|
--- |
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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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datasets: |
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- allenai/c4 |
|
language: en |
|
inference: false |
|
license: apache-2.0 |
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model-index: |
|
- name: jina-embedding-b-en-v2 |
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results: |
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- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 74.73134328358209 |
|
- type: ap |
|
value: 37.765427081831035 |
|
- type: f1 |
|
value: 68.79367444339518 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 88.544275 |
|
- type: ap |
|
value: 84.61328675662887 |
|
- type: f1 |
|
value: 88.51879035862375 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 45.263999999999996 |
|
- type: f1 |
|
value: 43.778759656699435 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.693 |
|
- type: map_at_10 |
|
value: 35.487 |
|
- type: map_at_100 |
|
value: 36.862 |
|
- type: map_at_1000 |
|
value: 36.872 |
|
- type: map_at_3 |
|
value: 30.049999999999997 |
|
- type: map_at_5 |
|
value: 32.966 |
|
- type: mrr_at_1 |
|
value: 21.977 |
|
- type: mrr_at_10 |
|
value: 35.565999999999995 |
|
- type: mrr_at_100 |
|
value: 36.948 |
|
- type: mrr_at_1000 |
|
value: 36.958 |
|
- type: mrr_at_3 |
|
value: 30.121 |
|
- type: mrr_at_5 |
|
value: 33.051 |
|
- type: ndcg_at_1 |
|
value: 21.693 |
|
- type: ndcg_at_10 |
|
value: 44.181 |
|
- type: ndcg_at_100 |
|
value: 49.982 |
|
- type: ndcg_at_1000 |
|
value: 50.233000000000004 |
|
- type: ndcg_at_3 |
|
value: 32.830999999999996 |
|
- type: ndcg_at_5 |
|
value: 38.080000000000005 |
|
- type: precision_at_1 |
|
value: 21.693 |
|
- type: precision_at_10 |
|
value: 7.248 |
|
- type: precision_at_100 |
|
value: 0.9769999999999999 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 13.632 |
|
- type: precision_at_5 |
|
value: 10.725 |
|
- type: recall_at_1 |
|
value: 21.693 |
|
- type: recall_at_10 |
|
value: 72.475 |
|
- type: recall_at_100 |
|
value: 97.653 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 40.896 |
|
- type: recall_at_5 |
|
value: 53.627 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 45.39242428696777 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 36.675626784714 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 62.247725694904034 |
|
- type: mrr |
|
value: 74.91359978894604 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.68003802970496 |
|
- type: cos_sim_spearman |
|
value: 81.23438110096286 |
|
- type: euclidean_pearson |
|
value: 81.87462986142582 |
|
- type: euclidean_spearman |
|
value: 81.23438110096286 |
|
- type: manhattan_pearson |
|
value: 81.61162566600755 |
|
- type: manhattan_spearman |
|
value: 81.11329400456184 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 84.01298701298701 |
|
- type: f1 |
|
value: 83.31690714969382 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 37.050108150972086 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 30.15731442819715 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
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split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.391999999999996 |
|
- type: map_at_10 |
|
value: 42.597 |
|
- type: map_at_100 |
|
value: 44.07 |
|
- type: map_at_1000 |
|
value: 44.198 |
|
- type: map_at_3 |
|
value: 38.957 |
|
- type: map_at_5 |
|
value: 40.961 |
|
- type: mrr_at_1 |
|
value: 37.196 |
|
- type: mrr_at_10 |
|
value: 48.152 |
|
- type: mrr_at_100 |
|
value: 48.928 |
|
- type: mrr_at_1000 |
|
value: 48.964999999999996 |
|
- type: mrr_at_3 |
|
value: 45.446 |
|
- type: mrr_at_5 |
|
value: 47.205999999999996 |
|
- type: ndcg_at_1 |
|
value: 37.196 |
|
- type: ndcg_at_10 |
|
value: 49.089 |
|
- type: ndcg_at_100 |
|
value: 54.471000000000004 |
|
- type: ndcg_at_1000 |
|
value: 56.385 |
|
- type: ndcg_at_3 |
|
value: 43.699 |
|
- type: ndcg_at_5 |
|
value: 46.22 |
|
- type: precision_at_1 |
|
value: 37.196 |
|
- type: precision_at_10 |
|
value: 9.313 |
|
- type: precision_at_100 |
|
value: 1.478 |
|
- type: precision_at_1000 |
|
value: 0.198 |
|
- type: precision_at_3 |
|
value: 20.839 |
|
- type: precision_at_5 |
|
value: 14.936 |
|
- type: recall_at_1 |
|
value: 31.391999999999996 |
|
- type: recall_at_10 |
|
value: 61.876 |
|
- type: recall_at_100 |
|
value: 84.214 |
|
- type: recall_at_1000 |
|
value: 95.985 |
|
- type: recall_at_3 |
|
value: 46.6 |
|
- type: recall_at_5 |
|
value: 53.588 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.083 |
|
- type: map_at_10 |
|
value: 38.812999999999995 |
|
- type: map_at_100 |
|
value: 40.053 |
|
- type: map_at_1000 |
|
value: 40.188 |
|
- type: map_at_3 |
|
value: 36.111 |
|
- type: map_at_5 |
|
value: 37.519000000000005 |
|
- type: mrr_at_1 |
|
value: 36.497 |
|
- type: mrr_at_10 |
|
value: 44.85 |
|
- type: mrr_at_100 |
|
value: 45.546 |
|
- type: mrr_at_1000 |
|
value: 45.593 |
|
- type: mrr_at_3 |
|
value: 42.686 |
|
- type: mrr_at_5 |
|
value: 43.909 |
|
- type: ndcg_at_1 |
|
value: 36.497 |
|
- type: ndcg_at_10 |
|
value: 44.443 |
|
- type: ndcg_at_100 |
|
value: 48.979 |
|
- type: ndcg_at_1000 |
|
value: 51.154999999999994 |
|
- type: ndcg_at_3 |
|
value: 40.660000000000004 |
|
- type: ndcg_at_5 |
|
value: 42.193000000000005 |
|
- type: precision_at_1 |
|
value: 36.497 |
|
- type: precision_at_10 |
|
value: 8.433 |
|
- type: precision_at_100 |
|
value: 1.369 |
|
- type: precision_at_1000 |
|
value: 0.185 |
|
- type: precision_at_3 |
|
value: 19.894000000000002 |
|
- type: precision_at_5 |
|
value: 13.873 |
|
- type: recall_at_1 |
|
value: 29.083 |
|
- type: recall_at_10 |
|
value: 54.313 |
|
- type: recall_at_100 |
|
value: 73.792 |
|
- type: recall_at_1000 |
|
value: 87.629 |
|
- type: recall_at_3 |
|
value: 42.257 |
|
- type: recall_at_5 |
|
value: 47.066 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.556000000000004 |
|
- type: map_at_10 |
|
value: 50.698 |
|
- type: map_at_100 |
|
value: 51.705 |
|
- type: map_at_1000 |
|
value: 51.768 |
|
- type: map_at_3 |
|
value: 47.848 |
|
- type: map_at_5 |
|
value: 49.358000000000004 |
|
- type: mrr_at_1 |
|
value: 43.95 |
|
- type: mrr_at_10 |
|
value: 54.191 |
|
- type: mrr_at_100 |
|
value: 54.852999999999994 |
|
- type: mrr_at_1000 |
|
value: 54.885 |
|
- type: mrr_at_3 |
|
value: 51.954 |
|
- type: mrr_at_5 |
|
value: 53.13 |
|
- type: ndcg_at_1 |
|
value: 43.95 |
|
- type: ndcg_at_10 |
|
value: 56.516 |
|
- type: ndcg_at_100 |
|
value: 60.477000000000004 |
|
- type: ndcg_at_1000 |
|
value: 61.746 |
|
- type: ndcg_at_3 |
|
value: 51.601 |
|
- type: ndcg_at_5 |
|
value: 53.795 |
|
- type: precision_at_1 |
|
value: 43.95 |
|
