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Bge Large En V1.5 model by BAAI
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Model
Bge Large En V1.5
by BAAI
ID: hf-model--baai--bge-large-en-v1.5
Scale 0.34B
Context Window 4.096K
Downloads 4.0M
FNI Rank 35
Percentile Top 0%
Activity
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--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-large-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision...
Audited 35 FNI Score
Tiny 0.34B Params
4k Context
Hot 4.0M Downloads
8G GPU ~2GB Est. VRAM
Dense BERTMODEL Architecture
| Entity Passport | |
| Registry ID | hf-model--baai--bge-large-en-v1.5 |
| Provider | huggingface |
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~1.6GB VRAM
Interactive
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* Static estimation for 4-Bit Quantization.
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Cite this model
Academic & Research Attribution
BibTeX
@misc{hf_model__baai__bge_large_en_v1.5,
author = {BAAI},
title = {Bge Large En V1.5 Model},
year = {2026},
howpublished = {\url{https://huggingface.co/BAAI/bge-large-en-v1.5}},
note = {Accessed via Free2AITools Knowledge Fortress}
} APA Style
BAAI. (2026). Bge Large En V1.5 [Model]. Free2AITools. https://huggingface.co/BAAI/bge-large-en-v1.5
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pip install -U transformers 35.0
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🚀 Velocity (V) 0
🛡️ Credibility (C) 0
🔧 Utility (U) 0
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The Nexus Index for Bge Large En V1.5 aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.
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tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index: - name: bge-large-en-v1.5
results:- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:- type: accuracy
value: 75.8507462686567 - type: ap
value: 38.566457320228245 - type: f1
value: 69.69386648043475
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:- type: accuracy
value: 92.416675 - type: ap
value: 89.1928861155922 - type: f1
value: 92.39477019574215
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:- type: accuracy
value: 48.175999999999995 - type: f1
value: 47.80712792870253
- type: accuracy
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 40.184999999999995 - type: map_at_10
value: 55.654 - type: map_at_100
value: 56.25 - type: map_at_1000
value: 56.255 - type: map_at_3
value: 51.742999999999995 - type: map_at_5
value: 54.129000000000005 - type: mrr_at_1
value: 40.967 - type: mrr_at_10
value: 55.96 - type: mrr_at_100
value: 56.54900000000001 - type: mrr_at_1000
value: 56.554 - type: mrr_at_3
value: 51.980000000000004 - type: mrr_at_5
value: 54.44 - type: ndcg_at_1
value: 40.184999999999995 - type: ndcg_at_10
value: 63.542 - type: ndcg_at_100
value: 65.96499999999999 - type: ndcg_at_1000
value: 66.08699999999999 - type: ndcg_at_3
value: 55.582 - type: ndcg_at_5
value: 59.855000000000004 - type: precision_at_1
value: 40.184999999999995 - type: precision_at_10
value: 8.841000000000001 - type: precision_at_100
value: 0.987 - type: precision_at_1000
value: 0.1 - type: precision_at_3
value: 22.238 - type: precision_at_5
value: 15.405 - type: recall_at_1
value: 40.184999999999995 - type: recall_at_10
value: 88.407 - type: recall_at_100
value: 98.72 - type: recall_at_1000
value: 99.644 - type: recall_at_3
value: 66.714 - type: recall_at_5
value: 77.027
- type: map_at_1
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:- type: v_measure
value: 48.567077926750066
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:- type: v_measure
