recall_at_10 | |
| value: 38.928000000000004 | |
| - type: recall_at_100 | |
| value: 62.529 | |
| - type: recall_at_1000 | |
| value: 84.44800000000001 | |
| - type: recall_at_3 | |
| value: 27.554000000000002 | |
| - type: recall_at_5 | |
| value: 32.915 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackUnixRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 27.297 | |
| - type: map_at_10 | |
| value: 36.957 | |
| - type: map_at_100 | |
| value: 38.252 | |
| - type: map_at_1000 | |
| value: 38.356 | |
| - type: map_at_3 | |
| value: 34.121 | |
| - type: map_at_5 | |
| value: 35.782000000000004 | |
| - type: mrr_at_1 | |
| value: 32.275999999999996 | |
| - type: mrr_at_10 | |
| value: 41.198 | |
| - type: mrr_at_100 | |
| value: 42.131 | |
| - type: mrr_at_1000 | |
| value: 42.186 | |
| - type: mrr_at_3 | |
| value: 38.557 | |
| - type: mrr_at_5 | |
| value: 40.12 | |
| - type: ndcg_at_1 | |
| value: 32.275999999999996 | |
| - type: ndcg_at_10 | |
| value: 42.516 | |
| - type: ndcg_at_100 | |
| value: 48.15 | |
| - type: ndcg_at_1000 | |
| value: 50.344 | |
| - type: ndcg_at_3 | |
| value: 37.423 | |
| - type: ndcg_at_5 | |
| value: 39.919 | |
| - type: precision_at_1 | |
| value: 32.275999999999996 | |
| - type: precision_at_10 | |
| value: 7.155 | |
| - type: precision_at_100 | |
| value: 1.123 | |
| - type: precision_at_1000 | |
| value: 0.14200000000000002 | |
| - type: precision_at_3 | |
| value: 17.163999999999998 | |
| - type: precision_at_5 | |
| value: 12.127 | |
| - type: recall_at_1 | |
| value: 27.297 | |
| - type: recall_at_10 | |
| value: 55.238 | |
| - type: recall_at_100 | |
| value: 79.2 | |
| - type: recall_at_1000 | |
| value: 94.258 | |
| - type: recall_at_3 | |
| value: 41.327000000000005 | |
| - type: recall_at_5 | |
| value: 47.588 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackWebmastersRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 29.142000000000003 | |
| - type: map_at_10 | |
| value: 38.769 | |
| - type: map_at_100 | |
| value: 40.292 | |
| - type: map_at_1000 | |
| value: 40.510000000000005 | |
| - type: map_at_3 | |
| value: 35.39 | |
| - type: map_at_5 | |
| value: 37.009 | |
| - type: mrr_at_1 | |
| value: 34.19 | |
| - type: mrr_at_10 | |
| value: 43.418 | |
| - type: mrr_at_100 | |
| value: 44.132 | |
| - type: mrr_at_1000 | |
| value: 44.175 | |
| - type: mrr_at_3 | |
| value: 40.547 | |
| - type: mrr_at_5 | |
| value: 42.088 | |
| - type: ndcg_at_1 | |
| value: 34.19 | |
| - type: ndcg_at_10 | |
| value: 45.14 | |
| - type: ndcg_at_100 | |
| value: 50.364 | |
| - type: ndcg_at_1000 | |
| value: 52.481 | |
| - type: ndcg_at_3 | |
| value: 39.466 | |
| - type: ndcg_at_5 | |
| value: 41.772 | |
| - type: precision_at_1 | |
| value: 34.19 | |
| - type: precision_at_10 | |
| value: 8.715 | |
| - type: precision_at_100 | |
| value: 1.6150000000000002 | |
| - type: precision_at_1000 | |
| value: 0.247 | |
| - type: precision_at_3 | |
| value: 18.248 | |
| - type: precision_at_5 | |
| value: 13.161999999999999 | |
| - type: recall_at_1 | |
| value: 29.142000000000003 | |
| - type: recall_at_10 | |
| value: 57.577999999999996 | |
| - type: recall_at_100 | |
| value: 81.428 | |
| - type: recall_at_1000 | |
| value: 94.017 | |
| - type: recall_at_3 | |
| value: 41.402 | |
| - type: recall_at_5 | |
| value: 47.695 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: BeIR/cqadupstack | |
| name: MTEB CQADupstackWordpressRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 22.039 | |
| - type: map_at_10 | |
| value: 30.669999999999998 | |
| - type: map_at_100 | |
| value: 31.682 | |
| - type: map_at_1000 | |
| value: 31.794 | |
| - type: map_at_3 | |
| value: 28.139999999999997 | |
| - type: map_at_5 | |
| value: 29.457 | |
| - type: mrr_at_1 | |
| value: 24.399 | |
| - type: mrr_at_10 | |
| value: 32.687 | |
| - type: mrr_at_100 | |
| value: 33.622 | |
| - type: mrr_at_1000 | |
| value: 33.698 | |
| - type: mrr_at_3 | |
| value: 30.407 | |
| - type: mrr_at_5 | |
| value: 31.552999999999997 | |
| - type: ndcg_at_1 | |
| value: 24.399 | |
| - type: ndcg_at_10 | |
| value: 35.472 | |
| - type: ndcg_at_100 | |
| value: 40.455000000000005 | |
| - type: ndcg_at_1000 | |
| value: 43.15 | |
| - type: ndcg_at_3 | |
| value: 30.575000000000003 | |
| - type: ndcg_at_5 | |
| value: 32.668 | |
| - type: precision_at_1 | |
| value: 24.399 | |
| - type: precision_at_10 | |
| value: 5.656 | |
| - type: precision_at_100 | |
| value: 0.874 | |
| - type: precision_at_1000 | |
| value: 0.121 | |
| - type: precision_at_3 | |
| value: 13.062000000000001 | |
| - type: precision_at_5 | |
| value: 9.242 | |
| - type: recall_at_1 | |
| value: 22.039 | |
| - type: recall_at_10 | |
| value: 48.379 | |
| - type: recall_at_100 | |
| value: 71.11800000000001 | |
| - type: recall_at_1000 | |
| value: 91.095 | |
| - type: recall_at_3 | |
| value: 35.108 | |
| - type: recall_at_5 | |
| value: 40.015 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: climate-fever | |
| name: MTEB ClimateFEVER | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 10.144 | |
| - type: map_at_10 | |
| value: 18.238 | |
| - type: map_at_100 | |
| value: 20.143 | |
| - type: map_at_1000 | |
| value: 20.346 | |
| - type: map_at_3 | |
