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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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- arctic |
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- snowflake-arctic-embed |
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model-index: |
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- name: snowflake-arctic-embed-m |
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results: |
|
- 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: |
|
- type: accuracy |
|
value: 76.80597014925374 |
|
- type: ap |
|
value: 39.31198155789558 |
|
- type: f1 |
|
value: 70.48198448222148 |
|
- 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 |
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metrics: |
|
- type: accuracy |
|
value: 82.831525 |
|
- type: ap |
|
value: 77.4474050181638 |
|
- type: f1 |
|
value: 82.77204845110204 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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 |
|
value: 38.93000000000001 |
|
- type: f1 |
|
value: 37.98013371053459 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
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: |
|
- type: map_at_1 |
|
value: 31.223 |
|
- type: map_at_10 |
|
value: 47.43 |
|
- type: map_at_100 |
|
value: 48.208 |
|
- type: map_at_1000 |
|
value: 48.211 |
|
- type: map_at_3 |
|
value: 42.579 |
|
- type: map_at_5 |
|
value: 45.263999999999996 |
|
- type: mrr_at_1 |
|
value: 31.65 |
|
- type: mrr_at_10 |
|
value: 47.573 |
|
- type: mrr_at_100 |
|
value: 48.359 |
|
- type: mrr_at_1000 |
|
value: 48.362 |
|
- type: mrr_at_3 |
|
value: 42.734 |
|
- type: mrr_at_5 |
|
value: 45.415 |
|
- type: ndcg_at_1 |
|
value: 31.223 |
|
- type: ndcg_at_10 |
|
value: 56.436 |
|
- type: ndcg_at_100 |
|
value: 59.657000000000004 |
|
- type: ndcg_at_1000 |
|
value: 59.731 |
|
- type: ndcg_at_3 |
|
value: 46.327 |
|
- type: ndcg_at_5 |
|
value: 51.178000000000004 |
|
- type: precision_at_1 |
|
value: 31.223 |
|
- type: precision_at_10 |
|
value: 8.527999999999999 |
|
- type: precision_at_100 |
|
value: 0.991 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 19.061 |
|
- type: precision_at_5 |
|
value: 13.797999999999998 |
|
- type: recall_at_1 |
|
value: 31.223 |
|
- type: recall_at_10 |
|
value: 85.277 |
|
- type: recall_at_100 |
|
value: 99.075 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 57.18299999999999 |
|
- type: recall_at_5 |
|
value: 68.99 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
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: 47.23625429411296 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
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: |
|
- type: v_measure |
|
value: 37.433880471403654 |
|
- 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: 60.53175025582013 |
|
- type: mrr |
|
value: 74.51160796728664 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
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config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.93746103286769 |
|
- type: cos_sim_spearman |
|
value: 86.62245567912619 |
|
- type: euclidean_pearson |
|
value: 87.154173907501 |
|
- type: euclidean_spearman |
|
value: 86.62245567912619 |
|
- type: manhattan_pearson |
|
value: 87.17682026633462 |
|
- type: manhattan_spearman |
|
value: 86.74775973908348 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
|
- type: accuracy |
|
value: 80.33766233766232 |
|
- type: f1 |
|
value: 79.64931422442245 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: jinaai/big-patent-clustering |
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name: MTEB BigPatentClustering |
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config: default |
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split: test |
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revision: 62d5330920bca426ce9d3c76ea914f15fc83e891 |
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metrics: |
|
- type: v_measure |
|
value: 19.116028913890613 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
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: |
|
- type: v_measure |
|
value: 36.966921852810174 |
|
- task: |
|
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: |
|
- type: v_measure |
|
value: 31.98019698537654 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
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 |
|
value: 34.079 |
|
- type: map_at_10 |
|
value: 46.35 |
|
- type: map_at_100 |
|
value: 47.785 |
|
- type: map_at_1000 |
|
value: 47.903 |
|
- type: map_at_3 |
|
value: 42.620999999999995 |
|
- type: map_at_5 |
|
value: 44.765 |
|
- type: mrr_at_1 |
|
value: 41.345 |
|
- type: mrr_at_10 |
|
value: 52.032000000000004 |
|
- type: mrr_at_100 |
|
value: 52.690000000000005 |
|
- type: mrr_at_1000 |
|
value: 52.727999999999994 |
|
- type: mrr_at_3 |
|
value: 49.428 |
|
- type: mrr_at_5 |
|
value: 51.093999999999994 |
|
- type: ndcg_at_1 |
|
value: 41.345 |
|
- type: ndcg_at_10 |
|
value: 53.027 |
|
- type: ndcg_at_100 |
|
value: 57.962 |
|
- type: ndcg_at_1000 |
|
value: 59.611999999999995 |
|
- type: ndcg_at_3 |
|
value: 47.687000000000005 |
|
- type: ndcg_at_5 |
|
value: 50.367 |
|
- type: precision_at_1 |
|
value: 41.345 |
|
- type: precision_at_10 |
|
value: 10.157 |
|
- type: precision_at_100 |
|
value: 1.567 |
|
- type: precision_at_1000 |
|
value: 0.199 |
|
- type: precision_at_3 |
|
value: 23.081 |
|
- type: precision_at_5 |
|
value: 16.738 |
|
- type: recall_at_1 |
|
value: 34.079 |
|
- type: recall_at_10 |
|
value: 65.93900000000001 |
|
- type: recall_at_100 |
|
value: 86.42699999999999 |
|
- type: recall_at_1000 |
|
value: 96.61 |
|
- type: recall_at_3 |
|
value: 50.56699999999999 |
|
- type: recall_at_5 |
|
value: 57.82000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-english |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.289 |
|
- type: map_at_10 |
|
value: 43.681 |
|
- type: map_at_100 |
|
value: 45.056000000000004 |
|
- type: map_at_1000 |
|
value: 45.171 |
|
- type: map_at_3 |
|
value: 40.702 |
|
- type: map_at_5 |
|
value: 42.292 |
|
- type: mrr_at_1 |
|
value: 41.146 |
|
- type: mrr_at_10 |
|
value: 49.604 |
|
- type: mrr_at_100 |
|
value: 50.28399999999999 |
|
- type: mrr_at_1000 |
|
value: 50.322 |
|
- type: mrr_at_3 |
|
value: 47.611 |
|
- type: mrr_at_5 |
|
value: 48.717 |
|
- type: ndcg_at_1 |
|
value: 41.146 |
|
- type: ndcg_at_10 |
|
value: 49.43 |
|
- type: ndcg_at_100 |
|
value: 54.01899999999999 |
|
- type: ndcg_at_1000 |
|
value: 55.803000000000004 |
|
- type: ndcg_at_3 |
|
value: 45.503 |
|
- type: ndcg_at_5 |
|
value: 47.198 |
|
- type: precision_at_1 |
|
value: 41.146 |
|
- type: precision_at_10 |
|
value: 9.268 |
|
- type: precision_at_100 |
|
value: 1.4749999999999999 |
|
- type: precision_at_1000 |
|
value: 0.19 |
|
- type: precision_at_3 |
|
value: 21.932 |
|
- type: precision_at_5 |
|
value: 15.389 |
|
- type: recall_at_1 |
|
value: 33.289 |
|
- type: recall_at_10 |
|
value: 59.209999999999994 |
|
- type: recall_at_100 |
|
value: 78.676 |
|
- type: recall_at_1000 |
|
value: 89.84100000000001 |
|
- type: recall_at_3 |
|
value: 47.351 |
|
- type: recall_at_5 |
|
value: 52.178999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gaming |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
|
metrics: |
|
- type: map_at_1 |
|
value: 44.483 |
|
- type: map_at_10 |
|
value: 56.862 |
|
- type: map_at_100 |
|
value: 57.901 |
|
- type: map_at_1000 |
|
value: 57.948 |
|
- type: map_at_3 |
|
value: 53.737 |
|
- type: map_at_5 |
|
value: 55.64 |
|
- type: mrr_at_1 |
|
value: 50.658 |
|
- type: mrr_at_10 |
|
value: 60.281 |
|
- type: mrr_at_100 |
|
value: 60.946 |
|
- type: mrr_at_1000 |
|
value: 60.967000000000006 |
|
- type: mrr_at_3 |
|
value: 58.192 |
|
- type: mrr_at_5 |
|
value: 59.531 |
|
- type: ndcg_at_1 |
|
value: 50.658 |
|
- type: ndcg_at_10 |
|
value: 62.339 |
|
- type: ndcg_at_100 |
|
value: 66.28399999999999 |
|
- type: ndcg_at_1000 |
|
value: 67.166 |
|
- type: ndcg_at_3 |
|