- type: precision_at_10 |
|
value: 9.009 |
|
- type: precision_at_100 |
|
value: 1.189 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 22.989 |
|
- type: precision_at_5 |
|
value: 15.473 |
|
- type: recall_at_1 |
|
value: 38.556000000000004 |
|
- type: recall_at_10 |
|
value: 70.159 |
|
- type: recall_at_100 |
|
value: 87.132 |
|
- type: recall_at_1000 |
|
value: 96.16 |
|
- type: recall_at_3 |
|
value: 56.906 |
|
- type: recall_at_5 |
|
value: 62.332 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.238 |
|
- type: map_at_10 |
|
value: 32.5 |
|
- type: map_at_100 |
|
value: 33.637 |
|
- type: map_at_1000 |
|
value: 33.719 |
|
- type: map_at_3 |
|
value: 30.026999999999997 |
|
- type: map_at_5 |
|
value: 31.555 |
|
- type: mrr_at_1 |
|
value: 26.328000000000003 |
|
- type: mrr_at_10 |
|
value: 34.44 |
|
- type: mrr_at_100 |
|
value: 35.455999999999996 |
|
- type: mrr_at_1000 |
|
value: 35.521 |
|
- type: mrr_at_3 |
|
value: 32.034 |
|
- type: mrr_at_5 |
|
value: 33.565 |
|
- type: ndcg_at_1 |
|
value: 26.328000000000003 |
|
- type: ndcg_at_10 |
|
value: 37.202 |
|
- type: ndcg_at_100 |
|
value: 42.728 |
|
- type: ndcg_at_1000 |
|
value: 44.792 |
|
- type: ndcg_at_3 |
|
value: 32.368 |
|
- type: ndcg_at_5 |
|
value: 35.008 |
|
- type: precision_at_1 |
|
value: 26.328000000000003 |
|
- type: precision_at_10 |
|
value: 5.7059999999999995 |
|
- type: precision_at_100 |
|
value: 0.8880000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 13.672 |
|
- type: precision_at_5 |
|
value: 9.74 |
|
- type: recall_at_1 |
|
value: 24.238 |
|
- type: recall_at_10 |
|
value: 49.829 |
|
- type: recall_at_100 |
|
value: 75.21 |
|
- type: recall_at_1000 |
|
value: 90.521 |
|
- type: recall_at_3 |
|
value: 36.867 |
|
- type: recall_at_5 |
|
value: 43.241 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.378 |
|
- type: map_at_10 |
|
value: 22.817999999999998 |
|
- type: map_at_100 |
|
value: 23.977999999999998 |
|
- type: map_at_1000 |
|
value: 24.108 |
|
- type: map_at_3 |
|
value: 20.719 |
|
- type: map_at_5 |
|
value: 21.889 |
|
- type: mrr_at_1 |
|
value: 19.03 |
|
- type: mrr_at_10 |
|
value: 27.022000000000002 |
|
- type: mrr_at_100 |
|
value: 28.011999999999997 |
|
- type: mrr_at_1000 |
|
value: 28.096 |
|
- type: mrr_at_3 |
|
value: 24.855 |
|
- type: mrr_at_5 |
|
value: 26.029999999999998 |
|
- type: ndcg_at_1 |
|
value: 19.03 |
|
- type: ndcg_at_10 |
|
value: 27.526 |
|
- type: ndcg_at_100 |
|
value: 33.040000000000006 |
|
- type: ndcg_at_1000 |
|
value: 36.187000000000005 |
|
- type: ndcg_at_3 |
|
value: 23.497 |
|
- type: ndcg_at_5 |
|
value: 25.334 |
|
- type: precision_at_1 |
|
value: 19.03 |
|
- type: precision_at_10 |
|
value: 4.963 |
|
- type: precision_at_100 |
|
value: 0.893 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 11.360000000000001 |
|
- type: precision_at_5 |
|
value: 8.134 |
|
- type: recall_at_1 |
|
value: 15.378 |
|
- type: recall_at_10 |
|
value: 38.061 |
|
- type: recall_at_100 |
|
value: 61.754 |
|
- type: recall_at_1000 |
|
value: 84.259 |
|
- type: recall_at_3 |
|
value: 26.788 |
|
- type: recall_at_5 |
|
value: 31.326999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.511999999999997 |
|
- type: map_at_10 |
|
value: 37.429 |
|
- type: map_at_100 |
|
value: 38.818000000000005 |
|
- type: map_at_1000 |
|
value: 38.924 |
|
- type: map_at_3 |
|
value: 34.625 |
|
- type: map_at_5 |
|
value: 36.064 |
|
- type: mrr_at_1 |
|
value: 33.300999999999995 |
|
- type: mrr_at_10 |
|
value: 43.036 |
|
- type: mrr_at_100 |
|
value: 43.894 |
|
- type: mrr_at_1000 |
|
value: 43.936 |
|
- type: mrr_at_3 |
|
value: 40.825 |
|
- type: mrr_at_5 |
|
value: 42.028 |
|
- type: ndcg_at_1 |
|
value: 33.300999999999995 |
|
- type: ndcg_at_10 |
|
value: 43.229 |
|
- type: ndcg_at_100 |
|
value: 48.992000000000004 |
|
- type: ndcg_at_1000 |
|
value: 51.02100000000001 |
|
- type: ndcg_at_3 |
|
value: 38.794000000000004 |
|
- type: ndcg_at_5 |
|
value: 40.65 |
|
- type: precision_at_1 |
|
value: 33.300999999999995 |
|
- type: precision_at_10 |
|
value: 7.777000000000001 |
|
- type: precision_at_100 |
|
value: 1.269 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 18.351 |
|
- type: precision_at_5 |
|
value: 12.762 |
|
- type: recall_at_1 |
|
value: 27.511999999999997 |
|
- type: recall_at_10 |
|
value: 54.788000000000004 |
|
- type: recall_at_100 |
|
value: 79.105 |
|
- type: recall_at_1000 |
|
value: 92.49199999999999 |
|
- type: recall_at_3 |
|
value: 41.924 |
|
- type: recall_at_5 |
|
value: 47.026 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.117 |
|
- type: map_at_10 |
|
value: 33.32 |
|
- type: map_at_100 |
|
value: 34.677 |
|
- type: map_at_1000 |
|
value: 34.78 |
|
- type: map_at_3 |
|
value: 30.233999999999998 |
|
- type: map_at_5 |
|
value: 31.668000000000003 |
|
- type: mrr_at_1 |
|
value: 29.566 |
|
- type: mrr_at_10 |
|
value: 38.244 |
|
- type: mrr_at_100 |
|
value: 39.245000000000005 |
|
- type: mrr_at_1000 |
|
value: 39.296 |
|
- type: mrr_at_3 |
|
value: 35.864000000000004 |
|
- type: mrr_at_5 |
|
value: 36.919999999999995 |
|
- type: ndcg_at_1 |
|
value: 29.566 |
|
- type: ndcg_at_10 |
|
value: 39.127 |
|
- type: ndcg_at_100 |
|
value: 44.989000000000004 |
|
- type: ndcg_at_1000 |
|
value: 47.189 |
|
- type: ndcg_at_3 |
|
value: 34.039 |
|
- type: ndcg_at_5 |
|
value: 35.744 |
|
- type: precision_at_1 |
|
value: 29.566 |
|
- type: precision_at_10 |
|
value: 7.385999999999999 |
|
- type: precision_at_100 |
|
value: 1.204 |
|
- type: precision_at_1000 |
|
value: 0.158 |
|
- type: precision_at_3 |
|
value: 16.286 |
|
- type: precision_at_5 |
|
value: 11.484 |
|
- type: recall_at_1 |
|
value: 24.117 |
|
- type: recall_at_10 |
|
value: 51.559999999999995 |
|
- type: recall_at_100 |
|
value: 77.104 |
|
- type: recall_at_1000 |
|
value: 91.79899999999999 |
|
- type: recall_at_3 |
|
value: 36.82 |
|
- type: recall_at_5 |
|
value: 41.453 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.17625 |
|
- type: map_at_10 |
|
value: 34.063916666666664 |
|
- type: map_at_100 |
|
value: 35.255500000000005 |
|
- type: map_at_1000 |
|
value: 35.37275 |
|
- type: map_at_3 |
|
value: 31.351666666666667 |
|
- type: map_at_5 |
|
value: 32.80608333333333 |
|
- type: mrr_at_1 |
|
value: 29.59783333333333 |
|
- type: mrr_at_10 |
|
value: 38.0925 |
|
- type: mrr_at_100 |
|
value: 38.957249999999995 |
|
- type: mrr_at_1000 |
|
value: 39.01608333333333 |
|
- type: mrr_at_3 |
|
value: 35.77625 |
|
- type: mrr_at_5 |
|
value: 37.04991666666667 |
|
- type: ndcg_at_1 |
|
value: 29.59783333333333 |
|
- type: ndcg_at_10 |
|
value: 39.343666666666664 |
|
- type: ndcg_at_100 |
|
value: 44.488249999999994 |
|
- type: ndcg_at_1000 |
|
value: 46.83358333333334 |
|
- type: ndcg_at_3 |
|
value: 34.69708333333333 |
|
- type: ndcg_at_5 |
|