| value: 14.809 | |
| - type: map_at_5 | |
| value: 16.567999999999998 | |
| - type: mrr_at_1 | |
| value: 22.671 | |
| - type: mrr_at_10 | |
| value: 34.906 | |
| - type: mrr_at_100 | |
| value: 35.858000000000004 | |
| - type: mrr_at_1000 | |
| value: 35.898 | |
| - type: mrr_at_3 | |
| value: 31.238 | |
| - type: mrr_at_5 | |
| value: 33.342 | |
| - type: ndcg_at_1 | |
| value: 22.671 | |
| - type: ndcg_at_10 | |
| value: 26.540000000000003 | |
| - type: ndcg_at_100 | |
| value: 34.138000000000005 | |
| - type: ndcg_at_1000 | |
| value: 37.72 | |
| - type: ndcg_at_3 | |
| value: 20.766000000000002 | |
| - type: ndcg_at_5 | |
| value: 22.927 | |
| - type: precision_at_1 | |
| value: 22.671 | |
| - type: precision_at_10 | |
| value: 8.619 | |
| - type: precision_at_100 | |
| value: 1.678 | |
| - type: precision_at_1000 | |
| value: 0.23500000000000001 | |
| - type: precision_at_3 | |
| value: 15.592 | |
| - type: precision_at_5 | |
| value: 12.43 | |
| - type: recall_at_1 | |
| value: 10.144 | |
| - type: recall_at_10 | |
| value: 33.46 | |
| - type: recall_at_100 | |
| value: 59.758 | |
| - type: recall_at_1000 | |
| value: 79.704 | |
| - type: recall_at_3 | |
| value: 19.604 | |
| - type: recall_at_5 | |
| value: 25.367 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: dbpedia-entity | |
| name: MTEB DBPedia | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 8.654 | |
| - type: map_at_10 | |
| value: 18.506 | |
| - type: map_at_100 | |
| value: 26.412999999999997 | |
| - type: map_at_1000 | |
| value: 28.13 | |
| - type: map_at_3 | |
| value: 13.379 | |
| - type: map_at_5 | |
| value: 15.529000000000002 | |
| - type: mrr_at_1 | |
| value: 66.0 | |
| - type: mrr_at_10 | |
| value: 74.13 | |
| - type: mrr_at_100 | |
| value: 74.48700000000001 | |
| - type: mrr_at_1000 | |
| value: 74.49799999999999 | |
| - type: mrr_at_3 | |
| value: 72.75 | |
| - type: mrr_at_5 | |
| value: 73.762 | |
| - type: ndcg_at_1 | |
| value: 54.50000000000001 | |
| - type: ndcg_at_10 | |
| value: 40.236 | |
| - type: ndcg_at_100 | |
| value: 44.690999999999995 | |
| - type: ndcg_at_1000 | |
| value: 52.195 | |
| - type: ndcg_at_3 | |
| value: 45.632 | |
| - type: ndcg_at_5 | |
| value: 42.952 | |
| - type: precision_at_1 | |
| value: 66.0 | |
| - type: precision_at_10 | |
| value: 31.724999999999998 | |
| - type: precision_at_100 | |
| value: 10.299999999999999 | |
| - type: precision_at_1000 | |
| value: 2.194 | |
| - type: precision_at_3 | |
| value: 48.75 | |
| - type: precision_at_5 | |
| value: 41.6 | |
| - type: recall_at_1 | |
| value: 8.654 | |
| - type: recall_at_10 | |
| value: 23.74 | |
| - type: recall_at_100 | |
| value: 50.346999999999994 | |
| - type: recall_at_1000 | |
| value: 74.376 | |
| - type: recall_at_3 | |
| value: 14.636 | |
| - type: recall_at_5 | |
| value: 18.009 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/emotion | |
| name: MTEB EmotionClassification | |
| config: default | |
| split: test | |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 | |
| metrics: | |
| - type: accuracy | |
| value: 53.245 | |
| - type: f1 | |
| value: 48.74520523753552 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: fever | |
| name: MTEB FEVER | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 51.729 | |
| - type: map_at_10 | |
| value: 63.904 | |
| - type: map_at_100 | |
| value: 64.363 | |
| - type: map_at_1000 | |
| value: 64.38199999999999 | |
| - type: map_at_3 | |
| value: 61.393 | |
| - type: map_at_5 | |
| value: 63.02100000000001 | |
| - type: mrr_at_1 | |
| value: 55.686 | |
| - type: mrr_at_10 | |
| value: 67.804 | |
| - type: mrr_at_100 | |
| value: 68.15299999999999 | |
| - type: mrr_at_1000 | |
| value: 68.161 | |
| - type: mrr_at_3 | |
| value: 65.494 | |
| - type: mrr_at_5 | |
| value: 67.01599999999999 | |
| - type: ndcg_at_1 | |
| value: 55.686 | |
| - type: ndcg_at_10 | |
| value: 70.025 | |
| - type: ndcg_at_100 | |
| value: 72.011 | |
| - type: ndcg_at_1000 | |
| value: 72.443 | |
| - type: ndcg_at_3 | |
| value: 65.32900000000001 | |
| - type: ndcg_at_5 | |
| value: 68.05600000000001 | |
| - type: precision_at_1 | |
| value: 55.686 | |
| - type: precision_at_10 | |
| value: 9.358 | |
| - type: precision_at_100 | |
| value: 1.05 | |
| - type: precision_at_1000 | |
| value: 0.11 | |
| - type: precision_at_3 | |
| value: 26.318 | |
| - type: precision_at_5 | |
| value: 17.321 | |
| - type: recall_at_1 | |
| value: 51.729 | |
| - type: recall_at_10 | |
| value: 85.04 | |
| - type: recall_at_100 | |
| value: 93.777 | |
| - type: recall_at_1000 | |
| value: 96.824 | |
| - type: recall_at_3 | |
| value: 72.521 | |
| - type: recall_at_5 | |
| value: 79.148 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: fiqa | |
| name: MTEB FiQA2018 | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 23.765 | |
| - type: map_at_10 | |
| value: 39.114 | |
| - type: map_at_100 | |
| value: 40.987 | |
| - type: map_at_1000 | |
| value: 41.155 | |
| - type: map_at_3 | |
| value: 34.028000000000006 | |
| - type: map_at_5 | |
| value: 36.925000000000004 | |
| - type: mrr_at_1 | |
| value: 46.451 | |
| - type: mrr_at_10 | |
| value: 54.711 | |
| - type: mrr_at_100 | |
| value: 55.509 | |
| - type: mrr_at_1000 | |
| value: 55.535000000000004 | |