value: 57.458 |
|
- type: ndcg_at_5 |
|
value: 60.112 |
|
- type: precision_at_1 |
|
value: 50.658 |
|
- type: precision_at_10 |
|
value: 9.762 |
|
- type: precision_at_100 |
|
value: 1.26 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 25.329 |
|
- type: precision_at_5 |
|
value: 17.254 |
|
- type: recall_at_1 |
|
value: 44.483 |
|
- type: recall_at_10 |
|
value: 74.819 |
|
- type: recall_at_100 |
|
value: 91.702 |
|
- type: recall_at_1000 |
|
value: 97.84 |
|
- type: recall_at_3 |
|
value: 62.13999999999999 |
|
- type: recall_at_5 |
|
value: 68.569 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-gis |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: 5003b3064772da1887988e05400cf3806fe491f2 |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.489 |
|
- type: map_at_10 |
|
value: 37.004999999999995 |
|
- type: map_at_100 |
|
value: 38.001000000000005 |
|
- type: map_at_1000 |
|
value: 38.085 |
|
- type: map_at_3 |
|
value: 34.239999999999995 |
|
- type: map_at_5 |
|
value: 35.934 |
|
- type: mrr_at_1 |
|
value: 28.362 |
|
- type: mrr_at_10 |
|
value: 38.807 |
|
- type: mrr_at_100 |
|
value: 39.671 |
|
- type: mrr_at_1000 |
|
value: 39.736 |
|
- type: mrr_at_3 |
|
value: 36.29 |
|
- type: mrr_at_5 |
|
value: 37.906 |
|
- type: ndcg_at_1 |
|
value: 28.362 |
|
- type: ndcg_at_10 |
|
value: 42.510999999999996 |
|
- type: ndcg_at_100 |
|
value: 47.226 |
|
- type: ndcg_at_1000 |
|
value: 49.226 |
|
- type: ndcg_at_3 |
|
value: 37.295 |
|
- type: ndcg_at_5 |
|
value: 40.165 |
|
- type: precision_at_1 |
|
value: 28.362 |
|
- type: precision_at_10 |
|
value: 6.633 |
|
- type: precision_at_100 |
|
value: 0.9490000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 16.234 |
|
- type: precision_at_5 |
|
value: 11.434999999999999 |
|
- type: recall_at_1 |
|
value: 26.489 |
|
- type: recall_at_10 |
|
value: 57.457 |
|
- type: recall_at_100 |
|
value: 78.712 |
|
- type: recall_at_1000 |
|
value: 93.565 |
|
- type: recall_at_3 |
|
value: 43.748 |
|
- type: recall_at_5 |
|
value: 50.589 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-mathematica |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.418999999999999 |
|
- type: map_at_10 |
|
value: 22.866 |
|
- type: map_at_100 |
|
value: 24.365000000000002 |
|
- type: map_at_1000 |
|
value: 24.479 |
|
- type: map_at_3 |
|
value: 19.965 |
|
- type: map_at_5 |
|
value: 21.684 |
|
- type: mrr_at_1 |
|
value: 14.677000000000001 |
|
- type: mrr_at_10 |
|
value: 26.316 |
|
- type: mrr_at_100 |
|
value: 27.514 |
|
- type: mrr_at_1000 |
|
value: 27.57 |
|
- type: mrr_at_3 |
|
value: 23.3 |
|
- type: mrr_at_5 |
|
value: 25.191000000000003 |
|
- type: ndcg_at_1 |
|
value: 14.677000000000001 |
|
- type: ndcg_at_10 |
|
value: 28.875 |
|
- type: ndcg_at_100 |
|
value: 35.607 |
|
- type: ndcg_at_1000 |
|
value: 38.237 |
|
- type: ndcg_at_3 |
|
value: 23.284 |
|
- type: ndcg_at_5 |
|
value: 26.226 |
|
- type: precision_at_1 |
|
value: 14.677000000000001 |
|
- type: precision_at_10 |
|
value: 5.771 |
|
- type: precision_at_100 |
|
value: 1.058 |
|
- type: precision_at_1000 |
|
value: 0.14200000000000002 |
|
- type: precision_at_3 |
|
value: 11.940000000000001 |
|
- type: precision_at_5 |
|
value: 9.229 |
|
- type: recall_at_1 |
|
value: 12.418999999999999 |
|
- type: recall_at_10 |
|
value: 43.333 |
|
- type: recall_at_100 |
|
value: 71.942 |
|
- type: recall_at_1000 |
|
value: 90.67399999999999 |
|
- type: recall_at_3 |
|
value: 28.787000000000003 |
|
- type: recall_at_5 |
|
value: 35.638 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-physics |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.686999999999998 |
|
- type: map_at_10 |
|
value: 42.331 |
|
- type: map_at_100 |
|
value: 43.655 |
|
- type: map_at_1000 |
|
value: 43.771 |
|
- type: map_at_3 |
|
value: 38.944 |
|
- type: map_at_5 |
|
value: 40.991 |
|
- type: mrr_at_1 |
|
value: 37.921 |
|
- type: mrr_at_10 |
|
value: 47.534 |
|
- type: mrr_at_100 |
|
value: 48.362 |
|
- type: mrr_at_1000 |
|
value: 48.405 |
|
- type: mrr_at_3 |
|
value: 44.995000000000005 |
|
- type: mrr_at_5 |
|
value: 46.617 |
|
- type: ndcg_at_1 |
|
value: 37.921 |
|
- type: ndcg_at_10 |
|
value: 48.236000000000004 |
|
- type: ndcg_at_100 |
|
value: 53.705000000000005 |
|
- type: ndcg_at_1000 |
|
value: 55.596000000000004 |
|
- type: ndcg_at_3 |
|
value: 43.11 |
|
- type: ndcg_at_5 |
|
value: 45.862 |
|
- type: precision_at_1 |
|
value: 37.921 |
|
- type: precision_at_10 |
|
value: 8.643 |
|
- type: precision_at_100 |
|
value: 1.336 |
|
- type: precision_at_1000 |
|
value: 0.166 |
|
- type: precision_at_3 |
|
value: 20.308 |
|
- type: precision_at_5 |
|
value: 14.514 |
|
- type: recall_at_1 |
|
value: 31.686999999999998 |
|
- type: recall_at_10 |
|
value: 60.126999999999995 |
|
- type: recall_at_100 |
|
value: 83.10600000000001 |
|
- type: recall_at_1000 |
|
value: 95.15 |
|
- type: recall_at_3 |
|
value: 46.098 |
|
- type: recall_at_5 |
|
value: 53.179 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-programmers |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.686 |
|
- type: map_at_10 |
|
value: 39.146 |
|
- type: map_at_100 |
|
value: 40.543 |
|
- type: map_at_1000 |
|
value: 40.644999999999996 |
|
- type: map_at_3 |
|
value: 36.195 |
|
- type: map_at_5 |
|
value: 37.919000000000004 |
|
- type: mrr_at_1 |
|
value: 35.160000000000004 |
|
- type: mrr_at_10 |
|
value: 44.711 |
|
- type: mrr_at_100 |
|
value: 45.609 |
|
- type: mrr_at_1000 |
|
value: 45.655 |
|
- type: mrr_at_3 |
|
value: 42.409 |
|
- type: mrr_at_5 |
|
value: 43.779 |
|
- type: ndcg_at_1 |
|
value: 35.160000000000004 |
|
- type: ndcg_at_10 |
|
value: 44.977000000000004 |
|
- type: ndcg_at_100 |
|
value: 50.663000000000004 |
|
- type: ndcg_at_1000 |
|
value: 52.794 |
|
- type: ndcg_at_3 |
|
value: 40.532000000000004 |
|
- type: ndcg_at_5 |
|
value: 42.641 |
|
- type: precision_at_1 |
|
value: 35.160000000000004 |
|
- type: precision_at_10 |
|
value: 8.014000000000001 |
|
- type: precision_at_100 |
|
value: 1.269 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 19.444 |
|
- type: precision_at_5 |
|
value: 13.653 |
|
- type: recall_at_1 |
|
value: 28.686 |
|
- type: recall_at_10 |
|
value: 56.801 |
|
- type: recall_at_100 |
|
value: 80.559 |
|
- type: recall_at_1000 |
|
value: 95.052 |
|
- type: recall_at_3 |
|
value: 43.675999999999995 |
|
- type: recall_at_5 |
|
value: 49.703 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.173833333333338 |
|
- type: map_at_10 |
|
value: 38.202083333333334 |
|
- type: map_at_100 |
|
value: 39.47475 |
|
- type: map_at_1000 |
|
value: 39.586499999999994 |
|
- type: map_at_3 |
|
value: 35.17308333333334 |
|
- type: map_at_5 |
|
value: 36.914 |
|
- type: mrr_at_1 |
|
value: 32.92958333333333 |
|
- type: mrr_at_10 |
|
value: 42.16758333333333 |
|
- type: mrr_at_100 |
|
value: 43.04108333333333 |
|
- type: mrr_at_1000 |
|
value: 43.092499999999994 |
|
- type: mrr_at_3 |
|
value: 39.69166666666666 |
|
- type: mrr_at_5 |
|
value: 41.19458333333333 |
|
- type: ndcg_at_1 |
|
value: 32.92958333333333 |
|
- type: ndcg_at_10 |
|
value: 43.80583333333333 |
|
- type: ndcg_at_100 |
|
value: 49.060916666666664 |
|
- type: ndcg_at_1000 |
|
value: 51.127250000000004 |
|
- type: ndcg_at_3 |
|
value: 38.80383333333333 |
|
- type: ndcg_at_5 |
|
value: 41.29658333333333 |
|
- type: precision_at_1 |
|
value: 32.92958333333333 |
|
- type: precision_at_10 |
|
value: 7.655666666666666 |
|
- type: precision_at_100 |
|
value: 1.2094166666666668 |
|
- type: precision_at_1000 |
|
value: 0.15750000000000003 |
|
- type: precision_at_3 |
|
value: 17.87975 |
|
- type: precision_at_5 |
|
value: 12.741833333333332 |
|
- type: recall_at_1 |
|
value: 28.173833333333338 |
|
- type: recall_at_10 |
|
value: 56.219249999999995 |
|
- type: recall_at_100 |
|
value: 79.01416666666665 |
|
- type: recall_at_1000 |
|
value: 93.13425000000001 |
|
- type: recall_at_3 |
|
value: 42.39241666666667 |
|
- type: recall_at_5 |
|
value: 48.764833333333335 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-stats |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.625999999999998 |
