|
--- |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- finetuner |
|
- mteb |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- alibi |
|
datasets: |
|
- allenai/c4 |
|
language: en |
|
license: apache-2.0 |
|
model-index: |
|
- name: jina-embedding-b-en-v2 |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 73.4179104477612 |
|
- type: ap |
|
value: 35.798378234524705 |
|
- type: f1 |
|
value: 67.27708504551819 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 88.977575 |
|
- type: ap |
|
value: 85.00359027707599 |
|
- type: f1 |
|
value: 88.9585285941142 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 44.455999999999996 |
|
- type: f1 |
|
value: 42.80615676169829 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.919 |
|
- type: map_at_10 |
|
value: 33.272 |
|
- type: map_at_100 |
|
value: 34.669 |
|
- type: map_at_1000 |
|
value: 34.68 |
|
- type: map_at_3 |
|
value: 28.011000000000003 |
|
- type: map_at_5 |
|
value: 30.767 |
|
- type: mrr_at_1 |
|
value: 19.061 |
|
- type: mrr_at_10 |
|
value: 33.352 |
|
- type: mrr_at_100 |
|
value: 34.75 |
|
- type: mrr_at_1000 |
|
value: 34.760999999999996 |
|
- type: mrr_at_3 |
|
value: 28.07 |
|
- type: mrr_at_5 |
|
value: 30.848 |
|
- type: ndcg_at_1 |
|
value: 18.919 |
|
- type: ndcg_at_10 |
|
value: 42.138 |
|
- type: ndcg_at_100 |
|
value: 48.165 |
|
- type: ndcg_at_1000 |
|
value: 48.435 |
|
- type: ndcg_at_3 |
|
value: 31.041 |
|
- type: ndcg_at_5 |
|
value: 36.015 |
|
- type: precision_at_1 |
|
value: 18.919 |
|
- type: precision_at_10 |
|
value: 7.098 |
|
- type: precision_at_100 |
|
value: 0.9740000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 13.276 |
|
- type: precision_at_5 |
|
value: 10.384 |
|
- type: recall_at_1 |
|
value: 18.919 |
|
- type: recall_at_10 |
|
value: 70.982 |
|
- type: recall_at_100 |
|
value: 97.44 |
|
- type: recall_at_1000 |
|
value: 99.502 |
|
- type: recall_at_3 |
|
value: 39.829 |
|
- type: recall_at_5 |
|
value: 51.92 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 45.38238451470738 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 37.12265635737745 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 62.473921100678695 |
|
- type: mrr |
|
value: 75.28195488721803 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.46030780641742 |
|
- type: cos_sim_spearman |
|
value: 83.29647627997147 |
|
- type: euclidean_pearson |
|
value: 83.63127685751004 |
|
- type: euclidean_spearman |
|
value: 83.29647627997147 |
|
- type: manhattan_pearson |
|
value: 83.29505322210208 |
|
- type: manhattan_spearman |
|
value: 82.8398393691656 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 83.94480519480521 |
|
- type: f1 |
|
value: 83.26406143364741 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 37.15926312173139 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 31.20469085642121 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.462 |
|
- type: map_at_10 |
|
value: 39.834 |
|
- type: map_at_100 |
|
value: 41.329 |
|
- type: map_at_1000 |
|
value: 41.465 |
|
- type: map_at_3 |
|
value: 36.586999999999996 |
|
- type: map_at_5 |
|
value: 38.239000000000004 |
|
- type: mrr_at_1 |
|
value: 34.335 |
|
- type: mrr_at_10 |
|
value: 45.493 |
|
- type: mrr_at_100 |
|
value: 46.323 |
|
- type: mrr_at_1000 |
|
value: 46.37 |
|
- type: mrr_at_3 |
|
value: 42.870999999999995 |
|
- type: mrr_at_5 |
|
value: 44.502 |
|
- type: ndcg_at_1 |
|
value: 34.335 |
|
- type: ndcg_at_10 |
|
value: 46.434 |
|
- type: ndcg_at_100 |
|
value: 52.013 |
|
- type: ndcg_at_1000 |
|
value: 54.079 |
|
- type: ndcg_at_3 |
|
value: 41.408 |
|
- type: ndcg_at_5 |
|
value: 43.562 |
|
- type: precision_at_1 |
|
value: 34.335 |
|
- type: precision_at_10 |
|
value: 8.913 |
|
- type: precision_at_100 |
|
value: 1.439 |
|
- type: precision_at_1000 |
|
value: 0.197 |
|
- type: precision_at_3 |
|
value: 20.029 |
|
- type: precision_at_5 |
|
value: 14.335 |
|
- type: recall_at_1 |
|
value: 28.462 |
|
- type: recall_at_10 |
|
value: 59.574000000000005 |
|
- type: recall_at_100 |
|
value: 82.631 |
|
- type: recall_at_1000 |
|
value: 95.45700000000001 |
|
- type: recall_at_3 |
|
value: 45.381 |
|
- type: recall_at_5 |
|
value: 51.18000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.245 |
|
- type: map_at_10 |
|
value: 37.156 |
|
- type: map_at_100 |
|
value: 38.464999999999996 |
|
- type: map_at_1000 |
|
value: 38.607 |
|
- type: map_at_3 |
|
value: 34.613 |
|
- type: map_at_5 |
|
value: 35.924 |
|
- type: mrr_at_1 |
|
value: 34.777 |
|
- type: mrr_at_10 |
|
value: 43.425000000000004 |
|
- type: mrr_at_100 |
|
value: 44.163000000000004 |
|
- type: mrr_at_1000 |
|
value: 44.211 |
|
- type: mrr_at_3 |
|
value: 41.391 |
|
- type: mrr_at_5 |
|
value: 42.461 |
|
- type: ndcg_at_1 |
|
value: 34.777 |
|
- type: ndcg_at_10 |
|
value: 42.807 |
|
- type: ndcg_at_100 |
|
value: 47.629 |
|
- type: ndcg_at_1000 |
|
value: 49.84 |
|
- type: ndcg_at_3 |
|
value: 39.28 |
|
- type: ndcg_at_5 |
|
value: 40.671 |
|
- type: precision_at_1 |
|
value: 34.777 |
|
- type: precision_at_10 |
|
value: 8.134 |
|
- type: precision_at_100 |
|
value: 1.3599999999999999 |
|
- type: precision_at_1000 |
|
value: 0.186 |
|
- type: precision_at_3 |
|
value: 19.320999999999998 |
|
- type: precision_at_5 |
|
value: 13.286999999999999 |
|
- type: recall_at_1 |
|
value: 27.245 |
|
- type: recall_at_10 |
|
value: 52.491 |
|
- type: recall_at_100 |
|
value: 73.065 |
|
- type: recall_at_1000 |
|
value: 86.931 |
|
- type: recall_at_3 |
|
value: 41.257 |
|
- type: recall_at_5 |
|
value: 45.811 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 37.088 |
|
- type: map_at_10 |
|
value: 49.003 |
|
- type: map_at_100 |
|
value: 50.017999999999994 |
|
- type: map_at_1000 |
|
value: 50.07899999999999 |
|
- type: map_at_3 |
|
value: 45.846 |
|
- type: map_at_5 |
|
value: 47.733 |
|
- type: mrr_at_1 |
|
value: 42.193999999999996 |
|
- type: mrr_at_10 |
|
value: 52.522999999999996 |
|
- type: mrr_at_100 |
|
value: 53.177 |
|
- type: mrr_at_1000 |
|
value: 53.205999999999996 |
|
- type: mrr_at_3 |
|
value: 49.916 |
|
- type: mrr_at_5 |
|
value: 51.50900000000001 |
|
- type: ndcg_at_1 |
|
value: 42.193999999999996 |
|
- type: ndcg_at_10 |
|
value: 54.99699999999999 |
|
- type: ndcg_at_100 |
|
value: 59.058 |
|
- type: ndcg_at_1000 |
|
value: 60.355000000000004 |
|
- type: ndcg_at_3 |
|
value: 49.515 |
|
- type: ndcg_at_5 |
|
value: 52.412000000000006 |