| - type: mrr_at_3 | |
| value: 52.649 | |
| - type: mrr_at_5 | |
| value: 53.729000000000006 | |
| - type: ndcg_at_1 | |
| value: 46.451 | |
| - type: ndcg_at_10 | |
| value: 46.955999999999996 | |
| - type: ndcg_at_100 | |
| value: 53.686 | |
| - type: ndcg_at_1000 | |
| value: 56.230000000000004 | |
| - type: ndcg_at_3 | |
| value: 43.374 | |
| - type: ndcg_at_5 | |
| value: 44.372 | |
| - type: precision_at_1 | |
| value: 46.451 | |
| - type: precision_at_10 | |
| value: 13.256 | |
| - type: precision_at_100 | |
| value: 2.019 | |
| - type: precision_at_1000 | |
| value: 0.247 | |
| - type: precision_at_3 | |
| value: 29.115000000000002 | |
| - type: precision_at_5 | |
| value: 21.389 | |
| - type: recall_at_1 | |
| value: 23.765 | |
| - type: recall_at_10 | |
| value: 53.452999999999996 | |
| - type: recall_at_100 | |
| value: 78.828 | |
| - type: recall_at_1000 | |
| value: 93.938 | |
| - type: recall_at_3 | |
| value: 39.023 | |
| - type: recall_at_5 | |
| value: 45.18 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: hotpotqa | |
| name: MTEB HotpotQA | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 31.918000000000003 | |
| - type: map_at_10 | |
| value: 46.741 | |
| - type: map_at_100 | |
| value: 47.762 | |
| - type: map_at_1000 | |
| value: 47.849000000000004 | |
| - type: map_at_3 | |
| value: 43.578 | |
| - type: map_at_5 | |
| value: 45.395 | |
| - type: mrr_at_1 | |
| value: 63.834999999999994 | |
| - type: mrr_at_10 | |
| value: 71.312 | |
| - type: mrr_at_100 | |
| value: 71.695 | |
| - type: mrr_at_1000 | |
| value: 71.714 | |
| - type: mrr_at_3 | |
| value: 69.82000000000001 | |
| - type: mrr_at_5 | |
| value: 70.726 | |
| - type: ndcg_at_1 | |
| value: 63.834999999999994 | |
| - type: ndcg_at_10 | |
| value: 55.879999999999995 | |
| - type: ndcg_at_100 | |
| value: 59.723000000000006 | |
| - type: ndcg_at_1000 | |
| value: 61.49400000000001 | |
| - type: ndcg_at_3 | |
| value: 50.964 | |
| - type: ndcg_at_5 | |
| value: 53.47 | |
| - type: precision_at_1 | |
| value: 63.834999999999994 | |
| - type: precision_at_10 | |
| value: 11.845 | |
| - type: precision_at_100 | |
| value: 1.4869999999999999 | |
| - type: precision_at_1000 | |
| value: 0.172 | |
| - type: precision_at_3 | |
| value: 32.158 | |
| - type: precision_at_5 | |
| value: 21.278 | |
| - type: recall_at_1 | |
| value: 31.918000000000003 | |
| - type: recall_at_10 | |
| value: 59.223000000000006 | |
| - type: recall_at_100 | |
| value: 74.328 | |
| - type: recall_at_1000 | |
| value: 86.05000000000001 | |
| - type: recall_at_3 | |
| value: 48.238 | |
| - type: recall_at_5 | |
| value: 53.193999999999996 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/imdb | |
| name: MTEB ImdbClassification | |
| config: default | |
| split: test | |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 | |
| metrics: | |
| - type: accuracy | |
| value: 79.7896 | |
| - type: ap | |
| value: 73.65166029460288 | |
| - type: f1 | |
| value: 79.71794693711813 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: msmarco | |
| name: MTEB MSMARCO | |
| config: default | |
| split: dev | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 22.239 | |
| - type: map_at_10 | |
| value: 34.542 | |
| - type: map_at_100 | |
| value: 35.717999999999996 | |
| - type: map_at_1000 | |
| value: 35.764 | |
| - type: map_at_3 | |
| value: 30.432 | |
| - type: map_at_5 | |
| value: 32.81 | |
| - type: mrr_at_1 | |
| value: 22.908 | |
| - type: mrr_at_10 | |
| value: 35.127 | |
| - type: mrr_at_100 | |
| value: 36.238 | |
| - type: mrr_at_1000 | |
| value: 36.278 | |
| - type: mrr_at_3 | |
| value: 31.076999999999998 | |
| - type: mrr_at_5 | |
| value: 33.419 | |
| - type: ndcg_at_1 | |
| value: 22.908 | |
| - type: ndcg_at_10 | |
| value: 41.607 | |
| - type: ndcg_at_100 | |
| value: 47.28 | |
| - type: ndcg_at_1000 | |
| value: 48.414 | |
| - type: ndcg_at_3 | |
| value: 33.253 | |
| - type: ndcg_at_5 | |
| value: 37.486000000000004 | |
| - type: precision_at_1 | |
| value: 22.908 | |
| - type: precision_at_10 | |
| value: 6.645 | |
| - type: precision_at_100 | |
| value: 0.9490000000000001 | |
| - type: precision_at_1000 | |
| value: 0.105 | |
| - type: precision_at_3 | |
| value: 14.130999999999998 | |
| - type: precision_at_5 | |
| value: 10.616 | |
| - type: recall_at_1 | |
| value: 22.239 | |
| - type: recall_at_10 | |
| value: 63.42 | |
| - type: recall_at_100 | |
| value: 89.696 | |
| - type: recall_at_1000 | |
| value: 98.351 | |
| - type: recall_at_3 | |
| value: 40.77 | |
| - type: recall_at_5 | |
| value: 50.93 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/mtop_domain | |
| name: MTEB MTOPDomainClassification (en) | |
| config: en | |
| split: test | |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf | |
| metrics: | |
| - type: accuracy | |
| value: 95.06839945280439 | |
| - type: f1 | |
| value: 94.74276398224072 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/mtop_intent | |
| name: MTEB MTOPIntentClassification (en) | |
| config: en | |
| split: test | |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba | |
| metrics: | |
| - type: accuracy | |
| value: 72.25718194254446 | |
| - type: f1 | |
