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--- |
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license: apache-2.0 |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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- arctic |
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- snowflake-arctic-embed |
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model-index: |
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- name: snowflake-arctic-embed-l |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
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- type: accuracy |
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value: 74.80597014925374 |
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- type: ap |
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value: 37.911466766189875 |
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- type: f1 |
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value: 68.88606927542106 |
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- task: |
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type: Classification |
|
dataset: |
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type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
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metrics: |
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- type: accuracy |
|
value: 78.402275 |
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- type: ap |
|
value: 73.03294793248114 |
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- type: f1 |
|
value: 78.3147786132161 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
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- type: accuracy |
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value: 36.717999999999996 |
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- type: f1 |
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value: 35.918044248787766 |
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- task: |
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type: Retrieval |
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dataset: |
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type: mteb/arguana |
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name: MTEB ArguAna |
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config: default |
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split: test |
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revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
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metrics: |
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- type: map_at_1 |
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value: 34.495 |
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- type: map_at_10 |
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value: 50.236000000000004 |
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- type: map_at_100 |
|
value: 50.944 |
|
- type: map_at_1000 |
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value: 50.94499999999999 |
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- type: map_at_3 |
|
value: 45.341 |
|
- type: map_at_5 |
|
value: 48.286 |
|
- type: mrr_at_1 |
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value: 35.135 |
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- type: mrr_at_10 |
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value: 50.471 |
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- type: mrr_at_100 |
|
value: 51.185 |
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- type: mrr_at_1000 |
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value: 51.187000000000005 |
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- type: mrr_at_3 |
|
value: 45.602 |
|
- type: mrr_at_5 |
|
value: 48.468 |
|
- type: ndcg_at_1 |
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value: 34.495 |
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- type: ndcg_at_10 |
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value: 59.086000000000006 |
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- type: ndcg_at_100 |
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value: 61.937 |
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- type: ndcg_at_1000 |
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value: 61.966 |
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- type: ndcg_at_3 |
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value: 49.062 |
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- type: ndcg_at_5 |
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value: 54.367 |
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- type: precision_at_1 |
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value: 34.495 |
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- type: precision_at_10 |
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value: 8.734 |
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- type: precision_at_100 |
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value: 0.9939999999999999 |
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- type: precision_at_1000 |
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value: 0.1 |
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- type: precision_at_3 |
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value: 19.962 |
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- type: precision_at_5 |
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value: 14.552000000000001 |
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- type: recall_at_1 |
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value: 34.495 |
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- type: recall_at_10 |
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value: 87.33999999999999 |
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- type: recall_at_100 |
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value: 99.431 |
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- type: recall_at_1000 |
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value: 99.644 |
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- type: recall_at_3 |
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value: 59.885999999999996 |
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- type: recall_at_5 |
|
value: 72.76 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/arxiv-clustering-p2p |
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name: MTEB ArxivClusteringP2P |
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config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
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metrics: |
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- type: v_measure |
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value: 47.46440874635501 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/arxiv-clustering-s2s |
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name: MTEB ArxivClusteringS2S |
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config: default |
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split: test |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
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- type: v_measure |
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value: 38.28720154213723 |
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- task: |
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type: Reranking |
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dataset: |
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type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
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- type: map |
|
value: 60.34614226394902 |
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- type: mrr |
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value: 75.05628105351096 |
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- task: |
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type: STS |
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dataset: |
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type: mteb/biosses-sts |
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name: MTEB BIOSSES |
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config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
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metrics: |
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- type: cos_sim_pearson |
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value: 87.41072716728198 |
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- type: cos_sim_spearman |
|
value: 86.34534093114372 |
|
- type: euclidean_pearson |
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value: 85.34009667750838 |
|
- type: euclidean_spearman |
|
value: 86.34534093114372 |
|
- type: manhattan_pearson |
|
value: 85.2158833586889 |
|
- type: manhattan_spearman |
