|
--- |
|
tags: |
|
- mteb |
|
- sentence transformers |
|
model-index: |
|
- name: bge-small-en |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 74.34328358208955 |
|
- type: ap |
|
value: 37.59947775195661 |
|
- type: f1 |
|
value: 68.548415491933 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 93.04527499999999 |
|
- type: ap |
|
value: 89.60696356772135 |
|
- type: f1 |
|
value: 93.03361469382438 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 46.08 |
|
- type: f1 |
|
value: 45.66249835363254 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 35.205999999999996 |
|
- type: map_at_10 |
|
value: 50.782000000000004 |
|
- type: map_at_100 |
|
value: 51.547 |
|
- type: map_at_1000 |
|
value: 51.554 |
|
- type: map_at_3 |
|
value: 46.515 |
|
- type: map_at_5 |
|
value: 49.296 |
|
- type: mrr_at_1 |
|
value: 35.632999999999996 |
|
- type: mrr_at_10 |
|
value: 50.958999999999996 |
|
- type: mrr_at_100 |
|
value: 51.724000000000004 |
|
- type: mrr_at_1000 |
|
value: 51.731 |
|
- type: mrr_at_3 |
|
value: 46.669 |
|
- type: mrr_at_5 |
|
value: 49.439 |
|
- type: ndcg_at_1 |
|
value: 35.205999999999996 |
|
- type: ndcg_at_10 |
|
value: 58.835 |
|
- type: ndcg_at_100 |
|
value: 62.095 |
|
- type: ndcg_at_1000 |
|
value: 62.255 |
|
- type: ndcg_at_3 |
|
value: 50.255 |
|
- type: ndcg_at_5 |
|
value: 55.296 |
|
- type: precision_at_1 |
|
value: 35.205999999999996 |
|
- type: precision_at_10 |
|
value: 8.421 |
|
- type: precision_at_100 |
|
value: 0.984 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 20.365 |
|
- type: precision_at_5 |
|
value: 14.680000000000001 |
|
- type: recall_at_1 |
|
value: 35.205999999999996 |
|
- type: recall_at_10 |
|
value: 84.211 |
|
- type: recall_at_100 |
|
value: 98.43499999999999 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 61.095 |
|
- type: recall_at_5 |
|
value: 73.4 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 47.52644476278646 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 39.973045724188964 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 62.28285314871488 |
|
- type: mrr |
|
value: 74.52743701358659 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.09041909160327 |
|
- type: cos_sim_spearman |
|
value: 79.96266537706944 |
|
- type: euclidean_pearson |
|
value: 79.50774978162241 |
|
- type: euclidean_spearman |
|
value: 79.9144715078551 |
|
- type: manhattan_pearson |
|
value: 79.2062139879302 |
|
- type: manhattan_spearman |
|
value: 79.35000081468212 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 85.31493506493506 |
|
- type: f1 |
|
value: 85.2704557977762 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 39.6837242810816 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 35.38881249555897 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.884999999999998 |
|
- type: map_at_10 |
|
value: 39.574 |
|
- type: map_at_100 |
|
value: 40.993 |
|
- type: map_at_1000 |
|
value: 41.129 |
|
- type: map_at_3 |
|
value: 36.089 |
|
- type: map_at_5 |
|
value: 38.191 |
|
- type: mrr_at_1 |
|
value: 34.477999999999994 |
|
- type: mrr_at_10 |
|
value: 45.411 |
|
- type: mrr_at_100 |
|
value: 46.089999999999996 |
|
- type: mrr_at_1000 |
|
value: 46.147 |
|
- type: mrr_at_3 |
|
value: 42.346000000000004 |
|
- type: mrr_at_5 |
|
value: 44.292 |
|
- type: ndcg_at_1 |
|
value: 34.477999999999994 |
|
- type: ndcg_at_10 |
|
value: 46.123999999999995 |
|
- type: ndcg_at_100 |
|
value: 51.349999999999994 |
|
- type: ndcg_at_1000 |
|
value: 53.578 |
|
- type: ndcg_at_3 |
|
value: 40.824 |
|
- type: ndcg_at_5 |
|
value: 43.571 |
|
- type: precision_at_1 |
|
value: 34.477999999999994 |
|
- type: precision_at_10 |
|
value: 8.841000000000001 |
|
- type: precision_at_100 |
|
value: 1.4460000000000002 |
|
- type: precision_at_1000 |
|
value: 0.192 |
|
- type: precision_at_3 |
|
value: 19.742 |
|
- type: precision_at_5 |
|
value: 14.421000000000001 |
|
- type: recall_at_1 |
|
value: 27.884999999999998 |
|
- type: recall_at_10 |
|
value: 59.087 |
|
- type: recall_at_100 |
|
value: 80.609 |
|
- type: recall_at_1000 |
|
value: 95.054 |
|
- type: recall_at_3 |
|
value: 44.082 |
|
- type: recall_at_5 |
|
value: 51.593999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.639 |
|
- type: map_at_10 |
|
value: 40.047 |
|
- type: map_at_100 |
|
value: 41.302 |
|
- type: map_at_1000 |
|
value: 41.425 |
|
- type: map_at_3 |
|
value: 37.406 |
|
- type: map_at_5 |
|
value: 38.934000000000005 |
|
- type: mrr_at_1 |
|
value: 37.707 |
|
- type: mrr_at_10 |
|
value: 46.082 |
|
- type: mrr_at_100 |
|
value: 46.745 |
|
- type: mrr_at_1000 |
|
value: 46.786 |
|
- type: mrr_at_3 |
|
value: 43.980999999999995 |
|
- type: mrr_at_5 |
|
value: 45.287 |
|
- type: ndcg_at_1 |
|
value: 37.707 |
|
- type: ndcg_at_10 |
|
value: 45.525 |
|
- type: ndcg_at_100 |
|
value: 49.976 |
|
- type: ndcg_at_1000 |
|
value: 51.94499999999999 |
|
- type: ndcg_at_3 |
|
value: 41.704 |
|
- type: ndcg_at_5 |
|
value: 43.596000000000004 |
|
- type: precision_at_1 |
|
value: 37.707 |
|
- type: precision_at_10 |
|
value: 8.465 |
|
- type: precision_at_100 |
|
value: 1.375 |
|
- type: precision_at_1000 |
|
value: 0.183 |
|
- type: precision_at_3 |
|
value: 19.979 |
|
- type: precision_at_5 |
|
value: 14.115 |
|
- type: recall_at_1 |
|
value: 30.639 |
|
- type: recall_at_10 |
|
value: 54.775 |
|
- type: recall_at_100 |
|
value: 73.678 |
|
- type: recall_at_1000 |
|
value: 86.142 |
|
- type: recall_at_3 |
|
value: 43.230000000000004 |
|
- type: recall_at_5 |
|
value: 48.622 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.038 |
|
- type: map_at_10 |
|
value: 49.922 |
|
- type: map_at_100 |
|
value: 51.032 |
|
- type: map_at_1000 |
|
value: 51.085 |
|
- type: map_at_3 |
|
value: 46.664 |
|
- type: map_at_5 |
|
value: 48.588 |
|
- type: mrr_at_1 |
|
value: 43.95 |
|
- type: mrr_at_10 |
|
value: 53.566 |
|
- type: mrr_at_100 |
|
value: 54.318999999999996 |
|
- type: mrr_at_1000 |
|
value: 54.348 |
|
- type: mrr_at_3 |
|
value: 51.066 |
|
- type: mrr_at_5 |
|
value: 52.649 |
|
- type: ndcg_at_1 |
|
value: 43.95 |
|
- type: ndcg_at_10 |
|
value: 55.676 |
|
- type: ndcg_at_100 |
|
value: 60.126000000000005 |
|
- type: ndcg_at_1000 |
|
value: 61.208 |
|
- type: ndcg_at_3 |
|
value: 50.20400000000001 |
|
- type: ndcg_at_5 |
|
value: 53.038 |
|
- type: precision_at_1 |
|
value: 43.95 |
|
- type: precision_at_10 |
|
value: 8.953 |
|
- type: precision_at_100 |
|
value: 1.2109999999999999 |
|
- type: precision_at_1000 |
|
value: 0.135 |
|
- type: precision_at_3 |
|
value: 22.256999999999998 |
|
- type: precision_at_5 |
|
value: 15.524 |
|
- type: recall_at_1 |
|
value: 38.038 |
|
- type: recall_at_10 |
|
value: 69.15 |
|
- type: recall_at_100 |
|
value: 88.31599999999999 |
|
- type: recall_at_1000 |
|
value: 95.993 |
|
- type: recall_at_3 |
|
value: 54.663 |
|
- type: recall_at_5 |
|
value: 61.373 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.872 |
|
- type: map_at_10 |
|
value: 32.912 |
|
- type: map_at_100 |
|
value: 33.972 |
|
- type: map_at_1000 |
|
value: 34.046 |
|
- type: map_at_3 |
|
value: 30.361 |
|
- type: map_at_5 |
|
value: 31.704 |
|
- type: mrr_at_1 |