value: 36.75075 |
|
- type: precision_at_1 |
|
value: 29.59783333333333 |
|
- type: precision_at_10 |
|
value: 6.884083333333332 |
|
- type: precision_at_100 |
|
value: 1.114 |
|
- type: precision_at_1000 |
|
value: 0.15108333333333332 |
|
- type: precision_at_3 |
|
value: 15.965250000000003 |
|
- type: precision_at_5 |
|
value: 11.246500000000001 |
|
- type: recall_at_1 |
|
value: 25.17625 |
|
- type: recall_at_10 |
|
value: 51.015999999999984 |
|
- type: recall_at_100 |
|
value: 73.60174999999998 |
|
- type: recall_at_1000 |
|
value: 89.849 |
|
- type: recall_at_3 |
|
value: 37.88399999999999 |
|
- type: recall_at_5 |
|
value: 43.24541666666666 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.537 |
|
- type: map_at_10 |
|
value: 31.081999999999997 |
|
- type: map_at_100 |
|
value: 32.042 |
|
- type: map_at_1000 |
|
value: 32.141 |
|
- type: map_at_3 |
|
value: 29.137 |
|
- type: map_at_5 |
|
value: 30.079 |
|
- type: mrr_at_1 |
|
value: 27.454 |
|
- type: mrr_at_10 |
|
value: 33.694 |
|
- type: mrr_at_100 |
|
value: 34.579 |
|
- type: mrr_at_1000 |
|
value: 34.649 |
|
- type: mrr_at_3 |
|
value: 32.004 |
|
- type: mrr_at_5 |
|
value: 32.794000000000004 |
|
- type: ndcg_at_1 |
|
value: 27.454 |
|
- type: ndcg_at_10 |
|
value: 34.915 |
|
- type: ndcg_at_100 |
|
value: 39.641 |
|
- type: ndcg_at_1000 |
|
value: 42.105 |
|
- type: ndcg_at_3 |
|
value: 31.276 |
|
- type: ndcg_at_5 |
|
value: 32.65 |
|
- type: precision_at_1 |
|
value: 27.454 |
|
- type: precision_at_10 |
|
value: 5.337 |
|
- type: precision_at_100 |
|
value: 0.8250000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 13.241 |
|
- type: precision_at_5 |
|
value: 8.895999999999999 |
|
- type: recall_at_1 |
|
value: 24.537 |
|
- type: recall_at_10 |
|
value: 44.324999999999996 |
|
- type: recall_at_100 |
|
value: 65.949 |
|
- type: recall_at_1000 |
|
value: 84.017 |
|
- type: recall_at_3 |
|
value: 33.857 |
|
- type: recall_at_5 |
|
value: 37.316 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.122 |
|
- type: map_at_10 |
|
value: 24.32 |
|
- type: map_at_100 |
|
value: 25.338 |
|
- type: map_at_1000 |
|
value: 25.462 |
|
- type: map_at_3 |
|
value: 22.064 |
|
- type: map_at_5 |
|
value: 23.322000000000003 |
|
- type: mrr_at_1 |
|
value: 20.647 |
|
- type: mrr_at_10 |
|
value: 27.858 |
|
- type: mrr_at_100 |
|
value: 28.743999999999996 |
|
- type: mrr_at_1000 |
|
value: 28.819 |
|
- type: mrr_at_3 |
|
value: 25.769 |
|
- type: mrr_at_5 |
|
value: 26.964 |
|
- type: ndcg_at_1 |
|
value: 20.647 |
|
- type: ndcg_at_10 |
|
value: 28.849999999999998 |
|
- type: ndcg_at_100 |
|
value: 33.849000000000004 |
|
- type: ndcg_at_1000 |
|
value: 36.802 |
|
- type: ndcg_at_3 |
|
value: 24.799 |
|
- type: ndcg_at_5 |
|
value: 26.682 |
|
- type: precision_at_1 |
|
value: 20.647 |
|
- type: precision_at_10 |
|
value: 5.2170000000000005 |
|
- type: precision_at_100 |
|
value: 0.906 |
|
- type: precision_at_1000 |
|
value: 0.134 |
|
- type: precision_at_3 |
|
value: 11.769 |
|
- type: precision_at_5 |
|
value: 8.486 |
|
- type: recall_at_1 |
|
value: 17.122 |
|
- type: recall_at_10 |
|
value: 38.999 |
|
- type: recall_at_100 |
|
value: 61.467000000000006 |
|
- type: recall_at_1000 |
|
value: 82.716 |
|
- type: recall_at_3 |
|
value: 27.601 |
|
- type: recall_at_5 |
|
value: 32.471 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.396 |
|
- type: map_at_10 |
|
value: 33.415 |
|
- type: map_at_100 |
|
value: 34.521 |
|
- type: map_at_1000 |
|
value: 34.631 |
|
- type: map_at_3 |
|
value: 30.703999999999997 |
|
- type: map_at_5 |
|
value: 32.166 |
|
- type: mrr_at_1 |
|
value: 28.825 |
|
- type: mrr_at_10 |
|
value: 37.397000000000006 |
|
- type: mrr_at_100 |
|
value: 38.286 |
|
- type: mrr_at_1000 |
|
value: 38.346000000000004 |
|
- type: mrr_at_3 |
|
value: 35.028 |
|
- type: mrr_at_5 |
|
value: 36.32 |
|
- type: ndcg_at_1 |
|
value: 28.825 |
|
- type: ndcg_at_10 |
|
value: 38.656 |
|
- type: ndcg_at_100 |
|
value: 43.856 |
|
- type: ndcg_at_1000 |
|
value: 46.31 |
|
- type: ndcg_at_3 |
|
value: 33.793 |
|
- type: ndcg_at_5 |
|
value: 35.909 |
|
- type: precision_at_1 |
|
value: 28.825 |
|
- type: precision_at_10 |
|
value: 6.567 |
|
- type: precision_at_100 |
|
value: 1.0330000000000001 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 15.516 |
|
- type: precision_at_5 |
|
value: 10.914 |
|
- type: recall_at_1 |
|
value: 24.396 |
|
- type: recall_at_10 |
|
value: 50.747 |
|
- type: recall_at_100 |
|
value: 73.477 |
|
- type: recall_at_1000 |
|
value: 90.801 |
|
- type: recall_at_3 |
|
value: 37.1 |
|
- type: recall_at_5 |
|
value: 42.589 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.072 |
|
- type: map_at_10 |
|
value: 34.307 |
|
- type: map_at_100 |
|
value: 35.725 |
|
- type: map_at_1000 |
|
value: 35.943999999999996 |
|
- type: map_at_3 |
|
value: 30.906 |
|
- type: map_at_5 |
|
value: 32.818000000000005 |
|
- type: mrr_at_1 |
|
value: 29.644 |
|
- type: mrr_at_10 |
|
value: 38.673 |
|
- type: mrr_at_100 |
|
value: 39.459 |
|
- type: mrr_at_1000 |
|
value: 39.527 |
|
- type: mrr_at_3 |
|
value: 35.771 |
|
- type: mrr_at_5 |
|
value: 37.332 |
|
- type: ndcg_at_1 |
|
value: 29.644 |
|
- type: ndcg_at_10 |
|
value: 40.548 |
|
- type: ndcg_at_100 |
|
value: 45.678999999999995 |
|
- type: ndcg_at_1000 |
|
value: 48.488 |
|
- type: ndcg_at_3 |
|
value: 34.887 |
|
- type: ndcg_at_5 |
|
value: 37.543 |
|
- type: precision_at_1 |
|
value: 29.644 |
|
- type: precision_at_10 |
|
value: 7.688000000000001 |
|
- type: precision_at_100 |
|
value: 1.482 |
|
- type: precision_at_1000 |
|
value: 0.23600000000000002 |
|
- type: precision_at_3 |
|
value: 16.206 |
|
- type: precision_at_5 |
|
value: 12.016 |
|
- type: recall_at_1 |
|
value: 25.072 |
|
- type: recall_at_10 |
|
value: 53.478 |
|
- type: recall_at_100 |
|
value: 76.07300000000001 |
|
- type: recall_at_1000 |
|
value: 93.884 |
|
- type: recall_at_3 |
|
value: 37.583 |
|
- type: recall_at_5 |
|
value: 44.464 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.712 |
|
- type: map_at_10 |
|
value: 27.467999999999996 |
|
- type: map_at_100 |
|
value: 28.502 |
|
- type: map_at_1000 |
|
value: 28.610000000000003 |
|
- type: map_at_3 |
|
value: 24.887999999999998 |
|
- type: map_at_5 |
|
value: 26.273999999999997 |
|
- type: mrr_at_1 |
|
value: 22.736 |
|
- type: mrr_at_10 |
|
value: 29.553 |
|
- type: mrr_at_100 |
|
value: 30.485 |
|
- type: mrr_at_1000 |
|
value: 30.56 |
|
- type: mrr_at_3 |
|
value: 27.078999999999997 |
|
- type: mrr_at_5 |
|
value: 28.401 |
|
- type: ndcg_at_1 |
|
value: 22.736 |
|
- type: ndcg_at_10 |
|
value: 32.023 |
|
- type: ndcg_at_100 |
|
value: 37.158 |
|
- type: ndcg_at_1000 |
|
value: 39.823 |
|
- type: ndcg_at_3 |
|
value: 26.951999999999998 |
|
- type: ndcg_at_5 |
|
value: 29.281000000000002 |
|