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value: 86.60920236509224 |
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- task: |
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type: Classification |
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dataset: |
|
type: mteb/banking77 |
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name: MTEB Banking77Classification |
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config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
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- type: accuracy |
|
value: 80.06493506493507 |
|
- type: f1 |
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value: 79.28108600339833 |
|
- task: |
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type: Clustering |
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dataset: |
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type: jinaai/big-patent-clustering |
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name: MTEB BigPatentClustering |
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config: default |
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split: test |
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revision: 62d5330920bca426ce9d3c76ea914f15fc83e891 |
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metrics: |
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- type: v_measure |
|
value: 20.545049432417287 |
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- task: |
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type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
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name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
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metrics: |
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- type: v_measure |
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value: 37.54369718479804 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/biorxiv-clustering-s2s |
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name: MTEB BiorxivClusteringS2S |
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config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
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metrics: |
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- type: v_measure |
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value: 32.64941588219162 |
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- task: |
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type: Retrieval |
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dataset: |
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type: mteb/cqadupstack-android |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: f46a197baaae43b4f621051089b82a364682dfeb |
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metrics: |
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- type: map_at_1 |
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value: 37.264 |
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- type: map_at_10 |
|
value: 49.43 |
|
- type: map_at_100 |
|
value: 50.967 |
|
- type: map_at_1000 |
|
value: 51.08200000000001 |
|
- type: map_at_3 |
|
value: 45.742 |
|
- type: map_at_5 |
|
value: 47.764 |
|
- type: mrr_at_1 |
|
value: 44.921 |
|
- type: mrr_at_10 |
|
value: 54.879999999999995 |
|
- type: mrr_at_100 |
|
value: 55.525000000000006 |
|
- type: mrr_at_1000 |
|
value: 55.565 |
|
- type: mrr_at_3 |
|
value: 52.480000000000004 |
|
- type: mrr_at_5 |
|
value: 53.86 |
|
- type: ndcg_at_1 |
|
value: 44.921 |
|
- type: ndcg_at_10 |
|
value: 55.664 |
|
- type: ndcg_at_100 |
|
value: 60.488 |
|
- type: ndcg_at_1000 |
|
value: 62.138000000000005 |
|
- type: ndcg_at_3 |
|
value: 50.797000000000004 |
|
- type: ndcg_at_5 |
|
value: 52.94799999999999 |
|
- type: precision_at_1 |
|
value: 44.921 |
|
- type: precision_at_10 |
|
value: 10.587 |
|
- type: precision_at_100 |
|
value: 1.629 |
|
- type: precision_at_1000 |
|
value: 0.203 |
|
- type: precision_at_3 |
|
value: 24.034 |
|
- type: precision_at_5 |
|
value: 17.224999999999998 |
|
- type: recall_at_1 |
|
value: 37.264 |
|
- type: recall_at_10 |
|
value: 67.15 |
|
- type: recall_at_100 |
|
value: 86.811 |
|
- type: recall_at_1000 |
|
value: 97.172 |
|
- type: recall_at_3 |
|
value: 53.15800000000001 |
|
- type: recall_at_5 |
|
value: 59.116 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-english |
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name: MTEB CQADupstackEnglishRetrieval |
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config: default |
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split: test |
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revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
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metrics: |
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- type: map_at_1 |
|
value: 36.237 |
|
- type: map_at_10 |
|
value: 47.941 |
|
- type: map_at_100 |
|
value: 49.131 |
|
- type: map_at_1000 |
|
value: 49.26 |
|
- type: map_at_3 |
|
value: 44.561 |
|
- type: map_at_5 |
|
value: 46.28 |
|
- type: mrr_at_1 |
|
value: 45.605000000000004 |
|
- type: mrr_at_10 |
|
value: 54.039 |
|
- type: mrr_at_100 |
|
value: 54.653 |
|
- type: mrr_at_1000 |
|
value: 54.688 |
|
- type: mrr_at_3 |
|
value: 52.006 |
|
- type: mrr_at_5 |
|
value: 53.096 |
|
- type: ndcg_at_1 |
|
value: 45.605000000000004 |
|
- type: ndcg_at_10 |
|
value: 53.916 |
|
- type: ndcg_at_100 |
|
value: 57.745999999999995 |
|
- type: ndcg_at_1000 |
|
value: 59.492999999999995 |
|
- type: ndcg_at_3 |
|
value: 49.774 |
|
- type: ndcg_at_5 |
|
value: 51.434999999999995 |
|
- type: precision_at_1 |
|
value: 45.605000000000004 |
|
- type: precision_at_10 |
|
value: 10.229000000000001 |
|
- type: precision_at_100 |
|
value: 1.55 |
|
- type: precision_at_1000 |
|
value: 0.2 |
|
- type: precision_at_3 |
|
value: 24.098 |
|
- type: precision_at_5 |
|
value: 16.726 |
|
- type: recall_at_1 |
|
value: 36.237 |
|
- type: recall_at_10 |
|
value: 64.03 |
|
- type: recall_at_100 |
|
value: 80.423 |
|
- type: recall_at_1000 |
|
value: 91.03 |
|
- type: recall_at_3 |
|
value: 51.20400000000001 |
|
- type: recall_at_5 |
|
value: 56.298 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gaming |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
|
metrics: |
|
- type: map_at_1 |
|
value: 47.278 |
|
- type: map_at_10 |
|
value: 59.757000000000005 |
|
- type: map_at_100 |
|
value: 60.67 |
|
- type: map_at_1000 |
|
value: 60.714 |
|
- type: map_at_3 |
|
value: 56.714 |
|
- type: map_at_5 |
|
value: 58.453 |
|
- type: mrr_at_1 |
|
value: 53.73 |
|
- type: mrr_at_10 |
|
value: 62.970000000000006 |
|
- type: mrr_at_100 |
|
value: 63.507999999999996 |
|
- type: mrr_at_1000 |
|
value: 63.53 |
|
- type: mrr_at_3 |
|
value: 60.909 |
|
- type: mrr_at_5 |
|
value: 62.172000000000004 |
|
- type: ndcg_at_1 |
|
value: 53.73 |
|
- type: ndcg_at_10 |
|
value: 64.97 |
|
- type: ndcg_at_100 |
|
value: 68.394 |
|
- type: ndcg_at_1000 |
|
value: 69.255 |
|
- type: ndcg_at_3 |
|
value: 60.228 |
|
- type: ndcg_at_5 |
|
value: 62.617999999999995 |
|
- type: precision_at_1 |
|
value: 53.73 |
|
- type: precision_at_10 |
|
value: 10.056 |
|
- type: precision_at_100 |
|
value: 1.265 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 26.332 |
|
- type: precision_at_5 |
|
value: 17.743000000000002 |
|
- type: recall_at_1 |
|
value: 47.278 |
|
- type: recall_at_10 |
|
value: 76.86500000000001 |
|
- type: recall_at_100 |
|
value: 91.582 |
|
- type: recall_at_1000 |
|
value: 97.583 |
|
- type: recall_at_3 |
|
value: 64.443 |
|
- type: recall_at_5 |
|
value: 70.283 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gis |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: 5003b3064772da1887988e05400cf3806fe491f2 |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.702 |
|
- type: map_at_10 |
|
value: 39.463 |
|
- type: map_at_100 |
|
value: 40.508 |
|
- type: map_at_1000 |
|
value: 40.579 |
|
- type: map_at_3 |
|
value: 36.748999999999995 |
|
- type: map_at_5 |
|
value: 38.296 |
|
- type: mrr_at_1 |
|
value: 31.977 |
|
- type: mrr_at_10 |
|
value: 41.739 |
|
- type: mrr_at_100 |
|
value: 42.586 |
|
- type: mrr_at_1000 |
|
value: 42.636 |
|
- type: mrr_at_3 |
|
value: 39.096 |
|
- type: mrr_at_5 |
|
value: 40.695 |
|
- type: ndcg_at_1 |
|
value: 31.977 |
|
- type: ndcg_at_10 |
|
value: 44.855000000000004 |
|
- type: ndcg_at_100 |
|
value: 49.712 |
|
- type: ndcg_at_1000 |
|
value: 51.443000000000005 |
|
- type: ndcg_at_3 |
|
value: 39.585 |
|
- type: ndcg_at_5 |
|
value: 42.244 |
|
- type: precision_at_1 |
|
value: 31.977 |
|
- type: precision_at_10 |
|
value: 6.768000000000001 |
|
- type: precision_at_100 |
|
value: 0.9690000000000001 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 16.761 |
|
- type: precision_at_5 |
|
value: 11.593 |
|
- type: recall_at_1 |
|
value: 29.702 |
|
- type: recall_at_10 |
|
value: 59.082 |
|
- type: recall_at_100 |