|
- type: map_at_10 |
|
value: 32.808 |
|
- type: map_at_100 |
|
value: 33.951 |
|
- type: map_at_1000 |
|
value: 34.052 |
|
- type: map_at_3 |
|
value: 30.536 |
|
- type: map_at_5 |
|
value: 31.77 |
|
- type: mrr_at_1 |
|
value: 28.374 |
|
- type: mrr_at_10 |
|
value: 35.527 |
|
- type: mrr_at_100 |
|
value: 36.451 |
|
- type: mrr_at_1000 |
|
value: 36.522 |
|
- type: mrr_at_3 |
|
value: 33.410000000000004 |
|
- type: mrr_at_5 |
|
value: 34.537 |
|
- type: ndcg_at_1 |
|
value: 28.374 |
|
- type: ndcg_at_10 |
|
value: 37.172 |
|
- type: ndcg_at_100 |
|
value: 42.474000000000004 |
|
- type: ndcg_at_1000 |
|
value: 44.853 |
|
- type: ndcg_at_3 |
|
value: 32.931 |
|
- type: ndcg_at_5 |
|
value: 34.882999999999996 |
|
- type: precision_at_1 |
|
value: 28.374 |
|
- type: precision_at_10 |
|
value: 5.813 |
|
- type: precision_at_100 |
|
value: 0.928 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 14.008000000000001 |
|
- type: precision_at_5 |
|
value: 9.754999999999999 |
|
- type: recall_at_1 |
|
value: 25.625999999999998 |
|
- type: recall_at_10 |
|
value: 47.812 |
|
- type: recall_at_100 |
|
value: 71.61800000000001 |
|
- type: recall_at_1000 |
|
value: 88.881 |
|
- type: recall_at_3 |
|
value: 35.876999999999995 |
|
- type: recall_at_5 |
|
value: 40.839 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-tex |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: 46989137a86843e03a6195de44b09deda022eec7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.233 |
|
- type: map_at_10 |
|
value: 26.375999999999998 |
|
- type: map_at_100 |
|
value: 27.575 |
|
- type: map_at_1000 |
|
value: 27.706999999999997 |
|
- type: map_at_3 |
|
value: 23.619 |
|
- type: map_at_5 |
|
value: 25.217 |
|
- type: mrr_at_1 |
|
value: 22.023 |
|
- type: mrr_at_10 |
|
value: 30.122 |
|
- type: mrr_at_100 |
|
value: 31.083 |
|
- type: mrr_at_1000 |
|
value: 31.163999999999998 |
|
- type: mrr_at_3 |
|
value: 27.541 |
|
- type: mrr_at_5 |
|
value: 29.061999999999998 |
|
- type: ndcg_at_1 |
|
value: 22.023 |
|
- type: ndcg_at_10 |
|
value: 31.476 |
|
- type: ndcg_at_100 |
|
value: 37.114000000000004 |
|
- type: ndcg_at_1000 |
|
value: 39.981 |
|
- type: ndcg_at_3 |
|
value: 26.538 |
|
- type: ndcg_at_5 |
|
value: 29.016 |
|
- type: precision_at_1 |
|
value: 22.023 |
|
- type: precision_at_10 |
|
value: 5.819 |
|
- type: precision_at_100 |
|
value: 1.018 |
|
- type: precision_at_1000 |
|
value: 0.14300000000000002 |
|
- type: precision_at_3 |
|
value: 12.583 |
|
- type: precision_at_5 |
|
value: 9.36 |
|
- type: recall_at_1 |
|
value: 18.233 |
|
- type: recall_at_10 |
|
value: 43.029 |
|
- type: recall_at_100 |
|
value: 68.253 |
|
- type: recall_at_1000 |
|
value: 88.319 |
|
- type: recall_at_3 |
|
value: 29.541 |
|
- type: recall_at_5 |
|
value: 35.783 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-unix |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.923 |
|
- type: map_at_10 |
|
value: 39.231 |
|
- type: map_at_100 |
|
value: 40.483000000000004 |
|
- type: map_at_1000 |
|
value: 40.575 |
|
- type: map_at_3 |
|
value: 35.94 |
|
- type: map_at_5 |
|
value: 37.683 |
|
- type: mrr_at_1 |
|
value: 33.955 |
|
- type: mrr_at_10 |
|
value: 43.163000000000004 |
|
- type: mrr_at_100 |
|
value: 44.054 |
|
- type: mrr_at_1000 |
|
value: 44.099 |
|
- type: mrr_at_3 |
|
value: 40.361000000000004 |
|
- type: mrr_at_5 |
|
value: 41.905 |
|
- type: ndcg_at_1 |
|
value: 33.955 |
|
- type: ndcg_at_10 |
|
value: 45.068000000000005 |
|
- type: ndcg_at_100 |
|
value: 50.470000000000006 |
|
- type: ndcg_at_1000 |
|
value: 52.349000000000004 |
|
- type: ndcg_at_3 |
|
value: 39.298 |
|
- type: ndcg_at_5 |
|
value: 41.821999999999996 |
|
- type: precision_at_1 |
|
value: 33.955 |
|
- type: precision_at_10 |
|
value: 7.649 |
|
- type: precision_at_100 |
|
value: 1.173 |
|
- type: precision_at_1000 |
|
value: 0.14200000000000002 |
|
- type: precision_at_3 |
|
value: 17.817 |
|
- type: precision_at_5 |
|
value: 12.537 |
|
- type: recall_at_1 |
|
value: 28.923 |
|
- type: recall_at_10 |
|
value: 58.934 |
|
- type: recall_at_100 |
|
value: 81.809 |
|
- type: recall_at_1000 |
|
value: 94.71300000000001 |
|
- type: recall_at_3 |
|
value: 42.975 |
|
- type: recall_at_5 |
|
value: 49.501 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-webmasters |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.596 |
|
- type: map_at_10 |
|
value: 38.735 |
|
- type: map_at_100 |
|
value: 40.264 |
|
- type: map_at_1000 |
|
value: 40.48 |
|
- type: map_at_3 |
|
value: 35.394999999999996 |
|
- type: map_at_5 |
|
value: 37.099 |
|
- type: mrr_at_1 |
|
value: 33.992 |
|
- type: mrr_at_10 |
|
value: 43.076 |
|
- type: mrr_at_100 |
|
value: 44.005 |
|
- type: mrr_at_1000 |
|
value: 44.043 |
|
- type: mrr_at_3 |
|
value: 40.415 |
|
- type: mrr_at_5 |
|
value: 41.957 |
|
- type: ndcg_at_1 |
|
value: 33.992 |
|
- type: ndcg_at_10 |
|
value: 44.896 |
|
- type: ndcg_at_100 |
|
value: 50.44499999999999 |
|
- type: ndcg_at_1000 |
|
value: 52.675000000000004 |
|
- type: ndcg_at_3 |
|
value: 39.783 |
|
- type: ndcg_at_5 |
|
value: 41.997 |
|
- type: precision_at_1 |
|
value: 33.992 |
|
- type: precision_at_10 |
|
value: 8.498 |
|
- type: precision_at_100 |
|
value: 1.585 |
|
- type: precision_at_1000 |
|
value: 0.248 |
|
- type: precision_at_3 |
|
value: 18.511 |
|
- type: precision_at_5 |
|
value: 13.241 |
|
- type: recall_at_1 |
|
value: 28.596 |
|
- type: recall_at_10 |
|
value: 56.885 |
|
- type: recall_at_100 |
|
value: 82.306 |
|
- type: recall_at_1000 |
|
value: 95.813 |
|
- type: recall_at_3 |
|
value: 42.168 |
|
- type: recall_at_5 |
|
value: 48.32 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/cqadupstack-wordpress |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.576 |
|
- type: map_at_10 |
|
value: 33.034 |
|
- type: map_at_100 |
|
value: 34.117999999999995 |
|
- type: map_at_1000 |
|
value: 34.222 |
|
- type: map_at_3 |
|
value: 30.183 |
|
- type: map_at_5 |
|
value: 31.974000000000004 |
|
- type: mrr_at_1 |
|
value: 27.542 |
|
- type: mrr_at_10 |
|
value: 34.838 |
|
- type: mrr_at_100 |
|
value: 35.824 |
|
- type: mrr_at_1000 |
|
value: 35.899 |
|
- type: mrr_at_3 |
|
value: 32.348 |
|
- type: mrr_at_5 |
|
value: 34.039 |
|
- type: ndcg_at_1 |
|
value: 27.542 |
|
- type: ndcg_at_10 |
|
value: 37.663000000000004 |
|
- type: ndcg_at_100 |
|
value: 42.762 |
|
- type: ndcg_at_1000 |
|
value: 45.235 |
|
- type: ndcg_at_3 |
|
value: 32.227 |
|
- type: ndcg_at_5 |
|
value: 35.27 |
|
- type: precision_at_1 |
|
value: 27.542 |
|
- type: precision_at_10 |
|
value: 5.840999999999999 |
|
- type: precision_at_100 |
|
value: 0.895 |
|
- type: precision_at_1000 |
|
value: 0.123 |
|
- type: precision_at_3 |
|
value: 13.370000000000001 |
|
- type: precision_at_5 |
|
value: 9.797 |
|
- type: recall_at_1 |
|
value: 25.576 |
|
- type: recall_at_10 |
|
value: 50.285000000000004 |
|
- type: recall_at_100 |
|
value: 73.06 |
|
- type: recall_at_1000 |
|
value: 91.15299999999999 |
|
- type: recall_at_3 |
|
value: 35.781 |
|
- type: recall_at_5 |
|
value: 43.058 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.061 |
|
- type: map_at_10 |
|
value: 29.464000000000002 |
|
- type: map_at_100 |
|
value: 31.552999999999997 |
|
- type: map_at_1000 |
|
value: 31.707 |
|
- type: map_at_3 |
|
value: 24.834999999999997 |
|
- type: map_at_5 |
|
value: 27.355 |
|
- type: mrr_at_1 |
|
value: 38.958 |
|
- type: mrr_at_10 |
|
value: 51.578 |
|
- type: mrr_at_100 |
|
value: 52.262 |
|
- type: mrr_at_1000 |
|
value: 52.283 |
|
- type: mrr_at_3 |
|
value: 48.599 |
|
- type: mrr_at_5 |
|
value: 50.404 |
|
- type: ndcg_at_1 |
|
value: 38.958 |
|
- type: ndcg_at_10 |
|
value: 39.367999999999995 |
|
- type: ndcg_at_100 |
|
value: 46.521 |
|
- type: ndcg_at_1000 |
|
value: 49.086999999999996 |
|
- type: ndcg_at_3 |
|
value: 33.442 |
|
- type: ndcg_at_5 |
|
value: 35.515 |
|
- type: precision_at_1 |
|
value: 38.958 |
|
- type: precision_at_10 |
|
value: 12.110999999999999 |
|
- type: precision_at_100 |
|
value: 1.982 |
