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--- |
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library_name: transformers |
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tags: |
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- sentence-transformers |
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- gte |
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- mteb |
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- transformers.js |
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- sentence-similarity |
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license: apache-2.0 |
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language: |
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- en |
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model-index: |
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- name: gte-base-en-v1.5 |
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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: 74.7910447761194 |
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- type: ap |
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value: 37.053785713650626 |
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- type: f1 |
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value: 68.51101510998551 |
|
- 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: |
|
- type: accuracy |
|
value: 93.016875 |
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- type: ap |
|
value: 89.17750268426342 |
|
- type: f1 |
|
value: 92.9970977240524 |
|
- 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: 53.312000000000005 |
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- type: f1 |
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value: 52.98175784163017 |
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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 |
|
value: 38.193 |
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- type: map_at_10 |
|
value: 54.848 |
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- type: map_at_100 |
|
value: 55.388000000000005 |
|
- type: map_at_1000 |
|
value: 55.388999999999996 |
|
- type: map_at_3 |
|
value: 50.427 |
|
- type: map_at_5 |
|
value: 53.105000000000004 |
|
- type: mrr_at_1 |
|
value: 39.047 |
|
- type: mrr_at_10 |
|
value: 55.153 |
|
- type: mrr_at_100 |
|
value: 55.686 |
|
- type: mrr_at_1000 |
|
value: 55.688 |
|
- type: mrr_at_3 |
|
value: 50.676 |
|
- type: mrr_at_5 |
|
value: 53.417 |
|
- type: ndcg_at_1 |
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value: 38.193 |
|
- type: ndcg_at_10 |
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value: 63.486 |
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- type: ndcg_at_100 |
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value: 65.58 |
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- type: ndcg_at_1000 |
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value: 65.61 |
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- type: ndcg_at_3 |
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value: 54.494 |
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- type: ndcg_at_5 |
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value: 59.339 |
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- type: precision_at_1 |
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value: 38.193 |
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- type: precision_at_10 |
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value: 9.075 |
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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: 22.096 |
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- type: precision_at_5 |
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value: 15.619 |
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- type: recall_at_1 |
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value: 38.193 |
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- type: recall_at_10 |
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value: 90.754 |
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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 |
|
- type: recall_at_3 |
|
value: 66.28699999999999 |
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- type: recall_at_5 |
|
value: 78.094 |
|
- 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: |
|
- type: v_measure |
|
value: 47.508221208908964 |
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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: 42.04668382560096 |
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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: 61.828759903716815 |
|
- type: mrr |
|
value: 74.37343358395991 |
|
- 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 |
|
value: 85.03673698773017 |
|
- type: cos_sim_spearman |
|
value: 83.6470866785058 |
|
- type: euclidean_pearson |
|
value: 82.64048673096565 |
|
- type: euclidean_spearman |
|
value: 83.63142367101115 |
|
- type: manhattan_pearson |
|
value: 82.71493099760228 |
|
- type: manhattan_spearman |
|
value: 83.60491704294326 |
|
- task: |
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type: Classification |
|
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: 86.73376623376623 |
|
- type: f1 |
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value: 86.70294049278262 |
|
- task: |
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type: Clustering |
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dataset: |
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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 |
|
value: 40.31923804167062 |
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- task: |
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type: Clustering |
|
dataset: |
|
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: 37.552547125348454 |
|
- 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: 30.567 |
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- type: map_at_10 |
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value: 41.269 |
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- type: map_at_100 |
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value: 42.689 |
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- type: map_at_1000 |
|
value: 42.84 |
|
- type: map_at_3 |
|
value: 37.567 |
|
- type: map_at_5 |
|
value: 39.706 |
|
- type: mrr_at_1 |
|
value: 37.053000000000004 |
|
- type: mrr_at_10 |
|
value: 46.900999999999996 |
|
- type: mrr_at_100 |
|
value: 47.662 |
|
- type: mrr_at_1000 |
|
value: 47.713 |
|
- type: mrr_at_3 |
|
value: 43.801 |
|
- type: mrr_at_5 |
|
value: 45.689 |
|
- type: ndcg_at_1 |
|
value: 37.053000000000004 |
|
- type: ndcg_at_10 |
|
value: 47.73 |
|
- type: ndcg_at_100 |
|
value: 53.128 |
|
- type: ndcg_at_1000 |
|
value: 55.300000000000004 |
|
- type: ndcg_at_3 |
|
value: 42.046 |
|
- type: ndcg_at_5 |
|
value: 44.782 |
|
- type: precision_at_1 |
|
value: 37.053000000000004 |
|
- type: precision_at_10 |
|
value: 9.142 |
|
- type: precision_at_100 |
|
value: 1.485 |
|
- type: precision_at_1000 |
|
value: 0.197 |
|