|
- type: precision_at_1 |
|
value: 42.193999999999996 |
|
- type: precision_at_10 |
|
value: 8.84 |
|
- type: precision_at_100 |
|
value: 1.1820000000000002 |
|
- type: precision_at_1000 |
|
value: 0.134 |
|
- type: precision_at_3 |
|
value: 21.944 |
|
- type: precision_at_5 |
|
value: 15.197 |
|
- type: recall_at_1 |
|
value: 37.088 |
|
- type: recall_at_10 |
|
value: 69.13 |
|
- type: recall_at_100 |
|
value: 86.612 |
|
- type: recall_at_1000 |
|
value: 95.946 |
|
- type: recall_at_3 |
|
value: 54.76 |
|
- type: recall_at_5 |
|
value: 61.76199999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.816 |
|
- type: map_at_10 |
|
value: 30.630000000000003 |
|
- type: map_at_100 |
|
value: 31.641000000000002 |
|
- type: map_at_1000 |
|
value: 31.730999999999998 |
|
- type: map_at_3 |
|
value: 28.153 |
|
- type: map_at_5 |
|
value: 29.433 |
|
- type: mrr_at_1 |
|
value: 23.842 |
|
- type: mrr_at_10 |
|
value: 32.432 |
|
- type: mrr_at_100 |
|
value: 33.354 |
|
- type: mrr_at_1000 |
|
value: 33.421 |
|
- type: mrr_at_3 |
|
value: 30.131999999999998 |
|
- type: mrr_at_5 |
|
value: 31.358000000000004 |
|
- type: ndcg_at_1 |
|
value: 23.842 |
|
- type: ndcg_at_10 |
|
value: 35.626000000000005 |
|
- type: ndcg_at_100 |
|
value: 40.855999999999995 |
|
- type: ndcg_at_1000 |
|
value: 43.111 |
|
- type: ndcg_at_3 |
|
value: 30.712 |
|
- type: ndcg_at_5 |
|
value: 32.912 |
|
- type: precision_at_1 |
|
value: 23.842 |
|
- type: precision_at_10 |
|
value: 5.627 |
|
- type: precision_at_100 |
|
value: 0.873 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 13.333 |
|
- type: precision_at_5 |
|
value: 9.266 |
|
- type: recall_at_1 |
|
value: 21.816 |
|
- type: recall_at_10 |
|
value: 49.370000000000005 |
|
- type: recall_at_100 |
|
value: 73.855 |
|
- type: recall_at_1000 |
|
value: 90.67399999999999 |
|
- type: recall_at_3 |
|
value: 35.85 |
|
- type: recall_at_5 |
|
value: 41.282000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.402000000000001 |
|
- type: map_at_10 |
|
value: 21.401999999999997 |
|
- type: map_at_100 |
|
value: 22.425 |
|
- type: map_at_1000 |
|
value: 22.561 |
|
- type: map_at_3 |
|
value: 19.238 |
|
- type: map_at_5 |
|
value: 20.213 |
|
- type: mrr_at_1 |
|
value: 17.91 |
|
- type: mrr_at_10 |
|
value: 25.629999999999995 |
|
- type: mrr_at_100 |
|
value: 26.529999999999998 |
|
- type: mrr_at_1000 |
|
value: 26.616 |
|
- type: mrr_at_3 |
|
value: 23.362 |
|
- type: mrr_at_5 |
|
value: 24.438 |
|
- type: ndcg_at_1 |
|
value: 17.91 |
|
- type: ndcg_at_10 |
|
value: 26.161 |
|
- type: ndcg_at_100 |
|
value: 31.474000000000004 |
|
- type: ndcg_at_1000 |
|
value: 34.802 |
|
- type: ndcg_at_3 |
|
value: 21.965 |
|
- type: ndcg_at_5 |
|
value: 23.511000000000003 |
|
- type: precision_at_1 |
|
value: 17.91 |
|
- type: precision_at_10 |
|
value: 4.8629999999999995 |
|
- type: precision_at_100 |
|
value: 0.869 |
|
- type: precision_at_1000 |
|
value: 0.129 |
|
- type: precision_at_3 |
|
value: 10.655000000000001 |
|
- type: precision_at_5 |
|
value: 7.5120000000000005 |
|
- type: recall_at_1 |
|
value: 14.402000000000001 |
|
- type: recall_at_10 |
|
value: 36.760999999999996 |
|
- type: recall_at_100 |
|
value: 60.549 |
|
- type: recall_at_1000 |
|
value: 84.414 |
|
- type: recall_at_3 |
|
value: 25.130000000000003 |
|
- type: recall_at_5 |
|
value: 29.079 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.176 |
|
- type: map_at_10 |
|
value: 35.789 |
|
- type: map_at_100 |
|
value: 37.092000000000006 |
|
- type: map_at_1000 |
|
value: 37.206 |
|
- type: map_at_3 |
|
value: 33.207 |
|
- type: map_at_5 |
|
value: 34.436 |
|
- type: mrr_at_1 |
|
value: 31.569000000000003 |
|
- type: mrr_at_10 |
|
value: 41.219 |
|
- type: mrr_at_100 |
|
value: 42.016999999999996 |
|
- type: mrr_at_1000 |
|
value: 42.065000000000005 |
|
- type: mrr_at_3 |
|
value: 39.012 |
|
- type: mrr_at_5 |
|
value: 40.22 |
|
- type: ndcg_at_1 |
|
value: 31.569000000000003 |
|
- type: ndcg_at_10 |
|
value: 41.515 |
|
- type: ndcg_at_100 |
|
value: 47.125 |
|
- type: ndcg_at_1000 |
|
value: 49.314 |
|
- type: ndcg_at_3 |
|
value: 37.201 |
|
- type: ndcg_at_5 |
|
value: 38.906 |
|
- type: precision_at_1 |
|
value: 31.569000000000003 |
|
- type: precision_at_10 |
|
value: 7.517 |
|
- type: precision_at_100 |
|
value: 1.225 |
|
- type: precision_at_1000 |
|
value: 0.161 |
|
- type: precision_at_3 |
|
value: 17.485 |
|
- type: precision_at_5 |
|
value: 12.089 |
|
- type: recall_at_1 |
|
value: 26.176 |
|
- type: recall_at_10 |
|
value: 53.076 |
|
- type: recall_at_100 |
|
value: 77.049 |
|
- type: recall_at_1000 |
|
value: 91.51 |
|
- type: recall_at_3 |
|
value: 40.82 |
|
- type: recall_at_5 |
|
value: 45.479 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.675 |
|
- type: map_at_10 |
|
value: 31.752999999999997 |
|
- type: map_at_100 |
|
value: 33.19 |
|
- type: map_at_1000 |
|
value: 33.303 |
|
- type: map_at_3 |
|
value: 28.89 |
|
- type: map_at_5 |
|
value: 30.451 |
|
- type: mrr_at_1 |
|
value: 27.854 |
|
- type: mrr_at_10 |
|
value: 36.736999999999995 |
|
- type: mrr_at_100 |
|
value: 37.783 |
|
- type: mrr_at_1000 |
|
value: 37.836 |
|
- type: mrr_at_3 |
|
value: 34.266000000000005 |
|
- type: mrr_at_5 |
|
value: 35.577999999999996 |
|
- type: ndcg_at_1 |
|
value: 27.854 |
|
- type: ndcg_at_10 |
|
value: 37.391999999999996 |
|
- type: ndcg_at_100 |
|
value: 43.682 |
|
- type: ndcg_at_1000 |
|
value: 46.005 |
|
- type: ndcg_at_3 |
|
value: 32.66 |
|
- type: ndcg_at_5 |
|
value: 34.73 |
|
- type: precision_at_1 |
|
value: 27.854 |
|
- type: precision_at_10 |
|
value: 6.963 |
|
- type: precision_at_100 |
|
value: 1.184 |
|
- type: precision_at_1000 |
|
value: 0.159 |
|
- type: precision_at_3 |
|
value: 15.715000000000002 |
|
- type: precision_at_5 |
|
value: 11.256 |
|
- type: recall_at_1 |
|
value: 22.675 |
|
- type: recall_at_10 |
|
value: 49.15 |
|
- type: recall_at_100 |
|
value: 76.542 |
|
- type: recall_at_1000 |
|
value: 92.19000000000001 |
|
- type: recall_at_3 |
|
value: 35.607 |
|
- type: recall_at_5 |
|
value: 41.288000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.214499999999997 |
|
- type: map_at_10 |
|
value: 31.979833333333335 |
|
- type: map_at_100 |
|
value: 33.20666666666666 |
|
- type: map_at_1000 |
|
value: 33.328583333333334 |
|
- type: map_at_3 |
|
value: 29.341416666666664 |
|
- type: map_at_5 |
|
value: 30.718083333333336 |
|
- type: mrr_at_1 |
|