- type: precision_at_1 |
|
value: 22.736 |
|
- type: precision_at_10 |
|
value: 5.213 |
|
- type: precision_at_100 |
|
value: 0.832 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 11.459999999999999 |
|
- type: precision_at_5 |
|
value: 8.244 |
|
- type: recall_at_1 |
|
value: 20.712 |
|
- type: recall_at_10 |
|
value: 44.057 |
|
- type: recall_at_100 |
|
value: 67.944 |
|
- type: recall_at_1000 |
|
value: 87.925 |
|
- type: recall_at_3 |
|
value: 30.305 |
|
- type: recall_at_5 |
|
value: 36.071999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.181999999999999 |
|
- type: map_at_10 |
|
value: 16.66 |
|
- type: map_at_100 |
|
value: 18.273 |
|
- type: map_at_1000 |
|
value: 18.45 |
|
- type: map_at_3 |
|
value: 14.141 |
|
- type: map_at_5 |
|
value: 15.455 |
|
- type: mrr_at_1 |
|
value: 22.15 |
|
- type: mrr_at_10 |
|
value: 32.062000000000005 |
|
- type: mrr_at_100 |
|
value: 33.116 |
|
- type: mrr_at_1000 |
|
value: 33.168 |
|
- type: mrr_at_3 |
|
value: 28.827 |
|
- type: mrr_at_5 |
|
value: 30.892999999999997 |
|
- type: ndcg_at_1 |
|
value: 22.15 |
|
- type: ndcg_at_10 |
|
value: 23.532 |
|
- type: ndcg_at_100 |
|
value: 30.358 |
|
- type: ndcg_at_1000 |
|
value: 33.783 |
|
- type: ndcg_at_3 |
|
value: 19.222 |
|
- type: ndcg_at_5 |
|
value: 20.919999999999998 |
|
- type: precision_at_1 |
|
value: 22.15 |
|
- type: precision_at_10 |
|
value: 7.185999999999999 |
|
- type: precision_at_100 |
|
value: 1.433 |
|
- type: precision_at_1000 |
|
value: 0.207 |
|
- type: precision_at_3 |
|
value: 13.941 |
|
- type: precision_at_5 |
|
value: 10.906 |
|
- type: recall_at_1 |
|
value: 10.181999999999999 |
|
- type: recall_at_10 |
|
value: 28.104000000000003 |
|
- type: recall_at_100 |
|
value: 51.998999999999995 |
|
- type: recall_at_1000 |
|
value: 71.311 |
|
- type: recall_at_3 |
|
value: 17.698 |
|
- type: recall_at_5 |
|
value: 22.262999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.669 |
|
- type: map_at_10 |
|
value: 15.552 |
|
- type: map_at_100 |
|
value: 21.865000000000002 |
|
- type: map_at_1000 |
|
value: 23.268 |
|
- type: map_at_3 |
|
value: 11.309 |
|
- type: map_at_5 |
|
value: 13.084000000000001 |
|
- type: mrr_at_1 |
|
value: 55.50000000000001 |
|
- type: mrr_at_10 |
|
value: 66.46600000000001 |
|
- type: mrr_at_100 |
|
value: 66.944 |
|
- type: mrr_at_1000 |
|
value: 66.956 |
|
- type: mrr_at_3 |
|
value: 64.542 |
|
- type: mrr_at_5 |
|
value: 65.717 |
|
- type: ndcg_at_1 |
|
value: 44.75 |
|
- type: ndcg_at_10 |
|
value: 35.049 |
|
- type: ndcg_at_100 |
|
value: 39.073 |
|
- type: ndcg_at_1000 |
|
value: 46.208 |
|
- type: ndcg_at_3 |
|
value: 39.525 |
|
- type: ndcg_at_5 |
|
value: 37.156 |
|
- type: precision_at_1 |
|
value: 55.50000000000001 |
|
- type: precision_at_10 |
|
value: 27.800000000000004 |
|
- type: precision_at_100 |
|
value: 9.013 |
|
- type: precision_at_1000 |
|
value: 1.8800000000000001 |
|
- type: precision_at_3 |
|
value: 42.667 |
|
- type: precision_at_5 |
|
value: 36.0 |
|
- type: recall_at_1 |
|
value: 6.669 |
|
- type: recall_at_10 |
|
value: 21.811 |
|
- type: recall_at_100 |
|
value: 45.112 |
|
- type: recall_at_1000 |
|
value: 67.806 |
|
- type: recall_at_3 |
|
value: 13.373 |
|
- type: recall_at_5 |
|
value: 16.615 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 48.769999999999996 |
|
- type: f1 |
|
value: 42.91448356376592 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 54.013 |
|
- type: map_at_10 |
|
value: 66.239 |
|
- type: map_at_100 |
|
value: 66.62599999999999 |
|
- type: map_at_1000 |
|
value: 66.644 |
|
- type: map_at_3 |
|
value: 63.965 |
|
- type: map_at_5 |
|
value: 65.45400000000001 |
|
- type: mrr_at_1 |
|
value: 58.221000000000004 |
|
- type: mrr_at_10 |
|
value: 70.43700000000001 |
|
- type: mrr_at_100 |
|
value: 70.744 |
|
- type: mrr_at_1000 |
|
value: 70.75099999999999 |
|
- type: mrr_at_3 |
|
value: 68.284 |
|
- type: mrr_at_5 |
|
value: 69.721 |
|
- type: ndcg_at_1 |
|
value: 58.221000000000004 |
|
- type: ndcg_at_10 |
|
value: 72.327 |
|
- type: ndcg_at_100 |
|
value: 73.953 |
|
- type: ndcg_at_1000 |
|
value: 74.312 |
|
- type: ndcg_at_3 |
|
value: 68.062 |
|
- type: ndcg_at_5 |
|
value: 70.56400000000001 |
|
- type: precision_at_1 |
|
value: 58.221000000000004 |
|
- type: precision_at_10 |
|
value: 9.521 |
|
- type: precision_at_100 |
|
value: 1.045 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_3 |
|
value: 27.348 |
|
- type: precision_at_5 |
|
value: 17.794999999999998 |
|
- type: recall_at_1 |
|
value: 54.013 |
|
- type: recall_at_10 |
|
value: 86.957 |
|
- type: recall_at_100 |
|
value: 93.911 |
|
- type: recall_at_1000 |
|
value: 96.38 |
|
- type: recall_at_3 |
|
value: 75.555 |
|
- type: recall_at_5 |
|
value: 81.671 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.254 |
|
- type: map_at_10 |
|
value: 33.723 |
|
- type: map_at_100 |
|
value: 35.574 |
|
- type: map_at_1000 |
|
value: 35.730000000000004 |
|
- type: map_at_3 |
|
value: 29.473 |
|
- type: map_at_5 |
|
value: 31.543 |
|
- type: mrr_at_1 |
|
value: 41.358 |
|
- type: mrr_at_10 |
|
value: 49.498 |
|
- type: mrr_at_100 |
|
value: 50.275999999999996 |
|
- type: mrr_at_1000 |
|
value: 50.308 |
|
- type: mrr_at_3 |
|
value: 47.016000000000005 |
|
- type: mrr_at_5 |
|
value: 48.336 |
|
- type: ndcg_at_1 |
|
value: 41.358 |
|
- type: ndcg_at_10 |
|
value: 41.579 |
|
- type: ndcg_at_100 |
|
value: 48.455 |
|
- type: ndcg_at_1000 |
|
value: 51.165000000000006 |
|
- type: ndcg_at_3 |
|
value: 37.681 |
|
- type: ndcg_at_5 |
|
value: 38.49 |
|
- type: precision_at_1 |
|
value: 41.358 |
|
- type: precision_at_10 |
|
value: 11.543000000000001 |
|
- type: precision_at_100 |
|
value: 1.87 |
|
- type: precision_at_1000 |
|
value: 0.23600000000000002 |
|
- type: precision_at_3 |
|
value: 24.743000000000002 |
|
- type: precision_at_5 |
|
value: 17.994 |
|
- type: recall_at_1 |
|
value: 21.254 |
|
- type: recall_at_10 |
|
value: 48.698 |
|
- type: recall_at_100 |
|
value: 74.588 |
|
- type: recall_at_1000 |
|
value: 91.00200000000001 |
|
- type: recall_at_3 |
|
value: 33.939 |
|
- type: recall_at_5 |
|
value: 39.367000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 35.922 |
|
- type: map_at_10 |
|
value: 52.32599999999999 |
|
- type: map_at_100 |
|
value: 53.18000000000001 |
|
- type: map_at_1000 |
|
value: 53.245 |
|
- type: map_at_3 |
|
value: 49.294 |
|
- type: map_at_5 |
|
value: 51.202999999999996 |
|
- type: mrr_at_1 |
|
value: 71.843 |
|
- type: mrr_at_10 |
|
value: 78.24600000000001 |
|
- type: mrr_at_100 |
|
value: 78.515 |
|
- type: mrr_at_1000 |
|
value: 78.527 |
|
- type: mrr_at_3 |
|
value: 77.17500000000001 |
|
- type: mrr_at_5 |
|
value: 77.852 |
|
- type: ndcg_at_1 |
|
value: 71.843 |
|
- type: ndcg_at_10 |
|
value: 61.379 |
|
- type: ndcg_at_100 |
|
value: 64.535 |