|
value: 80.92 |
|
- type: recall_at_1000 |
|
value: 93.728 |
|
- type: recall_at_3 |
|
value: 45.212 |
|
- type: recall_at_5 |
|
value: 51.449 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-mathematica |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.336 |
|
- type: map_at_10 |
|
value: 30.137999999999998 |
|
- type: map_at_100 |
|
value: 31.385 |
|
- type: map_at_1000 |
|
value: 31.495 |
|
- type: map_at_3 |
|
value: 27.481 |
|
- type: map_at_5 |
|
value: 28.772 |
|
- type: mrr_at_1 |
|
value: 25.871 |
|
- type: mrr_at_10 |
|
value: 34.686 |
|
- type: mrr_at_100 |
|
value: 35.649 |
|
- type: mrr_at_1000 |
|
value: 35.705 |
|
- type: mrr_at_3 |
|
value: 32.09 |
|
- type: mrr_at_5 |
|
value: 33.52 |
|
- type: ndcg_at_1 |
|
value: 25.871 |
|
- type: ndcg_at_10 |
|
value: 35.617 |
|
- type: ndcg_at_100 |
|
value: 41.272999999999996 |
|
- type: ndcg_at_1000 |
|
value: 43.725 |
|
- type: ndcg_at_3 |
|
value: 30.653999999999996 |
|
- type: ndcg_at_5 |
|
value: 32.714 |
|
- type: precision_at_1 |
|
value: 25.871 |
|
- type: precision_at_10 |
|
value: 6.4799999999999995 |
|
- type: precision_at_100 |
|
value: 1.0699999999999998 |
|
- type: precision_at_1000 |
|
value: 0.13999999999999999 |
|
- type: precision_at_3 |
|
value: 14.469000000000001 |
|
- type: precision_at_5 |
|
value: 10.274 |
|
- type: recall_at_1 |
|
value: 21.336 |
|
- type: recall_at_10 |
|
value: 47.746 |
|
- type: recall_at_100 |
|
value: 71.773 |
|
- type: recall_at_1000 |
|
value: 89.05199999999999 |
|
- type: recall_at_3 |
|
value: 34.172999999999995 |
|
- type: recall_at_5 |
|
value: 39.397999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-physics |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 34.424 |
|
- type: map_at_10 |
|
value: 45.647999999999996 |
|
- type: map_at_100 |
|
value: 46.907 |
|
- type: map_at_1000 |
|
value: 47.010999999999996 |
|
- type: map_at_3 |
|
value: 42.427 |
|
- type: map_at_5 |
|
value: 44.285000000000004 |
|
- type: mrr_at_1 |
|
value: 41.867 |
|
- type: mrr_at_10 |
|
value: 51.17699999999999 |
|
- type: mrr_at_100 |
|
value: 51.937 |
|
- type: mrr_at_1000 |
|
value: 51.975 |
|
- type: mrr_at_3 |
|
value: 48.941 |
|
- type: mrr_at_5 |
|
value: 50.322 |
|
- type: ndcg_at_1 |
|
value: 41.867 |
|
- type: ndcg_at_10 |
|
value: 51.534 |
|
- type: ndcg_at_100 |
|
value: 56.696999999999996 |
|
- type: ndcg_at_1000 |
|
value: 58.475 |
|
- type: ndcg_at_3 |
|
value: 46.835 |
|
- type: ndcg_at_5 |
|
value: 49.161 |
|
- type: precision_at_1 |
|
value: 41.867 |
|
- type: precision_at_10 |
|
value: 9.134 |
|
- type: precision_at_100 |
|
value: 1.362 |
|
- type: precision_at_1000 |
|
value: 0.17099999999999999 |
|
- type: precision_at_3 |
|
value: 22.073 |
|
- type: precision_at_5 |
|
value: 15.495999999999999 |
|
- type: recall_at_1 |
|
value: 34.424 |
|
- type: recall_at_10 |
|
value: 63.237 |
|
- type: recall_at_100 |
|
value: 84.774 |
|
- type: recall_at_1000 |
|
value: 95.987 |
|
- type: recall_at_3 |
|
value: 49.888 |
|
- type: recall_at_5 |
|
value: 55.940999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-programmers |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.72 |
|
- type: map_at_10 |
|
value: 41.327999999999996 |
|
- type: map_at_100 |
|
value: 42.651 |
|
- type: map_at_1000 |
|
value: 42.739 |
|
- type: map_at_3 |
|
value: 38.223 |
|
- type: map_at_5 |
|
value: 40.053 |
|
- type: mrr_at_1 |
|
value: 37.9 |
|
- type: mrr_at_10 |
|
value: 46.857 |
|
- type: mrr_at_100 |
|
value: 47.673 |
|
- type: mrr_at_1000 |
|
value: 47.711999999999996 |
|
- type: mrr_at_3 |
|
value: 44.292 |
|
- type: mrr_at_5 |
|
value: 45.845 |
|
- type: ndcg_at_1 |
|
value: 37.9 |
|
- type: ndcg_at_10 |
|
value: 47.105999999999995 |
|
- type: ndcg_at_100 |
|
value: 52.56999999999999 |
|
- type: ndcg_at_1000 |
|
value: 54.37800000000001 |
|
- type: ndcg_at_3 |
|
value: 42.282 |
|
- type: ndcg_at_5 |
|
value: 44.646 |
|
- type: precision_at_1 |
|
value: 37.9 |
|
- type: precision_at_10 |
|
value: 8.368 |
|
- type: precision_at_100 |
|
value: 1.283 |
|
- type: precision_at_1000 |
|
value: 0.16 |
|
- type: precision_at_3 |
|
value: 20.015 |
|
- type: precision_at_5 |
|
value: 14.132 |
|
- type: recall_at_1 |
|
value: 30.72 |
|
- type: recall_at_10 |
|
value: 58.826 |
|
- type: recall_at_100 |
|
value: 82.104 |
|
- type: recall_at_1000 |
|
value: 94.194 |
|
- type: recall_at_3 |
|
value: 44.962999999999994 |
|
- type: recall_at_5 |
|
value: 51.426 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.656583333333334 |
|
- type: map_at_10 |
|
value: 41.59883333333333 |
|
- type: map_at_100 |
|
value: 42.80350000000001 |
|
- type: map_at_1000 |
|
value: 42.91075 |
|
- type: map_at_3 |
|
value: 38.68908333333333 |
|
- type: map_at_5 |
|
value: 40.27733333333334 |
|
- type: mrr_at_1 |
|
value: 37.23483333333334 |
|
- type: mrr_at_10 |
|
value: 45.782000000000004 |
|
- type: mrr_at_100 |
|
value: 46.577083333333334 |
|
- type: mrr_at_1000 |
|
value: 46.62516666666667 |
|
- type: mrr_at_3 |
|
value: 43.480666666666664 |
|
- type: mrr_at_5 |
|
value: 44.79833333333333 |
|
- type: ndcg_at_1 |
|
value: 37.23483333333334 |
|
- type: ndcg_at_10 |
|
value: 46.971500000000006 |
|
- type: ndcg_at_100 |
|
value: 51.90125 |
|
- type: ndcg_at_1000 |
|
value: 53.86366666666667 |
|
- type: ndcg_at_3 |
|
value: 42.31791666666667 |
|
- type: ndcg_at_5 |
|
value: 44.458666666666666 |
|
- type: precision_at_1 |
|
value: 37.23483333333334 |
|
- type: precision_at_10 |
|
value: 8.044583333333332 |
|
- type: precision_at_100 |
|
value: 1.2334166666666666 |
|
- type: precision_at_1000 |
|
value: 0.15925 |
|
- type: precision_at_3 |
|
value: 19.240833333333327 |
|
- type: precision_at_5 |
|
value: 13.435083333333333 |
|
- type: recall_at_1 |
|
value: 31.656583333333334 |
|
- type: recall_at_10 |
|
value: 58.44758333333333 |
|
- type: recall_at_100 |
|
value: 79.93658333333332 |
|
- type: recall_at_1000 |
|
value: 93.32491666666668 |
|
- type: recall_at_3 |
|
value: 45.44266666666667 |
|
- type: recall_at_5 |
|
value: 50.99866666666666 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-stats |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.247 |
|
- type: map_at_10 |
|
value: 35.443999999999996 |
|
- type: map_at_100 |
|
value: 36.578 |
|
- type: map_at_1000 |
|
value: 36.675999999999995 |
|
- type: map_at_3 |
|
value: 33.276 |
|
- type: map_at_5 |
|
value: 34.536 |
|
- type: mrr_at_1 |
|
value: 31.747999999999998 |
|
- type: mrr_at_10 |
|
value: 38.413000000000004 |
|
- type: mrr_at_100 |
|
value: 39.327 |
|
- type: mrr_at_1000 |
|
value: 39.389 |
|
- type: mrr_at_3 |
|
value: 36.401 |
|
- type: mrr_at_5 |
|
value: 37.543 |
|
- type: ndcg_at_1 |
|
value: 31.747999999999998 |
|
- type: ndcg_at_10 |
|
value: 39.646 |
|
- type: ndcg_at_100 |
|
value: 44.861000000000004 |
|
- type: ndcg_at_1000 |
|
value: 47.197 |
|
- type: ndcg_at_3 |
|
value: 35.764 |
|
- type: ndcg_at_5 |
|
value: 37.635999999999996 |
|
- type: precision_at_1 |
|
value: 31.747999999999998 |
|
- type: precision_at_10 |
|
value: 6.12 |
|
- type: precision_at_100 |
|
value: 0.942 |
|
- type: precision_at_1000 |
|
value: 0.123 |
|
- type: precision_at_3 |
|
value: 15.235000000000001 |
|
- type: precision_at_5 |
|
value: 10.491 |
|
- type: recall_at_1 |
|
value: 28.247 |
|
- type: recall_at_10 |
|
value: 49.456 |
|
- type: recall_at_100 |
|
value: 73.02499999999999 |
|
- type: recall_at_1000 |
|
value: 89.898 |
|
- type: recall_at_3 |
|
value: 38.653999999999996 |
|
- type: recall_at_5 |
|
value: 43.259 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-tex |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: 46989137a86843e03a6195de44b09deda022eec7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.45 |
|
- type: map_at_10 |
|
value: 30.476999999999997 |
|
- type: map_at_100 |
|
value: 31.630999999999997 |
|
- type: map_at_1000 |
|
value: 31.755 |
|
- type: map_at_3 |
|
value: 27.989000000000004 |
|
- type: map_at_5 |
|
value: 29.410999999999998 |
|
- type: mrr_at_1 |
|
value: 26.979 |
|
- type: mrr_at_10 |
|
value: 34.316 |
|
- type: mrr_at_100 |
|
value: 35.272999999999996 |
|
- type: mrr_at_1000 |
|
value: 35.342 |
|
- type: mrr_at_3 |
|
value: 32.14 |
|
- type: mrr_at_5 |
|
value: 33.405 |
|
- type: ndcg_at_1 |
|
value: 26.979 |
|
- type: ndcg_at_10 |
|
value: 35.166 |
|
- type: ndcg_at_100 |
|
value: 40.583000000000006 |
|
- type: ndcg_at_1000 |
|
value: 43.282 |
|
- type: ndcg_at_3 |
|
value: 30.916 |
|
- type: ndcg_at_5 |
|
value: 32.973 |
|
- type: precision_at_1 |
|
value: 26.979 |
|
- type: precision_at_10 |
|
value: 6.132 |
|
- type: precision_at_100 |
|
value: 1.047 |
|
- type: precision_at_1000 |
|
value: 0.145 |
|
- type: precision_at_3 |
|
value: 14.360999999999999 |
|
- type: precision_at_5 |
|
value: 10.227 |
|
- type: recall_at_1 |
|
value: 22.45 |
|
- type: recall_at_10 |
|
value: 45.348 |
|
- type: recall_at_100 |
|
value: 69.484 |
|
- type: recall_at_1000 |
|
value: 88.628 |
|
- type: recall_at_3 |
|
value: 33.338 |
|
- type: recall_at_5 |
|