|
- type: precision_at_1000 |
|
value: 0.247 |
|
- type: precision_at_3 |
|
value: 25.102999999999998 |
|
- type: precision_at_5 |
|
value: 18.971 |
|
- type: recall_at_1 |
|
value: 17.061 |
|
- type: recall_at_10 |
|
value: 45.198 |
|
- type: recall_at_100 |
|
value: 69.18900000000001 |
|
- type: recall_at_1000 |
|
value: 83.38499999999999 |
|
- type: recall_at_3 |
|
value: 30.241 |
|
- type: recall_at_5 |
|
value: 36.851 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/dbpedia |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.398 |
|
- type: map_at_10 |
|
value: 21.421 |
|
- type: map_at_100 |
|
value: 31.649 |
|
- type: map_at_1000 |
|
value: 33.469 |
|
- type: map_at_3 |
|
value: 15.310000000000002 |
|
- type: map_at_5 |
|
value: 17.946 |
|
- type: mrr_at_1 |
|
value: 71 |
|
- type: mrr_at_10 |
|
value: 78.92099999999999 |
|
- type: mrr_at_100 |
|
value: 79.225 |
|
- type: mrr_at_1000 |
|
value: 79.23 |
|
- type: mrr_at_3 |
|
value: 77.792 |
|
- type: mrr_at_5 |
|
value: 78.467 |
|
- type: ndcg_at_1 |
|
value: 57.99999999999999 |
|
- type: ndcg_at_10 |
|
value: 44.733000000000004 |
|
- type: ndcg_at_100 |
|
value: 50.646 |
|
- type: ndcg_at_1000 |
|
value: 57.903999999999996 |
|
- type: ndcg_at_3 |
|
value: 49.175999999999995 |
|
- type: ndcg_at_5 |
|
value: 46.800999999999995 |
|
- type: precision_at_1 |
|
value: 71 |
|
- type: precision_at_10 |
|
value: 36.25 |
|
- type: precision_at_100 |
|
value: 12.135 |
|
- type: precision_at_1000 |
|
value: 2.26 |
|
- type: precision_at_3 |
|
value: 52.75 |
|
- type: precision_at_5 |
|
value: 45.65 |
|
- type: recall_at_1 |
|
value: 9.398 |
|
- type: recall_at_10 |
|
value: 26.596999999999998 |
|
- type: recall_at_100 |
|
value: 57.943 |
|
- type: recall_at_1000 |
|
value: 81.147 |
|
- type: recall_at_3 |
|
value: 16.634 |
|
- type: recall_at_5 |
|
value: 20.7 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 46.535000000000004 |
|
- type: f1 |
|
value: 42.53702746452163 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
|
metrics: |
|
- type: map_at_1 |
|
value: 77.235 |
|
- type: map_at_10 |
|
value: 85.504 |
|
- type: map_at_100 |
|
value: 85.707 |
|
- type: map_at_1000 |
|
value: 85.718 |
|
- type: map_at_3 |
|
value: 84.425 |
|
- type: map_at_5 |
|
value: 85.13 |
|
- type: mrr_at_1 |
|
value: 83.363 |
|
- type: mrr_at_10 |
|
value: 89.916 |
|
- type: mrr_at_100 |
|
value: 89.955 |
|
- type: mrr_at_1000 |
|
value: 89.956 |
|
- type: mrr_at_3 |
|
value: 89.32600000000001 |
|
- type: mrr_at_5 |
|
value: 89.79 |
|
- type: ndcg_at_1 |
|
value: 83.363 |
|
- type: ndcg_at_10 |
|
value: 89.015 |
|
- type: ndcg_at_100 |
|
value: 89.649 |
|
- type: ndcg_at_1000 |
|
value: 89.825 |
|
- type: ndcg_at_3 |
|
value: 87.45100000000001 |
|
- type: ndcg_at_5 |
|
value: 88.39399999999999 |
|
- type: precision_at_1 |
|
value: 83.363 |
|
- type: precision_at_10 |
|
value: 10.659 |
|
- type: precision_at_100 |
|
value: 1.122 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 33.338 |
|
- type: precision_at_5 |
|
value: 20.671999999999997 |
|
- type: recall_at_1 |
|
value: 77.235 |
|
- type: recall_at_10 |
|
value: 95.389 |
|
- type: recall_at_100 |
|
value: 97.722 |
|
- type: recall_at_1000 |
|
value: 98.744 |
|
- type: recall_at_3 |
|
value: 91.19800000000001 |
|
- type: recall_at_5 |
|
value: 93.635 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.835 |
|
- type: map_at_10 |
|
value: 34.459 |
|
- type: map_at_100 |
|
value: 36.335 |
|
- type: map_at_1000 |
|
value: 36.518 |
|
- type: map_at_3 |
|
value: 30.581000000000003 |
|
- type: map_at_5 |
|
value: 32.859 |
|
- type: mrr_at_1 |
|
value: 40.894999999999996 |
|
- type: mrr_at_10 |
|
value: 50.491 |
|
- type: mrr_at_100 |
|
value: 51.243 |
|
- type: mrr_at_1000 |
|
value: 51.286 |
|
- type: mrr_at_3 |
|
value: 47.994 |
|
- type: mrr_at_5 |
|
value: 49.429 |
|
- type: ndcg_at_1 |
|
value: 40.894999999999996 |
|
- type: ndcg_at_10 |
|
value: 42.403 |
|
- type: ndcg_at_100 |
|
value: 48.954 |
|
- type: ndcg_at_1000 |
|
value: 51.961 |
|
- type: ndcg_at_3 |
|
value: 39.11 |
|
- type: ndcg_at_5 |
|
value: 40.152 |
|
- type: precision_at_1 |
|
value: 40.894999999999996 |
|
- type: precision_at_10 |
|
value: 11.466 |
|
- type: precision_at_100 |
|
value: 1.833 |
|
- type: precision_at_1000 |
|
value: 0.23700000000000002 |
|
- type: precision_at_3 |
|
value: 25.874000000000002 |
|
- type: precision_at_5 |
|
value: 19.012 |
|
- type: recall_at_1 |
|
value: 20.835 |
|
- type: recall_at_10 |
|
value: 49.535000000000004 |
|
- type: recall_at_100 |
|
value: 73.39099999999999 |
|
- type: recall_at_1000 |
|
value: 91.01599999999999 |
|
- type: recall_at_3 |
|
value: 36.379 |
|
- type: recall_at_5 |
|
value: 42.059999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.945 |
|
- type: map_at_10 |
|
value: 65.376 |
|
- type: map_at_100 |
|
value: 66.278 |
|
- type: map_at_1000 |
|
value: 66.33 |
|
- type: map_at_3 |
|
value: 61.753 |
|
- type: map_at_5 |
|
value: 64.077 |
|
- type: mrr_at_1 |
|
value: 81.891 |
|
- type: mrr_at_10 |
|
value: 87.256 |
|
- type: mrr_at_100 |
|
value: 87.392 |
|
- type: mrr_at_1000 |
|
value: 87.395 |
|
- type: mrr_at_3 |
|
value: 86.442 |
|
- type: mrr_at_5 |
|
value: 86.991 |
|
- type: ndcg_at_1 |
|
value: 81.891 |
|
- type: ndcg_at_10 |
|
value: 73.654 |
|
- type: ndcg_at_100 |
|
value: 76.62299999999999 |
|
- type: ndcg_at_1000 |
|
value: 77.60000000000001 |
|
- type: ndcg_at_3 |
|
value: 68.71199999999999 |
|
- type: ndcg_at_5 |
|
value: 71.563 |
|
- type: precision_at_1 |
|
value: 81.891 |
|
- type: precision_at_10 |
|
value: 15.409 |
|
- type: precision_at_100 |
|
value: 1.77 |
|
- type: precision_at_1000 |
|
value: 0.19 |
|
- type: precision_at_3 |
|
value: 44.15 |
|
- type: precision_at_5 |
|
value: 28.732000000000003 |
|
- type: recall_at_1 |
|
value: 40.945 |
|
- type: recall_at_10 |
|
value: 77.04299999999999 |
|
- type: recall_at_100 |
|
value: 88.508 |
|
- type: recall_at_1000 |
|
value: 94.943 |
|
- type: recall_at_3 |
|
value: 66.226 |
|
- type: recall_at_5 |
|
value: 71.83 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 74.08200000000001 |
|
- type: ap |
|
value: 68.10929101713998 |
|
- type: f1 |
|
value: 73.98447117652009 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: c5a29a104738b98a9e76336939199e264163d4a0 |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.729000000000003 |
|
- type: map_at_10 |
|
value: 34.602 |
|
- type: map_at_100 |
|
value: 35.756 |
|
- type: map_at_1000 |
|
value: 35.803000000000004 |
|
- type: map_at_3 |
|
value: 30.619000000000003 |
|
- type: map_at_5 |
|
value: 32.914 |
|
- type: mrr_at_1 |
|
value: 22.364 |
|
- type: mrr_at_10 |
|
value: 35.183 |
|
- type: mrr_at_100 |
|
value: 36.287000000000006 |
|
- type: mrr_at_1000 |
|
value: 36.327999999999996 |
|
- type: mrr_at_3 |
|
value: 31.258000000000003 |
|
- type: mrr_at_5 |
|
value: 33.542 |
|
- type: ndcg_at_1 |
|
value: 22.364 |
|
- type: ndcg_at_10 |
|
value: 41.765 |
|
- type: ndcg_at_100 |
|
value: 47.293 |
|
- type: ndcg_at_1000 |
|
value: 48.457 |
|
- type: ndcg_at_3 |
|
value: 33.676 |
|
- type: ndcg_at_5 |
|
value: 37.783 |
|
- type: precision_at_1 |
|
value: 22.364 |
|
- type: precision_at_10 |
|
value: 6.662 |
|
- type: precision_at_100 |
|
value: 0.943 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.435999999999998 |
|
- type: precision_at_5 |
|
value: 10.764999999999999 |
|
- type: recall_at_1 |
|
value: 21.729000000000003 |
|
- type: recall_at_10 |
|
value: 63.815999999999995 |
|
- type: recall_at_100 |
|
value: 89.265 |
|
- type: recall_at_1000 |
|
value: 98.149 |
|
- type: recall_at_3 |
|
value: 41.898 |
|
- type: recall_at_5 |
|
value: 51.76500000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 92.73141814865483 |
|
- type: f1 |
|
value: 92.17518476408004 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 65.18011855905152 |
|
- type: f1 |
|
value: 46.70999638311856 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClassification (eng) |
|
config: eng |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: accuracy |