|
- type: ndcg_at_1000 |
|
value: 65.888 |
|
- type: ndcg_at_3 |
|
value: 56.958 |
|
- type: ndcg_at_5 |
|
value: 59.434 |
|
- type: precision_at_1 |
|
value: 71.843 |
|
- type: precision_at_10 |
|
value: 12.686 |
|
- type: precision_at_100 |
|
value: 1.517 |
|
- type: precision_at_1000 |
|
value: 0.16999999999999998 |
|
- type: precision_at_3 |
|
value: 35.778 |
|
- type: precision_at_5 |
|
value: 23.422 |
|
- type: recall_at_1 |
|
value: 35.922 |
|
- type: recall_at_10 |
|
value: 63.43 |
|
- type: recall_at_100 |
|
value: 75.868 |
|
- type: recall_at_1000 |
|
value: 84.88900000000001 |
|
- type: recall_at_3 |
|
value: 53.666000000000004 |
|
- type: recall_at_5 |
|
value: 58.555 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 79.4408 |
|
- type: ap |
|
value: 73.52820871620366 |
|
- type: f1 |
|
value: 79.36240238685001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.826999999999998 |
|
- type: map_at_10 |
|
value: 34.04 |
|
- type: map_at_100 |
|
value: 35.226 |
|
- type: map_at_1000 |
|
value: 35.275 |
|
- type: map_at_3 |
|
value: 30.165999999999997 |
|
- type: map_at_5 |
|
value: 32.318000000000005 |
|
- type: mrr_at_1 |
|
value: 22.464000000000002 |
|
- type: mrr_at_10 |
|
value: 34.631 |
|
- type: mrr_at_100 |
|
value: 35.752 |
|
- type: mrr_at_1000 |
|
value: 35.795 |
|
- type: mrr_at_3 |
|
value: 30.798 |
|
- type: mrr_at_5 |
|
value: 32.946999999999996 |
|
- type: ndcg_at_1 |
|
value: 22.464000000000002 |
|
- type: ndcg_at_10 |
|
value: 40.919 |
|
- type: ndcg_at_100 |
|
value: 46.632 |
|
- type: ndcg_at_1000 |
|
value: 47.833 |
|
- type: ndcg_at_3 |
|
value: 32.992 |
|
- type: ndcg_at_5 |
|
value: 36.834 |
|
- type: precision_at_1 |
|
value: 22.464000000000002 |
|
- type: precision_at_10 |
|
value: 6.494 |
|
- type: precision_at_100 |
|
value: 0.9369999999999999 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.021 |
|
- type: precision_at_5 |
|
value: 10.347000000000001 |
|
- type: recall_at_1 |
|
value: 21.826999999999998 |
|
- type: recall_at_10 |
|
value: 62.132 |
|
- type: recall_at_100 |
|
value: 88.55199999999999 |
|
- type: recall_at_1000 |
|
value: 97.707 |
|
- type: recall_at_3 |
|
value: 40.541 |
|
- type: recall_at_5 |
|
value: 49.739 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 95.68399452804377 |
|
- type: f1 |
|
value: 95.25490609832268 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 83.15321477428182 |
|
- type: f1 |
|
value: 60.35476439087966 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 71.92669804976462 |
|
- type: f1 |
|
value: 69.22815107207565 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 74.4855413584398 |
|
- type: f1 |
|
value: 72.92107516103387 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 32.412679360205544 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.09211869875204 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.540919056982545 |
|
- type: mrr |
|
value: 31.529904607063536 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.745 |
|
- type: map_at_10 |
|
value: 12.013 |
|
- type: map_at_100 |
|
value: 15.040000000000001 |
|
- type: map_at_1000 |
|
value: 16.427 |
|
- type: map_at_3 |
|
value: 8.841000000000001 |
|
- type: map_at_5 |
|
value: 10.289 |
|
- type: mrr_at_1 |
|
value: 45.201 |
|
- type: mrr_at_10 |
|
value: 53.483999999999995 |
|
- type: mrr_at_100 |
|
value: 54.20700000000001 |
|
- type: mrr_at_1000 |
|
value: 54.252 |
|
- type: mrr_at_3 |
|
value: 51.29 |
|
- type: mrr_at_5 |
|
value: 52.73 |
|
- type: ndcg_at_1 |
|
value: 43.808 |
|
- type: ndcg_at_10 |
|
value: 32.445 |
|
- type: ndcg_at_100 |
|
value: 30.031000000000002 |
|
- type: ndcg_at_1000 |
|
value: 39.007 |
|
- type: ndcg_at_3 |
|
value: 37.204 |
|
- type: ndcg_at_5 |
|
value: 35.07 |
|
- type: precision_at_1 |
|
value: 45.201 |
|
- type: precision_at_10 |
|
value: 23.684 |
|
- type: precision_at_100 |
|
value: 7.600999999999999 |
|
- type: precision_at_1000 |
|
value: 2.043 |
|
- type: precision_at_3 |
|
value: 33.953 |
|
- type: precision_at_5 |
|
value: 29.412 |
|
- type: recall_at_1 |
|
value: 5.745 |
|
- type: recall_at_10 |
|
value: 16.168 |
|
- type: recall_at_100 |
|
value: 30.875999999999998 |
|
- type: recall_at_1000 |
|
value: 62.686 |
|
- type: recall_at_3 |
|
value: 9.75 |
|
- type: recall_at_5 |
|
value: 12.413 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.828 |
|
- type: map_at_10 |
|
value: 53.239000000000004 |
|
- type: map_at_100 |
|
value: 54.035999999999994 |
|
- type: map_at_1000 |
|
value: 54.067 |
|
- type: map_at_3 |
|
value: 49.289 |
|
- type: map_at_5 |
|
value: 51.784 |
|
- type: mrr_at_1 |
|
value: 42.497 |
|
- type: mrr_at_10 |
|
value: 55.916999999999994 |
|
- type: mrr_at_100 |
|
value: 56.495 |
|
- type: mrr_at_1000 |
|
value: 56.516999999999996 |
|
- type: mrr_at_3 |
|
value: 52.800000000000004 |
|
- type: mrr_at_5 |
|
value: 54.722 |
|
- type: ndcg_at_1 |
|
value: 42.468 |
|
- type: ndcg_at_10 |
|
value: 60.437 |
|
- type: ndcg_at_100 |
|
value: 63.731 |
|
- type: ndcg_at_1000 |
|
value: 64.41799999999999 |
|
- type: ndcg_at_3 |
|
value: 53.230999999999995 |
|
- type: ndcg_at_5 |
|
value: 57.26 |
|
- type: precision_at_1 |
|
value: 42.468 |
|
- type: precision_at_10 |
|
value: 9.47 |
|
- type: precision_at_100 |
|
value: 1.1360000000000001 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 23.724999999999998 |
|
- type: precision_at_5 |
|
value: 16.593 |
|
- type: recall_at_1 |
|
value: 37.828 |
|
- type: recall_at_10 |
|
value: 79.538 |
|
- type: recall_at_100 |
|
value: 93.646 |
|
- type: recall_at_1000 |
|
value: 98.72999999999999 |
|
- type: recall_at_3 |
|
value: 61.134 |
|
- type: recall_at_5 |
|
value: 70.377 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.548 |
|
- type: map_at_10 |
|
value: 84.466 |
|
- type: map_at_100 |
|
value: 85.10600000000001 |
|
- type: map_at_1000 |
|
value: 85.123 |
|
- type: map_at_3 |
|
value: 81.57600000000001 |
|
- type: map_at_5 |
|
value: 83.399 |
|
- type: mrr_at_1 |
|
value: 81.24 |
|
- type: mrr_at_10 |
|
value: 87.457 |
|
- type: mrr_at_100 |
|
value: 87.574 |
|
- type: mrr_at_1000 |
|
value: 87.575 |
|
- type: mrr_at_3 |
|
value: 86.507 |
|
- type: mrr_at_5 |
|
value: 87.205 |
|
- type: ndcg_at_1 |
|
value: 81.25 |
|
- type: ndcg_at_10 |
|
value: 88.203 |
|
- type: ndcg_at_100 |
|
value: 89.457 |
|
- type: ndcg_at_1000 |
|
value: 89.563 |
|
- type: ndcg_at_3 |
|
value: 85.465 |
|
- type: ndcg_at_5 |
|
value: 87.007 |
|
- type: precision_at_1 |
|
value: 81.25 |
|
- type: precision_at_10 |
|
value: 13.373 |
|
- type: precision_at_100 |
|
value: 1.5270000000000001 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.417 |
|
- type: precision_at_5 |
|
value: 24.556 |
|
- type: recall_at_1 |
|
value: 70.548 |
|
- type: recall_at_10 |
|
value: 95.208 |
|
- type: recall_at_100 |