value: 27.328583333333338 |
|
- type: mrr_at_10 |
|
value: 35.88433333333333 |
|
- type: mrr_at_100 |
|
value: 36.80075000000001 |
|
- type: mrr_at_1000 |
|
value: 36.86175 |
|
- type: mrr_at_3 |
|
value: 33.51625 |
|
- type: mrr_at_5 |
|
value: 34.821416666666664 |
|
- type: ndcg_at_1 |
|
value: 27.328583333333338 |
|
- type: ndcg_at_10 |
|
value: 37.24475 |
|
- type: ndcg_at_100 |
|
value: 42.63825 |
|
- type: ndcg_at_1000 |
|
value: 45.08266666666667 |
|
- type: ndcg_at_3 |
|
value: 32.61783333333334 |
|
- type: ndcg_at_5 |
|
value: 34.631249999999994 |
|
- type: precision_at_1 |
|
value: 27.328583333333338 |
|
- type: precision_at_10 |
|
value: 6.5873333333333335 |
|
- type: precision_at_100 |
|
value: 1.094916666666667 |
|
- type: precision_at_1000 |
|
value: 0.15091666666666664 |
|
- type: precision_at_3 |
|
value: 15.073499999999997 |
|
- type: precision_at_5 |
|
value: 10.651916666666667 |
|
- type: recall_at_1 |
|
value: 23.214499999999997 |
|
- type: recall_at_10 |
|
value: 49.010250000000006 |
|
- type: recall_at_100 |
|
value: 72.70374999999999 |
|
- type: recall_at_1000 |
|
value: 89.66041666666666 |
|
- type: recall_at_3 |
|
value: 36.06008333333334 |
|
- type: recall_at_5 |
|
value: 41.289166666666674 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.497 |
|
- type: map_at_10 |
|
value: 29.176000000000002 |
|
- type: map_at_100 |
|
value: 30.218 |
|
- type: map_at_1000 |
|
value: 30.317 |
|
- type: map_at_3 |
|
value: 27.072000000000003 |
|
- type: map_at_5 |
|
value: 28.162 |
|
- type: mrr_at_1 |
|
value: 25.919999999999998 |
|
- type: mrr_at_10 |
|
value: 31.513 |
|
- type: mrr_at_100 |
|
value: 32.434000000000005 |
|
- type: mrr_at_1000 |
|
value: 32.507000000000005 |
|
- type: mrr_at_3 |
|
value: 29.576 |
|
- type: mrr_at_5 |
|
value: 30.45 |
|
- type: ndcg_at_1 |
|
value: 25.919999999999998 |
|
- type: ndcg_at_10 |
|
value: 32.958999999999996 |
|
- type: ndcg_at_100 |
|
value: 37.937 |
|
- type: ndcg_at_1000 |
|
value: 40.455000000000005 |
|
- type: ndcg_at_3 |
|
value: 28.969 |
|
- type: ndcg_at_5 |
|
value: 30.552 |
|
- type: precision_at_1 |
|
value: 25.919999999999998 |
|
- type: precision_at_10 |
|
value: 5.106999999999999 |
|
- type: precision_at_100 |
|
value: 0.8170000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 12.117 |
|
- type: precision_at_5 |
|
value: 8.373999999999999 |
|
- type: recall_at_1 |
|
value: 23.497 |
|
- type: recall_at_10 |
|
value: 42.506 |
|
- type: recall_at_100 |
|
value: 65.048 |
|
- type: recall_at_1000 |
|
value: 83.545 |
|
- type: recall_at_3 |
|
value: 31.078 |
|
- type: recall_at_5 |
|
value: 35.018 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.267 |
|
- type: map_at_10 |
|
value: 22.292 |
|
- type: map_at_100 |
|
value: 23.412 |
|
- type: map_at_1000 |
|
value: 23.543 |
|
- type: map_at_3 |
|
value: 19.993 |
|
- type: map_at_5 |
|
value: 21.256 |
|
- type: mrr_at_1 |
|
value: 18.445 |
|
- type: mrr_at_10 |
|
value: 25.698999999999998 |
|
- type: mrr_at_100 |
|
value: 26.682 |
|
- type: mrr_at_1000 |
|
value: 26.764 |
|
- type: mrr_at_3 |
|
value: 23.446 |
|
- type: mrr_at_5 |
|
value: 24.757 |
|
- type: ndcg_at_1 |
|
value: 18.445 |
|
- type: ndcg_at_10 |
|
value: 26.833000000000002 |
|
- type: ndcg_at_100 |
|
value: 32.151999999999994 |
|
- type: ndcg_at_1000 |
|
value: 35.235 |
|
- type: ndcg_at_3 |
|
value: 22.597 |
|
- type: ndcg_at_5 |
|
value: 24.585 |
|
- type: precision_at_1 |
|
value: 18.445 |
|
- type: precision_at_10 |
|
value: 4.942 |
|
- type: precision_at_100 |
|
value: 0.894 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 10.735999999999999 |
|
- type: precision_at_5 |
|
value: 7.915 |
|
- type: recall_at_1 |
|
value: 15.267 |
|
- type: recall_at_10 |
|
value: 37.198 |
|
- type: recall_at_100 |
|
value: 60.748999999999995 |
|
- type: recall_at_1000 |
|
value: 82.72699999999999 |
|
- type: recall_at_3 |
|
value: 25.419000000000004 |
|
- type: recall_at_5 |
|
value: 30.416999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.839000000000002 |
|
- type: map_at_10 |
|
value: 31.287 |
|
- type: map_at_100 |
|
value: 32.474 |
|
- type: map_at_1000 |
|
value: 32.586 |
|
- type: map_at_3 |
|
value: 28.735 |
|
- type: map_at_5 |
|
value: 30.11 |
|
- type: mrr_at_1 |
|
value: 26.959 |
|
- type: mrr_at_10 |
|
value: 34.943000000000005 |
|
- type: mrr_at_100 |
|
value: 35.957 |
|
- type: mrr_at_1000 |
|
value: 36.022 |
|
- type: mrr_at_3 |
|
value: 32.572 |
|
- type: mrr_at_5 |
|
value: 33.952 |
|
- type: ndcg_at_1 |
|
value: 26.959 |
|
- type: ndcg_at_10 |
|
value: 36.252 |
|
- type: ndcg_at_100 |
|
value: 41.915 |
|
- type: ndcg_at_1000 |
|
value: 44.461 |
|
- type: ndcg_at_3 |
|
value: 31.532 |
|
- type: ndcg_at_5 |
|
value: 33.674 |
|
- type: precision_at_1 |
|
value: 26.959 |
|
- type: precision_at_10 |
|
value: 6.166 |
|
- type: precision_at_100 |
|
value: 1.01 |
|
- type: precision_at_1000 |
|
value: 0.134 |
|
- type: precision_at_3 |
|
value: 14.302999999999999 |
|
- type: precision_at_5 |
|
value: 10.131 |
|
- type: recall_at_1 |
|
value: 22.839000000000002 |
|
- type: recall_at_10 |
|
value: 47.796 |
|
- type: recall_at_100 |
|
value: 72.68 |
|
- type: recall_at_1000 |
|
value: 90.556 |
|
- type: recall_at_3 |
|
value: 34.955000000000005 |
|
- type: recall_at_5 |
|
value: 40.293 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.676000000000002 |
|
- type: map_at_10 |
|
value: 30.742000000000004 |
|
- type: map_at_100 |
|
value: 32.332 |
|
- type: map_at_1000 |
|
value: 32.548 |
|
- type: map_at_3 |
|
value: 27.560000000000002 |
|
- type: map_at_5 |
|
value: 29.331000000000003 |
|
- type: mrr_at_1 |
|
value: 25.099 |
|
- type: mrr_at_10 |
|
value: 34.538999999999994 |
|
- type: mrr_at_100 |
|
value: 35.629 |
|
- type: mrr_at_1000 |
|
value: 35.687000000000005 |
|
- type: mrr_at_3 |
|
value: 31.621 |
|
- type: mrr_at_5 |
|
value: 33.419 |
|
- type: ndcg_at_1 |
|
value: 25.099 |
|
- type: ndcg_at_10 |
|
value: 36.741 |
|
- type: ndcg_at_100 |
|
value: 42.964 |
|
- type: ndcg_at_1000 |
|
value: 45.754 |
|
- type: ndcg_at_3 |
|
value: 31.356 |
|
- type: ndcg_at_5 |
|
value: 33.934999999999995 |
|
- type: precision_at_1 |
|
value: 25.099 |
|
- type: precision_at_10 |
|
value: 7.115 |
|
- type: precision_at_100 |
|
value: 1.46 |
|
- type: precision_at_1000 |
|
value: 0.23800000000000002 |
|
- type: precision_at_3 |
|
value: 14.954 |
|
- type: precision_at_5 |
|
value: 11.067 |
|