|
value: 26.779999999999998 |
|
- type: mrr_at_10 |
|
value: 34.812 |
|
- type: mrr_at_100 |
|
value: 35.754999999999995 |
|
- type: mrr_at_1000 |
|
value: 35.809000000000005 |
|
- type: mrr_at_3 |
|
value: 32.335 |
|
- type: mrr_at_5 |
|
value: 33.64 |
|
- type: ndcg_at_1 |
|
value: 26.779999999999998 |
|
- type: ndcg_at_10 |
|
value: 37.623 |
|
- type: ndcg_at_100 |
|
value: 42.924 |
|
- type: ndcg_at_1000 |
|
value: 44.856 |
|
- type: ndcg_at_3 |
|
value: 32.574 |
|
- type: ndcg_at_5 |
|
value: 34.842 |
|
- type: precision_at_1 |
|
value: 26.779999999999998 |
|
- type: precision_at_10 |
|
value: 5.729 |
|
- type: precision_at_100 |
|
value: 0.886 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_3 |
|
value: 13.559 |
|
- type: precision_at_5 |
|
value: 9.469 |
|
- type: recall_at_1 |
|
value: 24.872 |
|
- type: recall_at_10 |
|
value: 50.400999999999996 |
|
- type: recall_at_100 |
|
value: 74.954 |
|
- type: recall_at_1000 |
|
value: 89.56 |
|
- type: recall_at_3 |
|
value: 36.726 |
|
- type: recall_at_5 |
|
value: 42.138999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.803 |
|
- type: map_at_10 |
|
value: 24.348 |
|
- type: map_at_100 |
|
value: 25.56 |
|
- type: map_at_1000 |
|
value: 25.668000000000003 |
|
- type: map_at_3 |
|
value: 21.811 |
|
- type: map_at_5 |
|
value: 23.287 |
|
- type: mrr_at_1 |
|
value: 20.771 |
|
- type: mrr_at_10 |
|
value: 28.961 |
|
- type: mrr_at_100 |
|
value: 29.979 |
|
- type: mrr_at_1000 |
|
value: 30.046 |
|
- type: mrr_at_3 |
|
value: 26.555 |
|
- type: mrr_at_5 |
|
value: 28.060000000000002 |
|
- type: ndcg_at_1 |
|
value: 20.771 |
|
- type: ndcg_at_10 |
|
value: 29.335 |
|
- type: ndcg_at_100 |
|
value: 35.188 |
|
- type: ndcg_at_1000 |
|
value: 37.812 |
|
- type: ndcg_at_3 |
|
value: 24.83 |
|
- type: ndcg_at_5 |
|
value: 27.119 |
|
- type: precision_at_1 |
|
value: 20.771 |
|
- type: precision_at_10 |
|
value: 5.4350000000000005 |
|
- type: precision_at_100 |
|
value: 0.9480000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 11.982 |
|
- type: precision_at_5 |
|
value: 8.831 |
|
- type: recall_at_1 |
|
value: 16.803 |
|
- type: recall_at_10 |
|
value: 40.039 |
|
- type: recall_at_100 |
|
value: 65.83200000000001 |
|
- type: recall_at_1000 |
|
value: 84.478 |
|
- type: recall_at_3 |
|
value: 27.682000000000002 |
|
- type: recall_at_5 |
|
value: 33.535 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.345 |
|
- type: map_at_10 |
|
value: 37.757000000000005 |
|
- type: map_at_100 |
|
value: 39.141 |
|
- type: map_at_1000 |
|
value: 39.262 |
|
- type: map_at_3 |
|
value: 35.183 |
|
- type: map_at_5 |
|
value: 36.592 |
|
- type: mrr_at_1 |
|
value: 34.649 |
|
- type: mrr_at_10 |
|
value: 43.586999999999996 |
|
- type: mrr_at_100 |
|
value: 44.481 |
|
- type: mrr_at_1000 |
|
value: 44.542 |
|
- type: mrr_at_3 |
|
value: 41.29 |
|
- type: mrr_at_5 |
|
value: 42.642 |
|
- type: ndcg_at_1 |
|
value: 34.649 |
|
- type: ndcg_at_10 |
|
value: 43.161 |
|
- type: ndcg_at_100 |
|
value: 48.734 |
|
- type: ndcg_at_1000 |
|
value: 51.046 |
|
- type: ndcg_at_3 |
|
value: 39.118 |
|
- type: ndcg_at_5 |
|
value: 41.022 |
|
- type: precision_at_1 |
|
value: 34.649 |
|
- type: precision_at_10 |
|
value: 7.603 |
|
- type: precision_at_100 |
|
value: 1.209 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 18.319 |
|
- type: precision_at_5 |
|
value: 12.839 |
|
- type: recall_at_1 |
|
value: 28.345 |
|
- type: recall_at_10 |
|
value: 53.367 |
|
- type: recall_at_100 |
|
value: 76.453 |
|
- type: recall_at_1000 |
|
value: 91.82000000000001 |
|
- type: recall_at_3 |
|
value: 41.636 |
|
- type: recall_at_5 |
|
value: 46.760000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.419 |
|
- type: map_at_10 |
|
value: 31.716 |
|
- type: map_at_100 |
|
value: 33.152 |
|
- type: map_at_1000 |
|
value: 33.267 |
|
- type: map_at_3 |
|
value: 28.74 |
|
- type: map_at_5 |
|
value: 30.48 |
|
- type: mrr_at_1 |
|
value: 28.310999999999996 |
|
- type: mrr_at_10 |
|
value: 37.039 |
|
- type: mrr_at_100 |
|
value: 38.09 |
|
- type: mrr_at_1000 |
|
value: 38.145 |
|
- type: mrr_at_3 |
|
value: 34.437 |
|
- type: mrr_at_5 |
|
value: 36.024 |
|
- type: ndcg_at_1 |
|
value: 28.310999999999996 |
|
- type: ndcg_at_10 |
|
value: 37.41 |
|
- type: ndcg_at_100 |
|
value: 43.647999999999996 |
|
- type: ndcg_at_1000 |
|
value: 46.007 |
|
- type: ndcg_at_3 |
|
value: 32.509 |
|
- type: ndcg_at_5 |
|
value: 34.943999999999996 |
|
- type: precision_at_1 |
|
value: 28.310999999999996 |
|
- type: precision_at_10 |
|
value: 6.963 |
|
- type: precision_at_100 |
|
value: 1.1860000000000002 |
|
- type: precision_at_1000 |
|
value: 0.154 |
|
- type: precision_at_3 |
|
value: 15.867999999999999 |
|
- type: precision_at_5 |
|
value: 11.507000000000001 |
|
- type: recall_at_1 |
|
value: 22.419 |
|
- type: recall_at_10 |
|
value: 49.28 |
|
- type: recall_at_100 |
|
value: 75.802 |
|
- type: recall_at_1000 |
|
value: 92.032 |
|
- type: recall_at_3 |
|
value: 35.399 |
|
- type: recall_at_5 |
|
value: 42.027 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.669249999999998 |
|
- type: map_at_10 |
|
value: 33.332583333333325 |
|
- type: map_at_100 |
|
value: 34.557833333333335 |
|
- type: map_at_1000 |
|
value: 34.67141666666666 |
|
- type: map_at_3 |
|
value: 30.663166666666662 |
|
- type: map_at_5 |
|
value: 32.14883333333333 |
|
- type: mrr_at_1 |
|
value: 29.193833333333334 |
|
- type: mrr_at_10 |
|
value: 37.47625 |
|
- type: mrr_at_100 |
|
value: 38.3545 |
|
- type: mrr_at_1000 |
|
value: 38.413166666666676 |
|
- type: mrr_at_3 |
|
value: 35.06741666666667 |
|
- type: mrr_at_5 |
|
value: 36.450666666666656 |
|
- type: ndcg_at_1 |
|
value: 29.193833333333334 |
|
- type: ndcg_at_10 |
|
value: 38.505416666666676 |
|
- type: ndcg_at_100 |
|
value: 43.81125 |
|
- type: ndcg_at_1000 |
|
value: 46.09558333333333 |
|
- type: ndcg_at_3 |
|
value: 33.90916666666667 |
|
- type: ndcg_at_5 |
|
value: 36.07666666666666 |
|
- type: precision_at_1 |
|
value: 29.193833333333334 |
|
- type: precision_at_10 |
|
value: 6.7251666666666665 |
|
- type: precision_at_100 |
|
value: 1.1058333333333332 |
|
- type: precision_at_1000 |
|
value: 0.14833333333333332 |
|
- type: precision_at_3 |
|
value: 15.554166666666665 |
|
- type: precision_at_5 |
|
value: 11.079250000000002 |
|
- type: recall_at_1 |
|
value: 24.669249999999998 |
|
- type: recall_at_10 |
|
value: 49.75583333333332 |
|
- type: recall_at_100 |
|
value: 73.06908333333332 |
|
- type: recall_at_1000 |
|
value: 88.91316666666667 |
|
- type: recall_at_3 |
|
value: 36.913250000000005 |
|
- type: recall_at_5 |
|
value: 42.48641666666666 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.044999999999998 |
|
- type: map_at_10 |
|
value: 30.349999999999998 |
|
- type: map_at_100 |
|
value: 31.273 |
|
- type: map_at_1000 |
|
value: 31.362000000000002 |
|
- type: map_at_3 |
|
value: 28.508 |
|
- type: map_at_5 |
|
value: 29.369 |
|
- type: mrr_at_1 |
|
value: 26.994 |
|
- type: mrr_at_10 |
|
value: 33.12 |
|
- type: mrr_at_100 |
|
value: 33.904 |
|
- type: mrr_at_1000 |
|
value: 33.967000000000006 |
|
- type: mrr_at_3 |
|
value: 31.365 |
|