| value: 53.91164489161391 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/amazon_massive_intent | |
| name: MTEB MassiveIntentClassification (en) | |
| config: en | |
| split: test | |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 | |
| metrics: | |
| - type: accuracy | |
| value: 71.47948890383323 | |
| - type: f1 | |
| value: 69.98520247230257 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/amazon_massive_scenario | |
| name: MTEB MassiveScenarioClassification (en) | |
| config: en | |
| split: test | |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 | |
| metrics: | |
| - type: accuracy | |
| value: 76.46603900470748 | |
| - type: f1 | |
| value: 76.44111526065399 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/medrxiv-clustering-p2p | |
| name: MTEB MedrxivClusteringP2P | |
| config: default | |
| split: test | |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 | |
| metrics: | |
| - type: v_measure | |
| value: 33.19106070798198 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/medrxiv-clustering-s2s | |
| name: MTEB MedrxivClusteringS2S | |
| config: default | |
| split: test | |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 | |
| metrics: | |
| - type: v_measure | |
| value: 30.78772205248094 | |
| - task: | |
| type: Reranking | |
| dataset: | |
| type: mteb/mind_small | |
| name: MTEB MindSmallReranking | |
| config: default | |
| split: test | |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 | |
| metrics: | |
| - type: map | |
| value: 31.811231631488507 | |
| - type: mrr | |
| value: 32.98200485378021 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: nfcorpus | |
| name: MTEB NFCorpus | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 6.9 | |
| - type: map_at_10 | |
| value: 13.703000000000001 | |
| - type: map_at_100 | |
| value: 17.251 | |
| - type: map_at_1000 | |
| value: 18.795 | |
| - type: map_at_3 | |
| value: 10.366999999999999 | |
| - type: map_at_5 | |
| value: 11.675 | |
| - type: mrr_at_1 | |
| value: 47.059 | |
| - type: mrr_at_10 | |
| value: 55.816 | |
| - type: mrr_at_100 | |
| value: 56.434 | |
| - type: mrr_at_1000 | |
| value: 56.467 | |
| - type: mrr_at_3 | |
| value: 53.973000000000006 | |
| - type: mrr_at_5 | |
| value: 55.257999999999996 | |
| - type: ndcg_at_1 | |
| value: 44.737 | |
| - type: ndcg_at_10 | |
| value: 35.997 | |
| - type: ndcg_at_100 | |
| value: 33.487 | |
| - type: ndcg_at_1000 | |
| value: 41.897 | |
| - type: ndcg_at_3 | |
| value: 41.18 | |
| - type: ndcg_at_5 | |
| value: 38.721 | |
| - type: precision_at_1 | |
| value: 46.129999999999995 | |
| - type: precision_at_10 | |
| value: 26.533 | |
| - type: precision_at_100 | |
| value: 8.706 | |
| - type: precision_at_1000 | |
| value: 2.16 | |
| - type: precision_at_3 | |
| value: 38.493 | |
| - type: precision_at_5 | |
| value: 33.189 | |
| - type: recall_at_1 | |
| value: 6.9 | |
| - type: recall_at_10 | |
| value: 17.488999999999997 | |
| - type: recall_at_100 | |
| value: 34.583000000000006 | |
| - type: recall_at_1000 | |
| value: 64.942 | |
| - type: recall_at_3 | |
| value: 11.494 | |
| - type: recall_at_5 | |
| value: 13.496 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: nq | |
| name: MTEB NQ | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 33.028999999999996 | |
| - type: map_at_10 | |
| value: 49.307 | |
| - type: map_at_100 | |
| value: 50.205 | |
| - type: map_at_1000 | |
| value: 50.23 | |
| - type: map_at_3 | |
| value: 44.782 | |
| - type: map_at_5 | |
| value: 47.599999999999994 | |
| - type: mrr_at_1 | |
| value: 37.108999999999995 | |
| - type: mrr_at_10 | |
| value: 51.742999999999995 | |
| - type: mrr_at_100 | |
| value: 52.405 | |
| - type: mrr_at_1000 | |
| value: 52.422000000000004 | |
| - type: mrr_at_3 | |
| value: 48.087999999999994 | |
| - type: mrr_at_5 | |
| value: 50.414 | |
| - type: ndcg_at_1 | |
| value: 37.08 | |
| - type: ndcg_at_10 | |
| value: 57.236 | |
| - type: ndcg_at_100 | |
| value: 60.931999999999995 | |
| - type: ndcg_at_1000 | |
| value: 61.522 | |
| - type: ndcg_at_3 | |
| value: 48.93 | |
| - type: ndcg_at_5 | |
| value: 53.561 | |
| - type: precision_at_1 | |
| value: 37.08 | |
| - type: precision_at_10 | |
| value: 9.386 | |
| - type: precision_at_100 | |
| value: 1.1480000000000001 | |
| - type: precision_at_1000 | |
| value: 0.12 | |
| - type: precision_at_3 | |
| value: 22.258 | |
| - type: precision_at_5 | |
| value: 16.025 | |
| - type: recall_at_1 | |
| value: 33.028999999999996 | |
| - type: recall_at_10 | |
| value: 78.805 | |
| - type: recall_at_100 | |
| value: 94.643 | |
| - type: recall_at_1000 | |
| value: 99.039 | |
| - type: recall_at_3 | |
| value: 57.602 | |
| - type: recall_at_5 | |
| value: 68.253 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: quora | |
| name: MTEB QuoraRetrieval | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 71.122 | |
| - type: map_at_10 | |
| value: 85.237 | |
| - type: map_at_100 | |
| value: 85.872 | |
| - type: map_at_1000 | |
| value: 85.885 | |
| - type: map_at_3 | |
| value: 82.27499999999999 | |
| - type: map_at_5 | |
| value: 84.13199999999999 | |
| - type: mrr_at_1 | |
| value: 81.73 | |
| - type: mrr_at_10 | |
| value: 87.834 | |
| - type: mrr_at_100 | |
| value: 87.92 | |