value: 43.19453389182364
- type: v_measure
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:- type: map
value: 64.46555939623092 - type: mrr
value: 77.82361605768807
- type: map
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:- type: cos_sim_pearson
value: 84.9554128814735 - type: cos_sim_spearman
value: 84.65373612172036 - type: euclidean_pearson
value: 83.2905059954138 - type: euclidean_spearman
value: 84.52240782811128 - type: manhattan_pearson
value: 82.99533802997436 - type: manhattan_spearman
value: 84.20673798475734
- type: cos_sim_pearson
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:- type: accuracy
value: 87.78896103896103 - type: f1
value: 87.77189310964883
- type: accuracy
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:- type: v_measure
value: 39.714538337650495
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:- type: v_measure
value: 36.90108349284447
- type: v_measure
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 32.795 - type: map_at_10
value: 43.669000000000004 - type: map_at_100
value: 45.151 - type: map_at_1000
value: 45.278 - type: map_at_3
value: 40.006 - type: map_at_5
value: 42.059999999999995 - type: mrr_at_1
value: 39.771 - type: mrr_at_10
value: 49.826 - type: mrr_at_100
value: 50.504000000000005 - type: mrr_at_1000
value: 50.549 - type: mrr_at_3
value: 47.115 - type: mrr_at_5
value: 48.832 - type: ndcg_at_1
value: 39.771 - type: ndcg_at_10
value: 50.217999999999996 - type: ndcg_at_100
value: 55.454 - type: ndcg_at_1000
value: 57.37 - type: ndcg_at_3
value: 44.885000000000005 - type: ndcg_at_5
value: 47.419 - type: precision_at_1
value: 39.771 - type: precision_at_10
value: 9.642000000000001 - type: precision_at_100
value: 1.538 - type: precision_at_1000
value: 0.198 - type: precision_at_3
value: 21.268 - type: precision_at_5
value: 15.536 - type: recall_at_1
value: 32.795 - type: recall_at_10
value: 62.580999999999996 - type: recall_at_100
value: 84.438 - type: recall_at_1000
value: 96.492 - type: recall_at_3
value: 47.071000000000005 - type: recall_at_5
value: 54.079
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 32.671 - type: map_at_10
value: 43.334 - type: map_at_100
value: 44.566 - type: map_at_1000
value: 44.702999999999996 - type: map_at_3
value: 40.343 - type: map_at_5
value: 41.983 - type: mrr_at_1
value: 40.764 - type: mrr_at_10
value: 49.382 - type: mrr_at_100
value: 49.988 - type: mrr_at_1000
value: 50.03300000000001 - type: mrr_at_3
value: 47.293 - type: mrr_at_5
value: 48.51 - type: ndcg_at_1
value: 40.764 - type: ndcg_at_10
value: 49.039 - type: ndcg_at_100
value: 53.259 - type: ndcg_at_1000
value: 55.253 - type: ndcg_at_3
value: 45.091 - type: ndcg_at_5
value: 46.839999999999996 - type: precision_at_1
value: 40.764 - type: precision_at_10
value: 9.191 - type: precision_at_100
value: 1.476 - type: precision_at_1000
value: 0.19499999999999998 - type: precision_at_3
value: 21.72 - type: precision_at_5
value: 15.299 - type: recall_at_1
value: 32.671 - type: recall_at_10
value: 58.816 - type: recall_at_100
value: 76.654 - type: recall_at_1000
value: 89.05999999999999 - type: recall_at_3
value: 46.743 - type: recall_at_5
value: 51.783
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 40.328 - type: map_at_10
value: 53.32599999999999 - type: map_at_100
value: 54.37499999999999 - type: map_at_1000
value: 54.429 - type: map_at_3
value: 49.902 - type: map_at_5
value: 52.002 - type: mrr_at_1
value: 46.332 - type: mrr_at_10
value: 56.858 - type: mrr_at_100
value: 57.522 - type: mrr_at_1000
value: 57.54899999999999 - type: mrr_at_3
value: 54.472 - type: mrr_at_5
value: 55.996 - type: ndcg_at_1
value: 46.332 - type: ndcg_at_10