- type: precision_at_3 |
|
value: 20.076 |
|
- type: precision_at_5 |
|
value: 14.535 |
|
- type: recall_at_1 |
|
value: 30.567 |
|
- type: recall_at_10 |
|
value: 60.602999999999994 |
|
- type: recall_at_100 |
|
value: 83.22800000000001 |
|
- type: recall_at_1000 |
|
value: 96.696 |
|
- type: recall_at_3 |
|
value: 44.336999999999996 |
|
- type: recall_at_5 |
|
value: 51.949 |
|
- 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 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.538000000000004 |
|
- type: map_at_10 |
|
value: 38.757999999999996 |
|
- type: map_at_100 |
|
value: 40.129 |
|
- type: map_at_1000 |
|
value: 40.262 |
|
- type: map_at_3 |
|
value: 35.866 |
|
- type: map_at_5 |
|
value: 37.417 |
|
- type: mrr_at_1 |
|
value: 36.051 |
|
- type: mrr_at_10 |
|
value: 44.868 |
|
- type: mrr_at_100 |
|
value: 45.568999999999996 |
|
- type: mrr_at_1000 |
|
value: 45.615 |
|
- type: mrr_at_3 |
|
value: 42.558 |
|
- type: mrr_at_5 |
|
value: 43.883 |
|
- type: ndcg_at_1 |
|
value: 36.051 |
|
- type: ndcg_at_10 |
|
value: 44.584 |
|
- type: ndcg_at_100 |
|
value: 49.356 |
|
- type: ndcg_at_1000 |
|
value: 51.39 |
|
- type: ndcg_at_3 |
|
value: 40.389 |
|
- type: ndcg_at_5 |
|
value: 42.14 |
|
- type: precision_at_1 |
|
value: 36.051 |
|
- type: precision_at_10 |
|
value: 8.446 |
|
- type: precision_at_100 |
|
value: 1.411 |
|
- type: precision_at_1000 |
|
value: 0.19 |
|
- type: precision_at_3 |
|
value: 19.639 |
|
- type: precision_at_5 |
|
value: 13.796 |
|
- type: recall_at_1 |
|
value: 28.538000000000004 |
|
- type: recall_at_10 |
|
value: 54.99000000000001 |
|
- type: recall_at_100 |
|
value: 75.098 |
|
- type: recall_at_1000 |
|
value: 87.848 |
|
- type: recall_at_3 |
|
value: 42.236000000000004 |
|
- type: recall_at_5 |
|
value: 47.377 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gaming |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.188 |
|
- type: map_at_10 |
|
value: 50.861000000000004 |
|
- type: map_at_100 |
|
value: 51.917 |
|
- type: map_at_1000 |
|
value: 51.964999999999996 |
|
- type: map_at_3 |
|
value: 47.144000000000005 |
|
- type: map_at_5 |
|
value: 49.417 |
|
- type: mrr_at_1 |
|
value: 42.571 |
|
- type: mrr_at_10 |
|
value: 54.086999999999996 |
|
- type: mrr_at_100 |
|
value: 54.739000000000004 |
|
- type: mrr_at_1000 |
|
value: 54.762 |
|
- type: mrr_at_3 |
|
value: 51.285000000000004 |
|
- type: mrr_at_5 |
|
value: 53.0 |
|
- type: ndcg_at_1 |
|
value: 42.571 |
|
- type: ndcg_at_10 |
|
value: 57.282 |
|
- type: ndcg_at_100 |
|
value: 61.477000000000004 |
|
- type: ndcg_at_1000 |
|
value: 62.426 |
|
- type: ndcg_at_3 |
|
value: 51.0 |
|
- type: ndcg_at_5 |
|
value: 54.346000000000004 |
|
- type: precision_at_1 |
|
value: 42.571 |
|
- type: precision_at_10 |
|
value: 9.467 |
|
- type: precision_at_100 |
|
value: 1.2550000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 23.114 |
|
- type: precision_at_5 |
|
value: 16.250999999999998 |
|
- type: recall_at_1 |
|
value: 37.188 |
|
- type: recall_at_10 |
|
value: 73.068 |
|
- type: recall_at_100 |
|
value: 91.203 |
|
- type: recall_at_1000 |
|
value: 97.916 |
|
- type: recall_at_3 |
|
value: 56.552 |
|
- type: recall_at_5 |
|
value: 64.567 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gis |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: 5003b3064772da1887988e05400cf3806fe491f2 |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.041000000000004 |
|
- type: map_at_10 |
|
value: 33.86 |
|
- type: map_at_100 |
|
value: 34.988 |
|
- type: map_at_1000 |
|
value: 35.064 |
|
- type: map_at_3 |
|
value: 31.049 |
|
- type: map_at_5 |
|
value: 32.845 |
|
- type: mrr_at_1 |
|
value: 26.893 |
|
- type: mrr_at_10 |
|
value: 35.594 |
|
- type: mrr_at_100 |
|
value: 36.617 |
|
- type: mrr_at_1000 |
|
value: 36.671 |
|
- type: mrr_at_3 |
|
value: 33.051 |
|
- type: mrr_at_5 |
|
value: 34.61 |
|
- type: ndcg_at_1 |
|
value: 26.893 |
|
- type: ndcg_at_10 |
|
value: 38.674 |
|
- type: ndcg_at_100 |
|
value: 44.178 |
|
- type: ndcg_at_1000 |
|
value: 46.089999999999996 |
|
- type: ndcg_at_3 |
|
value: 33.485 |
|
- type: ndcg_at_5 |
|
value: 36.402 |
|
- type: precision_at_1 |
|
value: 26.893 |
|
- type: precision_at_10 |
|
value: 5.989 |
|
- type: precision_at_100 |
|
value: 0.918 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 14.2 |
|
- type: precision_at_5 |
|
value: 10.26 |
|
- type: recall_at_1 |
|
value: 25.041000000000004 |
|
- type: recall_at_10 |
|
value: 51.666000000000004 |
|
- type: recall_at_100 |
|
value: 76.896 |
|
- type: recall_at_1000 |
|
value: 91.243 |
|
- type: recall_at_3 |
|
value: 38.035999999999994 |
|
- type: recall_at_5 |
|
value: 44.999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-mathematica |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.909999999999998 |
|
- type: map_at_10 |
|
value: 23.901 |
|
- type: map_at_100 |
|
value: 25.165 |
|
- type: map_at_1000 |
|
value: 25.291000000000004 |
|
- type: map_at_3 |
|
value: 21.356 |
|
- type: map_at_5 |
|
value: 22.816 |
|
- type: mrr_at_1 |
|
value: 20.025000000000002 |
|
- type: mrr_at_10 |
|
value: 28.382 |
|
- type: mrr_at_100 |
|
value: 29.465000000000003 |
|
- type: mrr_at_1000 |
|
value: 29.535 |
|
- type: mrr_at_3 |
|
value: 25.933 |
|
- type: mrr_at_5 |
|
value: 27.332 |
|
- type: ndcg_at_1 |
|
value: 20.025000000000002 |
|
- type: ndcg_at_10 |
|
value: 29.099000000000004 |
|
- type: ndcg_at_100 |
|
value: 35.127 |
|
- type: ndcg_at_1000 |
|
value: 38.096000000000004 |
|
- type: ndcg_at_3 |
|
value: 24.464 |
|
- type: ndcg_at_5 |
|
value: 26.709 |
|
- type: precision_at_1 |
|
value: 20.025000000000002 |
|
- type: precision_at_10 |
|
value: 5.398 |
|
- type: precision_at_100 |
|
value: 0.9690000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 11.774 |
|
- type: precision_at_5 |
|
value: 8.632 |
|
- type: recall_at_1 |
|
value: 15.909999999999998 |
|
- type: recall_at_10 |
|
value: 40.672000000000004 |
|
- type: recall_at_100 |
|
value: 66.855 |
|
- type: recall_at_1000 |
|
value: 87.922 |
|
- type: recall_at_3 |
|
value: 28.069 |
|
- type: recall_at_5 |
|
value: 33.812 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-physics |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.175 |
|
- type: map_at_10 |
|
value: 41.36 |
|
- type: map_at_100 |
|
value: 42.701 |
|
- type: map_at_1000 |
|
value: 42.817 |
|
- type: map_at_3 |
|
value: 37.931 |
|
- type: map_at_5 |
|
value: 39.943 |
|
- type: mrr_at_1 |
|
value: 35.611 |
|
- type: mrr_at_10 |
|
value: 46.346 |
|
- type: mrr_at_100 |
|
value: 47.160000000000004 |
|
- type: mrr_at_1000 |
|
value: 47.203 |
|
- type: mrr_at_3 |
|
value: 43.712 |
|
- type: mrr_at_5 |
|
value: 45.367000000000004 |
|
- type: ndcg_at_1 |
|
value: 35.611 |
|
- type: ndcg_at_10 |
|
value: 47.532000000000004 |
|
- type: ndcg_at_100 |
|
value: 53.003 |
|
- type: ndcg_at_1000 |
|
value: 55.007 |
|
- type: ndcg_at_3 |
|
value: 42.043 |
|
- type: ndcg_at_5 |
|
value: 44.86 |
|
- type: precision_at_1 |
|
value: 35.611 |
|
- type: precision_at_10 |
|
value: 8.624 |
|
- type: precision_at_100 |
|
value: 1.332 |
|
- type: precision_at_1000 |
|
value: 0.169 |
|
- type: precision_at_3 |
|
value: 20.083000000000002 |
|
- type: precision_at_5 |