value: 38.746 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-unix |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.123000000000005 |
|
- type: map_at_10 |
|
value: 41.778 |
|
- type: map_at_100 |
|
value: 42.911 |
|
- type: map_at_1000 |
|
value: 42.994 |
|
- type: map_at_3 |
|
value: 38.558 |
|
- type: map_at_5 |
|
value: 40.318 |
|
- type: mrr_at_1 |
|
value: 37.687 |
|
- type: mrr_at_10 |
|
value: 45.889 |
|
- type: mrr_at_100 |
|
value: 46.672999999999995 |
|
- type: mrr_at_1000 |
|
value: 46.72 |
|
- type: mrr_at_3 |
|
value: 43.33 |
|
- type: mrr_at_5 |
|
value: 44.734 |
|
- type: ndcg_at_1 |
|
value: 37.687 |
|
- type: ndcg_at_10 |
|
value: 47.258 |
|
- type: ndcg_at_100 |
|
value: 52.331 |
|
- type: ndcg_at_1000 |
|
value: 54.152 |
|
- type: ndcg_at_3 |
|
value: 41.857 |
|
- type: ndcg_at_5 |
|
value: 44.283 |
|
- type: precision_at_1 |
|
value: 37.687 |
|
- type: precision_at_10 |
|
value: 7.892 |
|
- type: precision_at_100 |
|
value: 1.183 |
|
- type: precision_at_1000 |
|
value: 0.14300000000000002 |
|
- type: precision_at_3 |
|
value: 18.781 |
|
- type: precision_at_5 |
|
value: 13.134 |
|
- type: recall_at_1 |
|
value: 32.123000000000005 |
|
- type: recall_at_10 |
|
value: 59.760000000000005 |
|
- type: recall_at_100 |
|
value: 81.652 |
|
- type: recall_at_1000 |
|
value: 94.401 |
|
- type: recall_at_3 |
|
value: 44.996 |
|
- type: recall_at_5 |
|
value: 51.184 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-webmasters |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.196999999999996 |
|
- type: map_at_10 |
|
value: 42.012 |
|
- type: map_at_100 |
|
value: 43.663999999999994 |
|
- type: map_at_1000 |
|
value: 43.883 |
|
- type: map_at_3 |
|
value: 39.33 |
|
- type: map_at_5 |
|
value: 40.586 |
|
- type: mrr_at_1 |
|
value: 39.328 |
|
- type: mrr_at_10 |
|
value: 46.57 |
|
- type: mrr_at_100 |
|
value: 47.508 |
|
- type: mrr_at_1000 |
|
value: 47.558 |
|
- type: mrr_at_3 |
|
value: 44.532 |
|
- type: mrr_at_5 |
|
value: 45.58 |
|
- type: ndcg_at_1 |
|
value: 39.328 |
|
- type: ndcg_at_10 |
|
value: 47.337 |
|
- type: ndcg_at_100 |
|
value: 52.989 |
|
- type: ndcg_at_1000 |
|
value: 55.224 |
|
- type: ndcg_at_3 |
|
value: 43.362 |
|
- type: ndcg_at_5 |
|
value: 44.866 |
|
- type: precision_at_1 |
|
value: 39.328 |
|
- type: precision_at_10 |
|
value: 8.577 |
|
- type: precision_at_100 |
|
value: 1.5789999999999997 |
|
- type: precision_at_1000 |
|
value: 0.25 |
|
- type: precision_at_3 |
|
value: 19.697 |
|
- type: precision_at_5 |
|
value: 13.755 |
|
- type: recall_at_1 |
|
value: 33.196999999999996 |
|
- type: recall_at_10 |
|
value: 56.635000000000005 |
|
- type: recall_at_100 |
|
value: 81.882 |
|
- type: recall_at_1000 |
|
value: 95.342 |
|
- type: recall_at_3 |
|
value: 44.969 |
|
- type: recall_at_5 |
|
value: 49.266 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-wordpress |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.901000000000003 |
|
- type: map_at_10 |
|
value: 35.77 |
|
- type: map_at_100 |
|
value: 36.638999999999996 |
|
- type: map_at_1000 |
|
value: 36.741 |
|
- type: map_at_3 |
|
value: 33.219 |
|
- type: map_at_5 |
|
value: 34.574 |
|
- type: mrr_at_1 |
|
value: 29.205 |
|
- type: mrr_at_10 |
|
value: 37.848 |
|
- type: mrr_at_100 |
|
value: 38.613 |
|
- type: mrr_at_1000 |
|
value: 38.682 |
|
- type: mrr_at_3 |
|
value: 35.551 |
|
- type: mrr_at_5 |
|
value: 36.808 |
|
- type: ndcg_at_1 |
|
value: 29.205 |
|
- type: ndcg_at_10 |
|
value: 40.589 |
|
- type: ndcg_at_100 |
|
value: 45.171 |
|
- type: ndcg_at_1000 |
|
value: 47.602 |
|
- type: ndcg_at_3 |
|
value: 35.760999999999996 |
|
- type: ndcg_at_5 |
|
value: 37.980000000000004 |
|
- type: precision_at_1 |
|
value: 29.205 |
|
- type: precision_at_10 |
|
value: 6.192 |
|
- type: precision_at_100 |
|
value: 0.922 |
|
- type: precision_at_1000 |
|
value: 0.123 |
|
- type: precision_at_3 |
|
value: 15.034 |
|
- type: precision_at_5 |
|
value: 10.424999999999999 |
|
- type: recall_at_1 |
|
value: 26.901000000000003 |
|
- type: recall_at_10 |
|
value: 53.236000000000004 |
|
- type: recall_at_100 |
|
value: 74.809 |
|
- type: recall_at_1000 |
|
value: 92.884 |
|
- type: recall_at_3 |
|
value: 40.314 |
|
- type: recall_at_5 |
|
value: 45.617999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.794999999999998 |
|
- type: map_at_10 |
|
value: 29.322 |
|
- type: map_at_100 |
|
value: 31.463 |
|
- type: map_at_1000 |
|
value: 31.643 |
|
- type: map_at_3 |
|
value: 24.517 |
|
- type: map_at_5 |
|
value: 27.237000000000002 |
|
- type: mrr_at_1 |
|
value: 37.655 |
|
- type: mrr_at_10 |
|
value: 50.952 |
|
- type: mrr_at_100 |
|
value: 51.581999999999994 |
|
- type: mrr_at_1000 |
|
value: 51.61 |
|
- type: mrr_at_3 |
|
value: 47.991 |
|
- type: mrr_at_5 |
|
value: 49.744 |
|
- type: ndcg_at_1 |
|
value: 37.655 |
|
- type: ndcg_at_10 |
|
value: 39.328 |
|
- type: ndcg_at_100 |
|
value: 46.358 |
|
- type: ndcg_at_1000 |
|
value: 49.245 |
|
- type: ndcg_at_3 |
|
value: 33.052 |
|
- type: ndcg_at_5 |
|
value: 35.407 |
|
- type: precision_at_1 |
|
value: 37.655 |
|
- type: precision_at_10 |
|
value: 12.202 |
|
- type: precision_at_100 |
|
value: 1.9789999999999999 |
|
- type: precision_at_1000 |
|
value: 0.252 |
|
- type: precision_at_3 |
|
value: 24.973 |
|
- type: precision_at_5 |
|
value: 19.075 |
|
- type: recall_at_1 |
|
value: 16.794999999999998 |
|
- type: recall_at_10 |
|
value: 45.716 |
|
- type: recall_at_100 |
|
value: 68.919 |
|
- type: recall_at_1000 |
|
value: 84.71600000000001 |
|
- type: recall_at_3 |
|
value: 30.135 |
|
- type: recall_at_5 |
|
value: 37.141999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/dbpedia |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.817 |
|
- type: map_at_10 |
|
value: 22.058 |
|
- type: map_at_100 |
|
value: 31.805 |
|
- type: map_at_1000 |
|
value: 33.562999999999995 |
|
- type: map_at_3 |
|
value: 15.537 |
|
- type: map_at_5 |
|
value: 18.199 |
|
- type: mrr_at_1 |
|
value: 72.75 |
|
- type: mrr_at_10 |
|
value: 79.804 |
|
- type: mrr_at_100 |
|
value: 80.089 |
|
- type: mrr_at_1000 |
|
value: 80.09100000000001 |
|
- type: mrr_at_3 |
|
value: 78.75 |
|
- type: mrr_at_5 |
|
value: 79.325 |
|
- type: ndcg_at_1 |
|
value: 59.875 |
|
- type: ndcg_at_10 |
|
value: 45.972 |
|
- type: ndcg_at_100 |
|
value: 51.092999999999996 |
|
- type: ndcg_at_1000 |
|
value: 58.048 |
|
- type: ndcg_at_3 |
|
value: 50.552 |
|
- type: ndcg_at_5 |
|
value: 47.672 |
|
- type: precision_at_1 |
|
value: 72.75 |
|
- type: precision_at_10 |
|
value: 37.05 |
|
- type: precision_at_100 |
|
value: 12.005 |
|
- type: precision_at_1000 |
|
value: 2.221 |
|
- type: precision_at_3 |
|
value: 54.083000000000006 |
|
- type: precision_at_5 |
|
value: 46.2 |
|
- type: recall_at_1 |
|
value: 9.817 |
|
- type: recall_at_10 |
|
value: 27.877000000000002 |
|
- type: recall_at_100 |
|
value: 57.974000000000004 |
|
- type: recall_at_1000 |
|
value: 80.085 |
|
- type: recall_at_3 |
|
value: 16.911 |
|
- type: recall_at_5 |
|
value: 20.689 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 46.464999999999996 |
|
- type: f1 |
|
value: 42.759588662873796 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
|
metrics: |
|
- type: map_at_1 |
|
value: 75.82900000000001 |
|
- type: map_at_10 |
|
value: 84.613 |
|
- type: map_at_100 |
|
value: 84.845 |
|
- type: map_at_1000 |
|
value: 84.855 |
|
- type: map_at_3 |
|
value: 83.498 |
|
- type: map_at_5 |
|
value: 84.29299999999999 |
|
- type: mrr_at_1 |
|
value: 81.69800000000001 |
|
- type: mrr_at_10 |
|
value: 88.84100000000001 |
|
- type: mrr_at_100 |
|
value: 88.887 |
|
- type: mrr_at_1000 |
|
value: 88.888 |
|
- type: mrr_at_3 |
|
value: 88.179 |
|
- type: mrr_at_5 |
|
value: 88.69200000000001 |
|
- type: ndcg_at_1 |
|
value: 81.69800000000001 |
|
- type: ndcg_at_10 |
|
value: 88.21799999999999 |
|
- type: ndcg_at_100 |
|
value: 88.961 |
|
- type: ndcg_at_1000 |
|
value: 89.131 |
|
- type: ndcg_at_3 |
|
value: 86.591 |
|
- type: ndcg_at_5 |
|
value: 87.666 |
|
- type: precision_at_1 |
|
value: 81.69800000000001 |
|
- type: precision_at_10 |
|
value: 10.615 |
|
- type: precision_at_100 |
|
value: 1.125 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 33.208 |
|
- type: precision_at_5 |
|
value: 20.681 |
|
- type: recall_at_1 |
|
value: 75.82900000000001 |
|
- type: recall_at_10 |
|
value: 94.97 |
|
- type: recall_at_100 |
|
value: 97.786 |
|
- type: recall_at_1000 |
|
value: 98.809 |
|
- type: recall_at_3 |
|
value: 90.625 |
|
- type: recall_at_5 |
|
value: 93.345 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.788 |
|
- type: map_at_10 |
|
value: 36.71 |
|
- type: map_at_100 |
|
value: 38.527 |
|
- type: map_at_1000 |
|
value: 38.701 |
|
- type: map_at_3 |
|
value: 32.318999999999996 |
|
- type: map_at_5 |
|
value: 34.809 |
|
- type: mrr_at_1 |
|
value: 44.444 |
|
- type: mrr_at_10 |
|
value: 52.868 |
|
- type: mrr_at_100 |
|
value: 53.52400000000001 |
|
- type: mrr_at_1000 |
|
value: 53.559999999999995 |
|
- type: mrr_at_3 |
|
value: 50.153999999999996 |