- type: mrr_at_5 |
|
value: 32.124 |
|
- type: ndcg_at_1 |
|
value: 26.994 |
|
- type: ndcg_at_10 |
|
value: 34.214 |
|
- type: ndcg_at_100 |
|
value: 38.681 |
|
- type: ndcg_at_1000 |
|
value: 40.926 |
|
- type: ndcg_at_3 |
|
value: 30.725 |
|
- type: ndcg_at_5 |
|
value: 31.967000000000002 |
|
- type: precision_at_1 |
|
value: 26.994 |
|
- type: precision_at_10 |
|
value: 5.215 |
|
- type: precision_at_100 |
|
value: 0.807 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 12.986 |
|
- type: precision_at_5 |
|
value: 8.712 |
|
- type: recall_at_1 |
|
value: 24.044999999999998 |
|
- type: recall_at_10 |
|
value: 43.456 |
|
- type: recall_at_100 |
|
value: 63.675000000000004 |
|
- type: recall_at_1000 |
|
value: 80.05499999999999 |
|
- type: recall_at_3 |
|
value: 33.561 |
|
- type: recall_at_5 |
|
value: 36.767 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.672 |
|
- type: map_at_10 |
|
value: 22.641 |
|
- type: map_at_100 |
|
value: 23.75 |
|
- type: map_at_1000 |
|
value: 23.877000000000002 |
|
- type: map_at_3 |
|
value: 20.219 |
|
- type: map_at_5 |
|
value: 21.648 |
|
- type: mrr_at_1 |
|
value: 18.823 |
|
- type: mrr_at_10 |
|
value: 26.101999999999997 |
|
- type: mrr_at_100 |
|
value: 27.038 |
|
- type: mrr_at_1000 |
|
value: 27.118 |
|
- type: mrr_at_3 |
|
value: 23.669 |
|
- type: mrr_at_5 |
|
value: 25.173000000000002 |
|
- type: ndcg_at_1 |
|
value: 18.823 |
|
- type: ndcg_at_10 |
|
value: 27.176000000000002 |
|
- type: ndcg_at_100 |
|
value: 32.42 |
|
- type: ndcg_at_1000 |
|
value: 35.413 |
|
- type: ndcg_at_3 |
|
value: 22.756999999999998 |
|
- type: ndcg_at_5 |
|
value: 25.032 |
|
- type: precision_at_1 |
|
value: 18.823 |
|
- type: precision_at_10 |
|
value: 5.034000000000001 |
|
- type: precision_at_100 |
|
value: 0.895 |
|
- type: precision_at_1000 |
|
value: 0.132 |
|
- type: precision_at_3 |
|
value: 10.771 |
|
- type: precision_at_5 |
|
value: 8.1 |
|
- type: recall_at_1 |
|
value: 15.672 |
|
- type: recall_at_10 |
|
value: 37.296 |
|
- type: recall_at_100 |
|
value: 60.863 |
|
- type: recall_at_1000 |
|
value: 82.234 |
|
- type: recall_at_3 |
|
value: 25.330000000000002 |
|
- type: recall_at_5 |
|
value: 30.964000000000002 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.633 |
|
- type: map_at_10 |
|
value: 32.858 |
|
- type: map_at_100 |
|
value: 34.038000000000004 |
|
- type: map_at_1000 |
|
value: 34.141 |
|
- type: map_at_3 |
|
value: 30.209000000000003 |
|
- type: map_at_5 |
|
value: 31.567 |
|
- type: mrr_at_1 |
|
value: 28.358 |
|
- type: mrr_at_10 |
|
value: 36.433 |
|
- type: mrr_at_100 |
|
value: 37.352000000000004 |
|
- type: mrr_at_1000 |
|
value: 37.41 |
|
- type: mrr_at_3 |
|
value: 34.033 |
|
- type: mrr_at_5 |
|
value: 35.246 |
|
- type: ndcg_at_1 |
|
value: 28.358 |
|
- type: ndcg_at_10 |
|
value: 37.973 |
|
- type: ndcg_at_100 |
|
value: 43.411 |
|
- type: ndcg_at_1000 |
|
value: 45.747 |
|
- type: ndcg_at_3 |
|
value: 32.934999999999995 |
|
- type: ndcg_at_5 |
|
value: 35.013 |
|
- type: precision_at_1 |
|
value: 28.358 |
|
- type: precision_at_10 |
|
value: 6.418 |
|
- type: precision_at_100 |
|
value: 1.02 |
|
- type: precision_at_1000 |
|
value: 0.133 |
|
- type: precision_at_3 |
|
value: 14.677000000000001 |
|
- type: precision_at_5 |
|
value: 10.335999999999999 |
|
- type: recall_at_1 |
|
value: 24.633 |
|
- type: recall_at_10 |
|
value: 50.048 |
|
- type: recall_at_100 |
|
value: 73.821 |
|
- type: recall_at_1000 |
|
value: 90.046 |
|
- type: recall_at_3 |
|
value: 36.284 |
|
- type: recall_at_5 |
|
value: 41.370000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.133 |
|
- type: map_at_10 |
|
value: 31.491999999999997 |
|
- type: map_at_100 |
|
value: 33.062000000000005 |
|
- type: map_at_1000 |
|
value: 33.256 |
|
- type: map_at_3 |
|
value: 28.886 |
|
- type: map_at_5 |
|
value: 30.262 |
|
- type: mrr_at_1 |
|
value: 28.063 |
|
- type: mrr_at_10 |
|
value: 36.144 |
|
- type: mrr_at_100 |
|
value: 37.14 |
|
- type: mrr_at_1000 |
|
value: 37.191 |
|
- type: mrr_at_3 |
|
value: 33.762 |
|
- type: mrr_at_5 |
|
value: 34.997 |
|
- type: ndcg_at_1 |
|
value: 28.063 |
|
- type: ndcg_at_10 |
|
value: 36.951 |
|
- type: ndcg_at_100 |
|
value: 43.287 |
|
- type: ndcg_at_1000 |
|
value: 45.777 |
|
- type: ndcg_at_3 |
|
value: 32.786 |
|
- type: ndcg_at_5 |
|
value: 34.65 |
|
- type: precision_at_1 |
|
value: 28.063 |
|
- type: precision_at_10 |
|
value: 7.055 |
|
- type: precision_at_100 |
|
value: 1.476 |
|
- type: precision_at_1000 |
|
value: 0.22899999999999998 |
|
- type: precision_at_3 |
|
value: 15.481 |
|
- type: precision_at_5 |
|
value: 11.186 |
|
- type: recall_at_1 |
|
value: 23.133 |
|
- type: recall_at_10 |
|
value: 47.285 |
|
- type: recall_at_100 |
|
value: 76.176 |
|
- type: recall_at_1000 |
|
value: 92.176 |
|
- type: recall_at_3 |
|
value: 35.223 |
|
- type: recall_at_5 |
|
value: 40.142 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.547 |
|
- type: map_at_10 |
|
value: 26.374 |
|
- type: map_at_100 |
|
value: 27.419 |
|
- type: map_at_1000 |
|
value: 27.539 |
|
- type: map_at_3 |
|
value: 23.882 |
|
- type: map_at_5 |
|
value: 25.163999999999998 |
|
- type: mrr_at_1 |
|
value: 21.442 |
|
- type: mrr_at_10 |
|
value: 28.458 |
|
- type: mrr_at_100 |
|
value: 29.360999999999997 |
|
- type: mrr_at_1000 |
|
value: 29.448999999999998 |
|
- type: mrr_at_3 |
|
value: 25.97 |
|
- type: mrr_at_5 |
|
value: 27.273999999999997 |
|
- type: ndcg_at_1 |
|
value: 21.442 |
|
- type: ndcg_at_10 |
|
value: 30.897000000000002 |
|
- type: ndcg_at_100 |
|
value: 35.99 |
|
- type: ndcg_at_1000 |
|
value: 38.832 |
|
- type: ndcg_at_3 |
|
value: 25.944 |
|
- type: ndcg_at_5 |
|
value: 28.126 |
|
- type: precision_at_1 |
|
value: 21.442 |
|
- type: precision_at_10 |
|
value: 4.9910000000000005 |
|
- type: precision_at_100 |
|
value: 0.8109999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 11.029 |
|
- type: precision_at_5 |
|
value: 7.911 |
|
- type: recall_at_1 |
|
value: 19.547 |
|
- type: recall_at_10 |
|
value: 42.886 |
|
- type: recall_at_100 |
|
value: 66.64999999999999 |
|
- type: recall_at_1000 |
|
value: 87.368 |
|
- type: recall_at_3 |
|
value: 29.143 |
|
- type: recall_at_5 |
|
value: 34.544000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.572 |
|
- type: map_at_10 |
|
value: 25.312 |
|
- type: map_at_100 |
|
value: 27.062 |
|
- type: map_at_1000 |
|
value: 27.253 |
|
- type: map_at_3 |
|
value: 21.601 |
|
- type: map_at_5 |
|
value: 23.473 |
|
- type: mrr_at_1 |
|
value: 34.984 |
|
- type: mrr_at_10 |
|
value: 46.406 |
|
- type: mrr_at_100 |
|
value: 47.179 |
|
- type: mrr_at_1000 |
|
value: 47.21 |
|
- type: mrr_at_3 |
|
value: 43.485 |
|
- type: mrr_at_5 |
|
value: 45.322 |
|
- type: ndcg_at_1 |
|
value: 34.984 |
|
- type: ndcg_at_10 |
|
value: 34.344 |
|
- type: ndcg_at_100 |
|
value: 41.015 |
|
- type: ndcg_at_1000 |
|
value: 44.366 |
|
- type: ndcg_at_3 |
|
value: 29.119 |
|
- type: ndcg_at_5 |
|
value: 30.825999999999997 |
|
- type: precision_at_1 |
|
value: 34.984 |
|
- type: precision_at_10 |
|
value: 10.358 |
|
- type: precision_at_100 |
|
value: 1.762 |
|
- type: precision_at_1000 |
|