| - type: mrr_at_1000 | |
| value: 87.921 | |
| - type: mrr_at_3 | |
| value: 86.878 | |
| - type: mrr_at_5 | |
| value: 87.512 | |
| - type: ndcg_at_1 | |
| value: 81.73 | |
| - type: ndcg_at_10 | |
| value: 88.85499999999999 | |
| - type: ndcg_at_100 | |
| value: 89.992 | |
| - type: ndcg_at_1000 | |
| value: 90.07 | |
| - type: ndcg_at_3 | |
| value: 85.997 | |
| - type: ndcg_at_5 | |
| value: 87.55199999999999 | |
| - type: precision_at_1 | |
| value: 81.73 | |
| - type: precision_at_10 | |
| value: 13.491 | |
| - type: precision_at_100 | |
| value: 1.536 | |
| - type: precision_at_1000 | |
| value: 0.157 | |
| - type: precision_at_3 | |
| value: 37.623 | |
| - type: precision_at_5 | |
| value: 24.742 | |
| - type: recall_at_1 | |
| value: 71.122 | |
| - type: recall_at_10 | |
| value: 95.935 | |
| - type: recall_at_100 | |
| value: 99.657 | |
| - type: recall_at_1000 | |
| value: 99.996 | |
| - type: recall_at_3 | |
| value: 87.80799999999999 | |
| - type: recall_at_5 | |
| value: 92.161 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/reddit-clustering | |
| name: MTEB RedditClustering | |
| config: default | |
| split: test | |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb | |
| metrics: | |
| - type: v_measure | |
| value: 63.490029238193756 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/reddit-clustering-p2p | |
| name: MTEB RedditClusteringP2P | |
| config: default | |
| split: test | |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 | |
| metrics: | |
| - type: v_measure | |
| value: 65.13153408508836 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: scidocs | |
| name: MTEB SCIDOCS | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 4.202999999999999 | |
| - type: map_at_10 | |
| value: 10.174 | |
| - type: map_at_100 | |
| value: 12.138 | |
| - type: map_at_1000 | |
| value: 12.418 | |
| - type: map_at_3 | |
| value: 7.379 | |
| - type: map_at_5 | |
| value: 8.727 | |
| - type: mrr_at_1 | |
| value: 20.7 | |
| - type: mrr_at_10 | |
| value: 30.389 | |
| - type: mrr_at_100 | |
| value: 31.566 | |
| - type: mrr_at_1000 | |
| value: 31.637999999999998 | |
| - type: mrr_at_3 | |
| value: 27.133000000000003 | |
| - type: mrr_at_5 | |
| value: 29.078 | |
| - type: ndcg_at_1 | |
| value: 20.7 | |
| - type: ndcg_at_10 | |
| value: 17.355999999999998 | |
| - type: ndcg_at_100 | |
| value: 25.151 | |
| - type: ndcg_at_1000 | |
| value: 30.37 | |
| - type: ndcg_at_3 | |
| value: 16.528000000000002 | |
| - type: ndcg_at_5 | |
| value: 14.396999999999998 | |
| - type: precision_at_1 | |
| value: 20.7 | |
| - type: precision_at_10 | |
| value: 8.98 | |
| - type: precision_at_100 | |
| value: 2.015 | |
| - type: precision_at_1000 | |
| value: 0.327 | |
| - type: precision_at_3 | |
| value: 15.367 | |
| - type: precision_at_5 | |
| value: 12.559999999999999 | |
| - type: recall_at_1 | |
| value: 4.202999999999999 | |
| - type: recall_at_10 | |
| value: 18.197 | |
| - type: recall_at_100 | |
| value: 40.903 | |
| - type: recall_at_1000 | |
| value: 66.427 | |
| - type: recall_at_3 | |
| value: 9.362 | |
| - type: recall_at_5 | |
| value: 12.747 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sickr-sts | |
| name: MTEB SICK-R | |
| config: default | |
| split: test | |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 81.69890989765257 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts12-sts | |
| name: MTEB STS12 | |
| config: default | |
| split: test | |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 75.31953790551489 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts13-sts | |
| name: MTEB STS13 | |
| config: default | |
| split: test | |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 87.44050861280759 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts14-sts | |
| name: MTEB STS14 | |
| config: default | |
| split: test | |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 81.86922869270393 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts15-sts | |
| name: MTEB STS15 | |
| config: default | |
| split: test | |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 88.9399170304284 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts16-sts | |
| name: MTEB STS16 | |
| config: default | |
| split: test | |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 85.38015314088582 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts17-crosslingual-sts | |
| name: MTEB STS17 (en-en) | |
| config: en-en | |
| split: test | |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 90.53653527788835 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/sts22-crosslingual-sts | |
| name: MTEB STS22 (en) | |
| config: en | |
| split: test | |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 68.64526474250209 | |
| - task: | |
| type: STS | |
| dataset: | |
| type: mteb/stsbenchmark-sts | |
| name: MTEB STSBenchmark | |
| config: default | |
| split: test | |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 | |
| metrics: | |
| - type: cos_sim_spearman | |
| value: 86.56156983963042 | |
| - task: | |
| type: Reranking | |
| dataset: | |
| type: mteb/scidocs-reranking | |
| name: MTEB SciDocsRR | |
| config: default | |
| split: test | |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab | |
| metrics: | |
| - type: map | |
| value: 79.48610254648003 | |
| - type: mrr | |
| value: 94.02481505422682 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: scifact | |
| name: MTEB SciFact | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 48.983 | |
| - type: map_at_10 | |
| value: 59.077999999999996 | |
| - type: map_at_100 | |
| value: 59.536 | |
| - type: map_at_1000 | |
| value: 59.575 | |
| - type: map_at_3 | |
| value: 55.691 | |
| - type: map_at_5 | |
| value: 57.410000000000004 | |
| - type: mrr_at_1 | |
| value: 51.666999999999994 | |
| - type: mrr_at_10 | |
| value: 60.427 | |
| - type: mrr_at_100 | |
| value: 60.763 | |
| - type: mrr_at_1000 | |
| value: 60.79900000000001 | |
| - type: mrr_at_3 | |
| value: 57.556 | |
| - type: mrr_at_5 | |
| value: 59.089000000000006 | |
| - type: ndcg_at_1 | |
| value: 51.666999999999994 | |
| - type: ndcg_at_10 | |
| value: 64.559 | |
| - type: ndcg_at_100 | |
| value: 66.58 | |
| - type: ndcg_at_1000 | |
| value: 67.64 | |
| - type: ndcg_at_3 | |
| value: 58.287 | |
| - type: ndcg_at_5 | |
| value: 61.001000000000005 | |
| - type: precision_at_1 | |
| value: 51.666999999999994 | |
| - type: precision_at_10 | |
| value: 9.067 | |
| - type: precision_at_100 | |
| value: 1.0170000000000001 | |
| - type: precision_at_1000 | |
| value: 0.11100000000000002 | |
| - type: precision_at_3 | |
| value: 23.0 | |
| - type: precision_at_5 | |
| value: 15.6 | |
| - type: recall_at_1 | |
| value: 48.983 | |
| - type: recall_at_10 | |
| value: 80.289 | |
| - type: recall_at_100 | |
| value: 89.43299999999999 | |
| - type: recall_at_1000 | |
| value: 97.667 | |
| - type: recall_at_3 | |
| value: 62.978 | |
| - type: recall_at_5 | |
| value: 69.872 | |
| - task: | |
| type: PairClassification | |
| dataset: | |
| type: mteb/sprintduplicatequestions-pairclassification | |
| name: MTEB SprintDuplicateQuestions | |
| config: default | |
| split: test | |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 | |
| metrics: | |
| - type: cos_sim_accuracy | |
| value: 99.79009900990098 | |
| - type: cos_sim_ap | |
| value: 94.94115052608419 | |
| - type: cos_sim_f1 | |
| value: 89.1260162601626 | |
| - type: cos_sim_precision | |
| value: 90.599173553719 | |
| - type: cos_sim_recall | |
| value: 87.7 | |
| - type: dot_accuracy | |
| value: 99.79009900990098 | |
| - type: dot_ap | |
| value: 94.94115052608419 | |
| - type: dot_f1 | |
| value: 89.1260162601626 | |
| - type: dot_precision | |
| value: 90.599173553719 | |
| - type: dot_recall | |
| value: 87.7 | |
| - type: euclidean_accuracy | |
| value: 99.79009900990098 | |
| - type: euclidean_ap | |
| value: 94.94115052608419 | |
| - type: euclidean_f1 | |
| value: 89.1260162601626 | |
| - type: euclidean_precision | |
| value: 90.599173553719 | |
| - type: euclidean_recall | |
| value: 87.7 | |
| - type: manhattan_accuracy | |
| value: 99.7940594059406 | |
| - type: manhattan_ap | |
| value: 94.95271414642431 | |
| - type: manhattan_f1 | |
| value: 89.24508790072387 | |
| - type: manhattan_precision | |
| value: 92.3982869379015 | |
| - type: manhattan_recall | |
| value: 86.3 | |
| - type: max_accuracy | |
| value: 99.7940594059406 | |
| - type: max_ap | |
| value: 94.95271414642431 | |
| - type: max_f1 | |
| value: 89.24508790072387 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/stackexchange-clustering | |
| name: MTEB StackExchangeClustering | |
| config: default | |
| split: test | |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 | |
| metrics: | |
| - type: v_measure | |
| value: 68.43866571935851 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/stackexchange-clustering-p2p | |
| name: MTEB StackExchangeClusteringP2P | |
| config: default | |
| split: test | |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 | |
| metrics: | |
| - type: v_measure | |
| value: 35.16579026551532 | |
| - task: | |
| type: Reranking | |
| dataset: | |
| type: mteb/stackoverflowdupquestions-reranking | |
| name: MTEB StackOverflowDupQuestions | |
| config: default | |
| split: test | |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 | |
| metrics: | |
| - type: map | |
| value: 52.518952473513934 | |
| - type: mrr | |
| value: 53.292457134368895 | |
| - task: | |
| type: Summarization | |
| dataset: | |
| type: mteb/summeval | |
| name: MTEB SummEval | |
| config: default | |
| split: test | |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c | |
| metrics: | |
| - type: cos_sim_pearson | |
| value: 31.12529588316604 | |
| - type: cos_sim_spearman | |
| value: 32.31662126895294 | |
| - type: dot_pearson | |
| value: 31.125303796647056 | |
| - type: dot_spearman | |
| value: 32.31662126895294 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: trec-covid | |
| name: MTEB TRECCOVID | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 0.219 | |
| - type: map_at_10 | |
| value: 1.7469999999999999 | |
| - type: map_at_100 | |
| value: 10.177999999999999 | |
| - type: map_at_1000 | |
| value: 26.108999999999998 | |
| - type: map_at_3 | |
| value: 0.64 | |
| - type: map_at_5 | |
| value: 0.968 | |
| - type: mrr_at_1 | |
| value: 82.0 | |
| - type: mrr_at_10 | |
| value: 89.067 | |
| - type: mrr_at_100 | |
| value: 89.067 | |
