|
--- |
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language: |
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- en |
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library_name: sentence-transformers |
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license: mit |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- mteb |
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- sentence-similarity |
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- sentence-transformers |
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|
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model-index: |
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- name: GIST-all-MiniLM-L6-v2 |
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results: |
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- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 72.8955223880597 |
|
- type: ap |
|
value: 35.447605103320775 |
|
- type: f1 |
|
value: 66.82951715365854 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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 |
|
metrics: |
|
- type: accuracy |
|
value: 87.19474999999998 |
|
- type: ap |
|
value: 83.09577890808514 |
|
- type: f1 |
|
value: 87.13833121762009 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
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: |
|
- type: accuracy |
|
value: 42.556000000000004 |
|
- type: f1 |
|
value: 42.236256693772276 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: 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: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.884999999999998 |
|
- type: map_at_10 |
|
value: 42.364000000000004 |
|
- type: map_at_100 |
|
value: 43.382 |
|
- type: map_at_1000 |
|
value: 43.391000000000005 |
|
- type: map_at_3 |
|
value: 37.162 |
|
- type: map_at_5 |
|
value: 40.139 |
|
- type: mrr_at_1 |
|
value: 26.884999999999998 |
|
- type: mrr_at_10 |
|
value: 42.193999999999996 |
|
- type: mrr_at_100 |
|
value: 43.211 |
|
- type: mrr_at_1000 |
|
value: 43.221 |
|
- type: mrr_at_3 |
|
value: 36.949 |
|
- type: mrr_at_5 |
|
value: 40.004 |
|
- type: ndcg_at_1 |
|
value: 26.884999999999998 |
|
- type: ndcg_at_10 |
|
value: 51.254999999999995 |
|
- type: ndcg_at_100 |
|
value: 55.481 |
|
- type: ndcg_at_1000 |
|
value: 55.68300000000001 |
|
- type: ndcg_at_3 |
|
value: 40.565 |
|
- type: ndcg_at_5 |
|
value: 45.882 |
|
- type: precision_at_1 |
|
value: 26.884999999999998 |
|
- type: precision_at_10 |
|
value: 7.9799999999999995 |
|
- type: precision_at_100 |
|
value: 0.98 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 16.808999999999997 |
|
- type: precision_at_5 |
|
value: 12.645999999999999 |
|
- type: recall_at_1 |
|
value: 26.884999999999998 |
|
- type: recall_at_10 |
|
value: 79.801 |
|
- type: recall_at_100 |
|
value: 98.009 |
|
- type: recall_at_1000 |
|
value: 99.502 |
|
- type: recall_at_3 |
|
value: 50.427 |
|
- type: recall_at_5 |
|
value: 63.229 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 45.31044837358167 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
|
- type: v_measure |
|
value: 35.44751738734691 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
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: |
|
- type: map |
|
value: 62.96517580629869 |
|
- type: mrr |
|
value: 76.30051004704744 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.97262600499639 |
|
- type: cos_sim_spearman |
|
value: 81.25787561220484 |
|
- type: euclidean_pearson |
|
value: 64.96260261677082 |
|
- type: euclidean_spearman |
|
value: 64.17616109254686 |
|
- type: manhattan_pearson |
|
value: 65.05620628102835 |
|
- type: manhattan_spearman |
|
value: 64.71171546419122 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
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split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 84.2435064935065 |
|
- type: f1 |
|
value: 84.2334859253828 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 38.38358435972693 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
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config: default |
|
split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 31.093619653843124 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 35.016999999999996 |
|
- type: map_at_10 |
|
value: 47.019 |
|
- type: map_at_100 |
|
value: 48.634 |
|
- type: map_at_1000 |
|
value: 48.757 |
|
- type: map_at_3 |
|
value: 43.372 |
|
- type: map_at_5 |
|
value: 45.314 |
|
- type: mrr_at_1 |
|
value: 43.491 |
|
- type: mrr_at_10 |
|
value: 53.284 |
|
- type: mrr_at_100 |
|
value: 54.038 |
|
- type: mrr_at_1000 |
|
value: 54.071000000000005 |
|
- type: mrr_at_3 |
|
value: 51.001 |
|
- type: mrr_at_5 |
|
value: 52.282 |
|
- type: ndcg_at_1 |
|
value: 43.491 |
|
- type: ndcg_at_10 |
|
value: 53.498999999999995 |
|
- type: ndcg_at_100 |
|
value: 58.733999999999995 |
|
- type: ndcg_at_1000 |
|
value: 60.307 |
|
- type: ndcg_at_3 |
|
value: 48.841 |
|
- type: ndcg_at_5 |
|
value: 50.76199999999999 |
|
- type: precision_at_1 |
|
value: 43.491 |
|
- type: precision_at_10 |
|
value: 10.315000000000001 |
|
- type: precision_at_100 |
|
value: 1.6209999999999998 |
|
- type: precision_at_1000 |
|
value: 0.20500000000000002 |
|
- type: precision_at_3 |
|
value: 23.462 |
|
- type: precision_at_5 |
|
value: 16.652 |
|
- type: recall_at_1 |
|
value: 35.016999999999996 |
|
- type: recall_at_10 |
|
value: 64.92 |
|
- type: recall_at_100 |
|
value: 86.605 |
|
- type: recall_at_1000 |
|
value: 96.174 |
|
- type: recall_at_3 |
|
value: 50.99 |
|
- type: recall_at_5 |
|
value: 56.93 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.866 |
|
- type: map_at_10 |
|
value: 40.438 |
|
- type: map_at_100 |
|
value: 41.77 |
|
- type: map_at_1000 |
|
value: 41.913 |
|
- type: map_at_3 |
|
value: 37.634 |
|
- type: map_at_5 |
|
value: 39.226 |
|
- type: mrr_at_1 |
|
value: 37.834 |
|
- type: mrr_at_10 |
|
value: 46.765 |
|
- type: mrr_at_100 |
|
value: 47.410000000000004 |
|
- type: mrr_at_1000 |
|
value: 47.461 |
|
- type: mrr_at_3 |
|
value: 44.735 |
|
- type: mrr_at_5 |
|
value: 46.028000000000006 |
|
- type: ndcg_at_1 |
|
value: 37.834 |
|
- type: ndcg_at_10 |
|
value: 46.303 |
|
- type: ndcg_at_100 |
|
value: 50.879 |
|
- type: ndcg_at_1000 |
|
value: 53.112 |
|
- type: ndcg_at_3 |
|
value: 42.601 |
|
- type: ndcg_at_5 |
|
value: 44.384 |
|
- type: precision_at_1 |
|
value: 37.834 |
|