value: 0.23900000000000002 |
|
- type: precision_at_3 |
|
value: 21.368000000000002 |
|
- type: precision_at_5 |
|
value: 15.948 |
|
- type: recall_at_1 |
|
value: 15.572 |
|
- type: recall_at_10 |
|
value: 39.367999999999995 |
|
- type: recall_at_100 |
|
value: 62.183 |
|
- type: recall_at_1000 |
|
value: 80.92200000000001 |
|
- type: recall_at_3 |
|
value: 26.131999999999998 |
|
- type: recall_at_5 |
|
value: 31.635999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 8.848 |
|
- type: map_at_10 |
|
value: 19.25 |
|
- type: map_at_100 |
|
value: 27.193 |
|
- type: map_at_1000 |
|
value: 28.721999999999998 |
|
- type: map_at_3 |
|
value: 13.968 |
|
- type: map_at_5 |
|
value: 16.283 |
|
- type: mrr_at_1 |
|
value: 68.75 |
|
- type: mrr_at_10 |
|
value: 76.25 |
|
- type: mrr_at_100 |
|
value: 76.534 |
|
- type: mrr_at_1000 |
|
value: 76.53999999999999 |
|
- type: mrr_at_3 |
|
value: 74.667 |
|
- type: mrr_at_5 |
|
value: 75.86699999999999 |
|
- type: ndcg_at_1 |
|
value: 56.00000000000001 |
|
- type: ndcg_at_10 |
|
value: 41.426 |
|
- type: ndcg_at_100 |
|
value: 45.660000000000004 |
|
- type: ndcg_at_1000 |
|
value: 53.02 |
|
- type: ndcg_at_3 |
|
value: 46.581 |
|
- type: ndcg_at_5 |
|
value: 43.836999999999996 |
|
- type: precision_at_1 |
|
value: 68.75 |
|
- type: precision_at_10 |
|
value: 32.800000000000004 |
|
- type: precision_at_100 |
|
value: 10.440000000000001 |
|
- type: precision_at_1000 |
|
value: 1.9980000000000002 |
|
- type: precision_at_3 |
|
value: 49.667 |
|
- type: precision_at_5 |
|
value: 42.25 |
|
- type: recall_at_1 |
|
value: 8.848 |
|
- type: recall_at_10 |
|
value: 24.467 |
|
- type: recall_at_100 |
|
value: 51.344 |
|
- type: recall_at_1000 |
|
value: 75.235 |
|
- type: recall_at_3 |
|
value: 15.329 |
|
- type: recall_at_5 |
|
value: 18.892999999999997 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 48.95 |
|
- type: f1 |
|
value: 43.44563593360779 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 78.036 |
|
- type: map_at_10 |
|
value: 85.639 |
|
- type: map_at_100 |
|
value: 85.815 |
|
- type: map_at_1000 |
|
value: 85.829 |
|
- type: map_at_3 |
|
value: 84.795 |
|
- type: map_at_5 |
|
value: 85.336 |
|
- type: mrr_at_1 |
|
value: 84.353 |
|
- type: mrr_at_10 |
|
value: 90.582 |
|
- type: mrr_at_100 |
|
value: 90.617 |
|
- type: mrr_at_1000 |
|
value: 90.617 |
|
- type: mrr_at_3 |
|
value: 90.132 |
|
- type: mrr_at_5 |
|
value: 90.447 |
|
- type: ndcg_at_1 |
|
value: 84.353 |
|
- type: ndcg_at_10 |
|
value: 89.003 |
|
- type: ndcg_at_100 |
|
value: 89.60000000000001 |
|
- type: ndcg_at_1000 |
|
value: 89.836 |
|
- type: ndcg_at_3 |
|
value: 87.81400000000001 |
|
- type: ndcg_at_5 |
|
value: 88.478 |
|
- type: precision_at_1 |
|
value: 84.353 |
|
- type: precision_at_10 |
|
value: 10.482 |
|
- type: precision_at_100 |
|
value: 1.099 |
|
- type: precision_at_1000 |
|
value: 0.11399999999999999 |
|
- type: precision_at_3 |
|
value: 33.257999999999996 |
|
- type: precision_at_5 |
|
value: 20.465 |
|
- type: recall_at_1 |
|
value: 78.036 |
|
- type: recall_at_10 |
|
value: 94.517 |
|
- type: recall_at_100 |
|
value: 96.828 |
|
- type: recall_at_1000 |
|
value: 98.261 |
|
- type: recall_at_3 |
|
value: 91.12 |
|
- type: recall_at_5 |
|
value: 92.946 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.191 |
|
- type: map_at_10 |
|
value: 32.369 |
|
- type: map_at_100 |
|
value: 34.123999999999995 |
|
- type: map_at_1000 |
|
value: 34.317 |
|
- type: map_at_3 |
|
value: 28.71 |
|
- type: map_at_5 |
|
value: 30.607 |
|
- type: mrr_at_1 |
|
value: 40.894999999999996 |
|
- type: mrr_at_10 |
|
value: 48.842 |
|
- type: mrr_at_100 |
|
value: 49.599 |
|
- type: mrr_at_1000 |
|
value: 49.647000000000006 |
|
- type: mrr_at_3 |
|
value: 46.785 |
|
- type: mrr_at_5 |
|
value: 47.672 |
|
- type: ndcg_at_1 |
|
value: 40.894999999999996 |
|
- type: ndcg_at_10 |
|
value: 39.872 |
|
- type: ndcg_at_100 |
|
value: 46.126 |
|
- type: ndcg_at_1000 |
|
value: 49.476 |
|
- type: ndcg_at_3 |
|
value: 37.153000000000006 |
|
- type: ndcg_at_5 |
|
value: 37.433 |
|
- type: precision_at_1 |
|
value: 40.894999999999996 |
|
- type: precision_at_10 |
|
value: 10.818 |
|
- type: precision_at_100 |
|
value: 1.73 |
|
- type: precision_at_1000 |
|
value: 0.231 |
|
- type: precision_at_3 |
|
value: 25.051000000000002 |
|
- type: precision_at_5 |
|
value: 17.531 |
|
- type: recall_at_1 |
|
value: 20.191 |
|
- type: recall_at_10 |
|
value: 45.768 |
|
- type: recall_at_100 |
|
value: 68.82000000000001 |
|
- type: recall_at_1000 |
|
value: 89.133 |
|
- type: recall_at_3 |
|
value: 33.296 |
|
- type: recall_at_5 |
|
value: 38.022 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.257 |
|
- type: map_at_10 |
|
value: 61.467000000000006 |
|
- type: map_at_100 |
|
value: 62.364 |
|
- type: map_at_1000 |
|
value: 62.424 |
|
- type: map_at_3 |
|
value: 58.228 |
|
- type: map_at_5 |
|
value: 60.283 |
|
- type: mrr_at_1 |
|
value: 78.515 |
|
- type: mrr_at_10 |
|
value: 84.191 |
|
- type: mrr_at_100 |
|
value: 84.378 |
|
- type: mrr_at_1000 |
|
value: 84.385 |
|
- type: mrr_at_3 |
|
value: 83.284 |
|
- type: mrr_at_5 |
|
value: 83.856 |
|
- type: ndcg_at_1 |
|
value: 78.515 |
|
- type: ndcg_at_10 |
|
value: 69.78999999999999 |
|
- type: ndcg_at_100 |
|
value: 72.886 |
|
- type: ndcg_at_1000 |
|
value: 74.015 |
|
- type: ndcg_at_3 |
|
value: 65.23 |
|
- type: ndcg_at_5 |
|
value: 67.80199999999999 |
|
- type: precision_at_1 |
|
value: 78.515 |
|
- type: precision_at_10 |
|
value: 14.519000000000002 |
|
- type: precision_at_100 |
|
value: 1.694 |
|
- type: precision_at_1000 |
|
value: 0.184 |
|
- type: precision_at_3 |
|
value: 41.702 |
|
- type: precision_at_5 |
|
value: 27.046999999999997 |
|
- type: recall_at_1 |
|
value: 39.257 |
|
- type: recall_at_10 |
|
value: 72.59299999999999 |
|
- type: recall_at_100 |
|
value: 84.679 |
|
- type: recall_at_1000 |
|
value: 92.12 |
|
- type: recall_at_3 |
|
value: 62.552 |
|
- type: recall_at_5 |
|
value: 67.616 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 91.5152 |
|
- type: ap |
|
value: 87.64584669595709 |
|
- type: f1 |
|
value: 91.50605576428437 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.926000000000002 |
|
- type: map_at_10 |
|
value: 34.049 |
|
- type: map_at_100 |
|
value: 35.213 |
|
- type: map_at_1000 |
|
value: 35.265 |
|
- type: map_at_3 |
|
value: 30.309 |
|
- type: map_at_5 |
|
value: 32.407000000000004 |
|
- type: mrr_at_1 |
|
value: 22.55 |
|
- type: mrr_at_10 |
|
value: 34.657 |
|
- type: mrr_at_100 |
|
value: 35.760999999999996 |
|
- type: mrr_at_1000 |
|
value: 35.807 |
|
- type: mrr_at_3 |
|
value: 30.989 |
|
- type: mrr_at_5 |
|
value: 33.039 |
|
- type: ndcg_at_1 |
|
value: 22.55 |
|
- type: ndcg_at_10 |
|
value: 40.842 |
|
- type: ndcg_at_100 |
|
value: 46.436 |
|
- type: ndcg_at_1000 |
|
value: 47.721999999999994 |
|
- type: ndcg_at_3 |
|
value: 33.209 |
|
- type: ndcg_at_5 |
|
value: 36.943 |
|
- type: precision_at_1 |
|
value: 22.55 |
|
- type: precision_at_10 |
|
value: 6.447 |
|
- type: precision_at_100 |
|
value: 0.9249999999999999 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.136000000000001 |
|