| - type: mrr_at_1000 | |
| value: 89.067 | |
| - type: mrr_at_3 | |
| value: 88.333 | |
| - type: mrr_at_5 | |
| value: 88.73299999999999 | |
| - type: ndcg_at_1 | |
| value: 78.0 | |
| - type: ndcg_at_10 | |
| value: 71.398 | |
| - type: ndcg_at_100 | |
| value: 55.574999999999996 | |
| - type: ndcg_at_1000 | |
| value: 51.771 | |
| - type: ndcg_at_3 | |
| value: 77.765 | |
| - type: ndcg_at_5 | |
| value: 73.614 | |
| - type: precision_at_1 | |
| value: 82.0 | |
| - type: precision_at_10 | |
| value: 75.4 | |
| - type: precision_at_100 | |
| value: 58.040000000000006 | |
| - type: precision_at_1000 | |
| value: 23.516000000000002 | |
| - type: precision_at_3 | |
| value: 84.0 | |
| - type: precision_at_5 | |
| value: 78.4 | |
| - type: recall_at_1 | |
| value: 0.219 | |
| - type: recall_at_10 | |
| value: 1.958 | |
| - type: recall_at_100 | |
| value: 13.797999999999998 | |
| - type: recall_at_1000 | |
| value: 49.881 | |
| - type: recall_at_3 | |
| value: 0.672 | |
| - type: recall_at_5 | |
| value: 1.0370000000000001 | |
| - task: | |
| type: Retrieval | |
| dataset: | |
| type: webis-touche2020 | |
| name: MTEB Touche2020 | |
| config: default | |
| split: test | |
| revision: None | |
| metrics: | |
| - type: map_at_1 | |
| value: 1.8610000000000002 | |
| - type: map_at_10 | |
| value: 8.705 | |
| - type: map_at_100 | |
| value: 15.164 | |
| - type: map_at_1000 | |
| value: 16.78 | |
| - type: map_at_3 | |
| value: 4.346 | |
| - type: map_at_5 | |
| value: 6.151 | |
| - type: mrr_at_1 | |
| value: 22.448999999999998 | |
| - type: mrr_at_10 | |
| value: 41.556 | |
| - type: mrr_at_100 | |
| value: 42.484 | |
| - type: mrr_at_1000 | |
| value: 42.494 | |
| - type: mrr_at_3 | |
| value: 37.755 | |
| - type: mrr_at_5 | |
| value: 40.102 | |
| - type: ndcg_at_1 | |
| value: 21.429000000000002 | |
| - type: ndcg_at_10 | |
| value: 23.439 | |
| - type: ndcg_at_100 | |
| value: 36.948 | |
| - type: ndcg_at_1000 | |
| value: 48.408 | |
| - type: ndcg_at_3 | |
| value: 22.261 | |
| - type: ndcg_at_5 | |
| value: 23.085 | |
| - type: precision_at_1 | |
| value: 22.448999999999998 | |
| - type: precision_at_10 | |
| value: 21.633 | |
| - type: precision_at_100 | |
| value: 8.02 | |
| - type: precision_at_1000 | |
| value: 1.5939999999999999 | |
| - type: precision_at_3 | |
| value: 23.810000000000002 | |
| - type: precision_at_5 | |
| value: 24.490000000000002 | |
| - type: recall_at_1 | |
| value: 1.8610000000000002 | |
| - type: recall_at_10 | |
| value: 15.876000000000001 | |
| - type: recall_at_100 | |
| value: 50.300999999999995 | |
| - type: recall_at_1000 | |
| value: 86.098 | |
| - type: recall_at_3 | |
| value: 5.892 | |
| - type: recall_at_5 | |
| value: 9.443 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/toxic_conversations_50k | |
| name: MTEB ToxicConversationsClassification | |
| config: default | |
| split: test | |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c | |
| metrics: | |
| - type: accuracy | |
| value: 70.3264 | |
| - type: ap | |
| value: 13.249577616243794 | |
| - type: f1 | |
| value: 53.621518367695685 | |
| - task: | |
| type: Classification | |
| dataset: | |
| type: mteb/tweet_sentiment_extraction | |
| name: MTEB TweetSentimentExtractionClassification | |
| config: default | |
| split: test | |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a | |
| metrics: | |
| - type: accuracy | |
| value: 61.57611771363894 | |
| - type: f1 | |
| value: 61.79797478568639 | |
| - task: | |
| type: Clustering | |
| dataset: | |
| type: mteb/twentynewsgroups-clustering | |
| name: MTEB TwentyNewsgroupsClustering | |
| config: default | |
| split: test | |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 | |
| metrics: | |
| - type: v_measure | |
| value: 53.38315344479284 | |
| - task: | |
| type: PairClassification | |
| dataset: | |
| type: mteb/twittersemeval2015-pairclassification | |
| name: MTEB TwitterSemEval2015 | |
| config: default | |
| split: test | |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 | |
| metrics: | |
| - type: cos_sim_accuracy | |
| value: 87.55438993860642 | |
| - type: cos_sim_ap | |
| value: 77.98702600017738 | |
| - type: cos_sim_f1 | |
| value: 71.94971653931476 | |
| - type: cos_sim_precision | |
| value: 67.50693802035153 | |
| - type: cos_sim_recall | |
| value: 77.01846965699208 | |
| - type: dot_accuracy | |
| value: 87.55438993860642 | |
| - type: dot_ap | |
| value: 77.98702925907986 | |
| - type: dot_f1 | |
| value: 71.94971653931476 | |
| - type: dot_precision | |
| value: 67.50693802035153 | |
| - type: dot_recall | |
| value: 77.01846965699208 | |
| - type: euclidean_accuracy | |
| value: 87.55438993860642 | |
| - type: euclidean_ap | |
| value: 77.98702951957925 | |
| - type: euclidean_f1 | |
| value: 71.94971653931476 | |
| - type: euclidean_precision | |
| value: 67.50693802035153 | |
| - type: euclidean_recall | |
| value: 77.01846965699208 | |
| - type: manhattan_accuracy | |
| value: 87.54246885617214 | |
| - type: manhattan_ap | |
| value: 77.95531413902947 | |
| - type: manhattan_f1 | |
| value: 71.93605683836589 | |
| - type: manhattan_precision | |
| value: 69.28152492668622 | |
| - type: manhattan_recall | |
| value: 74.80211081794195 | |
| - type: max_accuracy | |