value: 59.313 - type: ndcg_at_100
value: 63.266999999999996 - type: ndcg_at_1000
value: 64.36 - type: ndcg_at_3
value: 53.815000000000005 - type: ndcg_at_5
value: 56.814 - type: precision_at_1
value: 46.332 - type: precision_at_10
value: 9.53 - type: precision_at_100
value: 1.238 - type: precision_at_1000
value: 0.13699999999999998 - type: precision_at_3
value: 24.054000000000002 - type: precision_at_5
value: 16.589000000000002 - type: recall_at_1
value: 40.328 - type: recall_at_10
value: 73.421 - type: recall_at_100
value: 90.059 - type: recall_at_1000
value: 97.81 - type: recall_at_3
value: 59.009 - type: recall_at_5
value: 66.352
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 27.424 - type: map_at_10
value: 36.332 - type: map_at_100
value: 37.347 - type: map_at_1000
value: 37.422 - type: map_at_3
value: 33.743 - type: map_at_5
value: 35.176 - type: mrr_at_1
value: 29.153000000000002 - type: mrr_at_10
value: 38.233 - type: mrr_at_100
value: 39.109 - type: mrr_at_1000
value: 39.164 - type: mrr_at_3
value: 35.876000000000005 - type: mrr_at_5
value: 37.169000000000004 - type: ndcg_at_1
value: 29.153000000000002 - type: ndcg_at_10
value: 41.439 - type: ndcg_at_100
value: 46.42 - type: ndcg_at_1000
value: 48.242000000000004 - type: ndcg_at_3
value: 36.362 - type: ndcg_at_5
value: 38.743 - type: precision_at_1
value: 29.153000000000002 - type: precision_at_10
value: 6.315999999999999 - type: precision_at_100
value: 0.927 - type: precision_at_1000
value: 0.11199999999999999 - type: precision_at_3
value: 15.443000000000001 - type: precision_at_5
value: 10.644 - type: recall_at_1
value: 27.424 - type: recall_at_10
value: 55.364000000000004 - type: recall_at_100
value: 78.211 - type: recall_at_1000
value: 91.74600000000001 - type: recall_at_3
value: 41.379 - type: recall_at_5
value: 47.14
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 19.601 - type: map_at_10
value: 27.826 - type: map_at_100
value: 29.017 - type: map_at_1000
value: 29.137 - type: map_at_3
value: 25.125999999999998 - type: map_at_5
value: 26.765 - type: mrr_at_1
value: 24.005000000000003 - type: mrr_at_10
value: 32.716 - type: mrr_at_100
value: 33.631 - type: mrr_at_1000
value: 33.694 - type: mrr_at_3
value: 29.934 - type: mrr_at_5
value: 31.630999999999997 - type: ndcg_at_1
value: 24.005000000000003 - type: ndcg_at_10
value: 33.158 - type: ndcg_at_100
value: 38.739000000000004 - type: ndcg_at_1000
value: 41.495 - type: ndcg_at_3
value: 28.185 - type: ndcg_at_5
value: 30.796 - type: precision_at_1
value: 24.005000000000003 - type: precision_at_10
value: 5.908 - type: precision_at_100
value: 1.005 - type: precision_at_1000
value: 0.13899999999999998 - type: precision_at_3
value: 13.391 - type: precision_at_5
value: 9.876 - type: recall_at_1
value: 19.601 - type: recall_at_10
value: 44.746 - type: recall_at_100
value: 68.82300000000001 - type: recall_at_1000
value: 88.215 - type: recall_at_3
value: 31.239 - type: recall_at_5
value: 37.695
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 30.130000000000003 - type: map_at_10
value: 40.96 - type: map_at_100
value: 42.282 - type: map_at_1000
value: 42.392 - type: map_at_3
value: 37.889 - type: map_at_5
value: 39.661 - type: mrr_at_1
value: 36.958999999999996 - type: mrr_at_10
value: 46.835 - type: mrr_at_100
value: 47.644 - type: mrr_at_1000
value: 47.688 - type: mrr_at_3
value: 44.562000000000005 - type: mrr_at_5
value: 45.938 - type: ndcg_at_1
value: 36.958999999999996 - type: ndcg_at_10
value: 47.06 - type: ndcg_at_100
value: 52.345 - type: ndcg_at_1000
value: 54.35 - type: ndcg_at_3
value: 42.301 - type: ndcg_at_5
value: 44.635999999999996 - type: precision_at_1