- type: precision_at_5 |
|
value: 10.381 |
|
- type: recall_at_1 |
|
value: 21.926000000000002 |
|
- type: recall_at_10 |
|
value: 61.724999999999994 |
|
- type: recall_at_100 |
|
value: 87.604 |
|
- type: recall_at_1000 |
|
value: 97.421 |
|
- type: recall_at_3 |
|
value: 40.944 |
|
- type: recall_at_5 |
|
value: 49.915 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.54765161878704 |
|
- type: f1 |
|
value: 93.3298945415573 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 75.71591427268582 |
|
- type: f1 |
|
value: 59.32113870474471 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 75.83053127101547 |
|
- type: f1 |
|
value: 73.60757944876475 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 78.72562205783457 |
|
- type: f1 |
|
value: 78.63761662505502 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 33.37935633767996 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.55270546130387 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.462692753143834 |
|
- type: mrr |
|
value: 31.497569753511563 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.646 |
|
- type: map_at_10 |
|
value: 12.498 |
|
- type: map_at_100 |
|
value: 15.486 |
|
- type: map_at_1000 |
|
value: 16.805999999999997 |
|
- type: map_at_3 |
|
value: 9.325 |
|
- type: map_at_5 |
|
value: 10.751 |
|
- type: mrr_at_1 |
|
value: 43.034 |
|
- type: mrr_at_10 |
|
value: 52.662 |
|
- type: mrr_at_100 |
|
value: 53.189 |
|
- type: mrr_at_1000 |
|
value: 53.25 |
|
- type: mrr_at_3 |
|
value: 50.929 |
|
- type: mrr_at_5 |
|
value: 51.92 |
|
- type: ndcg_at_1 |
|
value: 41.796 |
|
- type: ndcg_at_10 |
|
value: 33.477000000000004 |
|
- type: ndcg_at_100 |
|
value: 29.996000000000002 |
|
- type: ndcg_at_1000 |
|
value: 38.864 |
|
- type: ndcg_at_3 |
|
value: 38.940000000000005 |
|
- type: ndcg_at_5 |
|
value: 36.689 |
|
- type: precision_at_1 |
|
value: 43.034 |
|
- type: precision_at_10 |
|
value: 24.799 |
|
- type: precision_at_100 |
|
value: 7.432999999999999 |
|
- type: precision_at_1000 |
|
value: 1.9929999999999999 |
|
- type: precision_at_3 |
|
value: 36.842000000000006 |
|
- type: precision_at_5 |
|
value: 32.135999999999996 |
|
- type: recall_at_1 |
|
value: 5.646 |
|
- type: recall_at_10 |
|
value: 15.963 |
|
- type: recall_at_100 |
|
value: 29.492 |
|
- type: recall_at_1000 |
|
value: 61.711000000000006 |
|
- type: recall_at_3 |
|
value: 10.585 |
|
- type: recall_at_5 |
|
value: 12.753999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.602 |
|
- type: map_at_10 |
|
value: 41.545 |
|
- type: map_at_100 |
|
value: 42.644999999999996 |
|
- type: map_at_1000 |
|
value: 42.685 |
|
- type: map_at_3 |
|
value: 37.261 |
|
- type: map_at_5 |
|
value: 39.706 |
|
- type: mrr_at_1 |
|
value: 31.141000000000002 |
|
- type: mrr_at_10 |
|
value: 44.139 |
|
- type: mrr_at_100 |
|
value: 44.997 |
|
- type: mrr_at_1000 |
|
value: 45.025999999999996 |
|
- type: mrr_at_3 |
|
value: 40.503 |
|
- type: mrr_at_5 |
|
value: 42.64 |
|
- type: ndcg_at_1 |
|
value: 31.141000000000002 |
|
- type: ndcg_at_10 |
|
value: 48.995 |
|
- type: ndcg_at_100 |
|
value: 53.788000000000004 |
|
- type: ndcg_at_1000 |
|
value: 54.730000000000004 |
|
- type: ndcg_at_3 |
|
value: 40.844 |
|
- type: ndcg_at_5 |
|
value: 44.955 |
|
- type: precision_at_1 |
|
value: 31.141000000000002 |
|
- type: precision_at_10 |
|
value: 8.233 |
|
- type: precision_at_100 |
|
value: 1.093 |
|
- type: precision_at_1000 |
|
value: 0.11800000000000001 |
|
- type: precision_at_3 |
|
value: 18.579 |
|
- type: precision_at_5 |
|
value: 13.533999999999999 |
|
- type: recall_at_1 |
|
value: 27.602 |
|
- type: recall_at_10 |
|
value: 69.216 |
|
- type: recall_at_100 |
|
value: 90.252 |
|
- type: recall_at_1000 |
|
value: 97.27 |
|
- type: recall_at_3 |
|
value: 47.987 |
|
- type: recall_at_5 |
|
value: 57.438 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.949 |
|
- type: map_at_10 |
|
value: 84.89999999999999 |
|
- type: map_at_100 |
|
value: 85.531 |
|
- type: map_at_1000 |
|
value: 85.548 |
|
- type: map_at_3 |
|
value: 82.027 |
|
- type: map_at_5 |
|
value: 83.853 |
|
- type: mrr_at_1 |
|
value: 81.69999999999999 |
|
- type: mrr_at_10 |
|
value: 87.813 |
|
- type: mrr_at_100 |
|
value: 87.917 |
|
- type: mrr_at_1000 |
|
value: 87.91799999999999 |
|
- type: mrr_at_3 |
|
value: 86.938 |
|
- type: mrr_at_5 |
|
value: 87.53999999999999 |
|
- type: ndcg_at_1 |
|
value: 81.75 |
|
- type: ndcg_at_10 |
|
value: 88.55499999999999 |
|
- type: ndcg_at_100 |
|
value: 89.765 |
|
- type: ndcg_at_1000 |
|
value: 89.871 |
|
- type: ndcg_at_3 |
|
value: 85.905 |
|
- type: ndcg_at_5 |
|
value: 87.41 |
|
- type: precision_at_1 |
|
value: 81.75 |
|
- type: precision_at_10 |
|
value: 13.403 |
|
- type: precision_at_100 |
|
value: 1.528 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.597 |
|
- type: precision_at_5 |
|
value: 24.69 |
|
- type: recall_at_1 |
|
value: 70.949 |
|
- type: recall_at_10 |
|
value: 95.423 |
|
- type: recall_at_100 |
|
value: 99.509 |
|
- type: recall_at_1000 |
|
value: 99.982 |
|
- type: recall_at_3 |
|
value: 87.717 |
|
- type: recall_at_5 |
|
value: 92.032 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 51.76962893449579 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 62.32897690686379 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.478 |
|
- type: map_at_10 |
|
value: 11.994 |
|
- type: map_at_100 |
|
value: 13.977 |
|
- type: map_at_1000 |
|
value: 14.295 |
|
- type: map_at_3 |
|
value: 8.408999999999999 |
|
- type: map_at_5 |
|
value: 10.024 |
|
- type: mrr_at_1 |
|
value: 22.1 |
|
- type: mrr_at_10 |
|
value: 33.526 |
|
- type: mrr_at_100 |
|
value: 34.577000000000005 |
|
- type: mrr_at_1000 |
|
value: 34.632000000000005 |
|
- type: mrr_at_3 |
|
value: 30.217 |
|
- type: mrr_at_5 |
|
value: 31.962000000000003 |
|
- type: ndcg_at_1 |
|
value: 22.1 |
|
- type: ndcg_at_10 |
|
value: 20.191 |
|
- type: ndcg_at_100 |
|
value: 27.954 |
|
- type: ndcg_at_1000 |
|
value: 33.491 |
|
- type: ndcg_at_3 |
|
value: 18.787000000000003 |
|
- type: ndcg_at_5 |
|
value: 16.378999999999998 |
|
- type: precision_at_1 |
|
value: 22.1 |
|
- type: precision_at_10 |
|
value: 10.69 |
|
- type: precision_at_100 |
|
value: 2.1919999999999997 |
|
- type: precision_at_1000 |
|
value: 0.35200000000000004 |
|
- type: precision_at_3 |
|
value: 17.732999999999997 |
|
- type: precision_at_5 |
|
value: 14.499999999999998 |
|
- type: recall_at_1 |
|
value: 4.478 |
|
- type: recall_at_10 |
|
value: 21.657 |
|
- type: recall_at_100 |
|
value: 44.54 |
|
- type: recall_at_1000 |
|
value: 71.542 |
|
- type: recall_at_3 |
|
value: 10.778 |
|
- type: recall_at_5 |
|
value: 14.687 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.82325259156718 |
|
- type: cos_sim_spearman |
|
value: 79.2463589100662 |
|
- type: euclidean_pearson |
|
value: 80.48318380496771 |
|
- type: euclidean_spearman |
|
value: 79.34451935199979 |
|
- type: manhattan_pearson |
|
value: 80.39041824178759 |
|
- type: manhattan_spearman |
|
value: 79.23002892700211 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.74130231431258 |
|
- type: cos_sim_spearman |
|
value: 78.36856568042397 |
|