- type: recall_at_1 |
|
value: 21.676000000000002 |
|
- type: recall_at_10 |
|
value: 49.546 |
|
- type: recall_at_100 |
|
value: 76.544 |
|
- type: recall_at_1000 |
|
value: 94.39999999999999 |
|
- type: recall_at_3 |
|
value: 34.67 |
|
- type: recall_at_5 |
|
value: 41.528999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.431 |
|
- type: map_at_10 |
|
value: 24.694 |
|
- type: map_at_100 |
|
value: 25.884 |
|
- type: map_at_1000 |
|
value: 25.996999999999996 |
|
- type: map_at_3 |
|
value: 22.203 |
|
- type: map_at_5 |
|
value: 23.329 |
|
- type: mrr_at_1 |
|
value: 19.039 |
|
- type: mrr_at_10 |
|
value: 26.459 |
|
- type: mrr_at_100 |
|
value: 27.560000000000002 |
|
- type: mrr_at_1000 |
|
value: 27.636 |
|
- type: mrr_at_3 |
|
value: 24.03 |
|
- type: mrr_at_5 |
|
value: 25.213 |
|
- type: ndcg_at_1 |
|
value: 19.039 |
|
- type: ndcg_at_10 |
|
value: 29.220000000000002 |
|
- type: ndcg_at_100 |
|
value: 34.854 |
|
- type: ndcg_at_1000 |
|
value: 37.580999999999996 |
|
- type: ndcg_at_3 |
|
value: 24.218999999999998 |
|
- type: ndcg_at_5 |
|
value: 26.125 |
|
- type: precision_at_1 |
|
value: 19.039 |
|
- type: precision_at_10 |
|
value: 4.861 |
|
- type: precision_at_100 |
|
value: 0.826 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 10.290000000000001 |
|
- type: precision_at_5 |
|
value: 7.394 |
|
- type: recall_at_1 |
|
value: 17.431 |
|
- type: recall_at_10 |
|
value: 41.525 |
|
- type: recall_at_100 |
|
value: 67.121 |
|
- type: recall_at_1000 |
|
value: 87.575 |
|
- type: recall_at_3 |
|
value: 27.794 |
|
- type: recall_at_5 |
|
value: 32.332 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.767 |
|
- type: map_at_10 |
|
value: 17.456 |
|
- type: map_at_100 |
|
value: 19.097 |
|
- type: map_at_1000 |
|
value: 19.272 |
|
- type: map_at_3 |
|
value: 14.530000000000001 |
|
- type: map_at_5 |
|
value: 15.943999999999999 |
|
- type: mrr_at_1 |
|
value: 23.583000000000002 |
|
- type: mrr_at_10 |
|
value: 33.391 |
|
- type: mrr_at_100 |
|
value: 34.43 |
|
- type: mrr_at_1000 |
|
value: 34.479 |
|
- type: mrr_at_3 |
|
value: 30.239 |
|
- type: mrr_at_5 |
|
value: 31.923000000000002 |
|
- type: ndcg_at_1 |
|
value: 23.583000000000002 |
|
- type: ndcg_at_10 |
|
value: 24.84 |
|
- type: ndcg_at_100 |
|
value: 31.749 |
|
- type: ndcg_at_1000 |
|
value: 35.161 |
|
- type: ndcg_at_3 |
|
value: 19.906 |
|
- type: ndcg_at_5 |
|
value: 21.543 |
|
- type: precision_at_1 |
|
value: 23.583000000000002 |
|
- type: precision_at_10 |
|
value: 7.739 |
|
- type: precision_at_100 |
|
value: 1.5110000000000001 |
|
- type: precision_at_1000 |
|
value: 0.215 |
|
- type: precision_at_3 |
|
value: 14.506 |
|
- type: precision_at_5 |
|
value: 11.179 |
|
- type: recall_at_1 |
|
value: 10.767 |
|
- type: recall_at_10 |
|
value: 30.270000000000003 |
|
- type: recall_at_100 |
|
value: 54.467 |
|
- type: recall_at_1000 |
|
value: 73.71799999999999 |
|
- type: recall_at_3 |
|
value: 18.251 |
|
- type: recall_at_5 |
|
value: 22.831000000000003 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.493 |
|
- type: map_at_10 |
|
value: 15.290999999999999 |
|
- type: map_at_100 |
|
value: 21.523999999999997 |
|
- type: map_at_1000 |
|
value: 22.980999999999998 |
|
- type: map_at_3 |
|
value: 11.015 |
|
- type: map_at_5 |
|
value: 12.631 |
|
- type: mrr_at_1 |
|
value: 55.50000000000001 |
|
- type: mrr_at_10 |
|
value: 65.068 |
|
- type: mrr_at_100 |
|
value: 65.608 |
|
- type: mrr_at_1000 |
|
value: 65.622 |
|
- type: mrr_at_3 |
|
value: 62.625 |
|
- type: mrr_at_5 |
|
value: 64.2 |
|
- type: ndcg_at_1 |
|
value: 44.875 |
|
- type: ndcg_at_10 |
|
value: 35.046 |
|
- type: ndcg_at_100 |
|
value: 38.662 |
|
- type: ndcg_at_1000 |
|
value: 45.916000000000004 |
|
- type: ndcg_at_3 |
|
value: 38.888 |
|
- type: ndcg_at_5 |
|
value: 36.411 |
|
- type: precision_at_1 |
|
value: 55.50000000000001 |
|
- type: precision_at_10 |
|
value: 28.175 |
|
- type: precision_at_100 |
|
value: 8.938 |
|
- type: precision_at_1000 |
|
value: 1.894 |
|
- type: precision_at_3 |
|
value: 41.917 |
|
- type: precision_at_5 |
|
value: 34.949999999999996 |
|
- type: recall_at_1 |
|
value: 6.493 |
|
- type: recall_at_10 |
|
value: 20.992 |
|
- type: recall_at_100 |
|
value: 44.138 |
|
- type: recall_at_1000 |
|
value: 67.181 |
|
- type: recall_at_3 |
|
value: 12.546 |
|
- type: recall_at_5 |
|
value: 15.552 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 45.955 |
|
- type: f1 |
|
value: 40.97084067876041 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 43.765 |
|
- type: map_at_10 |
|
value: 56.566 |
|
- type: map_at_100 |
|
value: 57.154 |
|
- type: map_at_1000 |
|
value: 57.181000000000004 |
|
- type: map_at_3 |
|
value: 53.637 |
|
- type: map_at_5 |
|
value: 55.457 |
|
- type: mrr_at_1 |
|
value: 47.03 |
|
- type: mrr_at_10 |
|
value: 59.938 |
|
- type: mrr_at_100 |
|
value: 60.44500000000001 |
|
- type: mrr_at_1000 |
|
value: 60.458999999999996 |
|
- type: mrr_at_3 |
|
value: 57.141 |
|
- type: mrr_at_5 |
|
value: 58.862 |
|
- type: ndcg_at_1 |
|
value: 47.03 |
|
- type: ndcg_at_10 |
|
value: 63.227 |
|
- type: ndcg_at_100 |
|
value: 65.846 |
|
- type: ndcg_at_1000 |
|
value: 66.412 |
|
- type: ndcg_at_3 |
|
value: 57.546 |
|
- type: ndcg_at_5 |
|
value: 60.638000000000005 |
|
- type: precision_at_1 |
|
value: 47.03 |
|
- type: precision_at_10 |
|
value: 8.831 |
|
- type: precision_at_100 |
|
value: 1.027 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_3 |
|
value: 23.642 |
|
- type: precision_at_5 |
|
value: 15.884 |
|
- type: recall_at_1 |
|
value: 43.765 |
|
- type: recall_at_10 |
|
value: 80.537 |
|
- type: recall_at_100 |
|
value: 92.06400000000001 |
|
- type: recall_at_1000 |
|
value: 96.054 |
|
- type: recall_at_3 |
|
value: 65.27199999999999 |
|
- type: recall_at_5 |
|
value: 72.71 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.684 |
|
- type: map_at_10 |
|
value: 33.393 |
|
- type: map_at_100 |
|
value: 35.370000000000005 |
|
- type: map_at_1000 |
|
value: 35.539 |
|
- type: map_at_3 |
|
value: 28.810000000000002 |
|
- type: map_at_5 |
|
value: 31.484 |
|
- type: mrr_at_1 |
|
value: 41.049 |
|
- type: mrr_at_10 |
|
value: 49.736999999999995 |
|
- type: mrr_at_100 |
|
value: 50.541000000000004 |
|
- type: mrr_at_1000 |
|
value: 50.575 |
|
- type: mrr_at_3 |
|
value: 47.094 |
|
- type: mrr_at_5 |
|
value: 48.768 |
|
- type: ndcg_at_1 |
|
value: 41.049 |
|
- type: ndcg_at_10 |
|