value: 36.958999999999996 - type: precision_at_10
value: 8.479000000000001 - type: precision_at_100
value: 1.284 - type: precision_at_1000
value: 0.163 - type: precision_at_3
value: 20.244 - type: precision_at_5
value: 14.224999999999998 - type: recall_at_1
value: 30.130000000000003 - type: recall_at_10
value: 59.27 - type: recall_at_100
value: 81.195 - type: recall_at_1000
value: 94.21199999999999 - type: recall_at_3
value: 45.885 - type: recall_at_5
value: 52.016
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 26.169999999999998 - type: map_at_10
value: 36.451 - type: map_at_100
value: 37.791000000000004 - type: map_at_1000
value: 37.897 - type: map_at_3
value: 33.109 - type: map_at_5
value: 34.937000000000005 - type: mrr_at_1
value: 32.877 - type: mrr_at_10
value: 42.368 - type: mrr_at_100
value: 43.201 - type: mrr_at_1000
value: 43.259 - type: mrr_at_3
value: 39.763999999999996 - type: mrr_at_5
value: 41.260000000000005 - type: ndcg_at_1
value: 32.877 - type: ndcg_at_10
value: 42.659000000000006 - type: ndcg_at_100
value: 48.161 - type: ndcg_at_1000
value: 50.345 - type: ndcg_at_3
value: 37.302 - type: ndcg_at_5
value: 39.722 - type: precision_at_1
value: 32.877 - type: precision_at_10
value: 7.9 - type: precision_at_100
value: 1.236 - type: precision_at_1000
value: 0.158 - type: precision_at_3
value: 17.846 - type: precision_at_5
value: 12.9 - type: recall_at_1
value: 26.169999999999998 - type: recall_at_10
value: 55.35 - type: recall_at_100
value: 78.755 - type: recall_at_1000
value: 93.518 - type: recall_at_3
value: 40.176 - type: recall_at_5
value: 46.589000000000006
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 27.15516666666667 - type: map_at_10
value: 36.65741666666667 - type: map_at_100
value: 37.84991666666666 - type: map_at_1000
value: 37.96316666666667 - type: map_at_3
value: 33.74974999999999 - type: map_at_5
value: 35.3765 - type: mrr_at_1
value: 32.08233333333334 - type: mrr_at_10
value: 41.033833333333334 - type: mrr_at_100
value: 41.84524999999999 - type: mrr_at_1000
value: 41.89983333333333 - type: mrr_at_3
value: 38.62008333333333 - type: mrr_at_5
value: 40.03441666666666 - type: ndcg_at_1
value: 32.08233333333334 - type: ndcg_at_10
value: 42.229 - type: ndcg_at_100
value: 47.26716666666667 - type: ndcg_at_1000
value: 49.43466666666667 - type: ndcg_at_3
value: 37.36408333333333 - type: ndcg_at_5
value: 39.6715 - type: precision_at_1
value: 32.08233333333334 - type: precision_at_10
value: 7.382583333333334 - type: precision_at_100
value: 1.16625 - type: precision_at_1000
value: 0.15408333333333332 - type: precision_at_3
value: 17.218 - type: precision_at_5
value: 12.21875 - type: recall_at_1
value: 27.15516666666667 - type: recall_at_10
value: 54.36683333333333 - type: recall_at_100
value: 76.37183333333333 - type: recall_at_1000
value: 91.26183333333333 - type: recall_at_3
value: 40.769916666666674 - type: recall_at_5
value: 46.702333333333335
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 25.749 - type: map_at_10
value: 33.001999999999995 - type: map_at_100
value: 33.891 - type: map_at_1000
value: 33.993 - type: map_at_3
value: 30.703999999999997 - type: map_at_5
value: 31.959 - type: mrr_at_1
value: 28.834 - type: mrr_at_10
value: 35.955 - type: mrr_at_100
value: 36.709 - type: mrr_at_1000
value: 36.779 - type: mrr_at_3
value: 33.947 - type: mrr_at_5
value: 35.089 - type: ndcg_at_1
value: 28.834 - type: ndcg_at_10
value: 37.329 - type: ndcg_at_100
value: 41.79 - type: ndcg_at_1000
value: 44.169000000000004 - type: ndcg_at_3
value: 33.184999999999995 - type: ndcg_at_5
value: 35.107 - type: precision_at_1
value: 28.834 - type: precision_at_10