|
value: 14.437 |
|
- type: recall_at_1 |
|
value: 30.175 |
|
- type: recall_at_10 |
|
value: 60.5 |
|
- type: recall_at_100 |
|
value: 83.399 |
|
- type: recall_at_1000 |
|
value: 96.255 |
|
- type: recall_at_3 |
|
value: 45.448 |
|
- type: recall_at_5 |
|
value: 52.432 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-programmers |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.467000000000002 |
|
- type: map_at_10 |
|
value: 33.812999999999995 |
|
- type: map_at_100 |
|
value: 35.248000000000005 |
|
- type: map_at_1000 |
|
value: 35.359 |
|
- type: map_at_3 |
|
value: 30.316 |
|
- type: map_at_5 |
|
value: 32.233000000000004 |
|
- type: mrr_at_1 |
|
value: 28.310999999999996 |
|
- type: mrr_at_10 |
|
value: 38.979 |
|
- type: mrr_at_100 |
|
value: 39.937 |
|
- type: mrr_at_1000 |
|
value: 39.989999999999995 |
|
- type: mrr_at_3 |
|
value: 36.244 |
|
- type: mrr_at_5 |
|
value: 37.871 |
|
- type: ndcg_at_1 |
|
value: 28.310999999999996 |
|
- type: ndcg_at_10 |
|
value: 40.282000000000004 |
|
- type: ndcg_at_100 |
|
value: 46.22 |
|
- type: ndcg_at_1000 |
|
value: 48.507 |
|
- type: ndcg_at_3 |
|
value: 34.596 |
|
- type: ndcg_at_5 |
|
value: 37.267 |
|
- type: precision_at_1 |
|
value: 28.310999999999996 |
|
- type: precision_at_10 |
|
value: 7.831 |
|
- type: precision_at_100 |
|
value: 1.257 |
|
- type: precision_at_1000 |
|
value: 0.164 |
|
- type: precision_at_3 |
|
value: 17.275 |
|
- type: precision_at_5 |
|
value: 12.556999999999999 |
|
- type: recall_at_1 |
|
value: 22.467000000000002 |
|
- type: recall_at_10 |
|
value: 54.14099999999999 |
|
- type: recall_at_100 |
|
value: 79.593 |
|
- type: recall_at_1000 |
|
value: 95.063 |
|
- type: recall_at_3 |
|
value: 38.539 |
|
- type: recall_at_5 |
|
value: 45.403 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.18591666666667 |
|
- type: map_at_10 |
|
value: 33.84258333333333 |
|
- type: map_at_100 |
|
value: 35.11391666666666 |
|
- type: map_at_1000 |
|
value: 35.23258333333333 |
|
- type: map_at_3 |
|
value: 30.764249999999997 |
|
- type: map_at_5 |
|
value: 32.52333333333334 |
|
- type: mrr_at_1 |
|
value: 28.54733333333333 |
|
- type: mrr_at_10 |
|
value: 37.81725 |
|
- type: mrr_at_100 |
|
value: 38.716499999999996 |
|
- type: mrr_at_1000 |
|
value: 38.77458333333333 |
|
- type: mrr_at_3 |
|
value: 35.157833333333336 |
|
- type: mrr_at_5 |
|
value: 36.69816666666667 |
|
- type: ndcg_at_1 |
|
value: 28.54733333333333 |
|
- type: ndcg_at_10 |
|
value: 39.51508333333334 |
|
- type: ndcg_at_100 |
|
value: 44.95316666666666 |
|
- type: ndcg_at_1000 |
|
value: 47.257083333333334 |
|
- type: ndcg_at_3 |
|
value: 34.205833333333324 |
|
- type: ndcg_at_5 |
|
value: 36.78266666666667 |
|
- type: precision_at_1 |
|
value: 28.54733333333333 |
|
- type: precision_at_10 |
|
value: 7.082583333333334 |
|
- type: precision_at_100 |
|
value: 1.1590833333333332 |
|
- type: precision_at_1000 |
|
value: 0.15516666666666662 |
|
- type: precision_at_3 |
|
value: 15.908750000000001 |
|
- type: precision_at_5 |
|
value: 11.505416666666669 |
|
- type: recall_at_1 |
|
value: 24.18591666666667 |
|
- type: recall_at_10 |
|
value: 52.38758333333333 |
|
- type: recall_at_100 |
|
value: 76.13666666666667 |
|
- type: recall_at_1000 |
|
value: 91.99066666666667 |
|
- type: recall_at_3 |
|
value: 37.78333333333334 |
|
- type: recall_at_5 |
|
value: 44.30141666666666 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-stats |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.975 |
|
- type: map_at_10 |
|
value: 29.781000000000002 |
|
- type: map_at_100 |
|
value: 30.847 |
|
- type: map_at_1000 |
|
value: 30.94 |
|
- type: map_at_3 |
|
value: 27.167 |
|
- type: map_at_5 |
|
value: 28.633999999999997 |
|
- type: mrr_at_1 |
|
value: 24.387 |
|
- type: mrr_at_10 |
|
value: 32.476 |
|
- type: mrr_at_100 |
|
value: 33.337 |
|
- type: mrr_at_1000 |
|
value: 33.403 |
|
- type: mrr_at_3 |
|
value: 29.881999999999998 |
|
- type: mrr_at_5 |
|
value: 31.339 |
|
- type: ndcg_at_1 |
|
value: 24.387 |
|
- type: ndcg_at_10 |
|
value: 34.596 |
|
- type: ndcg_at_100 |
|
value: 39.635 |
|
- type: ndcg_at_1000 |
|
value: 42.079 |
|
- type: ndcg_at_3 |
|
value: 29.516 |
|
- type: ndcg_at_5 |
|
value: 31.959 |
|
- type: precision_at_1 |
|
value: 24.387 |
|
- type: precision_at_10 |
|
value: 5.6129999999999995 |
|
- type: precision_at_100 |
|
value: 0.8909999999999999 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 12.73 |
|
- type: precision_at_5 |
|
value: 9.171999999999999 |
|
- type: recall_at_1 |
|
value: 21.975 |
|
- type: recall_at_10 |
|
value: 46.826 |
|
- type: recall_at_100 |
|
value: 69.554 |
|
- type: recall_at_1000 |
|
value: 87.749 |
|
- type: recall_at_3 |
|
value: 33.016 |
|
- type: recall_at_5 |
|
value: 38.97 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-tex |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: 46989137a86843e03a6195de44b09deda022eec7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.614 |
|
- type: map_at_10 |
|
value: 22.927 |
|
- type: map_at_100 |
|
value: 24.185000000000002 |
|
- type: map_at_1000 |
|
value: 24.319 |
|
- type: map_at_3 |
|
value: 20.596 |
|
- type: map_at_5 |
|
value: 21.854000000000003 |
|
- type: mrr_at_1 |
|
value: 18.858 |
|
- type: mrr_at_10 |
|
value: 26.535999999999998 |
|
- type: mrr_at_100 |
|
value: 27.582 |
|
- type: mrr_at_1000 |
|
value: 27.665 |
|
- type: mrr_at_3 |
|
value: 24.295 |
|
- type: mrr_at_5 |
|
value: 25.532 |
|
- type: ndcg_at_1 |
|
value: 18.858 |
|
- type: ndcg_at_10 |
|
value: 27.583000000000002 |
|
- type: ndcg_at_100 |
|
value: 33.635 |
|
- type: ndcg_at_1000 |
|
value: 36.647 |
|
- type: ndcg_at_3 |
|
value: 23.348 |
|
- type: ndcg_at_5 |
|
value: 25.257 |
|
- type: precision_at_1 |
|
value: 18.858 |
|
- type: precision_at_10 |
|
value: 5.158 |
|
- type: precision_at_100 |
|
value: 0.964 |
|
- type: precision_at_1000 |
|
value: 0.13999999999999999 |
|
- type: precision_at_3 |
|
value: 11.092 |
|
- type: precision_at_5 |
|
value: 8.1 |
|
- type: recall_at_1 |
|
value: 15.614 |
|
- type: recall_at_10 |
|
value: 37.916 |
|
- type: recall_at_100 |
|
value: 65.205 |
|
- type: recall_at_1000 |
|
value: 86.453 |
|
- type: recall_at_3 |
|
value: 26.137 |
|
- type: recall_at_5 |
|
value: 31.087999999999997 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-unix |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.078000000000003 |
|
- type: map_at_10 |
|
value: 31.941999999999997 |
|
- type: map_at_100 |
|
value: 33.196999999999996 |
|
- type: map_at_1000 |
|
value: 33.303 |
|
- type: map_at_3 |
|
value: 28.927000000000003 |
|
- type: map_at_5 |
|
value: 30.707 |
|
- type: mrr_at_1 |
|
value: 26.866 |
|
- type: mrr_at_10 |
|
value: 35.557 |
|
- type: mrr_at_100 |
|
value: 36.569 |
|
- type: mrr_at_1000 |
|
value: 36.632 |
|
- type: mrr_at_3 |
|
value: 32.897999999999996 |
|
- type: mrr_at_5 |
|
value: 34.437 |
|
- type: ndcg_at_1 |
|
value: 26.866 |
|
- type: ndcg_at_10 |
|
value: 37.372 |
|
- type: ndcg_at_100 |
|
value: 43.248 |
|
- type: ndcg_at_1000 |
|
value: 45.632 |
|
- type: ndcg_at_3 |
|
value: 31.852999999999998 |
|
- type: ndcg_at_5 |
|
value: 34.582 |
|
- type: precision_at_1 |
|
value: 26.866 |
|