|
value: 75.24261603375525 |
|
- type: f1 |
|
value: 74.07895183913367 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringP2P (eng) |
|
config: eng |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 28.43855875387446 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringS2S (eng) |
|
config: eng |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 29.05331990256969 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 66.92333557498318 |
|
- type: f1 |
|
value: 64.29789389602692 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 72.74714189643578 |
|
- type: f1 |
|
value: 71.672585608315 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.503564225501613 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.410225127136457 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 29.170019896091908 |
|
- type: mrr |
|
value: 29.881276831500976 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.544 |
|
- type: map_at_10 |
|
value: 14.116999999999999 |
|
- type: map_at_100 |
|
value: 17.522 |
|
- type: map_at_1000 |
|
value: 19 |
|
- type: map_at_3 |
|
value: 10.369 |
|
- type: map_at_5 |
|
value: 12.189 |
|
- type: mrr_at_1 |
|
value: 47.988 |
|
- type: mrr_at_10 |
|
value: 56.84 |
|
- type: mrr_at_100 |
|
value: 57.367000000000004 |
|
- type: mrr_at_1000 |
|
value: 57.403000000000006 |
|
- type: mrr_at_3 |
|
value: 54.592 |
|
- type: mrr_at_5 |
|
value: 56.233 |
|
- type: ndcg_at_1 |
|
value: 45.82 |
|
- type: ndcg_at_10 |
|
value: 36.767 |
|
- type: ndcg_at_100 |
|
value: 33.356 |
|
- type: ndcg_at_1000 |
|
value: 42.062 |
|
- type: ndcg_at_3 |
|
value: 42.15 |
|
- type: ndcg_at_5 |
|
value: 40.355000000000004 |
|
- type: precision_at_1 |
|
value: 47.988 |
|
- type: precision_at_10 |
|
value: 27.121000000000002 |
|
- type: precision_at_100 |
|
value: 8.455 |
|
- type: precision_at_1000 |
|
value: 2.103 |
|
- type: precision_at_3 |
|
value: 39.628 |
|
- type: precision_at_5 |
|
value: 35.356 |
|
- type: recall_at_1 |
|
value: 6.544 |
|
- type: recall_at_10 |
|
value: 17.928 |
|
- type: recall_at_100 |
|
value: 32.843 |
|
- type: recall_at_1000 |
|
value: 65.752 |
|
- type: recall_at_3 |
|
value: 11.297 |
|
- type: recall_at_5 |
|
value: 14.357000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.262 |
|
- type: map_at_10 |
|
value: 55.095000000000006 |
|
- type: map_at_100 |
|
value: 55.93900000000001 |
|
- type: map_at_1000 |
|
value: 55.955999999999996 |
|
- type: map_at_3 |
|
value: 50.93 |
|
- type: map_at_5 |
|
value: 53.491 |
|
- type: mrr_at_1 |
|
value: 43.598 |
|
- type: mrr_at_10 |
|
value: 57.379999999999995 |
|
- type: mrr_at_100 |
|
value: 57.940999999999995 |
|
- type: mrr_at_1000 |
|
value: 57.952000000000005 |
|
- type: mrr_at_3 |
|
value: 53.998000000000005 |
|
- type: mrr_at_5 |
|
value: 56.128 |
|
- type: ndcg_at_1 |
|
value: 43.598 |
|
- type: ndcg_at_10 |
|
value: 62.427 |
|
- type: ndcg_at_100 |
|
value: 65.759 |
|
- type: ndcg_at_1000 |
|
value: 66.133 |
|
- type: ndcg_at_3 |
|
value: 54.745999999999995 |
|
- type: ndcg_at_5 |
|
value: 58.975 |
|
- type: precision_at_1 |
|
value: 43.598 |
|
- type: precision_at_10 |
|
value: 9.789 |
|
- type: precision_at_100 |
|
value: 1.171 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 24.295 |
|
- type: precision_at_5 |
|
value: 17.028 |
|
- type: recall_at_1 |
|
value: 39.262 |
|
- type: recall_at_10 |
|
value: 82.317 |
|
- type: recall_at_100 |
|
value: 96.391 |
|
- type: recall_at_1000 |
|
value: 99.116 |
|
- type: recall_at_3 |
|
value: 62.621 |
|
- type: recall_at_5 |
|
value: 72.357 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: ag_news |
|
name: MTEB NewsClassification |
|
config: default |
|
split: test |
|
revision: eb185aade064a813bc0b7f42de02595523103ca4 |
|
metrics: |
|
- type: accuracy |
|
value: 78.17500000000001 |
|
- type: f1 |
|
value: 78.01940892857273 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: GEM/opusparcus |
|
name: MTEB OpusparcusPC (en) |
|
config: en |
|
split: test |
|
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.89816700610999 |
|
- type: cos_sim_ap |
|
value: 100 |
|
- type: cos_sim_f1 |
|
value: 99.9490575649516 |
|
- type: cos_sim_precision |
|
value: 100 |
|
- type: cos_sim_recall |
|
value: 99.89816700610999 |
|
- type: dot_accuracy |
|
value: 99.89816700610999 |
|
- type: dot_ap |
|
value: 100 |
|
- type: dot_f1 |
|
value: 99.9490575649516 |
|
- type: dot_precision |
|
value: 100 |
|
- type: dot_recall |
|
value: 99.89816700610999 |
|
- type: euclidean_accuracy |
|
value: 99.89816700610999 |
|
- type: euclidean_ap |
|
value: 100 |
|
- type: euclidean_f1 |
|
value: 99.9490575649516 |
|
- type: euclidean_precision |
|
value: 100 |
|
- type: euclidean_recall |
|
value: 99.89816700610999 |
|
- type: manhattan_accuracy |
|
value: 99.89816700610999 |
|
- type: manhattan_ap |
|
value: 100 |
|
- type: manhattan_f1 |
|
value: 99.9490575649516 |
|
- type: manhattan_precision |
|
value: 100 |
|
- type: manhattan_recall |
|
value: 99.89816700610999 |
|
- type: max_accuracy |
|
value: 99.89816700610999 |
|
- type: max_ap |
|
value: 100 |
|
- type: max_f1 |
|
value: 99.9490575649516 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: paws-x |
|
name: MTEB PawsX (en) |
|
config: en |
|
split: test |
|
revision: 8a04d940a42cd40658986fdd8e3da561533a3646 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 61 |
|
- type: cos_sim_ap |
|
value: 59.630757252602464 |
|
- type: cos_sim_f1 |
|
value: 62.37521514629949 |
|
- type: cos_sim_precision |
|
value: 45.34534534534534 |
|
- type: cos_sim_recall |
|
value: 99.88974641675854 |
|
- type: dot_accuracy |
|
value: 61 |
|
- type: dot_ap |
|
value: 59.631527308059006 |
|
- type: dot_f1 |
|
value: 62.37521514629949 |
|
- type: dot_precision |
|
value: 45.34534534534534 |
|
- type: dot_recall |
|
value: 99.88974641675854 |
|
- type: euclidean_accuracy |
|
value: 61 |
|
- type: euclidean_ap |
|
value: 59.630757252602464 |
|
- type: euclidean_f1 |
|
value: 62.37521514629949 |
|
- type: euclidean_precision |
|
value: 45.34534534534534 |
|
- type: euclidean_recall |
|
value: 99.88974641675854 |
|
- type: manhattan_accuracy |
|
value: 60.9 |
|
- type: manhattan_ap |
|
value: 59.613947780462254 |
|
- type: manhattan_f1 |
|
value: 62.37521514629949 |
|
- type: manhattan_precision |
|
value: 45.34534534534534 |
|
- type: manhattan_recall |
|
value: 99.88974641675854 |
|
- type: max_accuracy |
|
value: 61 |
|
- type: max_ap |
|
value: 59.631527308059006 |
|
- type: max_f1 |
|
value: 62.37521514629949 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
|
metrics: |
|
- type: map_at_1 |
|
value: 69.963 |
|
- type: map_at_10 |
|
value: 83.59400000000001 |
|
- type: map_at_100 |
|
value: 84.236 |
|
- type: map_at_1000 |
|
value: 84.255 |
|
- type: map_at_3 |
|
value: 80.69800000000001 |
|
- type: map_at_5 |
|
value: 82.568 |
|
- type: mrr_at_1 |
|
value: 80.58999999999999 |
|
- type: mrr_at_10 |
|
value: 86.78200000000001 |
|
- type: mrr_at_100 |
|
value: 86.89099999999999 |
|
- type: mrr_at_1000 |
|
value: 86.893 |
|
- type: mrr_at_3 |
|
value: 85.757 |
|
- type: mrr_at_5 |
|
value: 86.507 |
|
- type: ndcg_at_1 |
|
value: 80.60000000000001 |
|
- type: ndcg_at_10 |
|
value: 87.41799999999999 |
|
- type: ndcg_at_100 |
|
value: 88.723 |
|
- type: ndcg_at_1000 |
|
value: 88.875 |
|
- type: ndcg_at_3 |
|
value: 84.565 |
|
- type: ndcg_at_5 |
|
value: 86.236 |
|
- type: precision_at_1 |
|
value: 80.60000000000001 |
|
- type: precision_at_10 |
|
value: 13.239 |
|
- type: precision_at_100 |
|
value: 1.5150000000000001 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 36.947 |
|
- type: precision_at_5 |
|
value: 24.354 |
|
- type: recall_at_1 |
|
value: 69.963 |
|
- type: recall_at_10 |
|
value: 94.553 |
|
- type: recall_at_100 |
|
value: 99.104 |
|
- type: recall_at_1000 |
|
value: 99.872 |
|
- type: recall_at_3 |
|
value: 86.317 |
|
- type: recall_at_5 |
|
value: 91.023 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 47.52890410998761 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
|
metrics: |