- type: precision_at_10 |
|
value: 8.898 |
|
- type: precision_at_100 |
|
value: 1.4409999999999998 |
|
- type: precision_at_1000 |
|
value: 0.19499999999999998 |
|
- type: precision_at_3 |
|
value: 20.977 |
|
- type: precision_at_5 |
|
value: 14.841 |
|
- type: recall_at_1 |
|
value: 29.866 |
|
- type: recall_at_10 |
|
value: 56.06100000000001 |
|
- type: recall_at_100 |
|
value: 75.809 |
|
- type: recall_at_1000 |
|
value: 89.875 |
|
- type: recall_at_3 |
|
value: 44.707 |
|
- type: recall_at_5 |
|
value: 49.846000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.985 |
|
- type: map_at_10 |
|
value: 51.165000000000006 |
|
- type: map_at_100 |
|
value: 52.17 |
|
- type: map_at_1000 |
|
value: 52.229000000000006 |
|
- type: map_at_3 |
|
value: 48.089999999999996 |
|
- type: map_at_5 |
|
value: 49.762 |
|
- type: mrr_at_1 |
|
value: 44.577 |
|
- type: mrr_at_10 |
|
value: 54.493 |
|
- type: mrr_at_100 |
|
value: 55.137 |
|
- type: mrr_at_1000 |
|
value: 55.167 |
|
- type: mrr_at_3 |
|
value: 52.079 |
|
- type: mrr_at_5 |
|
value: 53.518 |
|
- type: ndcg_at_1 |
|
value: 44.577 |
|
- type: ndcg_at_10 |
|
value: 56.825 |
|
- type: ndcg_at_100 |
|
value: 60.842 |
|
- type: ndcg_at_1000 |
|
value: 62.015 |
|
- type: ndcg_at_3 |
|
value: 51.699 |
|
- type: ndcg_at_5 |
|
value: 54.11 |
|
- type: precision_at_1 |
|
value: 44.577 |
|
- type: precision_at_10 |
|
value: 9.11 |
|
- type: precision_at_100 |
|
value: 1.206 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 23.156 |
|
- type: precision_at_5 |
|
value: 15.737000000000002 |
|
- type: recall_at_1 |
|
value: 38.985 |
|
- type: recall_at_10 |
|
value: 70.164 |
|
- type: recall_at_100 |
|
value: 87.708 |
|
- type: recall_at_1000 |
|
value: 95.979 |
|
- type: recall_at_3 |
|
value: 56.285 |
|
- type: recall_at_5 |
|
value: 62.303 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.137 |
|
- type: map_at_10 |
|
value: 36.729 |
|
- type: map_at_100 |
|
value: 37.851 |
|
- type: map_at_1000 |
|
value: 37.932 |
|
- type: map_at_3 |
|
value: 34.074 |
|
- type: map_at_5 |
|
value: 35.398 |
|
- type: mrr_at_1 |
|
value: 30.621 |
|
- type: mrr_at_10 |
|
value: 39.007 |
|
- type: mrr_at_100 |
|
value: 39.961 |
|
- type: mrr_at_1000 |
|
value: 40.02 |
|
- type: mrr_at_3 |
|
value: 36.591 |
|
- type: mrr_at_5 |
|
value: 37.806 |
|
- type: ndcg_at_1 |
|
value: 30.621 |
|
- type: ndcg_at_10 |
|
value: 41.772 |
|
- type: ndcg_at_100 |
|
value: 47.181 |
|
- type: ndcg_at_1000 |
|
value: 49.053999999999995 |
|
- type: ndcg_at_3 |
|
value: 36.577 |
|
- type: ndcg_at_5 |
|
value: 38.777 |
|
- type: precision_at_1 |
|
value: 30.621 |
|
- type: precision_at_10 |
|
value: 6.372999999999999 |
|
- type: precision_at_100 |
|
value: 0.955 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 15.367 |
|
- type: precision_at_5 |
|
value: 10.531 |
|
- type: recall_at_1 |
|
value: 28.137 |
|
- type: recall_at_10 |
|
value: 55.162 |
|
- type: recall_at_100 |
|
value: 79.931 |
|
- type: recall_at_1000 |
|
value: 93.67 |
|
- type: recall_at_3 |
|
value: 41.057 |
|
- type: recall_at_5 |
|
value: 46.327 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.798 |
|
- type: map_at_10 |
|
value: 25.267 |
|
- type: map_at_100 |
|
value: 26.579000000000004 |
|
- type: map_at_1000 |
|
value: 26.697 |
|
- type: map_at_3 |
|
value: 22.456 |
|
- type: map_at_5 |
|
value: 23.912 |
|
- type: mrr_at_1 |
|
value: 20.771 |
|
- type: mrr_at_10 |
|
value: 29.843999999999998 |
|
- type: mrr_at_100 |
|
value: 30.849 |
|
- type: mrr_at_1000 |
|
value: 30.916 |
|
- type: mrr_at_3 |
|
value: 27.156000000000002 |
|
- type: mrr_at_5 |
|
value: 28.518 |
|
- type: ndcg_at_1 |
|
value: 20.771 |
|
- type: ndcg_at_10 |
|
value: 30.792 |
|
- type: ndcg_at_100 |
|
value: 36.945 |
|
- type: ndcg_at_1000 |
|
value: 39.619 |
|
- type: ndcg_at_3 |
|
value: 25.52 |
|
- type: ndcg_at_5 |
|
value: 27.776 |
|
- type: precision_at_1 |
|
value: 20.771 |
|
- type: precision_at_10 |
|
value: 5.734 |
|
- type: precision_at_100 |
|
value: 1.031 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 12.148 |
|
- type: precision_at_5 |
|
value: 9.055 |
|
- type: recall_at_1 |
|
value: 16.798 |
|
- type: recall_at_10 |
|
value: 43.332 |
|
- type: recall_at_100 |
|
value: 70.016 |
|
- type: recall_at_1000 |
|
value: 88.90400000000001 |
|
- type: recall_at_3 |
|
value: 28.842000000000002 |
|
- type: recall_at_5 |
|
value: 34.37 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.180000000000003 |
|
- type: map_at_10 |
|
value: 41.78 |
|
- type: map_at_100 |
|
value: 43.102000000000004 |
|
- type: map_at_1000 |
|
value: 43.222 |
|
- type: map_at_3 |
|
value: 38.505 |
|
- type: map_at_5 |
|
value: 40.443 |
|
- type: mrr_at_1 |
|
value: 37.824999999999996 |
|
- type: mrr_at_10 |
|
value: 47.481 |
|
- type: mrr_at_100 |
|
value: 48.268 |
|
- type: mrr_at_1000 |
|
value: 48.313 |
|
- type: mrr_at_3 |
|
value: 44.946999999999996 |
|
- type: mrr_at_5 |
|
value: 46.492 |
|
- type: ndcg_at_1 |
|
value: 37.824999999999996 |
|
- type: ndcg_at_10 |
|
value: 47.827 |
|
- type: ndcg_at_100 |
|
value: 53.407000000000004 |
|
- type: ndcg_at_1000 |
|
value: 55.321 |
|
- type: ndcg_at_3 |
|
value: 42.815 |
|
- type: ndcg_at_5 |
|
value: 45.363 |
|
- type: precision_at_1 |
|
value: 37.824999999999996 |
|
- type: precision_at_10 |
|
value: 8.652999999999999 |
|
- type: precision_at_100 |
|
value: 1.354 |
|
- type: precision_at_1000 |
|
value: 0.172 |
|
- type: precision_at_3 |
|
value: 20.372 |
|
- type: precision_at_5 |
|
value: 14.591000000000001 |
|
- type: recall_at_1 |
|
value: 31.180000000000003 |
|
- type: recall_at_10 |
|
value: 59.894000000000005 |
|
- type: recall_at_100 |
|
value: 83.722 |
|
- type: recall_at_1000 |
|
value: 95.705 |
|
- type: recall_at_3 |
|
value: 45.824 |
|
- type: recall_at_5 |
|
value: 52.349999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.66 |
|
- type: map_at_10 |
|
value: 34.141 |
|
- type: map_at_100 |
|
value: 35.478 |
|
- type: map_at_1000 |
|
value: 35.594 |
|
- type: map_at_3 |
|
value: 30.446 |
|
- type: map_at_5 |
|
value: 32.583 |
|
- type: mrr_at_1 |
|
value: 29.909000000000002 |
|
- type: mrr_at_10 |
|
value: 38.949 |
|
- type: mrr_at_100 |
|
value: 39.803 |
|
- type: mrr_at_1000 |
|
value: 39.867999999999995 |
|
- type: mrr_at_3 |
|
value: 35.921 |
|
- type: mrr_at_5 |
|
value: 37.753 |
|
- type: ndcg_at_1 |
|
value: 29.909000000000002 |
|
- type: ndcg_at_10 |
|
value: 40.012 |
|
- type: ndcg_at_100 |