|
- type: mrr_at_5 |
|
value: 51.651 |
|
- type: ndcg_at_1 |
|
value: 44.444 |
|
- type: ndcg_at_10 |
|
value: 44.707 |
|
- type: ndcg_at_100 |
|
value: 51.174 |
|
- type: ndcg_at_1000 |
|
value: 53.996 |
|
- type: ndcg_at_3 |
|
value: 40.855999999999995 |
|
- type: ndcg_at_5 |
|
value: 42.113 |
|
- type: precision_at_1 |
|
value: 44.444 |
|
- type: precision_at_10 |
|
value: 12.021999999999998 |
|
- type: precision_at_100 |
|
value: 1.8950000000000002 |
|
- type: precision_at_1000 |
|
value: 0.241 |
|
- type: precision_at_3 |
|
value: 26.8 |
|
- type: precision_at_5 |
|
value: 19.66 |
|
- type: recall_at_1 |
|
value: 22.788 |
|
- type: recall_at_10 |
|
value: 51.793 |
|
- type: recall_at_100 |
|
value: 75.69500000000001 |
|
- type: recall_at_1000 |
|
value: 92.292 |
|
- type: recall_at_3 |
|
value: 37.375 |
|
- type: recall_at_5 |
|
value: 43.682 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
|
metrics: |
|
- type: map_at_1 |
|
value: 41.276 |
|
- type: map_at_10 |
|
value: 67.245 |
|
- type: map_at_100 |
|
value: 68.061 |
|
- type: map_at_1000 |
|
value: 68.11399999999999 |
|
- type: map_at_3 |
|
value: 63.693 |
|
- type: map_at_5 |
|
value: 65.90899999999999 |
|
- type: mrr_at_1 |
|
value: 82.552 |
|
- type: mrr_at_10 |
|
value: 87.741 |
|
- type: mrr_at_100 |
|
value: 87.868 |
|
- type: mrr_at_1000 |
|
value: 87.871 |
|
- type: mrr_at_3 |
|
value: 86.98599999999999 |
|
- type: mrr_at_5 |
|
value: 87.469 |
|
- type: ndcg_at_1 |
|
value: 82.552 |
|
- type: ndcg_at_10 |
|
value: 75.176 |
|
- type: ndcg_at_100 |
|
value: 77.902 |
|
- type: ndcg_at_1000 |
|
value: 78.852 |
|
- type: ndcg_at_3 |
|
value: 70.30499999999999 |
|
- type: ndcg_at_5 |
|
value: 73.00999999999999 |
|
- type: precision_at_1 |
|
value: 82.552 |
|
- type: precision_at_10 |
|
value: 15.765 |
|
- type: precision_at_100 |
|
value: 1.788 |
|
- type: precision_at_1000 |
|
value: 0.191 |
|
- type: precision_at_3 |
|
value: 45.375 |
|
- type: precision_at_5 |
|
value: 29.360999999999997 |
|
- type: recall_at_1 |
|
value: 41.276 |
|
- type: recall_at_10 |
|
value: 78.825 |
|
- type: recall_at_100 |
|
value: 89.41900000000001 |
|
- type: recall_at_1000 |
|
value: 95.625 |
|
- type: recall_at_3 |
|
value: 68.062 |
|
- type: recall_at_5 |
|
value: 73.40299999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 72.876 |
|
- type: ap |
|
value: 67.15477852410164 |
|
- type: f1 |
|
value: 72.65147370025373 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: c5a29a104738b98a9e76336939199e264163d4a0 |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.748 |
|
- type: map_at_10 |
|
value: 34.626000000000005 |
|
- type: map_at_100 |
|
value: 35.813 |
|
- type: map_at_1000 |
|
value: 35.859 |
|
- type: map_at_3 |
|
value: 30.753000000000004 |
|
- type: map_at_5 |
|
value: 33.049 |
|
- type: mrr_at_1 |
|
value: 22.35 |
|
- type: mrr_at_10 |
|
value: 35.23 |
|
- type: mrr_at_100 |
|
value: 36.359 |
|
- type: mrr_at_1000 |
|
value: 36.399 |
|
- type: mrr_at_3 |
|
value: 31.436999999999998 |
|
- type: mrr_at_5 |
|
value: 33.687 |
|
- type: ndcg_at_1 |
|
value: 22.364 |
|
- type: ndcg_at_10 |
|
value: 41.677 |
|
- type: ndcg_at_100 |
|
value: 47.355999999999995 |
|
- type: ndcg_at_1000 |
|
value: 48.494 |
|
- type: ndcg_at_3 |
|
value: 33.85 |
|
- type: ndcg_at_5 |
|
value: 37.942 |
|
- type: precision_at_1 |
|
value: 22.364 |
|
- type: precision_at_10 |
|
value: 6.6000000000000005 |
|
- type: precision_at_100 |
|
value: 0.9450000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.527000000000001 |
|
- type: precision_at_5 |
|
value: 10.796999999999999 |
|
- type: recall_at_1 |
|
value: 21.748 |
|
- type: recall_at_10 |
|
value: 63.292 |
|
- type: recall_at_100 |
|
value: 89.427 |
|
- type: recall_at_1000 |
|
value: 98.13499999999999 |
|
- type: recall_at_3 |
|
value: 42.126000000000005 |
|
- type: recall_at_5 |
|
value: 51.968 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 92.62425900592795 |
|
- type: f1 |
|
value: 92.08497761553683 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 64.51436388508893 |
|
- type: f1 |
|
value: 45.884016531912906 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClassification (eng) |
|
config: eng |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: accuracy |
|
value: 76.57172995780591 |
|
- type: f1 |
|
value: 75.52979910878491 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringP2P (eng) |
|
config: eng |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 44.84052695201612 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringS2S (eng) |
|
config: eng |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 21.443971229936494 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 65.79354404841965 |
|
- type: f1 |
|
value: 63.17260074126185 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 71.09616677874916 |
|
- type: f1 |
|
value: 69.74285784421075 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.474709231086184 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.93630367824217 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 29.08234393834005 |
|
- type: mrr |
|
value: 29.740466971605432 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.2059999999999995 |
|
- type: map_at_10 |
|
value: 14.442 |
|
- type: map_at_100 |
|
value: 18.005 |
|
- type: map_at_1000 |
|
value: 19.488 |
|
- type: map_at_3 |
|
value: 10.666 |
|
- type: map_at_5 |
|
value: 12.45 |
|
- type: mrr_at_1 |
|
value: 47.678 |
|
- type: mrr_at_10 |
|
value: 57.519 |
|
- type: mrr_at_100 |
|
value: 58.13700000000001 |
|
- type: mrr_at_1000 |
|
value: 58.167 |
|
- type: mrr_at_3 |
|
value: 55.779 |
|
- type: mrr_at_5 |
|
value: 56.940000000000005 |
|
- type: ndcg_at_1 |
|
value: 45.82 |
|
- type: ndcg_at_10 |
|
value: 37.651 |
|
- type: ndcg_at_100 |
|
value: 34.001999999999995 |
|
- type: ndcg_at_1000 |
|
value: 42.626 |
|
- type: ndcg_at_3 |
|
value: 43.961 |
|
- type: ndcg_at_5 |
|
value: 41.461 |
|
- type: precision_at_1 |
|
value: 47.678 |
|
- type: precision_at_10 |
|
value: 27.584999999999997 |
|
- type: precision_at_100 |
|
value: 8.455 |
|
- type: precision_at_1000 |
|
value: 2.118 |
|
- type: precision_at_3 |
|
value: 41.692 |
|
- type: precision_at_5 |
|
value: 36.161 |
|
- type: recall_at_1 |
|
value: 6.2059999999999995 |
|
- type: recall_at_10 |
|
value: 18.599 |
|
- type: recall_at_100 |
|
value: 33.608 |
|
- type: recall_at_1000 |
|
value: 65.429 |
|
- type: recall_at_3 |
|
value: 12.126000000000001 |
|
- type: recall_at_5 |
|
value: 14.902000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.117000000000004 |
|
- type: map_at_10 |
|
value: 55.535000000000004 |
|
- type: map_at_100 |
|
value: 56.32899999999999 |
|
- type: map_at_1000 |
|
value: 56.34400000000001 |
|
- type: map_at_3 |
|
value: 51.439 |
|
- type: map_at_5 |
|
value: 53.89699999999999 |
|
- type: mrr_at_1 |
|
value: 43.714 |
|
- type: mrr_at_10 |
|
value: 58.05200000000001 |
|
- type: mrr_at_100 |
|
value: 58.582 |
|
- type: mrr_at_1000 |
|
value: 58.592 |
|
- type: mrr_at_3 |
|
value: 54.896 |
|
- type: mrr_at_5 |
|
value: 56.874 |
|
- type: ndcg_at_1 |
|
value: 43.685 |
|
- type: ndcg_at_10 |
|
value: 63.108 |
|
- type: ndcg_at_100 |
|
value: 66.231 |
|
- type: ndcg_at_1000 |
|
value: 66.583 |
|
- type: ndcg_at_3 |
|
value: 55.659000000000006 |
|
- type: ndcg_at_5 |
|
value: 59.681 |
|
- type: precision_at_1 |
|
value: 43.685 |
|
- type: precision_at_10 |
|
value: 9.962 |
|
- type: precision_at_100 |
|
value: 1.174 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 24.961 |
|
- type: precision_at_5 |
|
value: 17.352 |
|
- type: recall_at_1 |
|
value: 39.117000000000004 |
|
- type: recall_at_10 |
|
value: 83.408 |
|
- type: recall_at_100 |
|
value: 96.553 |
|
- type: recall_at_1000 |
|
value: 99.136 |
|
- type: recall_at_3 |
|
value: 64.364 |
|
- type: recall_at_5 |
|
value: 73.573 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: ag_news |
|
name: MTEB NewsClassification |
|
config: default |
|
split: test |
|
revision: eb185aade064a813bc0b7f42de02595523103ca4 |
|
metrics: |
|
- type: accuracy |
|
value: 78.87763157894737 |
|
- type: f1 |
|
value: 78.69611753876177 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: GEM/opusparcus |
|
name: MTEB OpusparcusPC (en) |
|
config: en |
|
split: test |
|
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.89816700610999 |
|
- type: cos_sim_ap |
|
value: 100 |
|
- type: cos_sim_f1 |
|
value: 99.9490575649516 |
|
- type: cos_sim_precision |
|
value: 100 |
|
- type: cos_sim_recall |
|
value: 99.89816700610999 |
|
- type: dot_accuracy |
|
value: 99.89816700610999 |
|
- type: dot_ap |
|
value: 100 |
|
- type: dot_f1 |
|
value: 99.9490575649516 |
|
- type: dot_precision |
|
value: 100 |
|
- type: dot_recall |
|
value: 99.89816700610999 |
|
- type: euclidean_accuracy |
|
value: 99.89816700610999 |
|
- type: euclidean_ap |
|
value: 100 |
|
- type: euclidean_f1 |
|
value: 99.9490575649516 |
|
- type: euclidean_precision |
|
value: 100 |
|
- type: euclidean_recall |
|
value: 99.89816700610999 |