|
- type: v_measure |
|
value: 62.760692287940486 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.093 |
|
- type: map_at_10 |
|
value: 12.695 |
|
- type: map_at_100 |
|
value: 14.824000000000002 |
|
- type: map_at_1000 |
|
value: 15.123000000000001 |
|
- type: map_at_3 |
|
value: 8.968 |
|
- type: map_at_5 |
|
value: 10.828 |
|
- type: mrr_at_1 |
|
value: 25.1 |
|
- type: mrr_at_10 |
|
value: 35.894999999999996 |
|
- type: mrr_at_100 |
|
value: 36.966 |
|
- type: mrr_at_1000 |
|
value: 37.019999999999996 |
|
- type: mrr_at_3 |
|
value: 32.467 |
|
- type: mrr_at_5 |
|
value: 34.416999999999994 |
|
- type: ndcg_at_1 |
|
value: 25.1 |
|
- type: ndcg_at_10 |
|
value: 21.096999999999998 |
|
- type: ndcg_at_100 |
|
value: 29.202 |
|
- type: ndcg_at_1000 |
|
value: 34.541 |
|
- type: ndcg_at_3 |
|
value: 19.875 |
|
- type: ndcg_at_5 |
|
value: 17.497 |
|
- type: precision_at_1 |
|
value: 25.1 |
|
- type: precision_at_10 |
|
value: 10.9 |
|
- type: precision_at_100 |
|
value: 2.255 |
|
- type: precision_at_1000 |
|
value: 0.35400000000000004 |
|
- type: precision_at_3 |
|
value: 18.367 |
|
- type: precision_at_5 |
|
value: 15.299999999999999 |
|
- type: recall_at_1 |
|
value: 5.093 |
|
- type: recall_at_10 |
|
value: 22.092 |
|
- type: recall_at_100 |
|
value: 45.778 |
|
- type: recall_at_1000 |
|
value: 71.985 |
|
- type: recall_at_3 |
|
value: 11.167 |
|
- type: recall_at_5 |
|
value: 15.501999999999999 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 74.04386981759481 |
|
- type: cos_sim_spearman |
|
value: 69.12484963763646 |
|
- type: euclidean_pearson |
|
value: 71.49384353291062 |
|
- type: euclidean_spearman |
|
value: 69.12484548317074 |
|
- type: manhattan_pearson |
|
value: 71.49828173987272 |
|
- type: manhattan_spearman |
|
value: 69.08350274367014 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 66.95372527615659 |
|
- type: cos_sim_spearman |
|
value: 66.96821894433991 |
|
- type: euclidean_pearson |
|
value: 64.675348002074 |
|
- type: euclidean_spearman |
|
value: 66.96821894433991 |
|
- type: manhattan_pearson |
|
value: 64.5965887073831 |
|
- type: manhattan_spearman |
|
value: 66.88569076794741 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 77.34698437961983 |
|
- type: cos_sim_spearman |
|
value: 79.1153001117325 |
|
- type: euclidean_pearson |
|
value: 78.53562874696966 |
|
- type: euclidean_spearman |
|
value: 79.11530018205724 |
|
- type: manhattan_pearson |
|
value: 78.46484988944093 |
|
- type: manhattan_spearman |
|
value: 79.01416027493104 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 68.81220371935373 |
|
- type: cos_sim_spearman |
|
value: 68.50538405089604 |
|
- type: euclidean_pearson |
|
value: 68.69204272683749 |
|
- type: euclidean_spearman |
|
value: 68.50534223912419 |
|
- type: manhattan_pearson |
|
value: 68.67300120149523 |
|
- type: manhattan_spearman |
|
value: 68.45404301623115 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.2464678879813 |
|
- type: cos_sim_spearman |
|
value: 79.92003940566667 |
|
- type: euclidean_pearson |
|
value: 79.8080778793964 |
|
- type: euclidean_spearman |
|
value: 79.92003940566667 |
|
- type: manhattan_pearson |
|
value: 79.80153621444681 |
|
- type: manhattan_spearman |
|
value: 79.91293261418134 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 76.31179207708662 |
|
- type: cos_sim_spearman |
|
value: 78.65597349856115 |
|
- type: euclidean_pearson |
|
value: 78.76937027472678 |
|
- type: euclidean_spearman |
|
value: 78.65597349856115 |
|
- type: manhattan_pearson |
|
value: 78.77129513300605 |
|
- type: manhattan_spearman |
|
value: 78.62640467680775 |
|
- 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: 79.43158429552561 |
|
- type: cos_sim_spearman |
|
value: 81.46108646565362 |
|
- type: euclidean_pearson |
|
value: 81.47071791452292 |
|
- type: euclidean_spearman |
|
value: 81.46108646565362 |
|
- type: manhattan_pearson |
|
value: 81.56920643846031 |
|
- type: manhattan_spearman |
|
value: 81.42226241399516 |
|
- 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: 66.89546474141514 |
|
- type: cos_sim_spearman |
|
value: 65.8393752170531 |
|
- type: euclidean_pearson |
|
value: 67.2580522762307 |
|
- type: euclidean_spearman |
|
value: 65.8393752170531 |
|
- type: manhattan_pearson |
|
value: 67.45157729300522 |
|
- type: manhattan_spearman |
|
value: 66.19470854403802 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.39566306334434 |
|
- type: cos_sim_spearman |
|
value: 74.0981396086974 |
|
- type: euclidean_pearson |
|
value: 73.7834496259745 |
|
- type: euclidean_spearman |
|
value: 74.09803741302046 |
|
- type: manhattan_pearson |
|
value: 73.79958138780945 |
|
- type: manhattan_spearman |
|
value: 74.09894837555905 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: PhilipMay/stsb_multi_mt |
|
name: MTEB STSBenchmarkMultilingualSTS (en) |
|
config: en |
|
split: test |
|
revision: 93d57ef91790589e3ce9c365164337a8a78b7632 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.39566311006806 |
|
- type: cos_sim_spearman |
|
value: 74.0981396086974 |
|
- type: euclidean_pearson |
|
value: 73.78344970897099 |
|
- type: euclidean_spearman |
|
value: 74.09803741302046 |
|
- type: manhattan_pearson |
|
value: 73.79958147136705 |
|
- type: manhattan_spearman |
|
value: 74.09894837555905 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 80.81059564334683 |
|
- type: mrr |
|
value: 94.62696617108381 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 57.760999999999996 |
|
- type: map_at_10 |
|
value: 68.614 |
|
- type: map_at_100 |
|
value: 69.109 |
|
- type: map_at_1000 |
|
value: 69.134 |
|
- type: map_at_3 |
|
value: 65.735 |
|
- type: map_at_5 |
|
value: 67.42099999999999 |
|
- type: mrr_at_1 |
|
value: 60.667 |
|
- type: mrr_at_10 |
|
value: 69.94200000000001 |
|
- type: mrr_at_100 |
|
value: 70.254 |
|
- type: mrr_at_1000 |
|
value: 70.28 |
|
- type: mrr_at_3 |
|
value: 67.72200000000001 |
|
- type: mrr_at_5 |
|
value: 69.18900000000001 |
|
- type: ndcg_at_1 |
|
value: 60.667 |
|
- type: ndcg_at_10 |
|
value: 73.548 |
|
- type: ndcg_at_100 |
|
value: 75.381 |
|
- type: ndcg_at_1000 |
|
value: 75.991 |
|
- type: ndcg_at_3 |
|
value: 68.685 |
|
- type: ndcg_at_5 |
|
value: 71.26 |
|
- type: precision_at_1 |
|
value: 60.667 |
|
- type: precision_at_10 |
|
value: 9.833 |
|
- type: precision_at_100 |
|
value: 1.08 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 26.889000000000003 |
|
- type: precision_at_5 |
|
value: 17.8 |
|
- type: recall_at_1 |
|
value: 57.760999999999996 |
|
- type: recall_at_10 |
|
value: 87.13300000000001 |
|
- type: recall_at_100 |
|
value: 95 |
|
- type: recall_at_1000 |
|
value: 99.667 |
|
- type: recall_at_3 |
|
value: 74.211 |
|
- type: recall_at_5 |
|
value: 80.63900000000001 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81881188118813 |
|
- type: cos_sim_ap |
|
value: 95.21196473745837 |
|
- type: cos_sim_f1 |
|
value: 90.69767441860465 |
|
- type: cos_sim_precision |
|
value: 91.71779141104295 |
|
- type: cos_sim_recall |
|
value: 89.7 |
|
- type: dot_accuracy |
|
value: 99.81881188118813 |
|
- type: dot_ap |
|
value: 95.21196473745837 |
|
- type: dot_f1 |
|
value: 90.69767441860465 |
|
- type: dot_precision |
|
value: 91.71779141104295 |
|
- type: dot_recall |
|
value: 89.7 |
|
- type: euclidean_accuracy |
|
value: 99.81881188118813 |
|
- type: euclidean_ap |
|
value: 95.21196473745839 |
|
- type: euclidean_f1 |
|
value: 90.69767441860465 |
|
- type: euclidean_precision |
|
value: 91.71779141104295 |
|
- type: euclidean_recall |
|
value: 89.7 |
|
- type: manhattan_accuracy |
|
value: 99.81287128712871 |
|
- type: manhattan_ap |
|
value: 95.16667174835017 |
|
- type: manhattan_f1 |
|
value: 90.41095890410959 |
|
- type: manhattan_precision |
|
value: 91.7610710607621 |
|
- type: manhattan_recall |
|
value: 89.1 |
|
- type: max_accuracy |
|
value: 99.81881188118813 |
|
- type: max_ap |
|
value: 95.21196473745839 |
|
- type: max_f1 |
|