|
value: 45.707 |
|
- type: ndcg_at_1000 |
|
value: 48.15 |
|
- type: ndcg_at_3 |
|
value: 34.015 |
|
- type: ndcg_at_5 |
|
value: 37.002 |
|
- type: precision_at_1 |
|
value: 29.909000000000002 |
|
- type: precision_at_10 |
|
value: 7.693999999999999 |
|
- type: precision_at_100 |
|
value: 1.2229999999999999 |
|
- type: precision_at_1000 |
|
value: 0.16 |
|
- type: precision_at_3 |
|
value: 16.323999999999998 |
|
- type: precision_at_5 |
|
value: 12.306000000000001 |
|
- type: recall_at_1 |
|
value: 24.66 |
|
- type: recall_at_10 |
|
value: 52.478 |
|
- type: recall_at_100 |
|
value: 77.051 |
|
- type: recall_at_1000 |
|
value: 93.872 |
|
- type: recall_at_3 |
|
value: 36.382999999999996 |
|
- type: recall_at_5 |
|
value: 43.903999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.768416666666667 |
|
- type: map_at_10 |
|
value: 36.2485 |
|
- type: map_at_100 |
|
value: 37.520833333333336 |
|
- type: map_at_1000 |
|
value: 37.64033333333334 |
|
- type: map_at_3 |
|
value: 33.25791666666667 |
|
- type: map_at_5 |
|
value: 34.877250000000004 |
|
- type: mrr_at_1 |
|
value: 31.65408333333334 |
|
- type: mrr_at_10 |
|
value: 40.43866666666667 |
|
- type: mrr_at_100 |
|
value: 41.301249999999996 |
|
- type: mrr_at_1000 |
|
value: 41.357499999999995 |
|
- type: mrr_at_3 |
|
value: 37.938916666666664 |
|
- type: mrr_at_5 |
|
value: 39.35183333333334 |
|
- type: ndcg_at_1 |
|
value: 31.65408333333334 |
|
- type: ndcg_at_10 |
|
value: 41.76983333333334 |
|
- type: ndcg_at_100 |
|
value: 47.138 |
|
- type: ndcg_at_1000 |
|
value: 49.33816666666667 |
|
- type: ndcg_at_3 |
|
value: 36.76683333333333 |
|
- type: ndcg_at_5 |
|
value: 39.04441666666666 |
|
- type: precision_at_1 |
|
value: 31.65408333333334 |
|
- type: precision_at_10 |
|
value: 7.396249999999998 |
|
- type: precision_at_100 |
|
value: 1.1974166666666666 |
|
- type: precision_at_1000 |
|
value: 0.15791666666666668 |
|
- type: precision_at_3 |
|
value: 16.955583333333333 |
|
- type: precision_at_5 |
|
value: 12.09925 |
|
- type: recall_at_1 |
|
value: 26.768416666666667 |
|
- type: recall_at_10 |
|
value: 53.82366666666667 |
|
- type: recall_at_100 |
|
value: 77.39600000000002 |
|
- type: recall_at_1000 |
|
value: 92.46300000000001 |
|
- type: recall_at_3 |
|
value: 39.90166666666667 |
|
- type: recall_at_5 |
|
value: 45.754000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.369 |
|
- type: map_at_10 |
|
value: 32.025 |
|
- type: map_at_100 |
|
value: 33.08 |
|
- type: map_at_1000 |
|
value: 33.169 |
|
- type: map_at_3 |
|
value: 29.589 |
|
- type: map_at_5 |
|
value: 30.894 |
|
- type: mrr_at_1 |
|
value: 27.301 |
|
- type: mrr_at_10 |
|
value: 34.64 |
|
- type: mrr_at_100 |
|
value: 35.556 |
|
- type: mrr_at_1000 |
|
value: 35.616 |
|
- type: mrr_at_3 |
|
value: 32.515 |
|
- type: mrr_at_5 |
|
value: 33.666000000000004 |
|
- type: ndcg_at_1 |
|
value: 27.301 |
|
- type: ndcg_at_10 |
|
value: 36.386 |
|
- type: ndcg_at_100 |
|
value: 41.598 |
|
- type: ndcg_at_1000 |
|
value: 43.864999999999995 |
|
- type: ndcg_at_3 |
|
value: 32.07 |
|
- type: ndcg_at_5 |
|
value: 34.028999999999996 |
|
- type: precision_at_1 |
|
value: 27.301 |
|
- type: precision_at_10 |
|
value: 5.782 |
|
- type: precision_at_100 |
|
value: 0.923 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 13.804 |
|
- type: precision_at_5 |
|
value: 9.693 |
|
- type: recall_at_1 |
|
value: 24.369 |
|
- type: recall_at_10 |
|
value: 47.026 |
|
- type: recall_at_100 |
|
value: 70.76400000000001 |
|
- type: recall_at_1000 |
|
value: 87.705 |
|
- type: recall_at_3 |
|
value: 35.366 |
|
- type: recall_at_5 |
|
value: 40.077 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.878 |
|
- type: map_at_10 |
|
value: 25.582 |
|
- type: map_at_100 |
|
value: 26.848 |
|
- type: map_at_1000 |
|
value: 26.985 |
|
- type: map_at_3 |
|
value: 22.997 |
|
- type: map_at_5 |
|
value: 24.487000000000002 |
|
- type: mrr_at_1 |
|
value: 22.023 |
|
- type: mrr_at_10 |
|
value: 29.615000000000002 |
|
- type: mrr_at_100 |
|
value: 30.656 |
|
- type: mrr_at_1000 |
|
value: 30.737 |
|
- type: mrr_at_3 |
|
value: 27.322999999999997 |
|
- type: mrr_at_5 |
|
value: 28.665000000000003 |
|
- type: ndcg_at_1 |
|
value: 22.023 |
|
- type: ndcg_at_10 |
|
value: 30.476999999999997 |
|
- type: ndcg_at_100 |
|
value: 36.258 |
|
- type: ndcg_at_1000 |
|
value: 39.287 |
|
- type: ndcg_at_3 |
|
value: 25.995 |
|
- type: ndcg_at_5 |
|
value: 28.174 |
|
- type: precision_at_1 |
|
value: 22.023 |
|
- type: precision_at_10 |
|
value: 5.657 |
|
- type: precision_at_100 |
|
value: 1.01 |
|
- type: precision_at_1000 |
|
value: 0.145 |
|
- type: precision_at_3 |
|
value: 12.491 |
|
- type: precision_at_5 |
|
value: 9.112 |
|
- type: recall_at_1 |
|
value: 17.878 |
|
- type: recall_at_10 |
|
value: 41.155 |
|
- type: recall_at_100 |
|
value: 66.62599999999999 |
|
- type: recall_at_1000 |
|
value: 88.08200000000001 |
|
- type: recall_at_3 |
|
value: 28.505000000000003 |
|
- type: recall_at_5 |
|
value: 34.284 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.369999999999997 |
|
- type: map_at_10 |
|
value: 36.115 |
|
- type: map_at_100 |
|
value: 37.346000000000004 |
|
- type: map_at_1000 |
|
value: 37.449 |
|
- type: map_at_3 |
|
value: 32.976 |
|
- type: map_at_5 |
|
value: 34.782000000000004 |
|
- type: mrr_at_1 |
|
value: 30.784 |
|
- type: mrr_at_10 |
|
value: 40.014 |
|
- type: mrr_at_100 |
|
value: 40.913 |
|
- type: mrr_at_1000 |
|
value: 40.967999999999996 |
|
- type: mrr_at_3 |
|
value: 37.205 |
|
- type: mrr_at_5 |
|
value: 38.995999999999995 |
|
- type: ndcg_at_1 |
|
value: 30.784 |
|
- type: ndcg_at_10 |
|
value: 41.797000000000004 |
|
- type: ndcg_at_100 |
|
value: 47.355000000000004 |
|
- type: ndcg_at_1000 |
|
value: 49.535000000000004 |
|
- type: ndcg_at_3 |
|
value: 36.29 |
|
- type: ndcg_at_5 |
|
value: 39.051 |
|
- type: precision_at_1 |
|
value: 30.784 |
|
- type: precision_at_10 |
|
value: 7.164 |
|
- type: precision_at_100 |
|
value: 1.122 |
|
- type: precision_at_1000 |
|
value: 0.14200000000000002 |
|
- type: precision_at_3 |
|
value: 16.636 |
|
- type: precision_at_5 |
|
value: 11.996 |
|
- type: recall_at_1 |
|
value: 26.369999999999997 |
|
- type: recall_at_10 |
|
value: 55.010000000000005 |
|
- type: recall_at_100 |
|
value: 79.105 |
|
- type: recall_at_1000 |
|
value: 94.053 |
|
- type: recall_at_3 |
|
value: 40.139 |
|
- type: recall_at_5 |
|
value: 47.089 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.421 |