- type: precision_at_10 |
|
value: 6.511 |
|
- type: precision_at_100 |
|
value: 1.078 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 14.582999999999998 |
|
- type: precision_at_5 |
|
value: 10.634 |
|
- type: recall_at_1 |
|
value: 23.078000000000003 |
|
- type: recall_at_10 |
|
value: 50.334 |
|
- type: recall_at_100 |
|
value: 75.787 |
|
- type: recall_at_1000 |
|
value: 92.485 |
|
- type: recall_at_3 |
|
value: 35.386 |
|
- type: recall_at_5 |
|
value: 42.225 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-webmasters |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.203999999999997 |
|
- type: map_at_10 |
|
value: 31.276 |
|
- type: map_at_100 |
|
value: 32.844 |
|
- type: map_at_1000 |
|
value: 33.062999999999995 |
|
- type: map_at_3 |
|
value: 27.733999999999998 |
|
- type: map_at_5 |
|
value: 29.64 |
|
- type: mrr_at_1 |
|
value: 27.272999999999996 |
|
- type: mrr_at_10 |
|
value: 36.083 |
|
- type: mrr_at_100 |
|
value: 37.008 |
|
- type: mrr_at_1000 |
|
value: 37.076 |
|
- type: mrr_at_3 |
|
value: 33.004 |
|
- type: mrr_at_5 |
|
value: 34.664 |
|
- type: ndcg_at_1 |
|
value: 27.272999999999996 |
|
- type: ndcg_at_10 |
|
value: 37.763000000000005 |
|
- type: ndcg_at_100 |
|
value: 43.566 |
|
- type: ndcg_at_1000 |
|
value: 46.356 |
|
- type: ndcg_at_3 |
|
value: 31.673000000000002 |
|
- type: ndcg_at_5 |
|
value: 34.501 |
|
- type: precision_at_1 |
|
value: 27.272999999999996 |
|
- type: precision_at_10 |
|
value: 7.470000000000001 |
|
- type: precision_at_100 |
|
value: 1.502 |
|
- type: precision_at_1000 |
|
value: 0.24 |
|
- type: precision_at_3 |
|
value: 14.756 |
|
- type: precision_at_5 |
|
value: 11.225 |
|
- type: recall_at_1 |
|
value: 22.203999999999997 |
|
- type: recall_at_10 |
|
value: 51.437999999999995 |
|
- type: recall_at_100 |
|
value: 76.845 |
|
- type: recall_at_1000 |
|
value: 94.38600000000001 |
|
- type: recall_at_3 |
|
value: 34.258 |
|
- type: recall_at_5 |
|
value: 41.512 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-wordpress |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.474 |
|
- type: map_at_10 |
|
value: 26.362999999999996 |
|
- type: map_at_100 |
|
value: 27.456999999999997 |
|
- type: map_at_1000 |
|
value: 27.567999999999998 |
|
- type: map_at_3 |
|
value: 23.518 |
|
- type: map_at_5 |
|
value: 25.068 |
|
- type: mrr_at_1 |
|
value: 18.669 |
|
- type: mrr_at_10 |
|
value: 27.998 |
|
- type: mrr_at_100 |
|
value: 28.953 |
|
- type: mrr_at_1000 |
|
value: 29.03 |
|
- type: mrr_at_3 |
|
value: 25.230999999999998 |
|
- type: mrr_at_5 |
|
value: 26.654 |
|
- type: ndcg_at_1 |
|
value: 18.669 |
|
- type: ndcg_at_10 |
|
value: 31.684 |
|
- type: ndcg_at_100 |
|
value: 36.864999999999995 |
|
- type: ndcg_at_1000 |
|
value: 39.555 |
|
- type: ndcg_at_3 |
|
value: 26.057000000000002 |
|
- type: ndcg_at_5 |
|
value: 28.587 |
|
- type: precision_at_1 |
|
value: 18.669 |
|
- type: precision_at_10 |
|
value: 5.3420000000000005 |
|
- type: precision_at_100 |
|
value: 0.847 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 11.583 |
|
- type: precision_at_5 |
|
value: 8.466 |
|
- type: recall_at_1 |
|
value: 17.474 |
|
- type: recall_at_10 |
|
value: 46.497 |
|
- type: recall_at_100 |
|
value: 69.977 |
|
- type: recall_at_1000 |
|
value: 89.872 |
|
- type: recall_at_3 |
|
value: 31.385999999999996 |
|
- type: recall_at_5 |
|
value: 37.283 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.173 |
|
- type: map_at_10 |
|
value: 30.407 |
|
- type: map_at_100 |
|
value: 32.528 |
|
- type: map_at_1000 |
|
value: 32.698 |
|
- type: map_at_3 |
|
value: 25.523 |
|
- type: map_at_5 |
|
value: 28.038 |
|
- type: mrr_at_1 |
|
value: 38.958 |
|
- type: mrr_at_10 |
|
value: 51.515 |
|
- type: mrr_at_100 |
|
value: 52.214000000000006 |
|
- type: mrr_at_1000 |
|
value: 52.237 |
|
- type: mrr_at_3 |
|
value: 48.502 |
|
- type: mrr_at_5 |
|
value: 50.251000000000005 |
|
- type: ndcg_at_1 |
|
value: 38.958 |
|
- type: ndcg_at_10 |
|
value: 40.355000000000004 |
|
- type: ndcg_at_100 |
|
value: 47.68 |
|
- type: ndcg_at_1000 |
|
value: 50.370000000000005 |
|
- type: ndcg_at_3 |
|
value: 33.946 |
|
- type: ndcg_at_5 |
|
value: 36.057 |
|
- type: precision_at_1 |
|
value: 38.958 |
|
- type: precision_at_10 |
|
value: 12.508 |
|
- type: precision_at_100 |
|
value: 2.054 |
|
- type: precision_at_1000 |
|
value: 0.256 |
|
- type: precision_at_3 |
|
value: 25.581 |
|
- type: precision_at_5 |
|
value: 19.256999999999998 |
|
- type: recall_at_1 |
|
value: 17.173 |
|
- type: recall_at_10 |
|
value: 46.967 |
|
- type: recall_at_100 |
|
value: 71.47200000000001 |
|
- type: recall_at_1000 |
|
value: 86.238 |
|
- type: recall_at_3 |
|
value: 30.961 |
|
- type: recall_at_5 |
|
value: 37.539 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/dbpedia |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.999 |
|
- type: map_at_10 |
|
value: 18.989 |
|
- type: map_at_100 |
|
value: 26.133 |
|
- type: map_at_1000 |
|
value: 27.666 |
|
- type: map_at_3 |
|
value: 13.918 |
|
- type: map_at_5 |
|
value: 16.473 |
|
- type: mrr_at_1 |
|
value: 66.25 |
|
- type: mrr_at_10 |
|
value: 74.161 |
|
- type: mrr_at_100 |
|
value: 74.516 |
|
- type: mrr_at_1000 |
|
value: 74.524 |
|
- type: mrr_at_3 |
|
value: 72.875 |
|
- type: mrr_at_5 |
|
value: 73.613 |
|
- type: ndcg_at_1 |
|
value: 54.37499999999999 |
|
- type: ndcg_at_10 |
|
value: 39.902 |
|
- type: ndcg_at_100 |
|
value: 44.212 |
|
- type: ndcg_at_1000 |
|
value: 51.62 |
|
- type: ndcg_at_3 |
|
value: 45.193 |
|
- type: ndcg_at_5 |
|
value: 42.541000000000004 |
|
- type: precision_at_1 |
|
value: 66.25 |
|
- type: precision_at_10 |
|
value: 30.425 |
|
- type: precision_at_100 |
|
value: 9.754999999999999 |
|
- type: precision_at_1000 |
|
value: 2.043 |
|
- type: precision_at_3 |
|
value: 48.25 |
|
- type: precision_at_5 |
|
value: 40.65 |
|
- type: recall_at_1 |
|
value: 8.999 |
|
- type: recall_at_10 |
|
value: 24.133 |
|
- type: recall_at_100 |
|
value: 49.138999999999996 |
|
- type: recall_at_1000 |
|
value: 72.639 |
|
- type: recall_at_3 |
|
value: 15.287999999999998 |
|
- type: recall_at_5 |
|
value: 19.415 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 46.38999999999999 |
|
- type: f1 |
|
value: 41.444205512055234 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
|
metrics: |
|
- type: map_at_1 |
|
value: 87.35000000000001 |
|
- type: map_at_10 |
|
value: 92.837 |
|
- type: map_at_100 |
|
value: 92.996 |
|
- type: map_at_1000 |
|
value: 93.006 |
|
- type: map_at_3 |
|
value: 92.187 |
|
- type: map_at_5 |
|
value: 92.595 |
|
- type: mrr_at_1 |
|
value: 93.864 |
|
- type: mrr_at_10 |
|
value: 96.723 |
|
- type: mrr_at_100 |
|
value: 96.72500000000001 |
|
- type: mrr_at_1000 |
|
value: 96.72500000000001 |
|
- type: mrr_at_3 |
|
value: 96.64 |
|
- type: mrr_at_5 |
|
value: 96.71499999999999 |
|
- type: ndcg_at_1 |
|
value: 93.864 |
|
- type: ndcg_at_10 |
|
value: 94.813 |
|
- type: ndcg_at_100 |
|
value: 95.243 |
|
- type: ndcg_at_1000 |
|
value: 95.38600000000001 |
|
- type: ndcg_at_3 |
|
value: 94.196 |
|
- type: ndcg_at_5 |
|
value: 94.521 |
|
- type: precision_at_1 |
|
value: 93.864 |
|
- type: precision_at_10 |
|
value: 10.951 |
|
- type: precision_at_100 |