value: 90.69767441860465 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 59.54942204515638 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 39.42892282672948 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 51.189033075914324 |
|
- type: mrr |
|
value: 51.97014790764791 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.09466569775977 |
|
- type: cos_sim_spearman |
|
value: 30.31058660775912 |
|
- type: dot_pearson |
|
value: 30.09466438861689 |
|
- type: dot_spearman |
|
value: 30.31058660775912 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.253 |
|
- type: map_at_10 |
|
value: 2.07 |
|
- type: map_at_100 |
|
value: 12.679000000000002 |
|
- type: map_at_1000 |
|
value: 30.412 |
|
- type: map_at_3 |
|
value: 0.688 |
|
- type: map_at_5 |
|
value: 1.079 |
|
- type: mrr_at_1 |
|
value: 96 |
|
- type: mrr_at_10 |
|
value: 98 |
|
- type: mrr_at_100 |
|
value: 98 |
|
- type: mrr_at_1000 |
|
value: 98 |
|
- type: mrr_at_3 |
|
value: 98 |
|
- type: mrr_at_5 |
|
value: 98 |
|
- type: ndcg_at_1 |
|
value: 89 |
|
- type: ndcg_at_10 |
|
value: 79.646 |
|
- type: ndcg_at_100 |
|
value: 62.217999999999996 |
|
- type: ndcg_at_1000 |
|
value: 55.13400000000001 |
|
- type: ndcg_at_3 |
|
value: 83.458 |
|
- type: ndcg_at_5 |
|
value: 80.982 |
|
- type: precision_at_1 |
|
value: 96 |
|
- type: precision_at_10 |
|
value: 84.6 |
|
- type: precision_at_100 |
|
value: 64.34 |
|
- type: precision_at_1000 |
|
value: 24.534 |
|
- type: precision_at_3 |
|
value: 88.667 |
|
- type: precision_at_5 |
|
value: 85.6 |
|
- type: recall_at_1 |
|
value: 0.253 |
|
- type: recall_at_10 |
|
value: 2.253 |
|
- type: recall_at_100 |
|
value: 15.606 |
|
- type: recall_at_1000 |
|
value: 51.595 |
|
- type: recall_at_3 |
|
value: 0.7100000000000001 |
|
- type: recall_at_5 |
|
value: 1.139 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: mteb/touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.0540000000000003 |
|
- type: map_at_10 |
|
value: 13.078999999999999 |
|
- type: map_at_100 |
|
value: 19.468 |
|
- type: map_at_1000 |
|
value: 21.006 |
|
- type: map_at_3 |
|
value: 6.8629999999999995 |
|
- type: map_at_5 |
|
value: 9.187 |
|
- type: mrr_at_1 |
|
value: 42.857 |
|
- type: mrr_at_10 |
|
value: 56.735 |
|
- type: mrr_at_100 |
|
value: 57.352000000000004 |
|
- type: mrr_at_1000 |
|
value: 57.352000000000004 |
|
- type: mrr_at_3 |
|
value: 52.721 |
|
- type: mrr_at_5 |
|
value: 54.66 |
|
- type: ndcg_at_1 |
|
value: 38.775999999999996 |
|
- type: ndcg_at_10 |
|
value: 31.469 |
|
- type: ndcg_at_100 |
|
value: 42.016999999999996 |
|
- type: ndcg_at_1000 |
|
value: 52.60399999999999 |
|
- type: ndcg_at_3 |
|
value: 35.894 |
|
- type: ndcg_at_5 |
|
value: 33.873 |
|
- type: precision_at_1 |
|
value: 42.857 |
|
- type: precision_at_10 |
|
value: 27.346999999999998 |
|
- type: precision_at_100 |
|
value: 8.327 |
|
- type: precision_at_1000 |
|
value: 1.551 |
|
- type: precision_at_3 |
|
value: 36.735 |
|
- type: precision_at_5 |
|
value: 33.469 |
|
- type: recall_at_1 |
|
value: 3.0540000000000003 |
|
- type: recall_at_10 |
|
value: 19.185 |
|
- type: recall_at_100 |
|
value: 51.056000000000004 |
|
- type: recall_at_1000 |
|
value: 82.814 |
|
- type: recall_at_3 |
|
value: 7.961 |
|
- type: recall_at_5 |
|
value: 11.829 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
|
metrics: |
|
- type: accuracy |
|
value: 64.9346 |
|
- type: ap |
|
value: 12.121605736777527 |
|
- type: f1 |
|
value: 50.169902005887955 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 56.72608941709111 |
|
- type: f1 |
|
value: 57.0702928875253 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 37.72671554400943 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 82.84556237706384 |
|
- type: cos_sim_ap |
|
value: 63.28364215788651 |
|
- type: cos_sim_f1 |
|
value: 60.00000000000001 |
|
- type: cos_sim_precision |
|
value: 54.45161290322581 |
|
- type: cos_sim_recall |
|
value: 66.80738786279683 |
|
- type: dot_accuracy |
|
value: 82.84556237706384 |
|
- type: dot_ap |
|
value: 63.28364302860433 |
|
- type: dot_f1 |
|
value: 60.00000000000001 |
|
- type: dot_precision |
|
value: 54.45161290322581 |
|
- type: dot_recall |
|
value: 66.80738786279683 |
|
- type: euclidean_accuracy |
|
value: 82.84556237706384 |
|
- type: euclidean_ap |
|
value: 63.28363625097978 |
|
- type: euclidean_f1 |
|
value: 60.00000000000001 |
|
- type: euclidean_precision |
|
value: 54.45161290322581 |
|
- type: euclidean_recall |
|
value: 66.80738786279683 |
|
- type: manhattan_accuracy |
|
value: 82.86940454193241 |
|
- type: manhattan_ap |
|
value: 63.244773709836764 |
|
- type: manhattan_f1 |
|
value: 60.12680942696495 |
|
- type: manhattan_precision |
|
value: 55.00109433136353 |
|
- type: manhattan_recall |
|
value: 66.3060686015831 |
|
- type: max_accuracy |
|
value: 82.86940454193241 |
|
- type: max_ap |
|
value: 63.28364302860433 |
|
- type: max_f1 |
|
value: 60.12680942696495 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.32033220786278 |
|
- type: cos_sim_ap |
|
value: 84.71928176006863 |
|
- type: cos_sim_f1 |
|
value: 76.51483333969684 |
|
- type: cos_sim_precision |
|
value: 75.89184276300841 |
|
- type: cos_sim_recall |
|
value: 77.14813674160764 |
|
- type: dot_accuracy |
|
value: 88.32033220786278 |
|
- type: dot_ap |
|
value: 84.71928330149228 |
|
- type: dot_f1 |
|
value: 76.51483333969684 |
|
- type: dot_precision |
|
value: 75.89184276300841 |
|
- type: dot_recall |
|
value: 77.14813674160764 |
|
- type: euclidean_accuracy |
|
value: 88.32033220786278 |
|
- type: euclidean_ap |
|
value: 84.71928045384345 |
|
- type: euclidean_f1 |
|
value: 76.51483333969684 |
|
- type: euclidean_precision |
|
value: 75.89184276300841 |
|
- type: euclidean_recall |
|
value: 77.14813674160764 |
|
- type: manhattan_accuracy |
|
value: 88.27570147863545 |
|
- type: manhattan_ap |
|
value: 84.68523541579755 |
|
- type: manhattan_f1 |
|
value: 76.51512269355146 |
|
- type: manhattan_precision |
|
value: 75.62608107091825 |
|
- type: manhattan_recall |
|
value: 77.42531567600862 |
|
- type: max_accuracy |
|
value: 88.32033220786278 |
|
- type: max_ap |
|
value: 84.71928330149228 |
|
- type: max_f1 |
|
value: 76.51512269355146 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: jinaai/cities_wiki_clustering |
|
name: MTEB WikiCitiesClustering |
|
config: default |
|
split: test |
|
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa |
|
metrics: |
|
- type: v_measure |
|
value: 85.30624598674467 |
|
license: apache-2.0 |
|
--- |
|
<h1 align="center">Snowflake's Artic-embed-m</h1> |
|
<h4 align="center"> |
|
<p> |
|
<a href=#news>News</a> | |
|
<a href=#models>Models</a> | |
|
<a href=#usage>Usage</a> | |
|
<a href="#evaluation">Evaluation</a> | |
|
<a href="#contact">Contact</a> | |
|
<a href="#faq">FAQ</a> |
|
<a href="#license">License</a> | |
|
<a href="#acknowledgement">Acknowledgement</a> |
|
<p> |
|
</h4> |
|
|
|
|
|
## News |
|
|
|
|
|
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/snowflake-arctic-embed). |
|
|
|
|
|
## Models |
|
|
|
|
|
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance. |
|
|
|
|
|
The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models. |
|
|
|
|
|
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report will be available shortly. |
|
|
|
|
|
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension | |
|
| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- | |
|
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 | |
|
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 | |
|
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 | |
|
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 | |
|
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 | |
|
|
|
|
|
Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | |
|
| Google-gecko-text-embedding | 55.7 | |
|
| text-embedding-3-large | 55.44 | |
|
| Cohere-embed-english-v3.0 | 55.00 | |
|
| bge-large-en-v1.5 | 54.29 | |
|
|
|
|
|
### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) |
|
|
|
|
|
This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------- | -------------------------------- | |