|
- type: map_at_10 |
|
value: 35.253 |
|
- type: map_at_100 |
|
value: 36.97 |
|
- type: map_at_1000 |
|
value: 37.195 |
|
- type: map_at_3 |
|
value: 32.068000000000005 |
|
- type: map_at_5 |
|
value: 33.763 |
|
- type: mrr_at_1 |
|
value: 31.423000000000002 |
|
- type: mrr_at_10 |
|
value: 39.995999999999995 |
|
- type: mrr_at_100 |
|
value: 40.977999999999994 |
|
- type: mrr_at_1000 |
|
value: 41.024 |
|
- type: mrr_at_3 |
|
value: 36.989 |
|
- type: mrr_at_5 |
|
value: 38.629999999999995 |
|
- type: ndcg_at_1 |
|
value: 31.423000000000002 |
|
- type: ndcg_at_10 |
|
value: 41.382000000000005 |
|
- type: ndcg_at_100 |
|
value: 47.532000000000004 |
|
- type: ndcg_at_1000 |
|
value: 49.829 |
|
- type: ndcg_at_3 |
|
value: 35.809000000000005 |
|
- type: ndcg_at_5 |
|
value: 38.308 |
|
- type: precision_at_1 |
|
value: 31.423000000000002 |
|
- type: precision_at_10 |
|
value: 7.885000000000001 |
|
- type: precision_at_100 |
|
value: 1.609 |
|
- type: precision_at_1000 |
|
value: 0.246 |
|
- type: precision_at_3 |
|
value: 16.469 |
|
- type: precision_at_5 |
|
value: 12.174 |
|
- type: recall_at_1 |
|
value: 26.421 |
|
- type: recall_at_10 |
|
value: 53.618 |
|
- type: recall_at_100 |
|
value: 80.456 |
|
- type: recall_at_1000 |
|
value: 94.505 |
|
- type: recall_at_3 |
|
value: 37.894 |
|
- type: recall_at_5 |
|
value: 44.352999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.54 |
|
- type: map_at_10 |
|
value: 29.468 |
|
- type: map_at_100 |
|
value: 30.422 |
|
- type: map_at_1000 |
|
value: 30.542 |
|
- type: map_at_3 |
|
value: 26.888 |
|
- type: map_at_5 |
|
value: 27.962999999999997 |
|
- type: mrr_at_1 |
|
value: 23.29 |
|
- type: mrr_at_10 |
|
value: 31.176 |
|
- type: mrr_at_100 |
|
value: 32.046 |
|
- type: mrr_at_1000 |
|
value: 32.129000000000005 |
|
- type: mrr_at_3 |
|
value: 28.804999999999996 |
|
- type: mrr_at_5 |
|
value: 29.868 |
|
- type: ndcg_at_1 |
|
value: 23.29 |
|
- type: ndcg_at_10 |
|
value: 34.166000000000004 |
|
- type: ndcg_at_100 |
|
value: 39.217999999999996 |
|
- type: ndcg_at_1000 |
|
value: 41.964 |
|
- type: ndcg_at_3 |
|
value: 28.970000000000002 |
|
- type: ndcg_at_5 |
|
value: 30.797 |
|
- type: precision_at_1 |
|
value: 23.29 |
|
- type: precision_at_10 |
|
value: 5.489999999999999 |
|
- type: precision_at_100 |
|
value: 0.874 |
|
- type: precision_at_1000 |
|
value: 0.122 |
|
- type: precision_at_3 |
|
value: 12.261 |
|
- type: precision_at_5 |
|
value: 8.503 |
|
- type: recall_at_1 |
|
value: 21.54 |
|
- type: recall_at_10 |
|
value: 47.064 |
|
- type: recall_at_100 |
|
value: 70.959 |
|
- type: recall_at_1000 |
|
value: 91.032 |
|
- type: recall_at_3 |
|
value: 32.828 |
|
- type: recall_at_5 |
|
value: 37.214999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.102 |
|
- type: map_at_10 |
|
value: 17.469 |
|
- type: map_at_100 |
|
value: 19.244 |
|
- type: map_at_1000 |
|
value: 19.435 |
|
- type: map_at_3 |
|
value: 14.257 |
|
- type: map_at_5 |
|
value: 16.028000000000002 |
|
- type: mrr_at_1 |
|
value: 22.866 |
|
- type: mrr_at_10 |
|
value: 33.535 |
|
- type: mrr_at_100 |
|
value: 34.583999999999996 |
|
- type: mrr_at_1000 |
|
value: 34.622 |
|
- type: mrr_at_3 |
|
value: 29.946 |
|
- type: mrr_at_5 |
|
value: 32.157000000000004 |
|
- type: ndcg_at_1 |
|
value: 22.866 |
|
- type: ndcg_at_10 |
|
value: 25.16 |
|
- type: ndcg_at_100 |
|
value: 32.347 |
|
- type: ndcg_at_1000 |
|
value: 35.821 |
|
- type: ndcg_at_3 |
|
value: 19.816 |
|
- type: ndcg_at_5 |
|
value: 22.026 |
|
- type: precision_at_1 |
|
value: 22.866 |
|
- type: precision_at_10 |
|
value: 8.072 |
|
- type: precision_at_100 |
|
value: 1.5709999999999997 |
|
- type: precision_at_1000 |
|
value: 0.22200000000000003 |
|
- type: precision_at_3 |
|
value: 14.701 |
|
- type: precision_at_5 |
|
value: 11.960999999999999 |
|
- type: recall_at_1 |
|
value: 10.102 |
|
- type: recall_at_10 |
|
value: 31.086000000000002 |
|
- type: recall_at_100 |
|
value: 55.896 |
|
- type: recall_at_1000 |
|
value: 75.375 |
|
- type: recall_at_3 |
|
value: 18.343999999999998 |
|
- type: recall_at_5 |
|
value: 24.102 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.961 |
|
- type: map_at_10 |
|
value: 16.058 |
|
- type: map_at_100 |
|
value: 21.878 |
|
- type: map_at_1000 |
|
value: 23.156 |
|
- type: map_at_3 |
|
value: 12.206999999999999 |
|
- type: map_at_5 |
|
value: 13.747000000000002 |
|
- type: mrr_at_1 |
|
value: 60.5 |
|
- type: mrr_at_10 |
|
value: 68.488 |
|
- type: mrr_at_100 |
|
value: 69.02199999999999 |
|
- type: mrr_at_1000 |
|
value: 69.03200000000001 |
|
- type: mrr_at_3 |
|
value: 66.792 |
|
- type: mrr_at_5 |
|
value: 67.62899999999999 |
|
- type: ndcg_at_1 |
|
value: 49.125 |
|
- type: ndcg_at_10 |
|
value: 34.827999999999996 |
|
- type: ndcg_at_100 |
|
value: 38.723 |
|
- type: ndcg_at_1000 |
|
value: 45.988 |
|
- type: ndcg_at_3 |
|
value: 40.302 |
|
- type: ndcg_at_5 |
|
value: 36.781000000000006 |
|
- type: precision_at_1 |
|
value: 60.5 |
|
- type: precision_at_10 |
|
value: 26.825 |
|
- type: precision_at_100 |
|
value: 8.445 |
|
- type: precision_at_1000 |
|
value: 1.7000000000000002 |
|
- type: precision_at_3 |
|
value: 43.25 |
|
- type: precision_at_5 |
|
value: 34.5 |
|
- type: recall_at_1 |
|
value: 7.961 |
|
- type: recall_at_10 |
|
value: 20.843 |
|
- type: recall_at_100 |
|
value: 43.839 |
|
- type: recall_at_1000 |
|
value: 67.33 |
|
- type: recall_at_3 |
|
value: 13.516 |
|
- type: recall_at_5 |
|
value: 15.956000000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 52.06000000000001 |
|
- type: f1 |
|
value: 47.21494728335567 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 56.798 |
|
- type: map_at_10 |
|
value: 67.644 |
|
- type: map_at_100 |
|
value: 68.01700000000001 |
|
- type: map_at_1000 |
|
value: 68.038 |
|
- type: map_at_3 |
|
value: 65.539 |
|
- type: map_at_5 |
|
value: 66.912 |
|
- type: mrr_at_1 |
|
value: 61.221000000000004 |
|
- type: mrr_at_10 |
|
value: 71.97099999999999 |
|
- type: mrr_at_100 |
|
value: 72.262 |
|
- type: mrr_at_1000 |
|
value: 72.27 |
|
- type: mrr_at_3 |
|
value: 70.052 |
|
- type: mrr_at_5 |
|
value: 71.324 |
|
- type: ndcg_at_1 |
|
value: 61.221000000000004 |
|
- type: ndcg_at_10 |
|
value: 73.173 |
|
- type: ndcg_at_100 |
|
value: 74.779 |
|
- type: ndcg_at_1000 |
|
value: 75.229 |
|
- type: ndcg_at_3 |
|
value: 69.291 |
|
- type: ndcg_at_5 |
|
value: 71.552 |
|
- type: precision_at_1 |
|