|
value: 1.1400000000000001 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 35.114000000000004 |
|
- type: precision_at_5 |
|
value: 21.476 |
|
- type: recall_at_1 |
|
value: 87.35000000000001 |
|
- type: recall_at_10 |
|
value: 96.941 |
|
- type: recall_at_100 |
|
value: 98.397 |
|
- type: recall_at_1000 |
|
value: 99.21600000000001 |
|
- type: recall_at_3 |
|
value: 95.149 |
|
- type: recall_at_5 |
|
value: 96.131 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.476 |
|
- type: map_at_10 |
|
value: 40.11 |
|
- type: map_at_100 |
|
value: 42.229 |
|
- type: map_at_1000 |
|
value: 42.378 |
|
- type: map_at_3 |
|
value: 34.512 |
|
- type: map_at_5 |
|
value: 38.037 |
|
- type: mrr_at_1 |
|
value: 47.839999999999996 |
|
- type: mrr_at_10 |
|
value: 57.053 |
|
- type: mrr_at_100 |
|
value: 57.772 |
|
- type: mrr_at_1000 |
|
value: 57.799 |
|
- type: mrr_at_3 |
|
value: 54.552 |
|
- type: mrr_at_5 |
|
value: 56.011 |
|
- type: ndcg_at_1 |
|
value: 47.839999999999996 |
|
- type: ndcg_at_10 |
|
value: 48.650999999999996 |
|
- type: ndcg_at_100 |
|
value: 55.681000000000004 |
|
- type: ndcg_at_1000 |
|
value: 57.979 |
|
- type: ndcg_at_3 |
|
value: 43.923 |
|
- type: ndcg_at_5 |
|
value: 46.037 |
|
- type: precision_at_1 |
|
value: 47.839999999999996 |
|
- type: precision_at_10 |
|
value: 13.395000000000001 |
|
- type: precision_at_100 |
|
value: 2.0660000000000003 |
|
- type: precision_at_1000 |
|
value: 0.248 |
|
- type: precision_at_3 |
|
value: 29.064 |
|
- type: precision_at_5 |
|
value: 22.006 |
|
- type: recall_at_1 |
|
value: 24.476 |
|
- type: recall_at_10 |
|
value: 56.216 |
|
- type: recall_at_100 |
|
value: 81.798 |
|
- type: recall_at_1000 |
|
value: 95.48299999999999 |
|
- type: recall_at_3 |
|
value: 39.357 |
|
- type: recall_at_5 |
|
value: 47.802 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
|
metrics: |
|
- type: map_at_1 |
|
value: 42.728 |
|
- type: map_at_10 |
|
value: 57.737 |
|
- type: map_at_100 |
|
value: 58.531 |
|
- type: map_at_1000 |
|
value: 58.594 |
|
- type: map_at_3 |
|
value: 54.869 |
|
- type: map_at_5 |
|
value: 56.55 |
|
- type: mrr_at_1 |
|
value: 85.456 |
|
- type: mrr_at_10 |
|
value: 90.062 |
|
- type: mrr_at_100 |
|
value: 90.159 |
|
- type: mrr_at_1000 |
|
value: 90.16 |
|
- type: mrr_at_3 |
|
value: 89.37899999999999 |
|
- type: mrr_at_5 |
|
value: 89.81 |
|
- type: ndcg_at_1 |
|
value: 85.456 |
|
- type: ndcg_at_10 |
|
value: 67.755 |
|
- type: ndcg_at_100 |
|
value: 70.341 |
|
- type: ndcg_at_1000 |
|
value: 71.538 |
|
- type: ndcg_at_3 |
|
value: 63.735 |
|
- type: ndcg_at_5 |
|
value: 65.823 |
|
- type: precision_at_1 |
|
value: 85.456 |
|
- type: precision_at_10 |
|
value: 13.450000000000001 |
|
- type: precision_at_100 |
|
value: 1.545 |
|
- type: precision_at_1000 |
|
value: 0.16999999999999998 |
|
- type: precision_at_3 |
|
value: 38.861000000000004 |
|
- type: precision_at_5 |
|
value: 24.964 |
|
- type: recall_at_1 |
|
value: 42.728 |
|
- type: recall_at_10 |
|
value: 67.252 |
|
- type: recall_at_100 |
|
value: 77.265 |
|
- type: recall_at_1000 |
|
value: 85.246 |
|
- type: recall_at_3 |
|
value: 58.292 |
|
- type: recall_at_5 |
|
value: 62.41100000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 87.4836 |
|
- type: ap |
|
value: 82.29552224030336 |
|
- type: f1 |
|
value: 87.42791432227448 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: c5a29a104738b98a9e76336939199e264163d4a0 |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.015 |
|
- type: map_at_10 |
|
value: 35.621 |
|
- type: map_at_100 |
|
value: 36.809 |
|
- type: map_at_1000 |
|
value: 36.853 |
|
- type: map_at_3 |
|
value: 31.832 |
|
- type: map_at_5 |
|
value: 34.006 |
|
- type: mrr_at_1 |
|
value: 23.738999999999997 |
|
- type: mrr_at_10 |
|
value: 36.309999999999995 |
|
- type: mrr_at_100 |
|
value: 37.422 |
|
- type: mrr_at_1000 |
|
value: 37.461 |
|
- type: mrr_at_3 |
|
value: 32.592999999999996 |
|
- type: mrr_at_5 |
|
value: 34.736 |
|
- type: ndcg_at_1 |
|
value: 23.724999999999998 |
|
- type: ndcg_at_10 |
|
value: 42.617 |
|
- type: ndcg_at_100 |
|
value: 48.217999999999996 |
|
- type: ndcg_at_1000 |
|
value: 49.309 |
|
- type: ndcg_at_3 |
|
value: 34.905 |
|
- type: ndcg_at_5 |
|
value: 38.769 |
|
- type: precision_at_1 |
|
value: 23.724999999999998 |
|
- type: precision_at_10 |
|
value: 6.689 |
|
- type: precision_at_100 |
|
value: 0.9480000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.89 |
|
- type: precision_at_5 |
|
value: 10.897 |
|
- type: recall_at_1 |
|
value: 23.015 |
|
- type: recall_at_10 |
|
value: 64.041 |
|
- type: recall_at_100 |
|
value: 89.724 |
|
- type: recall_at_1000 |
|
value: 98.00999999999999 |
|
- type: recall_at_3 |
|
value: 43.064 |
|
- type: recall_at_5 |
|
value: 52.31099999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 96.49794801641588 |
|
- type: f1 |
|
value: 96.28931114498003 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 82.81121751025992 |
|
- type: f1 |
|
value: 63.18740125901853 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 77.66644250168123 |
|
- type: f1 |
|
value: 74.93211186867839 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 81.77202420981843 |
|
- type: f1 |
|
value: 81.63681969283554 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 34.596687684870645 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 32.26965660101405 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.33619694846802 |
|
- type: mrr |
|
value: 32.53719657720334 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.0729999999999995 |
|
- type: map_at_10 |
|
value: 13.245999999999999 |
|
- type: map_at_100 |
|
value: 16.747999999999998 |
|
- type: map_at_1000 |
|
value: 18.163 |
|
- type: map_at_3 |
|
value: 10.064 |
|
- type: map_at_5 |
|
value: 11.513 |
|
- type: mrr_at_1 |
|
value: 49.536 |
|
- type: mrr_at_10 |
|
value: 58.092 |
|
- type: mrr_at_100 |
|
value: 58.752 |
|
- type: mrr_at_1000 |
|
value: 58.78 |
|
- type: mrr_at_3 |
|
value: 56.398 |
|
- type: mrr_at_5 |
|
value: 57.389 |
|
- type: ndcg_at_1 |
|
value: 47.059 |
|
- type: ndcg_at_10 |
|
value: 35.881 |
|
- type: ndcg_at_100 |
|
value: 32.751999999999995 |
|
- type: ndcg_at_1000 |
|
value: 41.498000000000005 |
|
- type: ndcg_at_3 |
|
value: 42.518 |
|
- type: ndcg_at_5 |
|
value: 39.550999999999995 |
|
- type: precision_at_1 |
|
value: 49.536 |
|
- type: precision_at_10 |
|
value: 26.316 |
|
- type: precision_at_100 |
|
value: 8.084 |
|
- type: precision_at_1000 |
|
value: 2.081 |
|
- type: precision_at_3 |
|
value: 39.938 |
|
- type: precision_at_5 |
|
value: 34.056 |
|
- type: recall_at_1 |
|
value: 6.0729999999999995 |
|
- type: recall_at_10 |
|
value: 16.593 |
|
- type: recall_at_100 |
|
value: 32.883 |
|
- type: recall_at_1000 |
|
value: 64.654 |
|
- type: recall_at_3 |
|
value: 11.174000000000001 |
|
- type: recall_at_5 |
|
value: 13.528 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.043 |
|
- type: map_at_10 |
|
value: 45.318999999999996 |
|
- type: map_at_100 |
|
value: 46.381 |
|
- type: map_at_1000 |
|
value: 46.412 |
|
- type: map_at_3 |
|
value: 40.941 |
|
- type: map_at_5 |
|
value: 43.662 |
|
- type: mrr_at_1 |
|
value: 33.98 |
|
- type: mrr_at_10 |
|