|
- type: manhattan_accuracy |
|
value: 99.89816700610999 |
|
- type: manhattan_ap |
|
value: 100 |
|
- type: manhattan_f1 |
|
value: 99.9490575649516 |
|
- type: manhattan_precision |
|
value: 100 |
|
- type: manhattan_recall |
|
value: 99.89816700610999 |
|
- type: max_accuracy |
|
value: 99.89816700610999 |
|
- type: max_ap |
|
value: 100 |
|
- type: max_f1 |
|
value: 99.9490575649516 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: paws-x |
|
name: MTEB PawsX (en) |
|
config: en |
|
split: test |
|
revision: 8a04d940a42cd40658986fdd8e3da561533a3646 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 62 |
|
- type: cos_sim_ap |
|
value: 62.26837791655737 |
|
- type: cos_sim_f1 |
|
value: 62.607449856733524 |
|
- type: cos_sim_precision |
|
value: 46.36604774535809 |
|
- type: cos_sim_recall |
|
value: 96.36163175303197 |
|
- type: dot_accuracy |
|
value: 62 |
|
- type: dot_ap |
|
value: 62.26736459439965 |
|
- type: dot_f1 |
|
value: 62.607449856733524 |
|
- type: dot_precision |
|
value: 46.36604774535809 |
|
- type: dot_recall |
|
value: 96.36163175303197 |
|
- type: euclidean_accuracy |
|
value: 62 |
|
- type: euclidean_ap |
|
value: 62.26826112548132 |
|
- type: euclidean_f1 |
|
value: 62.607449856733524 |
|
- type: euclidean_precision |
|
value: 46.36604774535809 |
|
- type: euclidean_recall |
|
value: 96.36163175303197 |
|
- type: manhattan_accuracy |
|
value: 62 |
|
- type: manhattan_ap |
|
value: 62.26223761507973 |
|
- type: manhattan_f1 |
|
value: 62.585034013605444 |
|
- type: manhattan_precision |
|
value: 46.34146341463415 |
|
- type: manhattan_recall |
|
value: 96.36163175303197 |
|
- type: max_accuracy |
|
value: 62 |
|
- type: max_ap |
|
value: 62.26837791655737 |
|
- type: max_f1 |
|
value: 62.607449856733524 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
|
metrics: |
|
- type: map_at_1 |
|
value: 69.90899999999999 |
|
- type: map_at_10 |
|
value: 83.56700000000001 |
|
- type: map_at_100 |
|
value: 84.19200000000001 |
|
- type: map_at_1000 |
|
value: 84.212 |
|
- type: map_at_3 |
|
value: 80.658 |
|
- type: map_at_5 |
|
value: 82.473 |
|
- type: mrr_at_1 |
|
value: 80.4 |
|
- type: mrr_at_10 |
|
value: 86.699 |
|
- type: mrr_at_100 |
|
value: 86.798 |
|
- type: mrr_at_1000 |
|
value: 86.80099999999999 |
|
- type: mrr_at_3 |
|
value: 85.677 |
|
- type: mrr_at_5 |
|
value: 86.354 |
|
- type: ndcg_at_1 |
|
value: 80.43 |
|
- type: ndcg_at_10 |
|
value: 87.41 |
|
- type: ndcg_at_100 |
|
value: 88.653 |
|
- type: ndcg_at_1000 |
|
value: 88.81599999999999 |
|
- type: ndcg_at_3 |
|
value: 84.516 |
|
- type: ndcg_at_5 |
|
value: 86.068 |
|
- type: precision_at_1 |
|
value: 80.43 |
|
- type: precision_at_10 |
|
value: 13.234000000000002 |
|
- type: precision_at_100 |
|
value: 1.513 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 36.93 |
|
- type: precision_at_5 |
|
value: 24.26 |
|
- type: recall_at_1 |
|
value: 69.90899999999999 |
|
- type: recall_at_10 |
|
value: 94.687 |
|
- type: recall_at_100 |
|
value: 98.96000000000001 |
|
- type: recall_at_1000 |
|
value: 99.79599999999999 |
|
- type: recall_at_3 |
|
value: 86.25699999999999 |
|
- type: recall_at_5 |
|
value: 90.70700000000001 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 46.02256865360266 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
|
metrics: |
|
- type: v_measure |
|
value: 62.43157528757563 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.093 |
|
- type: map_at_10 |
|
value: 12.982 |
|
- type: map_at_100 |
|
value: 15.031 |
|
- type: map_at_1000 |
|
value: 15.334 |
|
- type: map_at_3 |
|
value: 9.339 |
|
- type: map_at_5 |
|
value: 11.183 |
|
- type: mrr_at_1 |
|
value: 25.1 |
|
- type: mrr_at_10 |
|
value: 36.257 |
|
- type: mrr_at_100 |
|
value: 37.351 |
|
- type: mrr_at_1000 |
|
value: 37.409 |
|
- type: mrr_at_3 |
|
value: 33.050000000000004 |
|
- type: mrr_at_5 |
|
value: 35.205 |
|
- type: ndcg_at_1 |
|
value: 25.1 |
|
- type: ndcg_at_10 |
|
value: 21.361 |
|
- type: ndcg_at_100 |
|
value: 29.396 |
|
- type: ndcg_at_1000 |
|
value: 34.849999999999994 |
|
- type: ndcg_at_3 |
|
value: 20.704 |
|
- type: ndcg_at_5 |
|
value: 18.086 |
|
- type: precision_at_1 |
|
value: 25.1 |
|
- type: precision_at_10 |
|
value: 10.94 |
|
- type: precision_at_100 |
|
value: 2.257 |
|
- type: precision_at_1000 |
|
value: 0.358 |
|
- type: precision_at_3 |
|
value: 19.467000000000002 |
|
- type: precision_at_5 |
|
value: 15.98 |
|
- type: recall_at_1 |
|
value: 5.093 |
|
- type: recall_at_10 |
|
value: 22.177 |
|
- type: recall_at_100 |
|
value: 45.842 |
|
- type: recall_at_1000 |
|
value: 72.598 |
|
- type: recall_at_3 |
|
value: 11.833 |
|
- type: recall_at_5 |
|
value: 16.173000000000002 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 73.56535226754596 |
|
- type: cos_sim_spearman |
|
value: 69.32425977603488 |
|
- type: euclidean_pearson |
|
value: 71.32425703470898 |
|
- type: euclidean_spearman |
|
value: 69.32425217267013 |
|
- type: manhattan_pearson |
|
value: 71.25897281394246 |
|
- type: manhattan_spearman |
|
value: 69.27132577049578 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 69.66387868726018 |
|
- type: cos_sim_spearman |
|
value: 67.85470749045027 |
|
- type: euclidean_pearson |
|
value: 66.62075098063795 |
|
- type: euclidean_spearman |
|
value: 67.85470749045027 |
|
- type: manhattan_pearson |
|
value: 66.61455061901262 |
|
- type: manhattan_spearman |
|
value: 67.87229618498695 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 75.65731331392575 |
|
- type: cos_sim_spearman |
|
value: 77.48991626780108 |
|
- type: euclidean_pearson |
|
value: 77.19884738623692 |
|
- type: euclidean_spearman |
|
value: 77.48985836619045 |
|
- type: manhattan_pearson |
|
value: 77.0656684243772 |
|
- type: manhattan_spearman |
|
value: 77.30289226582691 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 69.37003253666457 |
|
- type: cos_sim_spearman |
|
value: 69.77157648098141 |
|
- type: euclidean_pearson |
|
value: 69.39543876030432 |
|
- type: euclidean_spearman |
|
value: 69.77157648098141 |
|
- type: manhattan_pearson |
|
value: 69.29901600459745 |
|
- type: manhattan_spearman |
|
value: 69.65074167527128 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.56777256540136 |
|
- type: cos_sim_spearman |
|
value: 80.16458787843023 |
|
- type: euclidean_pearson |
|
value: 80.16475730686916 |
|
- type: euclidean_spearman |
|
value: 80.16458787843023 |
|
- type: manhattan_pearson |
|
value: 80.12814463670401 |
|
- type: manhattan_spearman |
|
value: 80.1357907984809 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 76.09572350919031 |
|
- type: cos_sim_spearman |
|
value: 77.94490233429326 |
|
- type: euclidean_pearson |
|
value: 78.36595251203524 |
|
- type: euclidean_spearman |
|
value: 77.94490233429326 |
|
- type: manhattan_pearson |
|
value: 78.41538768125166 |
|
- type: manhattan_spearman |
|
value: 78.01244379569542 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.7843552187951 |
|
- type: cos_sim_spearman |
|
value: 82.28085055047386 |
|
- type: euclidean_pearson |
|
value: 82.37373672515267 |
|
- type: euclidean_spearman |
|
value: 82.28085055047386 |
|
- type: manhattan_pearson |
|
value: 82.39387241346917 |
|
- type: manhattan_spearman |
|
value: 82.36503339515906 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: eea2b4fe26a775864c896887d910b76a8098ad3f |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 68.29963929962095 |
|
- type: cos_sim_spearman |
|
value: 67.96868942546051 |
|
- type: euclidean_pearson |
|
value: 68.93524903869285 |
|
- type: euclidean_spearman |
|
value: 67.96868942546051 |
|
- type: manhattan_pearson |
|
value: 68.79144468444811 |
|
- type: manhattan_spearman |
|
value: 67.69311483884324 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 72.84789696700685 |
|
- type: cos_sim_spearman |
|
value: 75.67875747588545 |
|
- type: euclidean_pearson |
|
value: 75.07752300463038 |
|
- type: euclidean_spearman |
|
value: 75.67875747588545 |
|
- type: manhattan_pearson |
|
value: 74.97934248140928 |
|
- type: manhattan_spearman |
|
value: 75.62525644178724 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: PhilipMay/stsb_multi_mt |
|
name: MTEB STSBenchmarkMultilingualSTS (en) |
|
config: en |
|
split: test |
|
revision: 93d57ef91790589e3ce9c365164337a8a78b7632 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 72.84789702519309 |
|
- type: cos_sim_spearman |
|
value: 75.67875747588545 |
|
- type: euclidean_pearson |
|
value: 75.07752310061133 |
|
- type: euclidean_spearman |
|
value: 75.67875747588545 |
|
- type: manhattan_pearson |
|
value: 74.97934257159595 |
|
- type: manhattan_spearman |
|
value: 75.62525644178724 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 81.55557720431086 |
|
- type: mrr |
|
value: 94.91178665198272 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 59.260999999999996 |
|
- type: map_at_10 |
|
value: 69.36099999999999 |
|
- type: map_at_100 |
|
value: 69.868 |
|
- type: map_at_1000 |
|
value: 69.877 |
|