value: 61.221000000000004 |
|
- type: precision_at_10 |
|
value: 9.449 |
|
- type: precision_at_100 |
|
value: 1.0370000000000001 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_3 |
|
value: 27.467999999999996 |
|
- type: precision_at_5 |
|
value: 17.744 |
|
- type: recall_at_1 |
|
value: 56.798 |
|
- type: recall_at_10 |
|
value: 85.991 |
|
- type: recall_at_100 |
|
value: 92.973 |
|
- type: recall_at_1000 |
|
value: 96.089 |
|
- type: recall_at_3 |
|
value: 75.576 |
|
- type: recall_at_5 |
|
value: 81.12 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.323 |
|
- type: map_at_10 |
|
value: 30.279 |
|
- type: map_at_100 |
|
value: 32.153999999999996 |
|
- type: map_at_1000 |
|
value: 32.339 |
|
- type: map_at_3 |
|
value: 26.336 |
|
- type: map_at_5 |
|
value: 28.311999999999998 |
|
- type: mrr_at_1 |
|
value: 35.339999999999996 |
|
- type: mrr_at_10 |
|
value: 44.931 |
|
- type: mrr_at_100 |
|
value: 45.818999999999996 |
|
- type: mrr_at_1000 |
|
value: 45.864 |
|
- type: mrr_at_3 |
|
value: 42.618 |
|
- type: mrr_at_5 |
|
value: 43.736999999999995 |
|
- type: ndcg_at_1 |
|
value: 35.339999999999996 |
|
- type: ndcg_at_10 |
|
value: 37.852999999999994 |
|
- type: ndcg_at_100 |
|
value: 44.888 |
|
- type: ndcg_at_1000 |
|
value: 48.069 |
|
- type: ndcg_at_3 |
|
value: 34.127 |
|
- type: ndcg_at_5 |
|
value: 35.026 |
|
- type: precision_at_1 |
|
value: 35.339999999999996 |
|
- type: precision_at_10 |
|
value: 10.617 |
|
- type: precision_at_100 |
|
value: 1.7930000000000001 |
|
- type: precision_at_1000 |
|
value: 0.23600000000000002 |
|
- type: precision_at_3 |
|
value: 22.582 |
|
- type: precision_at_5 |
|
value: 16.605 |
|
- type: recall_at_1 |
|
value: 18.323 |
|
- type: recall_at_10 |
|
value: 44.948 |
|
- type: recall_at_100 |
|
value: 71.11800000000001 |
|
- type: recall_at_1000 |
|
value: 90.104 |
|
- type: recall_at_3 |
|
value: 31.661 |
|
- type: recall_at_5 |
|
value: 36.498000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.668 |
|
- type: map_at_10 |
|
value: 43.669999999999995 |
|
- type: map_at_100 |
|
value: 44.646 |
|
- type: map_at_1000 |
|
value: 44.731 |
|
- type: map_at_3 |
|
value: 40.897 |
|
- type: map_at_5 |
|
value: 42.559999999999995 |
|
- type: mrr_at_1 |
|
value: 61.336999999999996 |
|
- type: mrr_at_10 |
|
value: 68.496 |
|
- type: mrr_at_100 |
|
value: 68.916 |
|
- type: mrr_at_1000 |
|
value: 68.938 |
|
- type: mrr_at_3 |
|
value: 66.90700000000001 |
|
- type: mrr_at_5 |
|
value: 67.91199999999999 |
|
- type: ndcg_at_1 |
|
value: 61.336999999999996 |
|
- type: ndcg_at_10 |
|
value: 52.588 |
|
- type: ndcg_at_100 |
|
value: 56.389 |
|
- type: ndcg_at_1000 |
|
value: 58.187999999999995 |
|
- type: ndcg_at_3 |
|
value: 48.109 |
|
- type: ndcg_at_5 |
|
value: 50.498 |
|
- type: precision_at_1 |
|
value: 61.336999999999996 |
|
- type: precision_at_10 |
|
value: 11.033 |
|
- type: precision_at_100 |
|
value: 1.403 |
|
- type: precision_at_1000 |
|
value: 0.164 |
|
- type: precision_at_3 |
|
value: 30.105999999999998 |
|
- type: precision_at_5 |
|
value: 19.954 |
|
- type: recall_at_1 |
|
value: 30.668 |
|
- type: recall_at_10 |
|
value: 55.165 |
|
- type: recall_at_100 |
|
value: 70.169 |
|
- type: recall_at_1000 |
|
value: 82.12 |
|
- type: recall_at_3 |
|
value: 45.159 |
|
- type: recall_at_5 |
|
value: 49.885000000000005 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 78.542 |
|
- type: ap |
|
value: 72.50692137216646 |
|
- type: f1 |
|
value: 78.40630687221642 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.613 |
|
- type: map_at_10 |
|
value: 29.98 |
|
- type: map_at_100 |
|
value: 31.136999999999997 |
|
- type: map_at_1000 |
|
value: 31.196 |
|
- type: map_at_3 |
|
value: 26.339000000000002 |
|
- type: map_at_5 |
|
value: 28.351 |
|
- type: mrr_at_1 |
|
value: 19.054 |
|
- type: mrr_at_10 |
|
value: 30.476 |
|
- type: mrr_at_100 |
|
value: 31.588 |
|
- type: mrr_at_1000 |
|
value: 31.641000000000002 |
|
- type: mrr_at_3 |
|
value: 26.834000000000003 |
|
- type: mrr_at_5 |
|
value: 28.849000000000004 |
|
- type: ndcg_at_1 |
|
value: 19.083 |
|
- type: ndcg_at_10 |
|
value: 36.541000000000004 |
|
- type: ndcg_at_100 |
|
value: 42.35 |
|
- type: ndcg_at_1000 |
|
value: 43.9 |
|
- type: ndcg_at_3 |
|
value: 29.015 |
|
- type: ndcg_at_5 |
|
value: 32.622 |
|
- type: precision_at_1 |
|
value: 19.083 |
|
- type: precision_at_10 |
|
value: 5.914 |
|
- type: precision_at_100 |
|
value: 0.889 |
|
- type: precision_at_1000 |
|
value: 0.10200000000000001 |
|
- type: precision_at_3 |
|
value: 12.483 |
|
- type: precision_at_5 |
|
value: 9.315 |
|
- type: recall_at_1 |
|
value: 18.613 |
|
- type: recall_at_10 |
|
value: 56.88999999999999 |
|
- type: recall_at_100 |
|
value: 84.207 |
|
- type: recall_at_1000 |
|
value: 96.20100000000001 |
|
- type: recall_at_3 |
|
value: 36.262 |
|
- type: recall_at_5 |
|
value: 44.925 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 94.77656178750571 |
|
- type: f1 |
|
value: 94.37966073742972 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 77.72457820337438 |
|
- type: f1 |
|
value: 59.11327646329634 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.17753866846 |
|
- type: f1 |
|
value: 71.22604635414544 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 76.67787491593813 |
|
- type: f1 |
|
value: 76.87653151298177 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 33.3485843514749 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 29.792796913883617 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.310305659169963 |
|
- type: mrr |
|
value: 32.38286775798406 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.968 |
|
- type: map_at_10 |
|
value: 11.379 |
|
- type: map_at_100 |
|
value: 14.618999999999998 |
|
- type: map_at_1000 |
|
value: 16.055 |
|
- type: map_at_3 |
|
value: 8.34 |
|
- type: map_at_5 |
|
value: 9.690999999999999 |
|
- type: mrr_at_1 |
|
value: 43.034 |
|
- type: mrr_at_10 |
|
value: 51.019999999999996 |
|
- type: mrr_at_100 |
|
value: 51.63100000000001 |
|
- type: mrr_at_1000 |
|
value: 51.681 |
|
- type: mrr_at_3 |
|
value: 49.174 |
|
- type: mrr_at_5 |
|
value: 50.181 |
|
- type: ndcg_at_1 |
|
value: 41.176 |
|
- type: ndcg_at_10 |
|
value: 31.341 |
|
- type: ndcg_at_100 |
|
value: 29.451 |
|
- type: ndcg_at_1000 |
|
value: 38.007000000000005 |
|
- type: ndcg_at_3 |
|
value: 36.494 |
|
- type: ndcg_at_5 |
|
value: 34.499 |
|
- type: precision_at_1 |
|
value: 43.034 |
|