- type: euclidean_pearson |
|
value: 82.48301631890303 |
|
- type: euclidean_spearman |
|
value: 78.28376980722732 |
|
- type: manhattan_pearson |
|
value: 82.43552075450525 |
|
- type: manhattan_spearman |
|
value: 78.22702443947126 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.96138619461459 |
|
- type: cos_sim_spearman |
|
value: 81.85436343502379 |
|
- type: euclidean_pearson |
|
value: 81.82895226665367 |
|
- type: euclidean_spearman |
|
value: 82.22707349602916 |
|
- type: manhattan_pearson |
|
value: 81.66303369445873 |
|
- type: manhattan_spearman |
|
value: 82.05030197179455 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.05481244198648 |
|
- type: cos_sim_spearman |
|
value: 80.85052504637808 |
|
- type: euclidean_pearson |
|
value: 80.86728419744497 |
|
- type: euclidean_spearman |
|
value: 81.033786401512 |
|
- type: manhattan_pearson |
|
value: 80.90107531061103 |
|
- type: manhattan_spearman |
|
value: 81.11374116827795 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.615220756399 |
|
- type: cos_sim_spearman |
|
value: 86.46858500002092 |
|
- type: euclidean_pearson |
|
value: 86.08307800247586 |
|
- type: euclidean_spearman |
|
value: 86.72691443870013 |
|
- type: manhattan_pearson |
|
value: 85.96155594487269 |
|
- type: manhattan_spearman |
|
value: 86.605909505275 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.14363913634436 |
|
- type: cos_sim_spearman |
|
value: 84.48430226487102 |
|
- type: euclidean_pearson |
|
value: 83.75303424801902 |
|
- type: euclidean_spearman |
|
value: 84.56762380734538 |
|
- type: manhattan_pearson |
|
value: 83.6135447165928 |
|
- type: manhattan_spearman |
|
value: 84.39898212616731 |
|
- 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: 85.09909252554525 |
|
- type: cos_sim_spearman |
|
value: 85.70951402743276 |
|
- type: euclidean_pearson |
|
value: 87.1991936239908 |
|
- type: euclidean_spearman |
|
value: 86.07745840612071 |
|
- type: manhattan_pearson |
|
value: 87.25039137549952 |
|
- type: manhattan_spearman |
|
value: 85.99938746659761 |
|
- 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: 63.529332093413615 |
|
- type: cos_sim_spearman |
|
value: 65.38177340147439 |
|
- type: euclidean_pearson |
|
value: 66.35278011412136 |
|
- type: euclidean_spearman |
|
value: 65.47147267032997 |
|
- type: manhattan_pearson |
|
value: 66.71804682408693 |
|
- type: manhattan_spearman |
|
value: 65.67406521423597 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.45802942885662 |
|
- type: cos_sim_spearman |
|
value: 84.8853341842566 |
|
- type: euclidean_pearson |
|
value: 84.60915021096707 |
|
- type: euclidean_spearman |
|
value: 85.11181242913666 |
|
- type: manhattan_pearson |
|
value: 84.38600521210364 |
|
- type: manhattan_spearman |
|
value: 84.89045417981723 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 85.92793380635129 |
|
- type: mrr |
|
value: 95.85834191226348 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 55.74400000000001 |
|
- type: map_at_10 |
|
value: 65.455 |
|
- type: map_at_100 |
|
value: 66.106 |
|
- type: map_at_1000 |
|
value: 66.129 |
|
- type: map_at_3 |
|
value: 62.719 |
|
- type: map_at_5 |
|
value: 64.441 |
|
- type: mrr_at_1 |
|
value: 58.667 |
|
- type: mrr_at_10 |
|
value: 66.776 |
|
- type: mrr_at_100 |
|
value: 67.363 |
|
- type: mrr_at_1000 |
|
value: 67.384 |
|
- type: mrr_at_3 |
|
value: 64.889 |
|
- type: mrr_at_5 |
|
value: 66.122 |
|
- type: ndcg_at_1 |
|
value: 58.667 |
|
- type: ndcg_at_10 |
|
value: 69.904 |
|
- type: ndcg_at_100 |
|
value: 72.807 |
|
- type: ndcg_at_1000 |
|
value: 73.423 |
|
- type: ndcg_at_3 |
|
value: 65.405 |
|
- type: ndcg_at_5 |
|
value: 67.86999999999999 |
|
- type: precision_at_1 |
|
value: 58.667 |
|
- type: precision_at_10 |
|
value: 9.3 |
|
- type: precision_at_100 |
|
value: 1.08 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 25.444 |
|
- type: precision_at_5 |
|
value: 17 |
|
- type: recall_at_1 |
|
value: 55.74400000000001 |
|
- type: recall_at_10 |
|
value: 82.122 |
|
- type: recall_at_100 |
|
value: 95.167 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 70.14399999999999 |
|
- type: recall_at_5 |
|
value: 76.417 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.86534653465347 |
|
- type: cos_sim_ap |
|
value: 96.54142419791388 |
|
- type: cos_sim_f1 |
|
value: 93.07535641547861 |
|
- type: cos_sim_precision |
|
value: 94.81327800829875 |
|
- type: cos_sim_recall |
|
value: 91.4 |
|
- type: dot_accuracy |
|
value: 99.86435643564356 |
|
- type: dot_ap |
|
value: 96.53682260449868 |
|
- type: dot_f1 |
|
value: 92.98515104966718 |
|
- type: dot_precision |
|
value: 95.27806925498426 |
|
- type: dot_recall |
|
value: 90.8 |
|
- type: euclidean_accuracy |
|
value: 99.86336633663366 |
|
- type: euclidean_ap |
|
value: 96.5228676185697 |
|
- type: euclidean_f1 |
|
value: 92.9735234215886 |
|
- type: euclidean_precision |
|
value: 94.70954356846472 |
|
- type: euclidean_recall |
|
value: 91.3 |
|
- type: manhattan_accuracy |
|
value: 99.85841584158416 |
|
- type: manhattan_ap |
|
value: 96.50392760934032 |
|
- type: manhattan_f1 |
|
value: 92.84642321160581 |
|
- type: manhattan_precision |
|
value: 92.8928928928929 |
|
- type: manhattan_recall |
|
value: 92.80000000000001 |
|
- type: max_accuracy |
|
value: 99.86534653465347 |
|
- type: max_ap |
|
value: 96.54142419791388 |
|
- type: max_f1 |
|
value: 93.07535641547861 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 61.08285408766616 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 35.640675309010604 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 53.20333913710715 |
|
- type: mrr |
|
value: 54.088813555725324 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.79465221925075 |
|
- type: cos_sim_spearman |
|
value: 30.530816059163634 |
|
- type: dot_pearson |
|
value: 31.364837244718043 |
|
- type: dot_spearman |
|
value: 30.79726823684003 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.22599999999999998 |
|
- type: map_at_10 |
|
value: 1.735 |
|
- type: map_at_100 |
|
value: 8.978 |
|
- type: map_at_1000 |
|
value: 20.851 |
|
- type: map_at_3 |
|
value: 0.613 |
|
- type: map_at_5 |
|
value: 0.964 |
|
- type: mrr_at_1 |
|
value: 88 |
|
- type: mrr_at_10 |
|
value: 92.867 |
|
- type: mrr_at_100 |
|
value: 92.867 |
|
- type: mrr_at_1000 |
|
value: 92.867 |
|
- type: mrr_at_3 |
|
value: 92.667 |
|
- type: mrr_at_5 |
|
value: 92.667 |
|
- type: ndcg_at_1 |
|
value: 82 |
|
- type: ndcg_at_10 |
|
value: 73.164 |
|
- type: ndcg_at_100 |
|
value: 51.878 |
|
- type: ndcg_at_1000 |
|
value: 44.864 |
|
- type: ndcg_at_3 |
|
value: 79.184 |
|
- type: ndcg_at_5 |
|
value: 76.39 |
|
- type: precision_at_1 |
|
value: 88 |
|
- type: precision_at_10 |
|
value: 76.2 |
|
- type: precision_at_100 |
|
value: 52.459999999999994 |
|
- type: precision_at_1000 |
|
value: 19.692 |
|
- type: precision_at_3 |
|
value: 82.667 |
|
- type: precision_at_5 |
|
value: 80 |
|
- type: recall_at_1 |
|
value: 0.22599999999999998 |
|
- type: recall_at_10 |
|
value: 1.942 |
|
- type: recall_at_100 |
|
value: 12.342 |
|
- type: recall_at_1000 |
|
value: 41.42 |
|