value: 41.338 |
|
- type: ndcg_at_100 |
|
value: 48.386 |
|
- type: ndcg_at_1000 |
|
value: 51.209 |
|
- type: ndcg_at_3 |
|
value: 37.208000000000006 |
|
- type: ndcg_at_5 |
|
value: 38.788 |
|
- type: precision_at_1 |
|
value: 41.049 |
|
- type: precision_at_10 |
|
value: 11.466 |
|
- type: precision_at_100 |
|
value: 1.8769999999999998 |
|
- type: precision_at_1000 |
|
value: 0.23800000000000002 |
|
- type: precision_at_3 |
|
value: 24.691 |
|
- type: precision_at_5 |
|
value: 18.519 |
|
- type: recall_at_1 |
|
value: 20.684 |
|
- type: recall_at_10 |
|
value: 48.431000000000004 |
|
- type: recall_at_100 |
|
value: 74.331 |
|
- type: recall_at_1000 |
|
value: 91.268 |
|
- type: recall_at_3 |
|
value: 33.267 |
|
- type: recall_at_5 |
|
value: 40.313 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.242 |
|
- type: map_at_10 |
|
value: 47.49 |
|
- type: map_at_100 |
|
value: 48.409 |
|
- type: map_at_1000 |
|
value: 48.489 |
|
- type: map_at_3 |
|
value: 44.519 |
|
- type: map_at_5 |
|
value: 46.298 |
|
- type: mrr_at_1 |
|
value: 64.483 |
|
- type: mrr_at_10 |
|
value: 71.364 |
|
- type: mrr_at_100 |
|
value: 71.734 |
|
- type: mrr_at_1000 |
|
value: 71.751 |
|
- type: mrr_at_3 |
|
value: 69.899 |
|
- type: mrr_at_5 |
|
value: 70.791 |
|
- type: ndcg_at_1 |
|
value: 64.483 |
|
- type: ndcg_at_10 |
|
value: 56.274 |
|
- type: ndcg_at_100 |
|
value: 59.855999999999995 |
|
- type: ndcg_at_1000 |
|
value: 61.538000000000004 |
|
- type: ndcg_at_3 |
|
value: 51.636 |
|
- type: ndcg_at_5 |
|
value: 54.089 |
|
- type: precision_at_1 |
|
value: 64.483 |
|
- type: precision_at_10 |
|
value: 11.858 |
|
- type: precision_at_100 |
|
value: 1.47 |
|
- type: precision_at_1000 |
|
value: 0.169 |
|
- type: precision_at_3 |
|
value: 32.635999999999996 |
|
- type: precision_at_5 |
|
value: 21.521 |
|
- type: recall_at_1 |
|
value: 32.242 |
|
- type: recall_at_10 |
|
value: 59.291000000000004 |
|
- type: recall_at_100 |
|
value: 73.518 |
|
- type: recall_at_1000 |
|
value: 84.747 |
|
- type: recall_at_3 |
|
value: 48.953 |
|
- type: recall_at_5 |
|
value: 53.801 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 80.9492 |
|
- type: ap |
|
value: 75.30846930618502 |
|
- type: f1 |
|
value: 80.89150705991759 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.033 |
|
- type: map_at_10 |
|
value: 34.331 |
|
- type: map_at_100 |
|
value: 35.536 |
|
- type: map_at_1000 |
|
value: 35.583 |
|
- type: map_at_3 |
|
value: 30.562 |
|
- type: map_at_5 |
|
value: 32.667 |
|
- type: mrr_at_1 |
|
value: 22.708000000000002 |
|
- type: mrr_at_10 |
|
value: 34.967999999999996 |
|
- type: mrr_at_100 |
|
value: 36.105 |
|
- type: mrr_at_1000 |
|
value: 36.147 |
|
- type: mrr_at_3 |
|
value: 31.256 |
|
- type: mrr_at_5 |
|
value: 33.322 |
|
- type: ndcg_at_1 |
|
value: 22.708000000000002 |
|
- type: ndcg_at_10 |
|
value: 41.211999999999996 |
|
- type: ndcg_at_100 |
|
value: 46.952 |
|
- type: ndcg_at_1000 |
|
value: 48.131 |
|
- type: ndcg_at_3 |
|
value: 33.501 |
|
- type: ndcg_at_5 |
|
value: 37.248999999999995 |
|
- type: precision_at_1 |
|
value: 22.708000000000002 |
|
- type: precision_at_10 |
|
value: 6.519 |
|
- type: precision_at_100 |
|
value: 0.9390000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.302999999999999 |
|
- type: precision_at_5 |
|
value: 10.481 |
|
- type: recall_at_1 |
|
value: 22.033 |
|
- type: recall_at_10 |
|
value: 62.348000000000006 |
|
- type: recall_at_100 |
|
value: 88.771 |
|
- type: recall_at_1000 |
|
value: 97.782 |
|
- type: recall_at_3 |
|
value: 41.331 |
|
- type: recall_at_5 |
|
value: 50.32600000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 92.69037847697219 |
|
- type: f1 |
|
value: 92.20814766144707 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 61.12859097127223 |
|
- type: f1 |
|
value: 44.859837744275346 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 67.59246805648958 |
|
- type: f1 |
|
value: 65.35653843975764 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 72.82447881640888 |
|
- type: f1 |
|
value: 71.74294810351809 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 32.623627054114884 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.715250618201516 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.268319417897434 |
|
- type: mrr |
|
value: 32.363138927039806 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.702 |
|
- type: map_at_10 |
|
value: 11.838999999999999 |
|
- type: map_at_100 |
|
value: 14.879999999999999 |
|
- type: map_at_1000 |
|
value: 16.277 |
|
- type: map_at_3 |
|
value: 8.912 |
|
- type: map_at_5 |
|
value: 10.213999999999999 |
|
- type: mrr_at_1 |
|
value: 44.891999999999996 |
|
- type: mrr_at_10 |
|
value: 53.15800000000001 |
|
- type: mrr_at_100 |
|
value: 53.830999999999996 |
|
- type: mrr_at_1000 |
|
value: 53.882 |
|
- type: mrr_at_3 |
|
value: 51.135 |
|
- type: mrr_at_5 |
|
value: 52.234 |
|
- type: ndcg_at_1 |
|
value: 43.808 |
|
- type: ndcg_at_10 |
|
value: 32.179 |
|
- type: ndcg_at_100 |
|
value: 29.842000000000002 |
|
- type: ndcg_at_1000 |
|
value: 38.858 |
|
- type: ndcg_at_3 |
|
value: 38.015 |
|
- type: ndcg_at_5 |
|
value: 35.574 |
|
- type: precision_at_1 |
|
value: 44.891999999999996 |
|
- type: precision_at_10 |
|
value: 23.375 |
|
- type: precision_at_100 |
|
value: 7.545 |
|
- type: precision_at_1000 |
|
value: 2.052 |
|
- type: precision_at_3 |
|
value: 35.088 |
|
- type: precision_at_5 |
|
value: 30.154999999999998 |
|
- type: recall_at_1 |
|
value: 5.702 |
|
- type: recall_at_10 |
|
value: 15.421000000000001 |
|
- type: recall_at_100 |
|
value: 30.708999999999996 |
|
- type: recall_at_1000 |
|
value: 62.487 |
|
- type: recall_at_3 |
|
value: 9.966999999999999 |
|
- type: recall_at_5 |
|
value: 12.059000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.117000000000004 |
|
- type: map_at_10 |
|
value: 54.041 |
|
- type: map_at_100 |
|
value: 54.845 |
|
- type: map_at_1000 |
|
value: 54.876999999999995 |
|
- type: map_at_3 |
|
value: 50.339999999999996 |
|
- type: map_at_5 |
|
value: 52.678999999999995 |
|
- type: mrr_at_1 |
|
value: 43.627 |
|
- type: mrr_at_10 |
|
value: 56.752 |
|
- type: mrr_at_100 |
|
value: 57.32899999999999 |
|
- type: mrr_at_1000 |
|
value: 57.35 |
|
- type: mrr_at_3 |
|
value: 53.818999999999996 |
|
- type: mrr_at_5 |
|
value: 55.684999999999995 |
|
- type: ndcg_at_1 |
|
value: 43.627 |
|
- type: ndcg_at_10 |
|
value: 60.934 |
|