value: 5.7669999999999995 - type: precision_at_100
value: 0.876 - type: precision_at_1000
value: 0.11399999999999999 - type: precision_at_3
value: 14.213000000000001 - type: precision_at_5
value: 9.754999999999999 - type: recall_at_1
value: 25.749 - type: recall_at_10
value: 47.791 - type: recall_at_100
value: 68.255 - type: recall_at_1000
value: 85.749 - type: recall_at_3
value: 36.199 - type: recall_at_5
value: 41.071999999999996
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 17.777 - type: map_at_10
value: 25.201 - type: map_at_100
value: 26.423999999999996 - type: map_at_1000
value: 26.544 - type: map_at_3
value: 22.869 - type: map_at_5
value: 24.023 - type: mrr_at_1
value: 21.473 - type: mrr_at_10
value: 29.12 - type: mrr_at_100
value: 30.144 - type: mrr_at_1000
value: 30.215999999999998 - type: mrr_at_3
value: 26.933 - type: mrr_at_5
value: 28.051 - type: ndcg_at_1
value: 21.473 - type: ndcg_at_10
value: 30.003 - type: ndcg_at_100
value: 35.766 - type: ndcg_at_1000
value: 38.501000000000005 - type: ndcg_at_3
value: 25.773000000000003 - type: ndcg_at_5
value: 27.462999999999997 - type: precision_at_1
value: 21.473 - type: precision_at_10
value: 5.482 - type: precision_at_100
value: 0.975 - type: precision_at_1000
value: 0.13799999999999998 - type: precision_at_3
value: 12.205 - type: precision_at_5
value: 8.692 - type: recall_at_1
value: 17.777 - type: recall_at_10
value: 40.582 - type: recall_at_100
value: 66.305 - type: recall_at_1000
value: 85.636 - type: recall_at_3
value: 28.687 - type: recall_at_5
value: 33.089
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 26.677 - type: map_at_10
value: 36.309000000000005 - type: map_at_100
value: 37.403999999999996 - type: map_at_1000
value: 37.496 - type: map_at_3
value: 33.382 - type: map_at_5
value: 34.98 - type: mrr_at_1
value: 31.343 - type: mrr_at_10
value: 40.549 - type: mrr_at_100
value: 41.342 - type: mrr_at_1000
value: 41.397 - type: mrr_at_3
value: 38.029 - type: mrr_at_5
value: 39.451 - type: ndcg_at_1
value: 31.343 - type: ndcg_at_10
value: 42.1 - type: ndcg_at_100
value: 47.089999999999996 - type: ndcg_at_1000
value: 49.222 - type: ndcg_at_3
value: 36.836999999999996 - type: ndcg_at_5
value: 39.21 - type: precision_at_1
value: 31.343 - type: precision_at_10
value: 7.164 - type: precision_at_100
value: 1.0959999999999999 - type: precision_at_1000
value: 0.13899999999999998 - type: precision_at_3
value: 16.915 - type: precision_at_5
value: 11.940000000000001 - type: recall_at_1
value: 26.677 - type: recall_at_10
value: 55.54599999999999 - type: recall_at_100
value: 77.094 - type: recall_at_1000
value: 92.01 - type: recall_at_3
value: 41.191 - type: recall_at_5
value: 47.006
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 24.501 - type: map_at_10
value: 33.102 - type: map_at_100
value: 34.676 - type: map_at_1000
value: 34.888000000000005 - type: map_at_3
value: 29.944 - type: map_at_5
value: 31.613999999999997 - type: mrr_at_1
value: 29.447000000000003 - type: mrr_at_10
value: 37.996 - type: mrr_at_100
value: 38.946 - type: mrr_at_1000
value: 38.995000000000005 - type: mrr_at_3
value: 35.079 - type: mrr_at_5
value: 36.69 - type: ndcg_at_1
value: 29.447000000000003 - type: ndcg_at_10
value: 39.232 - type: ndcg_at_100
value: 45.247 - type: ndcg_at_1000
value: 47.613 - type: ndcg_at_3
value: 33.922999999999995 - type: ndcg_at_5
value: 36.284 - type: precision_at_1
value: 29.447000000000003 - type: precision_at_10
value: 7.648000000000001 - type: precision_at_100
value: 1.516 - type: precision_at_1000
value: 0.23900000000000002 - type: precision_at_3
value: 16.008 - type: precision_at_5
value: 11.779 - type: recall_at_1