- type: recall_at_3 |
|
value: 0.637 |
|
- type: recall_at_5 |
|
value: 1.034 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.567 |
|
- type: map_at_10 |
|
value: 13.116 |
|
- type: map_at_100 |
|
value: 19.39 |
|
- type: map_at_1000 |
|
value: 20.988 |
|
- type: map_at_3 |
|
value: 7.109 |
|
- type: map_at_5 |
|
value: 9.950000000000001 |
|
- type: mrr_at_1 |
|
value: 42.857 |
|
- type: mrr_at_10 |
|
value: 57.404999999999994 |
|
- type: mrr_at_100 |
|
value: 58.021 |
|
- type: mrr_at_1000 |
|
value: 58.021 |
|
- type: mrr_at_3 |
|
value: 54.762 |
|
- type: mrr_at_5 |
|
value: 56.19 |
|
- type: ndcg_at_1 |
|
value: 38.775999999999996 |
|
- type: ndcg_at_10 |
|
value: 30.359 |
|
- type: ndcg_at_100 |
|
value: 41.284 |
|
- type: ndcg_at_1000 |
|
value: 52.30200000000001 |
|
- type: ndcg_at_3 |
|
value: 36.744 |
|
- type: ndcg_at_5 |
|
value: 34.326 |
|
- type: precision_at_1 |
|
value: 42.857 |
|
- type: precision_at_10 |
|
value: 26.122 |
|
- type: precision_at_100 |
|
value: 8.082 |
|
- type: precision_at_1000 |
|
value: 1.559 |
|
- type: precision_at_3 |
|
value: 40.136 |
|
- type: precision_at_5 |
|
value: 35.510000000000005 |
|
- type: recall_at_1 |
|
value: 3.567 |
|
- type: recall_at_10 |
|
value: 19.045 |
|
- type: recall_at_100 |
|
value: 49.979 |
|
- type: recall_at_1000 |
|
value: 84.206 |
|
- type: recall_at_3 |
|
value: 8.52 |
|
- type: recall_at_5 |
|
value: 13.103000000000002 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 68.8394 |
|
- type: ap |
|
value: 13.454399712443099 |
|
- type: f1 |
|
value: 53.04963076364322 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 60.546123372948514 |
|
- type: f1 |
|
value: 60.86952793277713 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 49.10042955060234 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.03308100375514 |
|
- type: cos_sim_ap |
|
value: 71.08284605869684 |
|
- type: cos_sim_f1 |
|
value: 65.42539436255494 |
|
- type: cos_sim_precision |
|
value: 64.14807302231237 |
|
- type: cos_sim_recall |
|
value: 66.75461741424802 |
|
- type: dot_accuracy |
|
value: 84.68736961316088 |
|
- type: dot_ap |
|
value: 69.20524036530992 |
|
- type: dot_f1 |
|
value: 63.54893953365829 |
|
- type: dot_precision |
|
value: 63.45698500394633 |
|
- type: dot_recall |
|
value: 63.641160949868066 |
|
- type: euclidean_accuracy |
|
value: 85.07480479227513 |
|
- type: euclidean_ap |
|
value: 71.14592761009864 |
|
- type: euclidean_f1 |
|
value: 65.43814432989691 |
|
- type: euclidean_precision |
|
value: 63.95465994962216 |
|
- type: euclidean_recall |
|
value: 66.99208443271768 |
|
- type: manhattan_accuracy |
|
value: 85.06288370984085 |
|
- type: manhattan_ap |
|
value: 71.07289742593868 |
|
- type: manhattan_f1 |
|
value: 65.37585421412301 |
|
- type: manhattan_precision |
|
value: 62.816147859922175 |
|
- type: manhattan_recall |
|
value: 68.15303430079156 |
|
- type: max_accuracy |
|
value: 85.07480479227513 |
|
- type: max_ap |
|
value: 71.14592761009864 |
|
- type: max_f1 |
|
value: 65.43814432989691 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.79058485659952 |
|
- type: cos_sim_ap |
|
value: 83.7183187008759 |
|
- type: cos_sim_f1 |
|
value: 75.86921142180798 |
|
- type: cos_sim_precision |
|
value: 73.00683371298405 |
|
- type: cos_sim_recall |
|
value: 78.96519864490298 |
|
- type: dot_accuracy |
|
value: 87.0085768618776 |
|
- type: dot_ap |
|
value: 81.87467488474279 |
|
- type: dot_f1 |
|
value: 74.04188363990559 |
|
- type: dot_precision |
|
value: 72.10507114191901 |
|
- type: dot_recall |
|
value: 76.08561749307053 |
|
- type: euclidean_accuracy |
|
value: 87.8332751193387 |
|
- type: euclidean_ap |
|
value: 83.83585648120315 |
|
- type: euclidean_f1 |
|
value: 76.02582177042369 |
|
- type: euclidean_precision |
|
value: 73.36388371759989 |
|
- type: euclidean_recall |
|
value: 78.88820449645827 |
|
- type: manhattan_accuracy |
|
value: 87.87208444910156 |
|
- type: manhattan_ap |
|
value: 83.8101950642973 |
|
- type: manhattan_f1 |
|
value: 75.90454195535027 |
|
- type: manhattan_precision |
|
value: 72.44419564761039 |
|
- type: manhattan_recall |
|
value: 79.71204188481676 |
|
- type: max_accuracy |
|
value: 87.87208444910156 |
|
- type: max_ap |
|
value: 83.83585648120315 |
|
- type: max_f1 |
|
value: 76.02582177042369 |
|
license: mit |
|
language: |
|
- en |
|
pipeline_tag: sentence-similarity |
|
--- |
|
|
|
|
|
<h1 align="center">FlagEmbedding</h1> |
|
|
|
|
|
<h4 align="center"> |
|
<p> |
|
<a href=#model-list>Model List</a> | |
|
<a href=#usage>Usage</a> | |
|
<a href="#evaluation">Evaluation</a> | |
|
<a href="#train">Train</a> | |
|
<a href="#contact">Contact</a> | |
|
<a href="#license">License</a> |
|
<p> |
|
</h4> |
|
|
|
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
|
|
|
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
|
|
|
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. |
|
And it also can be used in vector database for LLMs. |
|
|
|
************* 🌟**Updates**🌟 ************* |
|
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [**this**](#using-langchain); C-MTEB **leaderboard** is [avaliable](https://huggingface.co/spaces/mteb/leaderboard). |
|
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
|
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** |
|
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. |
|
|
|
|
|
## Model List |
|
|
|
`bge` is short for `BAAI general embedding`. |
|
|
|
| Model | Language | Description | query instruction for retrieval\* | |
|
|:-------------------------------|:--------:| :--------:| :--------:| |
|
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | |
|
| [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | |
|
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | |
|
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | |
|
|
|
\*: If you need to search the **long** relevant passages to a **short** query (s2p retrieval task), you need to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** need to be added to passages. |
|
|
|
## Usage |
|
|
|
Here are some examples to use `bge` models with |
|
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). |
|
|
|
#### Using FlagEmbedding |
|
``` |
|
pip install -U FlagEmbedding |
|
``` |
|
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. |
|
|
|
```python |
|
from FlagEmbedding import FlagModel |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：") |
|
embeddings_1 = model.encode(sentences) |
|
embeddings_2 = model.encode(sentences) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
|
|
# for s2p(short query to long passage) retrieval task, please use encode_queries() which will automatically add the instruction to each query |
|
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction |
|
queries = ['query_1', 'query_2'] |
|
passages = ["样例文档-1", "样例文档-2"] |
|
q_embeddings = model.encode_queries(queries) |
|
p_embeddings = model.encode(passages) |
|
scores = q_embeddings @ p_embeddings.T |
|
``` |
|
The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
|
|
|
FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU. |
|
|
|
|
|