|
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | |
|
| GIST-all-MiniLM-L6-v2 | 45.12 | |
|
| gte-tiny | 44.92 | |
|
| all-MiniLM-L6-v2 | 41.95 | |
|
| bge-micro-v2 | 42.56 | |
|
|
|
|
|
### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s) |
|
|
|
|
|
Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | |
|
| bge-small-en-v1.5 | 51.68 | |
|
| Cohere-embed-english-light-v3.0 | 51.34 | |
|
| text-embedding-3-small | 51.08 | |
|
| e5-small-v2 | 49.04 | |
|
|
|
|
|
### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) |
|
|
|
|
|
Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | |
|
| bge-base-en-v1.5 | 53.25 | |
|
| nomic-embed-text-v1.5 | 53.25 | |
|
| GIST-Embedding-v0 | 52.31 | |
|
| gte-base | 52.31 | |
|
|
|
### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) |
|
|
|
|
|
Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192! |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | |
|
| nomic-embed-text-v1.5 | 53.01 | |
|
| nomic-embed-text-v1 | 52.81 | |
|
|
|
|
|
|
|
|
|
### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) |
|
|
|
|
|
Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this small model does not sacrifice retrieval accuracy for its small size. |
|
|
|
|
|
| Model Name | MTEB Retrieval Score (NDCG @ 10) | |
|
| ------------------------------------------------------------------ | -------------------------------- | |
|
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | |
|
| UAE-Large-V1 | 54.66 | |
|
| bge-large-en-v1.5 | 54.29 | |
|
| mxbai-embed-large-v1 | 54.39 | |
|
| e5-Large-v2 | 50.56 | |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Using Sentence Transformers |
|
|
|
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below. |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m") |
|
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queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
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documents = ['The Data Cloud!', 'Mexico City of Course!'] |
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query_embeddings = model.encode(queries, prompt_name="query") |
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document_embeddings = model.encode(documents) |
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scores = query_embeddings @ document_embeddings.T |
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for query, query_scores in zip(queries, scores): |
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doc_score_pairs = list(zip(documents, query_scores)) |
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
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# Output passages & scores |
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print("Query:", query) |
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for document, score in doc_score_pairs: |
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print(score, document) |
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``` |
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``` |
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Query: what is snowflake? |
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0.20051965 The Data Cloud! |
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0.07660701 Mexico City of Course! |
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Query: Where can I get the best tacos? |
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0.24481852 Mexico City of Course! |
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0.15664819 The Data Cloud! |
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``` |
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### Using Huggingface transformers |
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You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query). |
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```python |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m') |
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m', add_pooling_layer=False) |
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model.eval() |
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query_prefix = 'Represent this sentence for searching relevant passages: ' |
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queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
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queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries] |
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query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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documents = ['The Data Cloud!', 'Mexico City of Course!'] |
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document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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# Compute token embeddings |
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with torch.no_grad(): |
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query_embeddings = model(**query_tokens)[0][:, 0] |
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doument_embeddings = model(**document_tokens)[0][:, 0] |
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# normalize embeddings |
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query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1) |
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doument_embeddings = torch.nn.functional.normalize(doument_embeddings, p=2, dim=1) |
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scores = torch.mm(query_embeddings, doument_embeddings.transpose(0, 1)) |
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for query, query_scores in zip(queries, scores): |
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doc_score_pairs = list(zip(documents, query_scores)) |
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doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) |
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#Output passages & scores |
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print("Query:", query) |
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for document, score in doc_score_pairs: |
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print(score, document) |
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``` |
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## FAQ |
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TBD |
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## Contact |
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Feel free to open an issue or pull request if you have any questions or suggestions about this project. |
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You also can email Daniel Campos([email protected]). |
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## License |
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Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge. |
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## Acknowledgement |
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We want to thank the open-source community, which has provided the great building blocks upon which we could make our models. |
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We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. |
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We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. |
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We also thank the open-source community for producing the great models we could build on top of and making these releases possible. |
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Finally, we thank the researchers who created BEIR and MTEB benchmarks. |
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It is largely thanks to their tireless work to define what better looks like that we could improve model performance. |
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