- type: ndcg_at_100 |
|
value: 64.277 |
|
- type: ndcg_at_1000 |
|
value: 64.97 |
|
- type: ndcg_at_3 |
|
value: 54.164 |
|
- type: ndcg_at_5 |
|
value: 57.994 |
|
- type: precision_at_1 |
|
value: 43.627 |
|
- type: precision_at_10 |
|
value: 9.383 |
|
- type: precision_at_100 |
|
value: 1.131 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 23.919 |
|
- type: precision_at_5 |
|
value: 16.541 |
|
- type: recall_at_1 |
|
value: 39.117000000000004 |
|
- type: recall_at_10 |
|
value: 79.012 |
|
- type: recall_at_100 |
|
value: 93.395 |
|
- type: recall_at_1000 |
|
value: 98.494 |
|
- type: recall_at_3 |
|
value: 61.714999999999996 |
|
- type: recall_at_5 |
|
value: 70.55799999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.832 |
|
- type: map_at_10 |
|
value: 84.82300000000001 |
|
- type: map_at_100 |
|
value: 85.44500000000001 |
|
- type: map_at_1000 |
|
value: 85.461 |
|
- type: map_at_3 |
|
value: 81.917 |
|
- type: map_at_5 |
|
value: 83.734 |
|
- type: mrr_at_1 |
|
value: 81.61 |
|
- type: mrr_at_10 |
|
value: 87.75500000000001 |
|
- type: mrr_at_100 |
|
value: 87.85300000000001 |
|
- type: mrr_at_1000 |
|
value: 87.854 |
|
- type: mrr_at_3 |
|
value: 86.855 |
|
- type: mrr_at_5 |
|
value: 87.465 |
|
- type: ndcg_at_1 |
|
value: 81.58999999999999 |
|
- type: ndcg_at_10 |
|
value: 88.536 |
|
- type: ndcg_at_100 |
|
value: 89.714 |
|
- type: ndcg_at_1000 |
|
value: 89.80799999999999 |
|
- type: ndcg_at_3 |
|
value: 85.8 |
|
- type: ndcg_at_5 |
|
value: 87.286 |
|
- type: precision_at_1 |
|
value: 81.58999999999999 |
|
- type: precision_at_10 |
|
value: 13.438 |
|
- type: precision_at_100 |
|
value: 1.5310000000000001 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.563 |
|
- type: precision_at_5 |
|
value: 24.65 |
|
- type: recall_at_1 |
|
value: 70.832 |
|
- type: recall_at_10 |
|
value: 95.574 |
|
- type: recall_at_100 |
|
value: 99.575 |
|
- type: recall_at_1000 |
|
value: 99.99 |
|
- type: recall_at_3 |
|
value: 87.61 |
|
- type: recall_at_5 |
|
value: 91.9 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 54.4131741738767 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 59.816632341901865 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.857 |
|
- type: map_at_10 |
|
value: 11.937000000000001 |
|
- type: map_at_100 |
|
value: 14.143 |
|
- type: map_at_1000 |
|
value: 14.451 |
|
- type: map_at_3 |
|
value: 8.376999999999999 |
|
- type: map_at_5 |
|
value: 10.172 |
|
- type: mrr_at_1 |
|
value: 23.799999999999997 |
|
- type: mrr_at_10 |
|
value: 34.134 |
|
- type: mrr_at_100 |
|
value: 35.285 |
|
- type: mrr_at_1000 |
|
value: 35.33 |
|
- type: mrr_at_3 |
|
value: 30.833 |
|
- type: mrr_at_5 |
|
value: 32.828 |
|
- type: ndcg_at_1 |
|
value: 23.799999999999997 |
|
- type: ndcg_at_10 |
|
value: 20.0 |
|
- type: ndcg_at_100 |
|
value: 28.486 |
|
- type: ndcg_at_1000 |
|
value: 33.781 |
|
- type: ndcg_at_3 |
|
value: 18.726000000000003 |
|
- type: ndcg_at_5 |
|
value: 16.587 |
|
- type: precision_at_1 |
|
value: 23.799999999999997 |
|
- type: precision_at_10 |
|
value: 10.39 |
|
- type: precision_at_100 |
|
value: 2.263 |
|
- type: precision_at_1000 |
|
value: 0.35300000000000004 |
|
- type: precision_at_3 |
|
value: 17.333000000000002 |
|
- type: precision_at_5 |
|
value: 14.56 |
|
- type: recall_at_1 |
|
value: 4.857 |
|
- type: recall_at_10 |
|
value: 21.02 |
|
- type: recall_at_100 |
|
value: 45.932 |
|
- type: recall_at_1000 |
|
value: 71.693 |
|
- type: recall_at_3 |
|
value: 10.552 |
|
- type: recall_at_5 |
|
value: 14.760000000000002 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.00513539036214 |
|
- type: cos_sim_spearman |
|
value: 79.19581558052613 |
|
- type: euclidean_pearson |
|
value: 82.46689229301268 |
|
- type: euclidean_spearman |
|
value: 79.19581263972574 |
|
- type: manhattan_pearson |
|
value: 82.46839559537645 |
|
- type: manhattan_spearman |
|
value: 79.19301791744469 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.44111721768361 |
|
- type: cos_sim_spearman |
|
value: 73.14524004507561 |
|
- type: euclidean_pearson |
|
value: 78.70346379990235 |
|
- type: euclidean_spearman |
|
value: 73.14518679640568 |
|
- type: manhattan_pearson |
|
value: 78.68478215009414 |
|
- type: manhattan_spearman |
|
value: 73.10912398034866 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.17030364533524 |
|
- type: cos_sim_spearman |
|
value: 82.88382996129783 |
|
- type: euclidean_pearson |
|
value: 82.25266887145027 |
|
- type: euclidean_spearman |
|
value: 82.88382996129783 |
|
- type: manhattan_pearson |
|
value: 82.21831434263969 |
|
- type: manhattan_spearman |
|
value: 82.83144970048046 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.73413303490618 |
|
- type: cos_sim_spearman |
|
value: 76.95203008005365 |
|
- type: euclidean_pearson |
|
value: 79.09169854088067 |
|
- type: euclidean_spearman |
|
value: 76.95202489005659 |
|
- type: manhattan_pearson |
|
value: 79.04289364751341 |
|
- type: manhattan_spearman |
|
value: 76.89976809512328 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.84421416279349 |
|
- type: cos_sim_spearman |
|
value: 87.67393507190887 |
|
- type: euclidean_pearson |
|
value: 86.81662915280972 |
|
- type: euclidean_spearman |
|
value: 87.67395576051472 |
|
- type: manhattan_pearson |
|
value: 86.76502179645067 |
|
- type: manhattan_spearman |
|
value: 87.60931601838358 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.47603001840406 |
|
- type: cos_sim_spearman |
|
value: 84.57363689562743 |
|
- type: euclidean_pearson |
|
value: 83.62746191773213 |
|
- type: euclidean_spearman |
|
value: 84.57363689562743 |
|
- type: manhattan_pearson |
|
value: 83.5049257196953 |
|
- type: manhattan_spearman |
|
value: 84.43576972291818 |
|
- 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: 89.17222804445805 |
|
- type: cos_sim_spearman |
|
value: 89.04642204765032 |
|
- type: euclidean_pearson |
|
value: 88.93412366747594 |
|
- type: euclidean_spearman |
|
value: 89.04642204765032 |
|
- type: manhattan_pearson |
|
value: 88.88891722217033 |
|
- type: manhattan_spearman |
|
value: 88.95405155642727 |
|
- 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: 63.4232873899918 |
|
- type: cos_sim_spearman |
|
value: 62.53261852485254 |
|
- type: euclidean_pearson |
|
value: 63.95808586267597 |
|
- type: euclidean_spearman |
|
value: 62.53261852485254 |
|
- type: manhattan_pearson |
|