value: 24.501 - type: recall_at_10
value: 51.18899999999999 - type: recall_at_100
value: 78.437 - type: recall_at_1000
value: 92.842 - type: recall_at_3
value: 35.808 - type: recall_at_5
value: 42.197
- type: map_at_1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 22.039 - type: map_at_10
value: 30.377 - type: map_at_100
value: 31.275 - type: map_at_1000
value: 31.379 - type: map_at_3
value: 27.98 - type: map_at_5
value: 29.358 - type: mrr_at_1
value: 24.03 - type: mrr_at_10
value: 32.568000000000005 - type: mrr_at_100
value: 33.403 - type: mrr_at_1000
value: 33.475 - type: mrr_at_3
value: 30.436999999999998 - type: mrr_at_5
value: 31.796000000000003 - type: ndcg_at_1
value: 24.03 - type: ndcg_at_10
value: 35.198 - type: ndcg_at_100
value: 39.668 - type: ndcg_at_1000
value: 42.296 - type: ndcg_at_3
value: 30.709999999999997 - type: ndcg_at_5
value: 33.024 - type: precision_at_1
value: 24.03 - type: precision_at_10
value: 5.564 - type: precision_at_100
value: 0.828 - type: precision_at_1000
value: 0.117 - type: precision_at_3
value: 13.309000000000001 - type: precision_at_5
value: 9.39 - type: recall_at_1
value: 22.039 - type: recall_at_10
value: 47.746 - type: recall_at_100
value: 68.23599999999999 - type: recall_at_1000
value: 87.852 - type: recall_at_3
value: 35.852000000000004 - type: recall_at_5
value: 41.410000000000004
- type: map_at_1
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 15.692999999999998 - type: map_at_10
value: 26.903 - type: map_at_100
value: 28.987000000000002 - type: map_at_1000
value: 29.176999999999996 - type: map_at_3
value: 22.137 - type: map_at_5
value: 24.758 - type: mrr_at_1
value: 35.57 - type: mrr_at_10
value: 47.821999999999996 - type: mrr_at_100
value: 48.608000000000004 - type: mrr_at_1000
value: 48.638999999999996 - type: mrr_at_3
value: 44.452000000000005 - type: mrr_at_5
value: 46.546 - type: ndcg_at_1
value: 35.57 - type: ndcg_at_10
value: 36.567 - type: ndcg_at_100
value: 44.085 - type: ndcg_at_1000
value: 47.24 - type: ndcg_at_3
value: 29.964000000000002 - type: ndcg_at_5
value: 32.511 - type: precision_at_1
value: 35.57 - type: precision_at_10
value: 11.485 - type: precision_at_100
value: 1.9619999999999997 - type: precision_at_1000
value: 0.256 - type: precision_at_3
value: 22.237000000000002 - type: precision_at_5
value: 17.471999999999998 - type: recall_at_1
value: 15.692999999999998 - type: recall_at_10
value: 43.056 - type: recall_at_100
value: 68.628 - type: recall_at_1000
value: 86.075 - type: recall_at_3
value: 26.918999999999997 - type: recall_at_5
value: 34.14
- type: map_at_1
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 9.53 - type: map_at_10
value: 20.951 - type: map_at_100
value: 30.136000000000003 - type: map_at_1000
value: 31.801000000000002 - type: map_at_3
value: 15.021 - type: map_at_5
value: 17.471999999999998 - type: mrr_at_1
value: 71.0 - type: mrr_at_10
value: 79.176 - type: mrr_at_100
value: 79.418 - type: mrr_at_1000
value: 79.426 - type: mrr_at_3
value: 78.125 - type: mrr_at_5
value: 78.61200000000001 - type: ndcg_at_1
value: 58.5 - type: ndcg_at_10
value: 44.106 - type: ndcg_at_100
value: 49.268 - type: ndcg_at_1000
value: 56.711999999999996 - type: ndcg_at_3
value: 48.934 - type: ndcg_at_5
value: 45.826 - type: precision_at_1
value: 71.0 - type: precision_at_10
value: 35.0 - type: precision_at_100
value: 11.360000000000001 - type: precision_at_1000
value: 2.046 - type: precision_at_3
value: 52.833 - type: precision_at_5
value: 44.15 - type: recall_at_1
value: 9.53 - type: recall_at_10
value: 26.811 - type: recall_at_100
value: 55.916999999999994 - type: recall_at_1000