- type: precision_at_10 |
|
value: 23.375 |
|
- type: precision_at_100 |
|
value: 7.799 |
|
- type: precision_at_1000 |
|
value: 2.059 |
|
- type: precision_at_3 |
|
value: 34.675 |
|
- type: precision_at_5 |
|
value: 30.154999999999998 |
|
- type: recall_at_1 |
|
value: 4.968 |
|
- type: recall_at_10 |
|
value: 15.104999999999999 |
|
- type: recall_at_100 |
|
value: 30.741000000000003 |
|
- type: recall_at_1000 |
|
value: 61.182 |
|
- type: recall_at_3 |
|
value: 9.338000000000001 |
|
- type: recall_at_5 |
|
value: 11.484 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.716 |
|
- type: map_at_10 |
|
value: 38.32 |
|
- type: map_at_100 |
|
value: 39.565 |
|
- type: map_at_1000 |
|
value: 39.602 |
|
- type: map_at_3 |
|
value: 33.848 |
|
- type: map_at_5 |
|
value: 36.471 |
|
- type: mrr_at_1 |
|
value: 26.912000000000003 |
|
- type: mrr_at_10 |
|
value: 40.607 |
|
- type: mrr_at_100 |
|
value: 41.589 |
|
- type: mrr_at_1000 |
|
value: 41.614000000000004 |
|
- type: mrr_at_3 |
|
value: 36.684 |
|
- type: mrr_at_5 |
|
value: 39.036 |
|
- type: ndcg_at_1 |
|
value: 26.883000000000003 |
|
- type: ndcg_at_10 |
|
value: 46.096 |
|
- type: ndcg_at_100 |
|
value: 51.513 |
|
- type: ndcg_at_1000 |
|
value: 52.366 |
|
- type: ndcg_at_3 |
|
value: 37.549 |
|
- type: ndcg_at_5 |
|
value: 41.971000000000004 |
|
- type: precision_at_1 |
|
value: 26.883000000000003 |
|
- type: precision_at_10 |
|
value: 8.004 |
|
- type: precision_at_100 |
|
value: 1.107 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 17.516000000000002 |
|
- type: precision_at_5 |
|
value: 13.019 |
|
- type: recall_at_1 |
|
value: 23.716 |
|
- type: recall_at_10 |
|
value: 67.656 |
|
- type: recall_at_100 |
|
value: 91.413 |
|
- type: recall_at_1000 |
|
value: 97.714 |
|
- type: recall_at_3 |
|
value: 45.449 |
|
- type: recall_at_5 |
|
value: 55.598000000000006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.486 |
|
- type: map_at_10 |
|
value: 84.292 |
|
- type: map_at_100 |
|
value: 84.954 |
|
- type: map_at_1000 |
|
value: 84.969 |
|
- type: map_at_3 |
|
value: 81.295 |
|
- type: map_at_5 |
|
value: 83.165 |
|
- type: mrr_at_1 |
|
value: 81.16 |
|
- type: mrr_at_10 |
|
value: 87.31 |
|
- type: mrr_at_100 |
|
value: 87.423 |
|
- type: mrr_at_1000 |
|
value: 87.423 |
|
- type: mrr_at_3 |
|
value: 86.348 |
|
- type: mrr_at_5 |
|
value: 86.991 |
|
- type: ndcg_at_1 |
|
value: 81.17 |
|
- type: ndcg_at_10 |
|
value: 88.067 |
|
- type: ndcg_at_100 |
|
value: 89.34 |
|
- type: ndcg_at_1000 |
|
value: 89.43900000000001 |
|
- type: ndcg_at_3 |
|
value: 85.162 |
|
- type: ndcg_at_5 |
|
value: 86.752 |
|
- type: precision_at_1 |
|
value: 81.17 |
|
- type: precision_at_10 |
|
value: 13.394 |
|
- type: precision_at_100 |
|
value: 1.5310000000000001 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.193 |
|
- type: precision_at_5 |
|
value: 24.482 |
|
- type: recall_at_1 |
|
value: 70.486 |
|
- type: recall_at_10 |
|
value: 95.184 |
|
- type: recall_at_100 |
|
value: 99.53999999999999 |
|
- type: recall_at_1000 |
|
value: 99.98700000000001 |
|
- type: recall_at_3 |
|
value: 86.89 |
|
- type: recall_at_5 |
|
value: 91.365 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 44.118229475102154 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 48.68049097629063 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.888 |
|
- type: map_at_10 |
|
value: 12.770999999999999 |
|
- type: map_at_100 |
|
value: 15.238 |
|
- type: map_at_1000 |
|
value: 15.616 |
|
- type: map_at_3 |
|
value: 8.952 |
|
- type: map_at_5 |
|
value: 10.639999999999999 |
|
- type: mrr_at_1 |
|
value: 24.099999999999998 |
|
- type: mrr_at_10 |
|
value: 35.375 |
|
- type: mrr_at_100 |
|
value: 36.442 |
|
- type: mrr_at_1000 |
|
value: 36.488 |
|
- type: mrr_at_3 |
|
value: 31.717000000000002 |
|
- type: mrr_at_5 |
|
value: 33.722 |
|
- type: ndcg_at_1 |
|
value: 24.099999999999998 |
|
- type: ndcg_at_10 |
|
value: 21.438 |
|
- type: ndcg_at_100 |
|
value: 30.601 |
|
- type: ndcg_at_1000 |
|
value: 36.678 |
|
- type: ndcg_at_3 |
|
value: 19.861 |
|
- type: ndcg_at_5 |
|
value: 17.263 |
|
- type: precision_at_1 |
|
value: 24.099999999999998 |
|
- type: precision_at_10 |
|
value: 11.4 |
|
- type: precision_at_100 |
|
value: 2.465 |
|
- type: precision_at_1000 |
|
value: 0.392 |
|
- type: precision_at_3 |
|
value: 18.733 |
|
- type: precision_at_5 |
|
value: 15.22 |
|
- type: recall_at_1 |
|
value: 4.888 |
|
- type: recall_at_10 |
|
value: 23.118 |
|
- type: recall_at_100 |
|
value: 49.995 |
|
- type: recall_at_1000 |
|
value: 79.577 |
|
- type: recall_at_3 |
|
value: 11.398 |
|
- type: recall_at_5 |
|
value: 15.428 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.33198632617024 |
|
- type: cos_sim_spearman |
|
value: 79.09232997136625 |
|
- type: euclidean_pearson |
|
value: 81.49986011523868 |
|
- type: euclidean_spearman |
|
value: 77.03530620283338 |
|
- type: manhattan_pearson |
|
value: 81.4741227286667 |
|
- type: manhattan_spearman |
|
value: 76.98641133116311 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.60103674582464 |
|
- type: cos_sim_spearman |
|
value: 75.03945035801914 |
|
- type: euclidean_pearson |
|
value: 80.82455267481467 |
|
- type: euclidean_spearman |
|
value: 70.3317366248871 |
|
- type: manhattan_pearson |
|
value: 80.8928091531445 |
|
- type: manhattan_spearman |
|
value: 70.43207370945672 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.52453177109315 |
|
- type: cos_sim_spearman |
|
value: 83.26431569305103 |
|
- type: euclidean_pearson |
|
value: 82.10494657997404 |
|
- type: euclidean_spearman |
|
value: 83.41028425949024 |
|
- type: manhattan_pearson |
|
value: 82.08669822983934 |
|
- type: manhattan_spearman |
|
value: 83.39959776442115 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.67472020277681 |
|
- type: cos_sim_spearman |
|
value: 78.61877889763109 |
|
- type: euclidean_pearson |
|
value: 80.07878012437722 |
|
- type: euclidean_spearman |
|
value: 77.44374494215397 |
|
- type: manhattan_pearson |
|
value: 79.95988483102258 |
|
- type: manhattan_spearman |
|
value: 77.36018101061366 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.55450610494437 |
|
- type: cos_sim_spearman |
|
value: 87.03494331841401 |
|
- type: euclidean_pearson |
|
value: 81.4319784394287 |
|
- type: euclidean_spearman |
|
value: 82.47893040599372 |
|
- type: manhattan_pearson |
|
value: 81.32627203699644 |
|
- type: manhattan_spearman |