value: 47.870000000000005 |
|
- type: mrr_at_100 |
|
value: 48.681999999999995 |
|
- type: mrr_at_1000 |
|
value: 48.703 |
|
- type: mrr_at_3 |
|
value: 44.341 |
|
- type: mrr_at_5 |
|
value: 46.547 |
|
- type: ndcg_at_1 |
|
value: 33.98 |
|
- type: ndcg_at_10 |
|
value: 52.957 |
|
- type: ndcg_at_100 |
|
value: 57.434 |
|
- type: ndcg_at_1000 |
|
value: 58.103 |
|
- type: ndcg_at_3 |
|
value: 44.896 |
|
- type: ndcg_at_5 |
|
value: 49.353 |
|
- type: precision_at_1 |
|
value: 33.98 |
|
- type: precision_at_10 |
|
value: 8.786 |
|
- type: precision_at_100 |
|
value: 1.1280000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 20.577 |
|
- type: precision_at_5 |
|
value: 14.942 |
|
- type: recall_at_1 |
|
value: 30.043 |
|
- type: recall_at_10 |
|
value: 73.593 |
|
- type: recall_at_100 |
|
value: 93.026 |
|
- type: recall_at_1000 |
|
value: 97.943 |
|
- type: recall_at_3 |
|
value: 52.955 |
|
- type: recall_at_5 |
|
value: 63.132 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.808 |
|
- type: map_at_10 |
|
value: 84.675 |
|
- type: map_at_100 |
|
value: 85.322 |
|
- type: map_at_1000 |
|
value: 85.33800000000001 |
|
- type: map_at_3 |
|
value: 81.68900000000001 |
|
- type: map_at_5 |
|
value: 83.543 |
|
- type: mrr_at_1 |
|
value: 81.5 |
|
- type: mrr_at_10 |
|
value: 87.59700000000001 |
|
- type: mrr_at_100 |
|
value: 87.705 |
|
- type: mrr_at_1000 |
|
value: 87.70599999999999 |
|
- type: mrr_at_3 |
|
value: 86.607 |
|
- type: mrr_at_5 |
|
value: 87.289 |
|
- type: ndcg_at_1 |
|
value: 81.51 |
|
- type: ndcg_at_10 |
|
value: 88.41799999999999 |
|
- type: ndcg_at_100 |
|
value: 89.644 |
|
- type: ndcg_at_1000 |
|
value: 89.725 |
|
- type: ndcg_at_3 |
|
value: 85.49900000000001 |
|
- type: ndcg_at_5 |
|
value: 87.078 |
|
- type: precision_at_1 |
|
value: 81.51 |
|
- type: precision_at_10 |
|
value: 13.438 |
|
- type: precision_at_100 |
|
value: 1.532 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.363 |
|
- type: precision_at_5 |
|
value: 24.57 |
|
- type: recall_at_1 |
|
value: 70.808 |
|
- type: recall_at_10 |
|
value: 95.575 |
|
- type: recall_at_100 |
|
value: 99.667 |
|
- type: recall_at_1000 |
|
value: 99.98899999999999 |
|
- type: recall_at_3 |
|
value: 87.223 |
|
- type: recall_at_5 |
|
value: 91.682 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 58.614831329137715 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 66.86580408560826 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.093 |
|
- type: map_at_10 |
|
value: 13.014000000000001 |
|
- type: map_at_100 |
|
value: 15.412999999999998 |
|
- type: map_at_1000 |
|
value: 15.756999999999998 |
|
- type: map_at_3 |
|
value: 9.216000000000001 |
|
- type: map_at_5 |
|
value: 11.036999999999999 |
|
- type: mrr_at_1 |
|
value: 25.1 |
|
- type: mrr_at_10 |
|
value: 37.133 |
|
- type: mrr_at_100 |
|
value: 38.165 |
|
- type: mrr_at_1000 |
|
value: 38.198 |
|
- type: mrr_at_3 |
|
value: 33.217 |
|
- type: mrr_at_5 |
|
value: 35.732 |
|
- type: ndcg_at_1 |
|
value: 25.1 |
|
- type: ndcg_at_10 |
|
value: 21.918000000000003 |
|
- type: ndcg_at_100 |
|
value: 30.983 |
|
- type: ndcg_at_1000 |
|
value: 36.629 |
|
- type: ndcg_at_3 |
|
value: 20.544999999999998 |
|
- type: ndcg_at_5 |
|
value: 18.192 |
|
- type: precision_at_1 |
|
value: 25.1 |
|
- type: precision_at_10 |
|
value: 11.44 |
|
- type: precision_at_100 |
|
value: 2.459 |
|
- type: precision_at_1000 |
|
value: 0.381 |
|
- type: precision_at_3 |
|
value: 19.267 |
|
- type: precision_at_5 |
|
value: 16.16 |
|
- type: recall_at_1 |
|
value: 5.093 |
|
- type: recall_at_10 |
|
value: 23.215 |
|
- type: recall_at_100 |
|
value: 49.902 |
|
- type: recall_at_1000 |
|
value: 77.403 |
|
- type: recall_at_3 |
|
value: 11.733 |
|
- type: recall_at_5 |
|
value: 16.372999999999998 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.9365442977452 |
|
- type: cos_sim_spearman |
|
value: 79.36960687383745 |
|
- type: euclidean_pearson |
|
value: 79.6045204840714 |
|
- type: euclidean_spearman |
|
value: 79.26382712751337 |
|
- type: manhattan_pearson |
|
value: 79.4805084789529 |
|
- type: manhattan_spearman |
|
value: 79.21847863209523 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.27906192961453 |
|
- type: cos_sim_spearman |
|
value: 74.38364712099211 |
|
- type: euclidean_pearson |
|
value: 78.54358927241223 |
|
- type: euclidean_spearman |
|
value: 74.22185560806376 |
|
- type: manhattan_pearson |
|
value: 78.50904327377751 |
|
- type: manhattan_spearman |
|
value: 74.2627500781748 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.66863742649639 |
|
- type: cos_sim_spearman |
|
value: 84.70630905216271 |
|
- type: euclidean_pearson |
|
value: 84.64498334705334 |
|
- type: euclidean_spearman |
|
value: 84.87204770690148 |
|
- type: manhattan_pearson |
|
value: 84.65774227976077 |
|
- type: manhattan_spearman |
|
value: 84.91251851797985 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.1577763924467 |
|
- type: cos_sim_spearman |
|
value: 80.10314039230198 |
|
- type: euclidean_pearson |
|
value: 81.51346991046043 |
|
- type: euclidean_spearman |
|
value: 80.08678485109435 |
|
- type: manhattan_pearson |
|
value: 81.57058914661894 |
|
- type: manhattan_spearman |
|
value: 80.1516230725106 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.40310839662533 |
|
- type: cos_sim_spearman |
|
value: 87.16293477217867 |
|
- type: euclidean_pearson |
|
value: 86.50688711184775 |
|
- type: euclidean_spearman |
|
value: 87.08651444923031 |
|
- type: manhattan_pearson |
|
value: 86.54674677557857 |
|
- type: manhattan_spearman |
|
value: 87.15079017870971 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.32886275207817 |
|
- type: cos_sim_spearman |
|
value: 85.0190460590732 |
|
- type: euclidean_pearson |
|
value: 84.42553652784679 |
|
- type: euclidean_spearman |
|
value: 85.20027364279328 |
|
- type: manhattan_pearson |
|
value: 84.42926246281078 |
|
- type: manhattan_spearman |
|
value: 85.20187419804306 |
|
- 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: 90.76732216967812 |
|
- type: cos_sim_spearman |
|
value: 90.63701653633909 |
|
- type: euclidean_pearson |
|
value: 90.26678186114682 |
|
- type: euclidean_spearman |
|
value: 90.67288073455427 |
|
- type: manhattan_pearson |
|
value: 90.20772020584582 |
|
- type: manhattan_spearman |
|
value: 90.60764863983702 |
|
- 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: 69.09280387698125 |
|
- type: cos_sim_spearman |
|
value: 68.62743151172162 |
|
- type: euclidean_pearson |
|
value: 69.89386398104689 |
|
- type: euclidean_spearman |
|
value: 68.71191066733556 |
|
- type: manhattan_pearson |
|
value: 69.92516500604872 |
|
- type: manhattan_spearman |
|
value: 68.80452846992576 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.13178592019887 |
|
- type: cos_sim_spearman |
|
value: 86.03947178806887 |
|
- type: euclidean_pearson |
|
value: 85.87029414285313 |
|
- type: euclidean_spearman |
|
value: 86.04960843306998 |
|
- type: manhattan_pearson |
|
value: 85.92946858580146 |
|
- type: manhattan_spearman |
|
value: 86.12575341860442 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 85.16657063002837 |
|
- type: mrr |
|