| value: 87.55438993860642 | |
| - type: max_ap | |
| value: 77.98702951957925 | |
| - type: max_f1 | |
| value: 71.94971653931476 | |
| - task: | |
| type: PairClassification | |
| dataset: | |
| type: mteb/twitterurlcorpus-pairclassification | |
| name: MTEB TwitterURLCorpus | |
| config: default | |
| split: test | |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf | |
| metrics: | |
| - type: cos_sim_accuracy | |
| value: 89.47296930182016 | |
| - type: cos_sim_ap | |
| value: 86.92853616302108 | |
| - type: cos_sim_f1 | |
| value: 79.35138351681047 | |
| - type: cos_sim_precision | |
| value: 76.74820143884892 | |
| - type: cos_sim_recall | |
| value: 82.13735756082538 | |
| - type: dot_accuracy | |
| value: 89.47296930182016 | |
| - type: dot_ap | |
| value: 86.92854339601595 | |
| - type: dot_f1 | |
| value: 79.35138351681047 | |
| - type: dot_precision | |
| value: 76.74820143884892 | |
| - type: dot_recall | |
| value: 82.13735756082538 | |
| - type: euclidean_accuracy | |
| value: 89.47296930182016 | |
| - type: euclidean_ap | |
| value: 86.92854191061649 | |
| - type: euclidean_f1 | |
| value: 79.35138351681047 | |
| - type: euclidean_precision | |
| value: 76.74820143884892 | |
| - type: euclidean_recall | |
| value: 82.13735756082538 | |
| - type: manhattan_accuracy | |
| value: 89.47685023479644 | |
| - type: manhattan_ap | |
| value: 86.90063722679578 | |
| - type: manhattan_f1 | |
| value: 79.30753865502702 | |
| - type: manhattan_precision | |
| value: 76.32066068631639 | |
| - type: manhattan_recall | |
| value: 82.53772713273791 | |
| - type: max_accuracy | |
| value: 89.47685023479644 | |
| - type: max_ap | |
| value: 86.92854339601595 | |
| - type: max_f1 | |
| value: 79.35138351681047 | |
| # retrainai/instructor-xl | |
| Its a fork of the original [hkunlp/instructor-xl](https://huggingface.co/hkunlp/instructor-xl) with minimal modifications to support running it in HuggingFace inference endpoint, just use the builtin custom handler. | |
| # hkunlp/instructor-xl | |
| We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks! | |
| The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! | |
| **************************** **Updates** **************************** | |
| * 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance. | |
| * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out! | |
| ## Quick start | |
| <hr /> | |
| ## Installation | |
| ```bash | |
| pip install InstructorEmbedding | |
| ``` | |
| ## Compute your customized embeddings | |
| Then you can use the model like this to calculate domain-specific and task-aware embeddings: | |
| ```python | |
| from InstructorEmbedding import INSTRUCTOR | |
| model = INSTRUCTOR('hkunlp/instructor-xl') | |
| sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" | |
| instruction = "Represent the Science title:" | |
| embeddings = model.encode([[instruction,sentence]]) | |
| print(embeddings) | |
| ``` | |
| ## Use cases | |
| <hr /> | |
| ## Calculate embeddings for your customized texts | |
| If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: | |
| Represent the `domain` `text_type` for `task_objective`: | |
| * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. | |
| * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. | |
| * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. | |
| ## Calculate Sentence similarities | |
| You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. | |
| ```python | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], | |
| ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] | |
| sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], | |
| ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] | |
| embeddings_a = model.encode(sentences_a) | |
| embeddings_b = model.encode(sentences_b) | |
| similarities = cosine_similarity(embeddings_a,embeddings_b) | |
| print(similarities) | |
| ``` | |
| ## Information Retrieval | |
| You can also use **customized embeddings** for information retrieval. | |
| ```python | |
| import numpy as np | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] | |
| corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], | |
| ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], | |
| ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] | |
| query_embeddings = model.encode(query) | |
| corpus_embeddings = model.encode(corpus) | |
| similarities = cosine_similarity(query_embeddings,corpus_embeddings) | |
| retrieved_doc_id = np.argmax(similarities) | |
| print(retrieved_doc_id) | |
| ``` | |
| ## Clustering | |
| Use **customized embeddings** for clustering texts in groups. | |
| ```python | |
| import sklearn.cluster | |
| sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], | |
| ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], | |
| ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], | |
| ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], | |
| ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] | |
| embeddings = model.encode(sentences) | |
| clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) | |
| clustering_model.fit(embeddings) | |
| cluster_assignment = clustering_model.labels_ | |
| print(cluster_assignment) | |
| ``` |