value: 64.07446205165546 |
|
- type: manhattan_spearman |
|
value: 62.86514483815617 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.324835033109 |
|
- type: cos_sim_spearman |
|
value: 84.75551248417419 |
|
- type: euclidean_pearson |
|
value: 84.98725144123726 |
|
- type: euclidean_spearman |
|
value: 84.75551248417419 |
|
- type: manhattan_pearson |
|
value: 84.9546533100131 |
|
- type: manhattan_spearman |
|
value: 84.73671830914728 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 83.62940531539546 |
|
- type: mrr |
|
value: 95.50283503714876 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 52.428 |
|
- type: map_at_10 |
|
value: 62.731 |
|
- type: map_at_100 |
|
value: 63.327 |
|
- type: map_at_1000 |
|
value: 63.356 |
|
- type: map_at_3 |
|
value: 60.17400000000001 |
|
- type: map_at_5 |
|
value: 61.461 |
|
- type: mrr_at_1 |
|
value: 55.333 |
|
- type: mrr_at_10 |
|
value: 63.788999999999994 |
|
- type: mrr_at_100 |
|
value: 64.27000000000001 |
|
- type: mrr_at_1000 |
|
value: 64.298 |
|
- type: mrr_at_3 |
|
value: 61.944 |
|
- type: mrr_at_5 |
|
value: 62.861 |
|
- type: ndcg_at_1 |
|
value: 55.333 |
|
- type: ndcg_at_10 |
|
value: 67.309 |
|
- type: ndcg_at_100 |
|
value: 70.033 |
|
- type: ndcg_at_1000 |
|
value: 70.842 |
|
- type: ndcg_at_3 |
|
value: 63.05500000000001 |
|
- type: ndcg_at_5 |
|
value: 64.8 |
|
- type: precision_at_1 |
|
value: 55.333 |
|
- type: precision_at_10 |
|
value: 9.1 |
|
- type: precision_at_100 |
|
value: 1.057 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 25.111 |
|
- type: precision_at_5 |
|
value: 16.333000000000002 |
|
- type: recall_at_1 |
|
value: 52.428 |
|
- type: recall_at_10 |
|
value: 80.156 |
|
- type: recall_at_100 |
|
value: 92.833 |
|
- type: recall_at_1000 |
|
value: 99.333 |
|
- type: recall_at_3 |
|
value: 68.73899999999999 |
|
- type: recall_at_5 |
|
value: 73.13300000000001 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.8069306930693 |
|
- type: cos_sim_ap |
|
value: 94.89496931806809 |
|
- type: cos_sim_f1 |
|
value: 90.0763358778626 |
|
- type: cos_sim_precision |
|
value: 91.70984455958549 |
|
- type: cos_sim_recall |
|
value: 88.5 |
|
- type: dot_accuracy |
|
value: 99.8069306930693 |
|
- type: dot_ap |
|
value: 94.89495820622456 |
|
- type: dot_f1 |
|
value: 90.0763358778626 |
|
- type: dot_precision |
|
value: 91.70984455958549 |
|
- type: dot_recall |
|
value: 88.5 |
|
- type: euclidean_accuracy |
|
value: 99.8069306930693 |
|
- type: euclidean_ap |
|
value: 94.8949693180681 |
|
- type: euclidean_f1 |
|
value: 90.0763358778626 |
|
- type: euclidean_precision |
|
value: 91.70984455958549 |
|
- type: euclidean_recall |
|
value: 88.5 |
|
- type: manhattan_accuracy |
|
value: 99.8009900990099 |
|
- type: manhattan_ap |
|
value: 94.81699021810266 |
|
- type: manhattan_f1 |
|
value: 89.82278481012658 |
|
- type: manhattan_precision |
|
value: 90.97435897435898 |
|
- type: manhattan_recall |
|
value: 88.7 |
|
- type: max_accuracy |
|
value: 99.8069306930693 |
|
- type: max_ap |
|
value: 94.8949693180681 |
|
- type: max_f1 |
|
value: 90.0763358778626 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 58.95255708336027 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 34.26328409998647 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 52.324949351182134 |
|
- type: mrr |
|
value: 53.08798329938036 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.286127875761963 |
|
- type: cos_sim_spearman |
|
value: 30.85723241148158 |
|
- type: dot_pearson |
|
value: 30.28613033184199 |
|
- type: dot_spearman |
|
value: 30.85723241148158 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.199 |
|
- type: map_at_10 |
|
value: 1.633 |
|
- type: map_at_100 |
|
value: 8.813 |
|
- type: map_at_1000 |
|
value: 21.015 |
|
- type: map_at_3 |
|
value: 0.577 |
|
- type: map_at_5 |
|
value: 0.907 |
|
- type: mrr_at_1 |
|
value: 72.0 |
|
- type: mrr_at_10 |
|
value: 82.667 |
|
- type: mrr_at_100 |
|
value: 82.667 |
|
- type: mrr_at_1000 |
|
value: 82.667 |
|
- type: mrr_at_3 |
|
value: 80.667 |
|
- type: mrr_at_5 |
|
value: 82.667 |
|
- type: ndcg_at_1 |
|
value: 67.0 |
|
- type: ndcg_at_10 |
|
value: 65.377 |
|
- type: ndcg_at_100 |
|
value: 50.693 |
|
- type: ndcg_at_1000 |
|
value: 45.449 |
|
- type: ndcg_at_3 |
|
value: 67.78800000000001 |
|
- type: ndcg_at_5 |
|
value: 67.19000000000001 |
|
- type: precision_at_1 |
|
value: 72.0 |
|
- type: precision_at_10 |
|
value: 70.6 |
|
- type: precision_at_100 |
|
value: 52.0 |
|
- type: precision_at_1000 |
|
value: 20.316000000000003 |
|
- type: precision_at_3 |
|
value: 72.667 |
|
- type: precision_at_5 |
|
value: 72.39999999999999 |
|
- type: recall_at_1 |
|
value: 0.199 |
|
- type: recall_at_10 |
|
value: 1.8800000000000001 |
|
- type: recall_at_100 |
|
value: 12.195 |
|
- type: recall_at_1000 |
|
value: 42.612 |
|
- type: recall_at_3 |
|
value: 0.608 |
|
- type: recall_at_5 |
|
value: 1.004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.34 |
|
- type: map_at_10 |
|
value: 7.983 |
|
- type: map_at_100 |
|
value: 14.488999999999999 |
|
- type: map_at_1000 |
|
value: 16.133 |
|
- type: map_at_3 |
|
value: 4.312 |
|
- type: map_at_5 |
|
value: 6.3420000000000005 |
|
- type: mrr_at_1 |
|
value: 26.531 |
|
- type: mrr_at_10 |
|
value: 41.558 |
|
- type: mrr_at_100 |
|
value: 42.211999999999996 |
|
- type: mrr_at_1000 |
|
value: 42.211999999999996 |
|
- type: mrr_at_3 |
|
value: 36.054 |
|
- type: mrr_at_5 |
|
value: 39.217999999999996 |
|
- type: ndcg_at_1 |
|
value: 23.469 |
|
- type: ndcg_at_10 |
|
value: 21.077 |
|
- type: ndcg_at_100 |
|
value: 35.497 |
|
- type: ndcg_at_1000 |
|
value: 47.282000000000004 |
|
- type: ndcg_at_3 |
|
value: 20.906 |
|
- type: ndcg_at_5 |
|
value: 21.78 |
|
- type: precision_at_1 |
|
value: 26.531 |
|
- type: precision_at_10 |
|
value: 18.570999999999998 |
|
- type: precision_at_100 |
|
value: 7.673000000000001 |
|
- type: precision_at_1000 |
|
value: 1.551 |
|
- type: precision_at_3 |
|
value: 21.769 |
|
- type: precision_at_5 |
|
value: 22.448999999999998 |
|
- type: recall_at_1 |
|
value: 2.34 |
|
- type: recall_at_10 |
|
value: 14.154 |
|
- type: recall_at_100 |
|
value: 48.355 |
|
- type: recall_at_1000 |
|
value: 84.872 |
|
- type: recall_at_3 |
|
value: 5.19 |
|
- type: recall_at_5 |
|
value: 9.211 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 71.9318 |
|
- type: ap |
|
value: 14.755439516631267 |
|
- type: f1 |