value: 95.73671063867141 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 63.510999999999996 |
|
- type: map_at_10 |
|
value: 72.76899999999999 |
|
- type: map_at_100 |
|
value: 73.303 |
|
- type: map_at_1000 |
|
value: 73.32499999999999 |
|
- type: map_at_3 |
|
value: 70.514 |
|
- type: map_at_5 |
|
value: 71.929 |
|
- type: mrr_at_1 |
|
value: 66.333 |
|
- type: mrr_at_10 |
|
value: 73.75 |
|
- type: mrr_at_100 |
|
value: 74.119 |
|
- type: mrr_at_1000 |
|
value: 74.138 |
|
- type: mrr_at_3 |
|
value: 72.222 |
|
- type: mrr_at_5 |
|
value: 73.122 |
|
- type: ndcg_at_1 |
|
value: 66.333 |
|
- type: ndcg_at_10 |
|
value: 76.774 |
|
- type: ndcg_at_100 |
|
value: 78.78500000000001 |
|
- type: ndcg_at_1000 |
|
value: 79.254 |
|
- type: ndcg_at_3 |
|
value: 73.088 |
|
- type: ndcg_at_5 |
|
value: 75.002 |
|
- type: precision_at_1 |
|
value: 66.333 |
|
- type: precision_at_10 |
|
value: 9.833 |
|
- type: precision_at_100 |
|
value: 1.093 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 28.222 |
|
- type: precision_at_5 |
|
value: 18.333 |
|
- type: recall_at_1 |
|
value: 63.510999999999996 |
|
- type: recall_at_10 |
|
value: 87.98899999999999 |
|
- type: recall_at_100 |
|
value: 96.5 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 77.86699999999999 |
|
- type: recall_at_5 |
|
value: 82.73899999999999 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.78514851485149 |
|
- type: cos_sim_ap |
|
value: 94.94214383862038 |
|
- type: cos_sim_f1 |
|
value: 89.02255639097744 |
|
- type: cos_sim_precision |
|
value: 89.2462311557789 |
|
- type: cos_sim_recall |
|
value: 88.8 |
|
- type: dot_accuracy |
|
value: 99.78217821782178 |
|
- type: dot_ap |
|
value: 94.69965247836805 |
|
- type: dot_f1 |
|
value: 88.78695208970439 |
|
- type: dot_precision |
|
value: 90.54054054054053 |
|
- type: dot_recall |
|
value: 87.1 |
|
- type: euclidean_accuracy |
|
value: 99.78118811881188 |
|
- type: euclidean_ap |
|
value: 94.9865187695411 |
|
- type: euclidean_f1 |
|
value: 88.99950223992036 |
|
- type: euclidean_precision |
|
value: 88.60257680872151 |
|
- type: euclidean_recall |
|
value: 89.4 |
|
- type: manhattan_accuracy |
|
value: 99.78811881188119 |
|
- type: manhattan_ap |
|
value: 95.0021236766459 |
|
- type: manhattan_f1 |
|
value: 89.12071535022356 |
|
- type: manhattan_precision |
|
value: 88.54886475814413 |
|
- type: manhattan_recall |
|
value: 89.7 |
|
- type: max_accuracy |
|
value: 99.78811881188119 |
|
- type: max_ap |
|
value: 95.0021236766459 |
|
- type: max_f1 |
|
value: 89.12071535022356 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 68.93190546593995 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 37.602808534760655 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 52.29214480978073 |
|
- type: mrr |
|
value: 53.123169722434426 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.967800769650022 |
|
- type: cos_sim_spearman |
|
value: 31.168490040206926 |
|
- type: dot_pearson |
|
value: 30.888603021128553 |
|
- type: dot_spearman |
|
value: 31.028241262520385 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.22300000000000003 |
|
- type: map_at_10 |
|
value: 1.781 |
|
- type: map_at_100 |
|
value: 9.905999999999999 |
|
- type: map_at_1000 |
|
value: 23.455000000000002 |
|
- type: map_at_3 |
|
value: 0.569 |
|
- type: map_at_5 |
|
value: 0.918 |
|
- type: mrr_at_1 |
|
value: 84.0 |
|
- type: mrr_at_10 |
|
value: 91.067 |
|
- type: mrr_at_100 |
|
value: 91.067 |
|
- type: mrr_at_1000 |
|
value: 91.067 |
|
- type: mrr_at_3 |
|
value: 90.667 |
|
- type: mrr_at_5 |
|
value: 91.067 |
|
- type: ndcg_at_1 |
|
value: 78.0 |
|
- type: ndcg_at_10 |
|
value: 73.13499999999999 |
|
- type: ndcg_at_100 |
|
value: 55.32 |
|
- type: ndcg_at_1000 |
|
value: 49.532 |
|
- type: ndcg_at_3 |
|
value: 73.715 |
|
- type: ndcg_at_5 |
|
value: 72.74199999999999 |
|
- type: precision_at_1 |
|
value: 84.0 |
|
- type: precision_at_10 |
|
value: 78.8 |
|
- type: precision_at_100 |
|
value: 56.32 |
|
- type: precision_at_1000 |
|
value: 21.504 |
|
- type: precision_at_3 |
|
value: 77.333 |
|
- type: precision_at_5 |
|
value: 78.0 |
|
- type: recall_at_1 |
|
value: 0.22300000000000003 |
|
- type: recall_at_10 |
|
value: 2.049 |
|
- type: recall_at_100 |
|
value: 13.553 |
|
- type: recall_at_1000 |
|
value: 46.367999999999995 |
|
- type: recall_at_3 |
|
value: 0.604 |
|
- type: recall_at_5 |
|
value: 1.015 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.0380000000000003 |
|
- type: map_at_10 |
|
value: 10.188 |
|
- type: map_at_100 |
|
value: 16.395 |
|
- type: map_at_1000 |
|
value: 18.024 |
|
- type: map_at_3 |
|
value: 6.236 |
|
- type: map_at_5 |
|
value: 7.276000000000001 |
|
- type: mrr_at_1 |
|
value: 34.694 |
|
- type: mrr_at_10 |
|
value: 46.292 |
|
- type: mrr_at_100 |
|
value: 47.446 |
|
- type: mrr_at_1000 |
|
value: 47.446 |
|
- type: mrr_at_3 |
|
value: 41.156 |
|
- type: mrr_at_5 |
|
value: 44.32 |
|
- type: ndcg_at_1 |
|
value: 32.653 |
|
- type: ndcg_at_10 |
|
value: 25.219 |
|
- type: ndcg_at_100 |
|
value: 37.802 |
|
- type: ndcg_at_1000 |
|
value: 49.274 |
|
- type: ndcg_at_3 |
|
value: 28.605999999999998 |
|
- type: ndcg_at_5 |
|
value: 26.21 |
|
- type: precision_at_1 |
|
value: 34.694 |
|
- type: precision_at_10 |
|
value: 21.837 |
|
- type: precision_at_100 |
|
value: 7.776 |
|
- type: precision_at_1000 |
|
value: 1.522 |
|
- type: precision_at_3 |
|
value: 28.571 |
|
- type: precision_at_5 |
|
value: 25.306 |
|
- type: recall_at_1 |
|
value: 3.0380000000000003 |
|
- type: recall_at_10 |
|
value: 16.298000000000002 |
|
- type: recall_at_100 |
|
value: 48.712 |
|
- type: recall_at_1000 |
|
value: 83.16799999999999 |
|
- type: recall_at_3 |
|
value: 7.265000000000001 |
|
- type: recall_at_5 |
|
value: 9.551 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 83.978 |
|
- type: ap |
|
value: 24.751887949330015 |
|
- type: f1 |
|
value: 66.8685134049279 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 61.573288058856825 |
|
- type: f1 |
|
value: 61.973261751726604 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 48.75483298792469 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.36824223639506 |
|
- type: cos_sim_ap |
|
value: 75.53126388573047 |
|
- type: cos_sim_f1 |
|
value: 67.9912831688245 |
|
- type: cos_sim_precision |
|
value: 66.11817501869858 |
|
- type: cos_sim_recall |
|
value: 69.9736147757256 |
|
- type: dot_accuracy |
|
value: 86.39804494248078 |
|
- type: dot_ap |
|
value: 75.27598891718046 |
|
- type: dot_f1 |
|
value: 67.91146284159763 |
|
- type: dot_precision |
|
value: 63.90505003490807 |
|
- type: dot_recall |
|
value: 72.45382585751979 |
|
- type: euclidean_accuracy |
|
value: 86.36228169517793 |
|
- type: euclidean_ap |
|
value: 75.51438087434647 |
|
- type: euclidean_f1 |
|
value: 68.02370523061066 |
|
- type: euclidean_precision |
|
value: 66.46525679758308 |
|
- type: euclidean_recall |
|
value: 69.65699208443272 |
|
- type: manhattan_accuracy |
|
value: 86.46361089586935 |
|
- type: manhattan_ap |
|
value: 75.50800785730111 |
|
- type: manhattan_f1 |
|
value: 67.9220437187253 |
|
- type: manhattan_precision |
|
value: 67.79705573080967 |
|
- type: manhattan_recall |
|
value: 68.04749340369392 |