- type: map_at_3 |
|
value: 66.617 |
|
- type: map_at_5 |
|
value: 68.061 |
|
- type: mrr_at_1 |
|
value: 62.333000000000006 |
|
- type: mrr_at_10 |
|
value: 70.533 |
|
- type: mrr_at_100 |
|
value: 70.966 |
|
- type: mrr_at_1000 |
|
value: 70.975 |
|
- type: mrr_at_3 |
|
value: 68.667 |
|
- type: mrr_at_5 |
|
value: 69.717 |
|
- type: ndcg_at_1 |
|
value: 62.333000000000006 |
|
- type: ndcg_at_10 |
|
value: 73.82300000000001 |
|
- type: ndcg_at_100 |
|
value: 76.122 |
|
- type: ndcg_at_1000 |
|
value: 76.374 |
|
- type: ndcg_at_3 |
|
value: 69.27499999999999 |
|
- type: ndcg_at_5 |
|
value: 71.33 |
|
- type: precision_at_1 |
|
value: 62.333000000000006 |
|
- type: precision_at_10 |
|
value: 9.8 |
|
- type: precision_at_100 |
|
value: 1.097 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 26.889000000000003 |
|
- type: precision_at_5 |
|
value: 17.599999999999998 |
|
- type: recall_at_1 |
|
value: 59.260999999999996 |
|
- type: recall_at_10 |
|
value: 86.2 |
|
- type: recall_at_100 |
|
value: 96.667 |
|
- type: recall_at_1000 |
|
value: 98.667 |
|
- type: recall_at_3 |
|
value: 74.006 |
|
- type: recall_at_5 |
|
value: 79.167 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81881188118813 |
|
- type: cos_sim_ap |
|
value: 95.20169041096409 |
|
- type: cos_sim_f1 |
|
value: 90.76224129227664 |
|
- type: cos_sim_precision |
|
value: 91.64118246687055 |
|
- type: cos_sim_recall |
|
value: 89.9 |
|
- type: dot_accuracy |
|
value: 99.81881188118813 |
|
- type: dot_ap |
|
value: 95.20169041096409 |
|
- type: dot_f1 |
|
value: 90.76224129227664 |
|
- type: dot_precision |
|
value: 91.64118246687055 |
|
- type: dot_recall |
|
value: 89.9 |
|
- type: euclidean_accuracy |
|
value: 99.81881188118813 |
|
- type: euclidean_ap |
|
value: 95.2016904109641 |
|
- type: euclidean_f1 |
|
value: 90.76224129227664 |
|
- type: euclidean_precision |
|
value: 91.64118246687055 |
|
- type: euclidean_recall |
|
value: 89.9 |
|
- type: manhattan_accuracy |
|
value: 99.81881188118813 |
|
- type: manhattan_ap |
|
value: 95.22680188132777 |
|
- type: manhattan_f1 |
|
value: 90.79013588324108 |
|
- type: manhattan_precision |
|
value: 91.38804457953394 |
|
- type: manhattan_recall |
|
value: 90.2 |
|
- type: max_accuracy |
|
value: 99.81881188118813 |
|
- type: max_ap |
|
value: 95.22680188132777 |
|
- type: max_f1 |
|
value: 90.79013588324108 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 57.8638628701308 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 37.82028248106046 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 50.870860210170946 |
|
- type: mrr |
|
value: 51.608084521687466 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.60384207444685 |
|
- type: cos_sim_spearman |
|
value: 30.84047452209471 |
|
- type: dot_pearson |
|
value: 31.60384104417333 |
|
- type: dot_spearman |
|
value: 30.84047452209471 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.246 |
|
- type: map_at_10 |
|
value: 2.051 |
|
- type: map_at_100 |
|
value: 13.129 |
|
- type: map_at_1000 |
|
value: 31.56 |
|
- type: map_at_3 |
|
value: 0.681 |
|
- type: map_at_5 |
|
value: 1.105 |
|
- type: mrr_at_1 |
|
value: 94 |
|
- type: mrr_at_10 |
|
value: 97 |
|
- type: mrr_at_100 |
|
value: 97 |
|
- type: mrr_at_1000 |
|
value: 97 |
|
- type: mrr_at_3 |
|
value: 97 |
|
- type: mrr_at_5 |
|
value: 97 |
|
- type: ndcg_at_1 |
|
value: 87 |
|
- type: ndcg_at_10 |
|
value: 80.716 |
|
- type: ndcg_at_100 |
|
value: 63.83 |
|
- type: ndcg_at_1000 |
|
value: 56.215 |
|
- type: ndcg_at_3 |
|
value: 84.531 |
|
- type: ndcg_at_5 |
|
value: 84.777 |
|
- type: precision_at_1 |
|
value: 94 |
|
- type: precision_at_10 |
|
value: 84.6 |
|
- type: precision_at_100 |
|
value: 66.03999999999999 |
|
- type: precision_at_1000 |
|
value: 24.878 |
|
- type: precision_at_3 |
|
value: 88.667 |
|
- type: precision_at_5 |
|
value: 89.60000000000001 |
|
- type: recall_at_1 |
|
value: 0.246 |
|
- type: recall_at_10 |
|
value: 2.2079999999999997 |
|
- type: recall_at_100 |
|
value: 15.895999999999999 |
|
- type: recall_at_1000 |
|
value: 52.683 |
|
- type: recall_at_3 |
|
value: 0.7040000000000001 |
|
- type: recall_at_5 |
|
value: 1.163 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.852 |
|
- type: map_at_10 |
|
value: 14.316 |
|
- type: map_at_100 |
|
value: 20.982 |
|
- type: map_at_1000 |
|
value: 22.58 |
|
- type: map_at_3 |
|
value: 7.767 |
|
- type: map_at_5 |
|
value: 10.321 |
|
- type: mrr_at_1 |
|
value: 51.019999999999996 |
|
- type: mrr_at_10 |
|
value: 66.365 |
|
- type: mrr_at_100 |
|
value: 66.522 |
|
- type: mrr_at_1000 |
|
value: 66.522 |
|
- type: mrr_at_3 |
|
value: 62.925 |
|
- type: mrr_at_5 |
|
value: 64.762 |
|
- type: ndcg_at_1 |
|
value: 46.939 |
|
- type: ndcg_at_10 |
|
value: 34.516999999999996 |
|
- type: ndcg_at_100 |
|
value: 44.25 |
|
- type: ndcg_at_1000 |
|
value: 54.899 |
|
- type: ndcg_at_3 |
|
value: 40.203 |
|
- type: ndcg_at_5 |
|
value: 37.004 |
|
- type: precision_at_1 |
|
value: 51.019999999999996 |
|
- type: precision_at_10 |
|
value: 29.796 |
|
- type: precision_at_100 |
|
value: 8.633000000000001 |
|
- type: precision_at_1000 |
|
value: 1.584 |
|
- type: precision_at_3 |
|
value: 40.816 |
|
- type: precision_at_5 |
|
value: 35.918 |
|
- type: recall_at_1 |
|
value: 3.852 |
|
- type: recall_at_10 |
|
value: 20.891000000000002 |
|
- type: recall_at_100 |
|
value: 52.428 |
|
- type: recall_at_1000 |
|
value: 84.34899999999999 |
|
- type: recall_at_3 |
|
value: 8.834 |
|
- type: recall_at_5 |
|
value: 12.909 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
|
metrics: |
|
- type: accuracy |
|
value: 64.7092 |
|
- type: ap |
|
value: 11.972915012305819 |
|
- type: f1 |
|
value: 49.91050149892115 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 56.737408036219584 |
|
- type: f1 |
|
value: 57.07235266246011 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 35.9147539025798 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 82.52369315133814 |
|
- type: cos_sim_ap |
|
value: 62.34858091376534 |
|
- type: cos_sim_f1 |
|
value: 58.18225190839694 |
|
- type: cos_sim_precision |
|
value: 53.09098824553766 |
|
- type: cos_sim_recall |
|
value: 64.35356200527704 |
|
- type: dot_accuracy |
|
value: 82.52369315133814 |
|
- type: dot_ap |
|
value: 62.34857753814992 |
|
- type: dot_f1 |
|
value: 58.18225190839694 |
|
- type: dot_precision |
|
value: 53.09098824553766 |
|
- type: dot_recall |
|
value: 64.35356200527704 |
|
- type: euclidean_accuracy |
|
value: 82.52369315133814 |
|
- type: euclidean_ap |
|
value: 62.34857756663386 |
|
- type: euclidean_f1 |
|
value: 58.18225190839694 |
|
- type: euclidean_precision |
|
value: 53.09098824553766 |
|
- type: euclidean_recall |
|
value: 64.35356200527704 |
|
- type: manhattan_accuracy |
|
value: 82.49389044525243 |
|
- type: manhattan_ap |
|
value: 62.32245347238179 |
|
- type: manhattan_f1 |
|
value: 58.206309819213054 |
|
- type: manhattan_precision |
|
value: 52.70704044511021 |
|
- type: manhattan_recall |
|
value: 64.9868073878628 |
|
- type: max_accuracy |
|
value: 82.52369315133814 |
|
- type: max_ap |
|
value: 62.34858091376534 |
|
- type: max_f1 |
|
value: 58.206309819213054 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.34555827220863 |
|
- type: cos_sim_ap |
|
value: 84.84152481680071 |
|
- type: cos_sim_f1 |
|
value: 76.860456739428 |
|
- type: cos_sim_precision |
|
value: 72.21470150263978 |
|
- type: cos_sim_recall |
|
value: 82.14505697566985 |
|
- type: dot_accuracy |
|
value: 88.34555827220863 |
|
- type: dot_ap |
|
value: 84.84152743322608 |
|
- type: dot_f1 |
|
value: 76.860456739428 |
|
- type: dot_precision |
|
value: 72.21470150263978 |
|
- type: dot_recall |
|
value: 82.14505697566985 |
|
- type: euclidean_accuracy |
|
value: 88.34555827220863 |
|
- type: euclidean_ap |
|
value: 84.84152589453169 |
|
- type: euclidean_f1 |
|
value: 76.860456739428 |
|
- type: euclidean_precision |
|
value: 72.21470150263978 |
|
- type: euclidean_recall |
|
value: 82.14505697566985 |
|
- type: manhattan_accuracy |
|
value: 88.38242713548337 |
|
- type: manhattan_ap |
|
value: 84.8112124970968 |
|
- type: manhattan_f1 |
|
value: 76.83599206057487 |
|
- type: manhattan_precision |
|
value: 73.51244900829934 |
|
- type: manhattan_recall |
|
value: 80.47428395441946 |
|
- type: max_accuracy |
|
value: 88.38242713548337 |
|
- type: max_ap |
|
value: 84.84152743322608 |
|
- type: max_f1 |
|
value: 76.860456739428 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: jinaai/cities_wiki_clustering |
|
name: MTEB WikiCitiesClustering |
|
config: default |
|
split: test |
|
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa |
|
metrics: |
|
- type: v_measure |
|
value: 85.5314389263015 |
|
--- |
|
<h1 align="center">Snowflake's Artic-embed-l</h1> |
|
<h4 align="center"> |
|
<p> |
|
<a href=#news>News</a> | |
|
<a href=#models>Models</a> | |
|
<a href=#usage>Usage</a> | |
|
<a href="#evaluation">Evaluation</a> | |
|
<a href="#contact">Contact</a> | |
|
<a href="#faq">FAQ</a> |
|