|
value: 55.39101096477449 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 61.06395019807584 |
|
- type: f1 |
|
value: 61.18513886850968 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 43.68814723462553 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.8258329856351 |
|
- type: cos_sim_ap |
|
value: 73.51953909054856 |
|
- type: cos_sim_f1 |
|
value: 68.17958783120707 |
|
- type: cos_sim_precision |
|
value: 63.70930765703806 |
|
- type: cos_sim_recall |
|
value: 73.3245382585752 |
|
- type: dot_accuracy |
|
value: 85.8258329856351 |
|
- type: dot_ap |
|
value: 73.51954936569123 |
|
- type: dot_f1 |
|
value: 68.17958783120707 |
|
- type: dot_precision |
|
value: 63.70930765703806 |
|
- type: dot_recall |
|
value: 73.3245382585752 |
|
- type: euclidean_accuracy |
|
value: 85.8258329856351 |
|
- type: euclidean_ap |
|
value: 73.51954390509214 |
|
- type: euclidean_f1 |
|
value: 68.17958783120707 |
|
- type: euclidean_precision |
|
value: 63.70930765703806 |
|
- type: euclidean_recall |
|
value: 73.3245382585752 |
|
- type: manhattan_accuracy |
|
value: 85.8258329856351 |
|
- type: manhattan_ap |
|
value: 73.44954175022839 |
|
- type: manhattan_f1 |
|
value: 68.08816482989938 |
|
- type: manhattan_precision |
|
value: 62.351908731899954 |
|
- type: manhattan_recall |
|
value: 74.9868073878628 |
|
- type: max_accuracy |
|
value: 85.8258329856351 |
|
- type: max_ap |
|
value: 73.51954936569123 |
|
- type: max_f1 |
|
value: 68.17958783120707 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.6094617145962 |
|
- type: cos_sim_ap |
|
value: 85.4121913477208 |
|
- type: cos_sim_f1 |
|
value: 77.61548157484985 |
|
- type: cos_sim_precision |
|
value: 74.84627484627485 |
|
- type: cos_sim_recall |
|
value: 80.59747459193102 |
|
- type: dot_accuracy |
|
value: 88.6094617145962 |
|
- type: dot_ap |
|
value: 85.41219830675979 |
|
- type: dot_f1 |
|
value: 77.61548157484985 |
|
- type: dot_precision |
|
value: 74.84627484627485 |
|
- type: dot_recall |
|
value: 80.59747459193102 |
|
- type: euclidean_accuracy |
|
value: 88.6094617145962 |
|
- type: euclidean_ap |
|
value: 85.41219328124808 |
|
- type: euclidean_f1 |
|
value: 77.61548157484985 |
|
- type: euclidean_precision |
|
value: 74.84627484627485 |
|
- type: euclidean_recall |
|
value: 80.59747459193102 |
|
- type: manhattan_accuracy |
|
value: 88.53960492102301 |
|
- type: manhattan_ap |
|
value: 85.35022078482446 |
|
- type: manhattan_f1 |
|
value: 77.56588974387569 |
|
- type: manhattan_precision |
|
value: 74.98742183569324 |
|
- type: manhattan_recall |
|
value: 80.3279950723745 |
|
- type: max_accuracy |
|
value: 88.6094617145962 |
|
- type: max_ap |
|
value: 85.41219830675979 |
|
- type: max_f1 |
|
value: 77.61548157484985 |
|
--- |
|
<!-- TODO: add evaluation results here --> |
|
<br><br> |
|
|
|
<p align="center"> |
|
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" 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>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b> |
|
</p> |
|
|
|
|
|
## Intended Usage & Model Info |
|
|
|
`jina-embedding-b-en-v2` is an English, monolingual embedding model supporting 8k sequence length. |
|
It is based on a Bert architecture that supports the symmetric bidirectional variant of ALiBi to support longer sequence length. |
|
The backbone Jina Bert Small model is pretrained on the C4 dataset. |
|
The model is further trained on Jina AI's collection of more than 40 datasets 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 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,... |
|
|
|
This model has 33 million parameters, which enables lightning-fast and memory efficient inference on long documents, while still delivering impressive performance. |
|
Additionally, we provide the following embedding models, supporting 8k sequence length as well: |
|
|
|
- [`jina-embedding-s-en-v2`](https://huggingface.co/jinaai/jina-embedding-s-en-v2): 33 million parameters. |
|
- [`jina-embedding-b-en-v2`](https://huggingface.co/jinaai/jina-embedding-b-en-v2): 137 million parameters **(you are here)**. |
|
- [`jina-embedding-l-en-v2`](https://huggingface.co/jinaai/jina-embedding-l-en-v2): 435 million parameters. |
|
|
|
## Data & Parameters |
|
<!-- TODO: update the paper ID once it is published on arxiv --> |
|
Please checkout our [technical blog](https://arxiv.org/abs/2307.11224). |
|
|
|
## Metrics |
|
|
|
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI: |
|
|
|
<!-- TODO: add evaluation table here --> |
|
|
|
## Usage |
|
|
|
You can use Jina Embedding models directly from transformers package: |
|
```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-embedding-b-en-v2', 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])) |
|
``` |
|
|
|
For long sequences, it's recommended to perform inference using Flash Attention. Using Flash Attention allows you to increase the batch size and throughput for long sequence length. |
|
We include an experimental implementation for Flash Attention, shipped with the model. |
|
Install the following triton version: |
|
`pip install triton==2.0.0.dev20221202`. |
|
Now run the same code above, but make sure to set the parameter `with_flash` to `True` when you load the model. You also have to use either `fp16` or `bf16`: |
|
```python |
|
from transformers import AutoModel |
|
from numpy.linalg import norm |
|
import torch |
|
|
|
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) |
|
model = AutoModel.from_pretrained('jinaai/jina-embedding-b-en-v2', trust_remote_code=True, with_flash=True, torch_dtype=torch.float16).cuda() # 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])) |
|
``` |
|
|
|
## Fine-tuning |
|
|
|
Please consider [Finetuner](https://github.com/jina-ai/finetuner). |
|
|
|
## Plans |
|
The development of new multilingual models is currently underway. We will be targeting mainly the German and Spanish languages. The upcoming models will be called `jina-embedding-s/b/l-de/es-v2`. |
|
|
|
## 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: |
|
|
|
<!-- TODO: update the paper ID once it is published on arxiv --> |
|
``` latex |
|
@misc{günther2023jina, |
|
title={Beyond the 512-Token Barrier: Training General-Purpose Text |
|
Embeddings for Large 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}, |
|
year={2023}, |
|
eprint={2307.11224}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