|
- type: max_accuracy |
|
value: 86.46361089586935 |
|
- type: max_ap |
|
value: 75.53126388573047 |
|
- type: max_f1 |
|
value: 68.02370523061066 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.80350836341057 |
|
- type: cos_sim_ap |
|
value: 85.51101933260743 |
|
- type: cos_sim_f1 |
|
value: 77.9152271629704 |
|
- type: cos_sim_precision |
|
value: 75.27815662910056 |
|
- type: cos_sim_recall |
|
value: 80.74376347397599 |
|
- type: dot_accuracy |
|
value: 88.84425815966158 |
|
- type: dot_ap |
|
value: 85.49726945962519 |
|
- type: dot_f1 |
|
value: 77.94445269567801 |
|
- type: dot_precision |
|
value: 75.27251864601261 |
|
- type: dot_recall |
|
value: 80.81305820757623 |
|
- type: euclidean_accuracy |
|
value: 88.80350836341057 |
|
- type: euclidean_ap |
|
value: 85.4882880790211 |
|
- type: euclidean_f1 |
|
value: 77.87063284615103 |
|
- type: euclidean_precision |
|
value: 74.61022927689595 |
|
- type: euclidean_recall |
|
value: 81.42901139513397 |
|
- type: manhattan_accuracy |
|
value: 88.7161873714441 |
|
- type: manhattan_ap |
|
value: 85.45753871906821 |
|
- type: manhattan_f1 |
|
value: 77.8686401480111 |
|
- type: manhattan_precision |
|
value: 74.95903683123174 |
|
- type: manhattan_recall |
|
value: 81.01324299353249 |
|
- type: max_accuracy |
|
value: 88.84425815966158 |
|
- type: max_ap |
|
value: 85.51101933260743 |
|
- type: max_f1 |
|
value: 77.94445269567801 |
|
--- |
|
|
|
<!-- **English** | [中文](./README_zh.md) --> |
|
|
|
# gte-base-en-v1.5 |
|
|
|
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. |
|
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU). |
|
|
|
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)). |
|
|
|
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct), |
|
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB. |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
- **Developed by:** Institute for Intelligent Computing, Alibaba Group |
|
- **Model type:** Text Embeddings |
|
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking |
|
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669) |
|
|
|
<!-- - **Demo [optional]:** [More Information Needed] --> |
|
|
|
### Model list |
|
|
|
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo | |
|
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: | |
|
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 | |
|
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 | |
|
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 | |
|
|
|
|
|
## How to Get Started with the Model |
|
|
|
Use the code below to get started with the model. |
|
|
|
```python |
|
# Requires transformers>=4.36.0 |
|
|
|
import torch.nn.functional as F |
|
from transformers import AutoModel, AutoTokenizer |
|
|
|
input_texts = [ |
|
"what is the capital of China?", |
|
"how to implement quick sort in python?", |
|
"Beijing", |
|
"sorting algorithms" |
|
] |
|
|
|
model_path = 'Alibaba-NLP/gte-base-en-v1.5' |
|
tokenizer = AutoTokenizer.from_pretrained(model_path) |
|
model = AutoModel.from_pretrained(model_path, trust_remote_code=True) |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = outputs.last_hidden_state[:, 0] |
|
|
|
# (Optionally) normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:1] @ embeddings[1:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** |
|
|
|
|
|
Use with `sentence-transformers`: |
|
|
|
```python |
|
# Requires sentence_transformers>=2.7.0 |
|
|
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
sentences = ['That is a happy person', 'That is a very happy person'] |
|
|
|
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True) |
|
embeddings = model.encode(sentences) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
Use with `transformers.js`: |
|
|
|
```js |
|
// npm i @xenova/transformers |
|
import { pipeline, dot } from '@xenova/transformers'; |
|
|
|
// Create feature extraction pipeline |
|
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', { |
|
quantized: false, // Comment out this line to use the quantized version |
|
}); |
|
|
|
// Generate sentence embeddings |
|
const sentences = [ |
|
"what is the capital of China?", |
|
"how to implement quick sort in python?", |
|
"Beijing", |
|
"sorting algorithms" |
|
] |
|
const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); |
|
|
|
// Compute similarity scores |
|
const [source_embeddings, ...document_embeddings ] = output.tolist(); |
|
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x)); |
|
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362] |
|
``` |
|
|
|
Use with infinity: |
|
[Infinity](https://github.com/michaelfeil/infinity) is a MIT licensed server for OpenAI-compatible deployment. |
|
``` |
|
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ |
|
michaelf34/infinity:0.0.68 \ |
|
v2 --model-id Alibaba-NLP/gte-base-en-v1.5 --revision "4c742dc2b781e4ab062a4a77f4f7cbad4bdee970" --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997 |
|
``` |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
- Masked language modeling (MLM): `c4-en` |
|
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data |
|
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data |
|
|
|
### Training Procedure |
|
|
|
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. |
|
The model first undergoes preliminary MLM pre-training on shorter lengths. |
|
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training. |
|
|
|
The entire training process is as follows: |
|
- MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000 |
|
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000 |
|
- CPT: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000 |
|
- Fine-tuning: TODO |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### MTEB |
|
|
|
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). |
|
|
|
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2). |
|
|
|
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | |
|
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | |
|
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | |
|
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | |
|
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | |
|
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | |
|
|
|
|
|
### LoCo |
|
|
|
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | |
|
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | |
|
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | |
|
|
|
|
|
|
|
## Citation |
|
If you find our paper or models helpful, please consider citing them as follows: |
|
|
|
``` |
|
@misc{zhang2024mgte, |
|
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, |
|
author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang}, |
|
year={2024}, |
|
eprint={2407.19669}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL}, |
|
url={https://arxiv.org/abs/2407.19669}, |
|
} |
|
@misc{li2023gte, |
|
title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, |
|
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang}, |
|
year={2023}, |
|
eprint={2308.03281}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL}, |
|
url={https://arxiv.org/abs/2308.03281}, |
|
} |
|
``` |
|
|