|
value: 99.514 |
|
- type: recall_at_1000 |
|
value: 99.988 |
|
- type: recall_at_3 |
|
value: 87.214 |
|
- type: recall_at_5 |
|
value: 91.696 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 53.04822095496839 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 60.30778476474675 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.692 |
|
- type: map_at_10 |
|
value: 11.766 |
|
- type: map_at_100 |
|
value: 13.904 |
|
- type: map_at_1000 |
|
value: 14.216999999999999 |
|
- type: map_at_3 |
|
value: 8.245 |
|
- type: map_at_5 |
|
value: 9.92 |
|
- type: mrr_at_1 |
|
value: 23.0 |
|
- type: mrr_at_10 |
|
value: 33.78 |
|
- type: mrr_at_100 |
|
value: 34.922 |
|
- type: mrr_at_1000 |
|
value: 34.973 |
|
- type: mrr_at_3 |
|
value: 30.2 |
|
- type: mrr_at_5 |
|
value: 32.565 |
|
- type: ndcg_at_1 |
|
value: 23.0 |
|
- type: ndcg_at_10 |
|
value: 19.863 |
|
- type: ndcg_at_100 |
|
value: 28.141 |
|
- type: ndcg_at_1000 |
|
value: 33.549 |
|
- type: ndcg_at_3 |
|
value: 18.434 |
|
- type: ndcg_at_5 |
|
value: 16.384 |
|
- type: precision_at_1 |
|
value: 23.0 |
|
- type: precision_at_10 |
|
value: 10.39 |
|
- type: precision_at_100 |
|
value: 2.235 |
|
- type: precision_at_1000 |
|
value: 0.35300000000000004 |
|
- type: precision_at_3 |
|
value: 17.133000000000003 |
|
- type: precision_at_5 |
|
value: 14.44 |
|
- type: recall_at_1 |
|
value: 4.692 |
|
- type: recall_at_10 |
|
value: 21.025 |
|
- type: recall_at_100 |
|
value: 45.324999999999996 |
|
- type: recall_at_1000 |
|
value: 71.675 |
|
- type: recall_at_3 |
|
value: 10.440000000000001 |
|
- type: recall_at_5 |
|
value: 14.64 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.96178184892842 |
|
- type: cos_sim_spearman |
|
value: 79.6487740813199 |
|
- type: euclidean_pearson |
|
value: 82.06661161625023 |
|
- type: euclidean_spearman |
|
value: 79.64876769031183 |
|
- type: manhattan_pearson |
|
value: 82.07061164575131 |
|
- type: manhattan_spearman |
|
value: 79.65197039464537 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.15305604100027 |
|
- type: cos_sim_spearman |
|
value: 74.27447427941591 |
|
- type: euclidean_pearson |
|
value: 80.52737337565307 |
|
- type: euclidean_spearman |
|
value: 74.27416077132192 |
|
- type: manhattan_pearson |
|
value: 80.53728571140387 |
|
- type: manhattan_spearman |
|
value: 74.28853605753457 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.44386080639279 |
|
- type: cos_sim_spearman |
|
value: 84.17947648159536 |
|
- type: euclidean_pearson |
|
value: 83.34145388129387 |
|
- type: euclidean_spearman |
|
value: 84.17947648159536 |
|
- type: manhattan_pearson |
|
value: 83.30699061927966 |
|
- type: manhattan_spearman |
|
value: 84.18125737380451 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.57392220985612 |
|
- type: cos_sim_spearman |
|
value: 78.80745014464101 |
|
- type: euclidean_pearson |
|
value: 80.01660371487199 |
|
- type: euclidean_spearman |
|
value: 78.80741240102256 |
|
- type: manhattan_pearson |
|
value: 79.96810779507953 |
|
- type: manhattan_spearman |
|
value: 78.75600400119448 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.85421063026625 |
|
- type: cos_sim_spearman |
|
value: 87.55320285299192 |
|
- type: euclidean_pearson |
|
value: 86.69750143323517 |
|
- type: euclidean_spearman |
|
value: 87.55320284326378 |
|
- type: manhattan_pearson |
|
value: 86.63379169960379 |
|
- type: manhattan_spearman |
|
value: 87.4815029877984 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.31314130411842 |
|
- type: cos_sim_spearman |
|
value: 85.3489588181433 |
|
- type: euclidean_pearson |
|
value: 84.13240933463535 |
|
- type: euclidean_spearman |
|
value: 85.34902871403281 |
|
- type: manhattan_pearson |
|
value: 84.01183086503559 |
|
- type: manhattan_spearman |
|
value: 85.19316703166102 |
|
- 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: 89.09979781689536 |
|
- type: cos_sim_spearman |
|
value: 88.87813323759015 |
|
- type: euclidean_pearson |
|
value: 88.65413031123792 |
|
- type: euclidean_spearman |
|
value: 88.87813323759015 |
|
- type: manhattan_pearson |
|
value: 88.61818758256024 |
|
- type: manhattan_spearman |
|
value: 88.81044100494604 |
|
- 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: 62.30693258111531 |
|
- type: cos_sim_spearman |
|
value: 62.195516523251946 |
|
- type: euclidean_pearson |
|
value: 62.951283701049476 |
|
- type: euclidean_spearman |
|
value: 62.195516523251946 |
|
- type: manhattan_pearson |
|
value: 63.068322281439535 |
|
- type: manhattan_spearman |
|
value: 62.10621171028406 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.27092833763909 |
|
- type: cos_sim_spearman |
|
value: 84.84429717949759 |
|
- type: euclidean_pearson |
|
value: 84.8516966060792 |
|
- type: euclidean_spearman |
|
value: 84.84429717949759 |
|
- type: manhattan_pearson |
|
value: 84.82203139242881 |
|
- type: manhattan_spearman |
|
value: 84.8358503952945 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 83.10290863981409 |
|
- type: mrr |
|
value: 95.31168450286097 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 52.161 |
|
- type: map_at_10 |
|
value: 62.138000000000005 |
|
- type: map_at_100 |
|
value: 62.769 |
|
- type: map_at_1000 |
|
value: 62.812 |
|
- type: map_at_3 |
|
value: 59.111000000000004 |
|
- type: map_at_5 |
|
value: 60.995999999999995 |
|
- type: mrr_at_1 |
|
value: 55.333 |
|
- type: mrr_at_10 |
|
value: 63.504000000000005 |
|
- type: mrr_at_100 |
|
value: 64.036 |
|
- type: mrr_at_1000 |
|
value: 64.08 |
|
- type: mrr_at_3 |
|
value: 61.278 |
|
- type: mrr_at_5 |
|
value: 62.778 |
|
- type: ndcg_at_1 |
|
value: 55.333 |
|
- type: ndcg_at_10 |
|
value: 66.678 |
|
- type: ndcg_at_100 |
|
value: 69.415 |
|
- type: ndcg_at_1000 |
|
value: 70.453 |
|
- type: ndcg_at_3 |
|
value: 61.755 |
|
- type: ndcg_at_5 |
|
value: 64.546 |
|
- type: precision_at_1 |
|
value: 55.333 |
|
- type: precision_at_10 |
|
value: 9.033 |
|
- type: precision_at_100 |
|
value: 1.043 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 24.221999999999998 |
|
- type: precision_at_5 |
|
value: 16.333000000000002 |
|
- type: recall_at_1 |
|
value: 52.161 |
|
- type: recall_at_10 |
|
value: 79.156 |
|
- type: recall_at_100 |
|
value: 91.333 |
|
- type: recall_at_1000 |
|
value: 99.333 |
|
- type: recall_at_3 |
|
value: 66.43299999999999 |
|
- type: recall_at_5 |
|
value: 73.272 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81287128712871 |
|
- type: cos_sim_ap |
|
value: 95.30034785910676 |
|
- type: cos_sim_f1 |
|
value: 90.28629856850716 |
|
- type: cos_sim_precision |
|
value: 92.36401673640168 |
|
- type: cos_sim_recall |
|
value: 88.3 |
|
- type: dot_accuracy |
|
value: 99.81287128712871 |
|
- type: dot_ap |
|
value: 95.30034785910676 |
|
- type: dot_f1 |