value: 79.973 - type: recall_at_3
value: 16.413 - type: recall_at_5
value: 19.980999999999998
- type: map_at_1
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:- type: accuracy
value: 51.519999999999996 - type: f1
value: 46.36601294761231
- type: accuracy
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 74.413 - type: map_at_10
value: 83.414 - type: map_at_100
value: 83.621 - type: map_at_1000
value: 83.635 - type: map_at_3
value: 82.337 - type: map_at_5
value: 83.039 - type: mrr_at_1
value: 80.19800000000001 - type: mrr_at_10
value: 87.715 - type: mrr_at_100
value: 87.778 - type: mrr_at_1000
value: 87.779 - type: mrr_at_3
value: 87.106 - type: mrr_at_5
value: 87.555 - type: ndcg_at_1
value: 80.19800000000001 - type: ndcg_at_10
value: 87.182 - type: ndcg_at_100
value: 87.90299999999999 - type: ndcg_at_1000
value: 88.143 - type: ndcg_at_3
value: 85.60600000000001 - type: ndcg_at_5
value: 86.541 - type: precision_at_1
value: 80.19800000000001 - type: precision_at_10
value: 10.531 - type: precision_at_100
value: 1.113 - type: precision_at_1000
value: 0.11499999999999999 - type: precision_at_3
value: 32.933 - type: precision_at_5
value: 20.429 - type: recall_at_1
value: 74.413 - type: recall_at_10
value: 94.363 - type: recall_at_100
value: 97.165 - type: recall_at_1000
value: 98.668 - type: recall_at_3
value: 90.108 - type: recall_at_5
value: 92.52
- type: map_at_1
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 22.701 - type: map_at_10
value: 37.122 - type: map_at_100
value: 39.178000000000004 - type: map_at_1000
value: 39.326 - type: map_at_3
value: 32.971000000000004 - type: map_at_5
value: 35.332 - type: mrr_at_1
value: 44.753 - type: mrr_at_10
value: 53.452 - type: mrr_at_100
value: 54.198 - type: mrr_at_1000
value: 54.225 - type: mrr_at_3
value: 50.952 - type: mrr_at_5
value: 52.464 - type: ndcg_at_1
value: 44.753 - type: ndcg_at_10
value: 45.021 - type: ndcg_at_100
value: 52.028 - type: ndcg_at_1000
value: 54.596000000000004 - type: ndcg_at_3
value: 41.622 - type: ndcg_at_5
value: 42.736000000000004 - type: precision_at_1
value: 44.753 - type: precision_at_10
value: 12.284 - type: precision_at_100
value: 1.955 - type: precision_at_1000
value: 0.243 - type: precision_at_3
value: 27.828999999999997 - type: precision_at_5
value: 20.061999999999998 - type: recall_at_1
value: 22.701 - type: recall_at_10
value: 51.432 - type: recall_at_100
value: 77.009 - type: recall_at_1000
value: 92.511 - type: recall_at_3
value: 37.919000000000004 - type: recall_at_5
value: 44.131
- type: map_at_1
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 40.189 - type: map_at_10
value: 66.24600000000001 - type: map_at_100
value: 67.098 - type: map_at_1000
value: 67.149 - type: map_at_3
value: 62.684 - type: map_at_5
value: 64.974 - type: mrr_at_1
value: 80.378 - type: mrr_at_10
value: 86.127 - type: mrr_at_100
value: 86.29299999999999 - type: mrr_at_1000
value: 86.297 - type: mrr_at_3
value: 85.31400000000001 - type: mrr_at_5
value: 85.858 - type: ndcg_at_1
value: 80.378 - type: ndcg_at_10
value: 74.101 - type: ndcg_at_100
value: 76.993 - type: ndcg_at_1000
value: 77.948 - type: ndcg_at_3
value: 69.232 - type: ndcg_at_5
value: 72.04599999999999 - type: precision_at_1
value: 80.378 - type: precision_at_10
value: 15.595999999999998 - type: precision_at_100
value: 1.7840000000000003 - type: precision_at_1000
value: 0.191 - type: precision_at_3
value: 44.884 - type: precision_at_5
value: 29.145 - type: recall_at_1
value: 40.189 - type: recall_at_10
value: 77.981 - type: recall_at_100
value: 89.21 - type: recall_at_1000
value: 95.48299999999999 - type: recall_at_3
value: 67.326 - type: recall_at_5
value: 72.863