|
value: 82.40660565070675 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.51576965454805 |
|
- type: cos_sim_spearman |
|
value: 83.0062959588245 |
|
- type: euclidean_pearson |
|
value: 79.98888882568556 |
|
- type: euclidean_spearman |
|
value: 81.08948911791873 |
|
- type: manhattan_pearson |
|
value: 79.77952719568583 |
|
- type: manhattan_spearman |
|
value: 80.79471040445408 |
|
- 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: 87.28313046682885 |
|
- type: cos_sim_spearman |
|
value: 87.35865211085007 |
|
- type: euclidean_pearson |
|
value: 84.11501613667811 |
|
- type: euclidean_spearman |
|
value: 82.82038954956121 |
|
- type: manhattan_pearson |
|
value: 83.891278147302 |
|
- type: manhattan_spearman |
|
value: 82.59947685165902 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 67.80653738006102 |
|
- type: cos_sim_spearman |
|
value: 68.11259151179601 |
|
- type: euclidean_pearson |
|
value: 43.16707985094242 |
|
- type: euclidean_spearman |
|
value: 58.96200382968696 |
|
- type: manhattan_pearson |
|
value: 43.84146858566507 |
|
- type: manhattan_spearman |
|
value: 59.05193977207514 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.62068205073571 |
|
- type: cos_sim_spearman |
|
value: 84.40071593577095 |
|
- type: euclidean_pearson |
|
value: 80.90824726252514 |
|
- type: euclidean_spearman |
|
value: 80.54974812534094 |
|
- type: manhattan_pearson |
|
value: 80.6759008187939 |
|
- type: manhattan_spearman |
|
value: 80.31149103896973 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 87.13774787530915 |
|
- type: mrr |
|
value: 96.22233793802422 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 49.167 |
|
- type: map_at_10 |
|
value: 59.852000000000004 |
|
- type: map_at_100 |
|
value: 60.544 |
|
- type: map_at_1000 |
|
value: 60.577000000000005 |
|
- type: map_at_3 |
|
value: 57.242000000000004 |
|
- type: map_at_5 |
|
value: 58.704 |
|
- type: mrr_at_1 |
|
value: 51.0 |
|
- type: mrr_at_10 |
|
value: 60.575 |
|
- type: mrr_at_100 |
|
value: 61.144 |
|
- type: mrr_at_1000 |
|
value: 61.175000000000004 |
|
- type: mrr_at_3 |
|
value: 58.667 |
|
- type: mrr_at_5 |
|
value: 59.599999999999994 |
|
- type: ndcg_at_1 |
|
value: 51.0 |
|
- type: ndcg_at_10 |
|
value: 64.398 |
|
- type: ndcg_at_100 |
|
value: 67.581 |
|
- type: ndcg_at_1000 |
|
value: 68.551 |
|
- type: ndcg_at_3 |
|
value: 59.928000000000004 |
|
- type: ndcg_at_5 |
|
value: 61.986 |
|
- type: precision_at_1 |
|
value: 51.0 |
|
- type: precision_at_10 |
|
value: 8.7 |
|
- type: precision_at_100 |
|
value: 1.047 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 23.666999999999998 |
|
- type: precision_at_5 |
|
value: 15.6 |
|
- type: recall_at_1 |
|
value: 49.167 |
|
- type: recall_at_10 |
|
value: 77.333 |
|
- type: recall_at_100 |
|
value: 91.833 |
|
- type: recall_at_1000 |
|
value: 99.667 |
|
- type: recall_at_3 |
|
value: 65.594 |
|
- type: recall_at_5 |
|
value: 70.52199999999999 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.77227722772277 |
|
- type: cos_sim_ap |
|
value: 94.14261011689366 |
|
- type: cos_sim_f1 |
|
value: 88.37209302325581 |
|
- type: cos_sim_precision |
|
value: 89.36605316973414 |
|
- type: cos_sim_recall |
|
value: 87.4 |
|
- type: dot_accuracy |
|
value: 99.07128712871287 |
|
- type: dot_ap |
|
value: 27.325649239129486 |
|
- type: dot_f1 |
|
value: 33.295838020247466 |
|
- type: dot_precision |
|
value: 38.04627249357326 |
|
- type: dot_recall |
|
value: 29.599999999999998 |
|
- type: euclidean_accuracy |
|
value: 99.74158415841585 |
|
- type: euclidean_ap |
|
value: 92.32695359979576 |
|
- type: euclidean_f1 |
|
value: 86.90534575772439 |
|
- type: euclidean_precision |
|
value: 85.27430221366699 |
|
- type: euclidean_recall |
|
value: 88.6 |
|
- type: manhattan_accuracy |
|
value: 99.74257425742574 |
|
- type: manhattan_ap |
|
value: 92.40335687760499 |
|
- type: manhattan_f1 |
|
value: 86.96507624200687 |
|
- type: manhattan_precision |
|
value: 85.57599225556632 |
|
- type: manhattan_recall |
|
value: 88.4 |
|
- type: max_accuracy |
|
value: 99.77227722772277 |
|
- type: max_ap |
|
value: 94.14261011689366 |
|
- type: max_f1 |
|
value: 88.37209302325581 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 53.113809982945035 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 33.90915908471812 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 50.36481271702464 |
|
- type: mrr |
|
value: 51.05628236142942 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.311305530381826 |
|
- type: cos_sim_spearman |
|
value: 31.22029657606254 |
|
- type: dot_pearson |
|
value: 12.157032445910177 |
|
- type: dot_spearman |
|
value: 13.275185888551805 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.167 |
|
- type: map_at_10 |
|
value: 1.113 |
|
- type: map_at_100 |
|
value: 5.926 |
|
- type: map_at_1000 |
|
value: 15.25 |
|
- type: map_at_3 |
|
value: 0.414 |
|
- type: map_at_5 |
|
value: 0.633 |
|
- type: mrr_at_1 |
|
value: 64.0 |
|
- type: mrr_at_10 |
|
value: 74.444 |
|
- type: mrr_at_100 |
|
value: 74.667 |
|
- type: mrr_at_1000 |
|
value: 74.679 |
|
- type: mrr_at_3 |
|
value: 72.0 |
|
- type: mrr_at_5 |
|
value: 74.0 |
|
- type: ndcg_at_1 |
|
value: 59.0 |
|
- type: ndcg_at_10 |
|
value: 51.468 |
|
- type: ndcg_at_100 |
|
value: 38.135000000000005 |
|
- type: ndcg_at_1000 |
|
value: 36.946 |
|
- type: ndcg_at_3 |
|
value: 55.827000000000005 |
|
- type: ndcg_at_5 |
|
value: 53.555 |
|
- type: precision_at_1 |
|
value: 64.0 |
|
- type: precision_at_10 |
|
value: 54.400000000000006 |
|
- type: precision_at_100 |
|
value: 39.08 |
|
- type: precision_at_1000 |
|
value: 16.618 |
|
- type: precision_at_3 |
|
value: 58.667 |
|
- type: precision_at_5 |
|
value: 56.8 |
|
- type: recall_at_1 |
|
value: 0.167 |
|
- type: recall_at_10 |
|
value: 1.38 |
|
- type: recall_at_100 |
|
value: 9.189 |
|
- type: recall_at_1000 |
|
value: 35.737 |
|
- type: recall_at_3 |
|
value: 0.455 |
|
- type: recall_at_5 |
|
value: 0.73 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.4299999999999997 |
|
- type: map_at_10 |
|
value: 8.539 |
|
- type: map_at_100 |
|
value: 14.155999999999999 |
|
- type: map_at_1000 |
|
value: 15.684999999999999 |
|
- type: map_at_3 |
|
value: 3.857 |
|
- type: map_at_5 |
|
value: 5.583 |
|
- type: mrr_at_1 |
|
value: 26.531 |
|
- type: mrr_at_10 |
|
value: 40.489999999999995 |
|
- type: mrr_at_100 |
|
value: 41.772999999999996 |
|