|
value: 90.28629856850716 |
|
- type: dot_precision |
|
value: 92.36401673640168 |
|
- type: dot_recall |
|
value: 88.3 |
|
- type: euclidean_accuracy |
|
value: 99.81287128712871 |
|
- type: euclidean_ap |
|
value: 95.30034785910676 |
|
- type: euclidean_f1 |
|
value: 90.28629856850716 |
|
- type: euclidean_precision |
|
value: 92.36401673640168 |
|
- type: euclidean_recall |
|
value: 88.3 |
|
- type: manhattan_accuracy |
|
value: 99.80990099009901 |
|
- type: manhattan_ap |
|
value: 95.26880751950654 |
|
- type: manhattan_f1 |
|
value: 90.22177419354838 |
|
- type: manhattan_precision |
|
value: 90.95528455284553 |
|
- type: manhattan_recall |
|
value: 89.5 |
|
- type: max_accuracy |
|
value: 99.81287128712871 |
|
- type: max_ap |
|
value: 95.30034785910676 |
|
- type: max_f1 |
|
value: 90.28629856850716 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 58.518662504351184 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 34.96168178378587 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 52.04862593471896 |
|
- type: mrr |
|
value: 52.97238402936932 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.092545236479946 |
|
- type: cos_sim_spearman |
|
value: 31.599851000175498 |
|
- type: dot_pearson |
|
value: 30.092542723901676 |
|
- type: dot_spearman |
|
value: 31.599851000175498 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.189 |
|
- type: map_at_10 |
|
value: 1.662 |
|
- type: map_at_100 |
|
value: 9.384 |
|
- type: map_at_1000 |
|
value: 22.669 |
|
- type: map_at_3 |
|
value: 0.5559999999999999 |
|
- type: map_at_5 |
|
value: 0.9039999999999999 |
|
- type: mrr_at_1 |
|
value: 68.0 |
|
- type: mrr_at_10 |
|
value: 81.01899999999999 |
|
- type: mrr_at_100 |
|
value: 81.01899999999999 |
|
- type: mrr_at_1000 |
|
value: 81.01899999999999 |
|
- type: mrr_at_3 |
|
value: 79.333 |
|
- type: mrr_at_5 |
|
value: 80.733 |
|
- type: ndcg_at_1 |
|
value: 63.0 |
|
- type: ndcg_at_10 |
|
value: 65.913 |
|
- type: ndcg_at_100 |
|
value: 51.895 |
|
- type: ndcg_at_1000 |
|
value: 46.967 |
|
- type: ndcg_at_3 |
|
value: 65.49199999999999 |
|
- type: ndcg_at_5 |
|
value: 66.69699999999999 |
|
- type: precision_at_1 |
|
value: 68.0 |
|
- type: precision_at_10 |
|
value: 71.6 |
|
- type: precision_at_100 |
|
value: 53.66 |
|
- type: precision_at_1000 |
|
value: 21.124000000000002 |
|
- type: precision_at_3 |
|
value: 72.667 |
|
- type: precision_at_5 |
|
value: 74.0 |
|
- type: recall_at_1 |
|
value: 0.189 |
|
- type: recall_at_10 |
|
value: 1.913 |
|
- type: recall_at_100 |
|
value: 12.601999999999999 |
|
- type: recall_at_1000 |
|
value: 44.296 |
|
- type: recall_at_3 |
|
value: 0.605 |
|
- type: recall_at_5 |
|
value: 1.018 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.701 |
|
- type: map_at_10 |
|
value: 10.445 |
|
- type: map_at_100 |
|
value: 17.324 |
|
- type: map_at_1000 |
|
value: 19.161 |
|
- type: map_at_3 |
|
value: 5.497 |
|
- type: map_at_5 |
|
value: 7.278 |
|
- type: mrr_at_1 |
|
value: 30.612000000000002 |
|
- type: mrr_at_10 |
|
value: 45.534 |
|
- type: mrr_at_100 |
|
value: 45.792 |
|
- type: mrr_at_1000 |
|
value: 45.806999999999995 |
|
- type: mrr_at_3 |
|
value: 37.755 |
|
- type: mrr_at_5 |
|
value: 43.469 |
|
- type: ndcg_at_1 |
|
value: 26.531 |
|
- type: ndcg_at_10 |
|
value: 26.235000000000003 |
|
- type: ndcg_at_100 |
|
value: 39.17 |
|
- type: ndcg_at_1000 |
|
value: 51.038 |
|
- type: ndcg_at_3 |
|
value: 23.625 |
|
- type: ndcg_at_5 |
|
value: 24.338 |
|
- type: precision_at_1 |
|
value: 30.612000000000002 |
|
- type: precision_at_10 |
|
value: 24.285999999999998 |
|
- type: precision_at_100 |
|
value: 8.224 |
|
- type: precision_at_1000 |
|
value: 1.6179999999999999 |
|
- type: precision_at_3 |
|
value: 24.490000000000002 |
|
- type: precision_at_5 |
|
value: 24.898 |
|
- type: recall_at_1 |
|
value: 2.701 |
|
- type: recall_at_10 |
|
value: 17.997 |
|
- type: recall_at_100 |
|
value: 51.766999999999996 |
|
- type: recall_at_1000 |
|
value: 87.863 |
|
- type: recall_at_3 |
|
value: 6.295000000000001 |
|
- type: recall_at_5 |
|
value: 9.993 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 73.3474 |
|
- type: ap |
|
value: 15.393431414459924 |
|
- type: f1 |
|
value: 56.466681887882416 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 62.062818336163 |
|
- type: f1 |
|
value: 62.11230840463252 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 42.464892820845115 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.15962329379508 |
|
- type: cos_sim_ap |
|
value: 74.73674057919256 |
|
- type: cos_sim_f1 |
|
value: 68.81245642574947 |
|
- type: cos_sim_precision |
|
value: 61.48255813953488 |
|
- type: cos_sim_recall |
|
value: 78.12664907651715 |
|
- type: dot_accuracy |
|
value: 86.15962329379508 |
|
- type: dot_ap |
|
value: 74.7367634988281 |
|
- type: dot_f1 |
|
value: 68.81245642574947 |
|
- type: dot_precision |
|
value: 61.48255813953488 |
|
- type: dot_recall |
|
value: 78.12664907651715 |
|
- type: euclidean_accuracy |
|
value: 86.15962329379508 |
|
- type: euclidean_ap |
|
value: 74.7367761466634 |
|
- type: euclidean_f1 |
|
value: 68.81245642574947 |
|
- type: euclidean_precision |
|
value: 61.48255813953488 |
|
- type: euclidean_recall |
|
value: 78.12664907651715 |
|
- type: manhattan_accuracy |
|
value: 86.21326816474935 |
|
- type: manhattan_ap |
|
value: 74.64416473733951 |
|
- type: manhattan_f1 |
|
value: 68.80924855491331 |
|
- type: manhattan_precision |
|
value: 61.23456790123457 |
|
- type: manhattan_recall |
|
value: 78.52242744063325 |
|
- type: max_accuracy |
|
value: 86.21326816474935 |
|
- type: max_ap |
|
value: 74.7367761466634 |
|
- type: max_f1 |
|
value: 68.81245642574947 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.97620988085536 |
|
- type: cos_sim_ap |
|
value: 86.08680845745758 |
|
- type: cos_sim_f1 |
|
value: 78.02793637114438 |
|
- type: cos_sim_precision |
|
value: 73.11082699683736 |
|
- type: cos_sim_recall |
|
value: 83.65414228518632 |
|
- type: dot_accuracy |
|
value: 88.97620988085536 |
|
- type: dot_ap |
|
value: 86.08681149437946 |
|
- type: dot_f1 |
|
value: 78.02793637114438 |
|
- type: dot_precision |
|
value: 73.11082699683736 |
|
- type: dot_recall |
|
value: 83.65414228518632 |
|
- type: euclidean_accuracy |
|
value: 88.97620988085536 |
|
- type: euclidean_ap |
|
value: 86.08681215460771 |
|
- type: euclidean_f1 |
|
value: 78.02793637114438 |
|
- type: euclidean_precision |
|
value: 73.11082699683736 |
|
- type: euclidean_recall |
|
value: 83.65414228518632 |
|
- type: manhattan_accuracy |
|
value: 88.88888888888889 |
|
- type: manhattan_ap |
|
value: 86.02916327562438 |
|
- type: manhattan_f1 |
|
value: 78.02063045516843 |
|
- type: manhattan_precision |
|
value: 73.38851947346994 |
|
- type: manhattan_recall |
|