<a href="#license">License</a> | |
|
<a href="#acknowledgement">Acknowledgement</a> |
|
<p> |
|
</h4> |
|
|
|
|
|
## News |
|
|
|
|
|
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/snowflake-arctic-embed). |
|
|
|
|
|
## Models |
|
|
|
|
|
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance. |
|
|
|
|
|
The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models. |
|
|
|
|
|
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report will be available shortly. |
|
|
|
|
|
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension | |
|
| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- | |
|
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 | |
|
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 | |
|
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 | |
|
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 | |
|
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 | |
|
|
|
|
|
Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | |
|
| Google-gecko-text-embedding | 55.7 | |
|
| text-embedding-3-large | 55.44 | |
|
| Cohere-embed-english-v3.0 | 55.00 | |
|
| bge-large-en-v1.5 | 54.29 | |
|
|
|
|
|
### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) |
|
|
|
|
|
This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------- | -------------------------------- | |
|
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | |
|
| GIST-all-MiniLM-L6-v2 | 45.12 | |
|
| gte-tiny | 44.92 | |
|
| all-MiniLM-L6-v2 | 41.95 | |
|
| bge-micro-v2 | 42.56 | |
|
|
|
|
|
### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s) |
|
|
|
|
|
Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | |
|
| bge-small-en-v1.5 | 51.68 | |
|
| Cohere-embed-english-light-v3.0 | 51.34 | |
|
| text-embedding-3-small | 51.08 | |
|
| e5-small-v2 | 49.04 | |
|
|
|
|
|
### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) |
|
|
|
|
|
Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | |
|
| bge-base-en-v1.5 | 53.25 | |
|
| nomic-embed-text-v1.5 | 53.25 | |
|
| GIST-Embedding-v0 | 52.31 | |
|
| gte-base | 52.31 | |
|
|
|
### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) |
|
|
|
|
|
Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192! |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | |
|
| nomic-embed-text-v1.5 | 53.01 | |
|
| nomic-embed-text-v1 | 52.81 | |
|
|
|
|
|
|
|
|
|
### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) |
|
|
|
|
|
Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this small model does not sacrifice retrieval accuracy for its small size. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | |
|
| UAE-Large-V1 | 54.66 | |
|
| bge-large-en-v1.5 | 54.29 | |
|
| mxbai-embed-large-v1 | 54.39 | |
|
| e5-Large-v2 | 50.56 | |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Using Sentence Transformers |
|
|
|
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below. |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-l") |
|
|
|
queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
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documents = ['The Data Cloud!', 'Mexico City of Course!'] |
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query_embeddings = model.encode(queries, prompt_name="query") |
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document_embeddings = model.encode(documents) |
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scores = query_embeddings @ document_embeddings.T |
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for query, query_scores in zip(queries, scores): |
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doc_score_pairs = list(zip(documents, query_scores)) |
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
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# Output passages & scores |
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print("Query:", query) |
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for document, score in doc_score_pairs: |
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print(score, document) |
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``` |
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``` |
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Query: what is snowflake? |
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0.28976774 The Data Cloud! |
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0.19071159 Mexico City of Course! |
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Query: Where can I get the best tacos? |
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0.38650584 Mexico City of Course! |
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0.25145516 The Data Cloud! |
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``` |
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### Using Huggingface transformers |
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You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query). |
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```python |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-l') |
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-l', add_pooling_layer=False) |
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model.eval() |
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query_prefix = 'Represent this sentence for searching relevant passages: ' |
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queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
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queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries] |
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query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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documents = ['The Data Cloud!', 'Mexico City of Course!'] |
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document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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# Compute token embeddings |
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with torch.no_grad(): |
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query_embeddings = model(**query_tokens)[0][:, 0] |
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doument_embeddings = model(**document_tokens)[0][:, 0] |
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# normalize embeddings |
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query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1) |
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doument_embeddings = torch.nn.functional.normalize(doument_embeddings, p=2, dim=1) |
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scores = torch.mm(query_embeddings, doument_embeddings.transpose(0, 1)) |
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for query, query_scores in zip(queries, scores): |
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doc_score_pairs = list(zip(documents, query_scores)) |
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
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#Output passages & scores |
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print("Query:", query) |
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for document, score in doc_score_pairs: |
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print(score, document) |
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``` |
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## FAQ |
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TBD |
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## Contact |
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Feel free to open an issue or pull request if you have any questions or suggestions about this project. |
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You also can email Daniel Campos([email protected]). |
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## License |
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Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge. |
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## Acknowledgement |
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We want to thank the open-source community, which has provided the great building blocks upon which we could make our models. |
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We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. |
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We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. |
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We also thank the open-source community for producing the great models we could build on top of and making these releases possible. |
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Finally, we thank the researchers who created BEIR and MTEB benchmarks. |
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It is largely thanks to their tireless work to define what better looks like that we could improve model performance. |