#### Using Sentence-Transformers |
|
|
|
Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
model = SentenceTransformer('BAAI/bge-large-zh') |
|
embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
|
embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
``` |
|
For s2p(short query to long passage) retrieval task, |
|
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
|
But the instruction is not needed for passages. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
queries = ['query_1', 'query_2'] |
|
passages = ["样例文档-1", "样例文档-2"] |
|
instruction = "为这个句子生成表示以用于检索相关文章：" |
|
|
|
model = SentenceTransformer('BAAI/bge-large-zh') |
|
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
|
p_embeddings = model.encode(passages, normalize_embeddings=True) |
|
scores = q_embeddings @ p_embeddings.T |
|
``` |
|
|
|
#### Using Langchain |
|
|
|
You can use `bge` in langchain like this: |
|
```python |
|
from langchain.embeddings import HuggingFaceBgeEmbeddings |
|
model_name = "BAAI/bge-small-en" |
|
model_kwargs = {'device': 'cuda'} |
|
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
|
model_norm = HuggingFaceBgeEmbeddings( |
|
model_name=model_name, |
|
model_kwargs=model_kwargs, |
|
encode_kwargs=encode_kwargs |
|
) |
|
``` |
|
|
|
|
|
#### Using HuggingFace Transformers |
|
|
|
With transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of first token (i.e., [CLS]) as the sentence embedding. |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModel |
|
import torch |
|
# Sentences we want sentence embeddings for |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
|
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh') |
|
model = AutoModel.from_pretrained('BAAI/bge-large-zh') |
|
|
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
|
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
|
|
|
# Compute token embeddings |
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
# Perform pooling. In this case, cls pooling. |
|
sentence_embeddings = model_output[0][:, 0] |
|
# normalize embeddings |
|
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
|
print("Sentence embeddings:", sentence_embeddings) |
|
``` |
|
|
|
|
|
## Evaluation |
|
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
|
More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
|
|
|
- **MTEB**: |
|
|
|
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**bge-large-en**](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | **63.98** | **53.9** | **46.98** | 85.8 | **59.48** | 81.56 | 32.06 | **76.21** | |
|
| [**bge-base-en**](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | |
|
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | |
|
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | |
|
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | |
|
| [**bge-small-en**](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | |
|
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | |
|
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | |
|
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | |
|
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | |
|
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | |
|
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | |
|
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | |
|
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | |
|
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 384 | 512 | 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 | |
|
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 | |
|
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 768 | 512 | 56.00 | 41.88 | 41.1 | 82.54 | 53.14 | 76.51 | 30.36 | 66.68 | |
|
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 768 | 512 | 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 | |
|
|
|
|
|
|
|
- **C-MTEB**: |
|
We create a benchmark C-MTEB for chinese text embedding which consists of 31 datasets from 6 tasks. |
|
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. |
|
|
|
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 | |
|
| [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** | |
|
| [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 | |
|
| [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 | |
|
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 | |
|
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 | |
|
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 | |
|
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | |
|
| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 | |
|
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 | |
|
|
|
|
|
|
|
## Train |
|
This section will introduce the way we used to train the general embedding. |
|
The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), |
|
and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md). |
|
|
|
|
|
**1. RetroMAE Pre-train** |
|
We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), |
|
which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). |
|
The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. |
|
In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively. |
|
We used the AdamW optimizer and the learning rate is 2e-5. |
|
|
|
**Pre-training data**: |
|
- English: |
|
- [Pile](https://pile.eleuther.ai/) |
|
- [wikipedia](https://huggingface.co/datasets/wikipedia) |
|
- [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus) |
|
- Chinese: |
|
- [wudao](https://github.com/BAAI-WuDao/Data) |
|
|
|
|
|
**2. Finetune** |
|
We fine-tune the model using a contrastive objective. |
|
The format of input data is a triple`(query, positive, negative)`. |
|
Besides the negative in the triple, we also adopt in-batch negatives strategy. |
|
We employ the cross-device negatives sharing method to share negatives among different GPUs, |
|
which can dramatically **increase the number of negatives**. |
|
|
|
We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). |
|
We used the AdamW optimizer and the learning rate is 1e-5. |
|
The temperature for contrastive loss is 0.01. |
|
|
|
Besides, we add instruction to the query for s2p(short query to long passage) retrieval task in the training (add nothing to passages). |
|
For English, the instruction is `Represent this sentence for searching relevant passages: `; |
|
For Chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`. |
|
In the evaluation, the instruction should be added for queries in retrieval task, not be added for other tasks. |
|
Noted that the instruction is not needed for passages. |
|
|
|
The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
|
You can easily finetune your model with it. |
|
|
|
**Training data**: |
|
|
|
- For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on. |
|
|
|
- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on. |
|
|
|
**The data collection is to be released in the future.** |
|
|
|
We will continually update the embedding models and training codes, |
|
hoping to promote the development of the embedding model community. |
|
|
|
|
|
|
|
## License |
|
FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
|
|
|
|
|
|
|
|