value: 83.2768709578072 |
|
- type: max_accuracy |
|
value: 88.97620988085536 |
|
- type: max_ap |
|
value: 86.08681215460771 |
|
- type: max_f1 |
|
value: 78.02793637114438 |
|
--- |
|
<!-- TODO: add evaluation results here --> |
|
<br><br> |
|
|
|
<p align="center"> |
|
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> |
|
</p> |
|
|
|
|
|
<p align="center"> |
|
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> |
|
</p> |
|
|
|
## Quick Start |
|
|
|
The easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). |
|
|
|
## Intended Usage & Model Info |
|
|
|
`jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. |
|
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length. |
|
The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset. |
|
The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. |
|
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. |
|
|
|
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. |
|
This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc. |
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|
|
With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference. |
|
Additionally, we provide the following embedding models: |
|
|
|
- [`jina-embeddings-v2-small-en`](https://huggingface.co./jinaai/jina-embeddings-v2-small-en): 33 million parameters. |
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- [`jina-embeddings-v2-base-en`](https://huggingface.co./jinaai/jina-embeddings-v2-base-en): 137 million parameters **(you are here)**. |
|
- [`jina-embeddings-v2-base-zh`](https://huggingface.co./jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings. |
|
- [`jina-embeddings-v2-base-de`](https://huggingface.co./jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings. |
|
- [`jina-embeddings-v2-base-es`](https://huggingface.co./jinaai/jina-embeddings-v2-base-es): Spanish-English Bilingual embeddings. |
|
|
|
## Data & Parameters |
|
|
|
Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923) |
|
|
|
## Usage |
|
|
|
**<details><summary>Please apply mean pooling when integrating the model.</summary>** |
|
<p> |
|
|
|
### Why mean pooling? |
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|
|
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. |
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It has been proved to be the most effective way to produce high-quality sentence embeddings. |
|
We offer an `encode` function to deal with this. |
|
|
|
However, if you would like to do it without using the default `encode` function: |
|
|
|
```python |
|
import torch |
|
import torch.nn.functional as F |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output[0] |
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
|
|
|
sentences = ['How is the weather today?', 'What is the current weather like today?'] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en') |
|
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) |
|
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
|
|
embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
``` |
|
|
|
</p> |
|
</details> |
|
|
|
You can use Jina Embedding models directly from transformers package. |
|
|
|
First, you need to make sure that you are logged into huggingface. You can either use the huggingface-cli tool (after installing the `transformers` package) and pass your [hugginface access token](https://huggingface.co./docs/hub/security-tokens): |
|
```bash |
|
huggingface-cli login |
|
``` |
|
Alternatively, you can provide the access token as an environment variable in the shell: |
|
```bash |
|
export HF_TOKEN="<your token here>" |
|
``` |
|
or in Python: |
|
```python |
|
import os |
|
|
|
os.environ['HF_TOKEN'] = "<your token here>" |
|
``` |
|
|
|
Then, you can use load and use the model via the `AutoModel` class: |
|
|
|
```python |
|
!pip install transformers |
|
from transformers import AutoModel |
|
from numpy.linalg import norm |
|
|
|
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) |
|
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method |
|
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?']) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function: |
|
|
|
```python |
|
embeddings = model.encode( |
|
['Very long ... document'], |
|
max_length=2048 |
|
) |
|
``` |
|
|
|
Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well): |
|
|
|
```python |
|
!pip install -U sentence-transformers |
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
model = SentenceTransformer( |
|
"jinaai/jina-embeddings-v2-base-en", # switch to en/zh for English or Chinese |
|
trust_remote_code=True |
|
) |
|
|
|
# control your input sequence length up to 8192 |
|
model.max_seq_length = 1024 |
|
|
|
embeddings = model.encode([ |
|
'How is the weather today?', |
|
'What is the current weather like today?' |
|
]) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
## Alternatives to Using Transformers (or SentencTransformers) Package |
|
|
|
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). |
|
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy). |
|
|
|
|
|
## Use Jina Embeddings for RAG |
|
|
|
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83), |
|
|
|
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out. |
|
|
|
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px"> |
|
|
|
|
|
## Plans |
|
|
|
1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese. |
|
2. Multimodal embedding models enable Multimodal RAG applications. |
|
3. High-performt rerankers. |
|
|
|
## Trouble Shooting |
|
|
|
**Loading of Model Code failed** |
|
|
|
If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized. |
|
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model: |
|
|
|
```bash |
|
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ... |
|
``` |
|
|
|
|
|
**User is not logged into Huggingface** |
|
|
|
The model is only availabe under [gated access](https://huggingface.co./docs/hub/models-gated). |
|
This means you need to be logged into huggingface load load it. |
|
If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above: |
|
```bash |
|
OSError: jinaai/jina-embeddings-v2-base-en is not a local folder and is not a valid model identifier listed on 'https://huggingface.co./models' |
|
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`. |
|
``` |
|
|
|
## Contact |
|
|
|
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
|
|
|
## Citation |
|
|
|
If you find Jina Embeddings useful in your research, please cite the following paper: |
|
|
|
``` |
|
@misc{günther2023jina, |
|
title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, |
|
author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao}, |
|
year={2023}, |
|
eprint={2310.19923}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