- type: mrr_at_1000 |
|
value: 41.772999999999996 |
|
- type: mrr_at_3 |
|
value: 35.034 |
|
- type: mrr_at_5 |
|
value: 38.81 |
|
- type: ndcg_at_1 |
|
value: 21.429000000000002 |
|
- type: ndcg_at_10 |
|
value: 20.787 |
|
- type: ndcg_at_100 |
|
value: 33.202 |
|
- type: ndcg_at_1000 |
|
value: 45.167 |
|
- type: ndcg_at_3 |
|
value: 18.233 |
|
- type: ndcg_at_5 |
|
value: 19.887 |
|
- type: precision_at_1 |
|
value: 26.531 |
|
- type: precision_at_10 |
|
value: 19.796 |
|
- type: precision_at_100 |
|
value: 7.4079999999999995 |
|
- type: precision_at_1000 |
|
value: 1.5310000000000001 |
|
- type: precision_at_3 |
|
value: 19.728 |
|
- type: precision_at_5 |
|
value: 21.633 |
|
- type: recall_at_1 |
|
value: 2.4299999999999997 |
|
- type: recall_at_10 |
|
value: 14.901 |
|
- type: recall_at_100 |
|
value: 46.422000000000004 |
|
- type: recall_at_1000 |
|
value: 82.83500000000001 |
|
- type: recall_at_3 |
|
value: 4.655 |
|
- type: recall_at_5 |
|
value: 8.092 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 72.90140000000001 |
|
- type: ap |
|
value: 15.138716624430662 |
|
- type: f1 |
|
value: 56.08803013269606 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 59.85285795132994 |
|
- type: f1 |
|
value: 60.17575819903709 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 41.125150148437065 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.96751505036657 |
|
- type: cos_sim_ap |
|
value: 70.45642872444971 |
|
- type: cos_sim_f1 |
|
value: 65.75274793133259 |
|
- type: cos_sim_precision |
|
value: 61.806361736707686 |
|
- type: cos_sim_recall |
|
value: 70.23746701846966 |
|
- type: dot_accuracy |
|
value: 77.84466829588126 |
|
- type: dot_ap |
|
value: 32.49904328313596 |
|
- type: dot_f1 |
|
value: 37.903122189387126 |
|
- type: dot_precision |
|
value: 25.050951086956523 |
|
- type: dot_recall |
|
value: 77.83641160949868 |
|
- type: euclidean_accuracy |
|
value: 84.5920009536866 |
|
- type: euclidean_ap |
|
value: 68.83700633574043 |
|
- type: euclidean_f1 |
|
value: 64.92803542871202 |
|
- type: euclidean_precision |
|
value: 60.820465545056464 |
|
- type: euclidean_recall |
|
value: 69.63060686015831 |
|
- type: manhattan_accuracy |
|
value: 84.52643500029802 |
|
- type: manhattan_ap |
|
value: 68.63286046599892 |
|
- type: manhattan_f1 |
|
value: 64.7476540705047 |
|
- type: manhattan_precision |
|
value: 62.3291015625 |
|
- type: manhattan_recall |
|
value: 67.36147757255937 |
|
- type: max_accuracy |
|
value: 84.96751505036657 |
|
- type: max_ap |
|
value: 70.45642872444971 |
|
- type: max_f1 |
|
value: 65.75274793133259 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.65603291031164 |
|
- type: cos_sim_ap |
|
value: 85.58148320880878 |
|
- type: cos_sim_f1 |
|
value: 77.63202920041064 |
|
- type: cos_sim_precision |
|
value: 76.68444377675957 |
|
- type: cos_sim_recall |
|
value: 78.60332614721281 |
|
- type: dot_accuracy |
|
value: 79.71048239996895 |
|
- type: dot_ap |
|
value: 59.31114839296281 |
|
- type: dot_f1 |
|
value: 57.13895527483783 |
|
- type: dot_precision |
|
value: 51.331125015335545 |
|
- type: dot_recall |
|
value: 64.4287034185402 |
|
- type: euclidean_accuracy |
|
value: 86.99305312997244 |
|
- type: euclidean_ap |
|
value: 81.87075965254876 |
|
- type: euclidean_f1 |
|
value: 73.53543008715421 |
|
- type: euclidean_precision |
|
value: 72.39964184450082 |
|
- type: euclidean_recall |
|
value: 74.70742223591007 |
|
- type: manhattan_accuracy |
|
value: 87.04156479217605 |
|
- type: manhattan_ap |
|
value: 81.7850497283247 |
|
- type: manhattan_f1 |
|
value: 73.52951955143475 |
|
- type: manhattan_precision |
|
value: 70.15875236030492 |
|
- type: manhattan_recall |
|
value: 77.2405297197413 |
|
- type: max_accuracy |
|
value: 88.65603291031164 |
|
- type: max_ap |
|
value: 85.58148320880878 |
|
- type: max_f1 |
|
value: 77.63202920041064 |
|
--- |
|
<h1 align="center">GIST Embedding v0 - all-MiniLM-L6-v2</h1> |
|
|
|
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* |
|
|
|
The model is fine-tuned on top of the [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). |
|
|
|
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. |
|
|
|
Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) |
|
|
|
|
|
# Data |
|
|
|
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: |
|
|
|
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) |
|
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb |
|
|
|
The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). |
|
|
|
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). |
|
|
|
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. |
|
|
|
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. |
|
|
|
# Usage |
|
|
|
The model can be easily loaded using the Sentence Transformers library. |
|
|
|
```Python |
|
import torch.nn.functional as F |
|
from sentence_transformers import SentenceTransformer |
|
|
|
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. |
|
|
|
model = SentenceTransformer("avsolatorio/GIST-all-MiniLM-L6-v2", revision=revision) |
|
|
|
texts = [ |
|
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", |
|
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", |
|
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" |
|
] |
|
|
|
# Compute embeddings |
|
embeddings = model.encode(texts, convert_to_tensor=True) |
|
|
|
# Compute cosine-similarity for each pair of sentences |
|
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) |
|
|
|
print(scores.cpu().numpy()) |
|
``` |
|
|
|
# Training Parameters |
|
|
|
Below are the training parameters used to fine-tune the model: |
|
|
|
``` |
|
Epochs = 40 |
|
Warmup ratio = 0.1 |
|
Learning rate = 5e-6 |
|
Batch size = 16 |
|
Checkpoint step = 102000 |
|
Contrastive loss temperature = 0.01 |
|
``` |
|
|
|
|
|
# Evaluation |
|
|
|
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. |
|
|
|
|
|
# Citation |
|
|
|
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 |
|
|
|
``` |
|
@article{solatorio2024gistembed, |
|
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, |
|
author={Aivin V. Solatorio}, |
|
journal={arXiv preprint arXiv:2402.16829}, |
|
year={2024}, |
|
URL={https://arxiv.org/abs/2402.16829} |
|
eprint={2402.16829}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
# Acknowledgements |
|
|
|
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. |
|
|
|
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. |