- type: map_at_1
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:- type: accuracy
value: 92.84599999999999 - type: ap
value: 89.4710787567357 - type: f1
value: 92.83752676932258
- type: accuracy
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:- type: map_at_1
value: 23.132 - type: map_at_10
value: 35.543 - type: map_at_100
value: 36.702 - type: map_at_1000
value: 36.748999999999995 - type: map_at_3
value: 31.737 - type: map_at_5
value: 33.927 - type: mrr_at_1
value: 23.782 - type: mrr_at_10
value: 36.204 - type: mrr_at_100
value: 37.29 - type: mrr_at_1000
value: 37.330999999999996 - type: mrr_at_3
value: 32.458999999999996 - type: mrr_at_5
value: 34.631 - type: ndcg_at_1
value: 23.782 - type: ndcg_at_10
value: 42.492999999999995 - type: ndcg_at_100
value: 47.985 - type: ndcg_at_1000
value: 49.141 - type: ndcg_at_3
value: 34.748000000000005 - type: ndcg_at_5
value: 38.651 - type: precision_at_1
value: 23.782 - type: precision_at_10
value: 6.665 - type: precision_at_100
value: 0.941 - type: precision_at_1000
value: 0.104 - type: precision_at_3
value: 14.776 - type: precision_at_5
value: 10.84 - type: recall_at_1
value: 23.132 - type: recall_at_10
value: 63.794 - type: recall_at_100
value: 89.027 - type: recall_at_1000
value: 97.807 - type: recall_at_3
value: 42.765 - type: recall_at_5
value: 52.11
- type: map_at_1
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:- type: accuracy
value: 94.59188326493388 - type: f1
value: 94.3842594786827
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:- type: accuracy
value: 79.49384404924761 - type: f1
value: 59.7580539534629
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:- type: accuracy
value: 77.56220578345663 - type: f1
value: 75.27228165561478
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:- type: accuracy
value: 80.53463349024884 - type: f1
value: 80.4893958236536
- type: accuracy
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:- type: v_measure
value: 32.56100273484962
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:- type: v_measure
value: 31.470380028839607
- type: v_measure
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:- type: map
value: 32.06102792457849 - type: mrr
value: 33.30709199672238
- type: map
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 6.776999999999999 - type: map_at_10
value: 14.924000000000001 - type: map_at_100
value: 18.955 - type: map_at_1000
value: 20.538999999999998 - type: map_at_3
value: 10.982 - type: map_at_5
value: 12.679000000000002 - type: mrr_at_1
value: 47.988 - type: mrr_at_10
value: 57.232000000000006 - type: mrr_at_100
value: 57.818999999999996 - type: mrr_at_1000
value: 57.847 - type: mrr_at_3
value: 54.901999999999994 - type: mrr_at_5
value: 56.481 - type: ndcg_at_1
value: 46.594 - type: ndcg_at_10
value: 38.129000000000005 - type: ndcg_at_100
value: 35.54 - type: ndcg_at_1000
value: 44.172 - type: ndcg_at_3
value: 43.025999999999996 - type: ndcg_at_5
value: 41.052 - type: precision_at_1
value: 47.988 - type: precision_at_10
value: 28.111000000000004 - type: precision_at_100
value: 8.929 - type: precision_at_1000
value: 2.185 - type: precision_at_3
value: 40.144000000000005 - type: precision_at_5
value: 35.232 - type: recall_at_1
value: 6.776999999999999 - type: recall_at_10
value: 19.289 - type: recall_at_100
value: 36.359 - type: recall_at_1000
value: 67.54 - type: recall_at_3
value: 11.869 - type: recall_at_5
value: 14.999
- type: map_at_1
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 31.108000000000004 - type: map_at_10
value: 47.126000000000005 - type: map_at_100
value: 48.171 - type: map_at_1000
value: 48.199 - type: map_at_3
value: 42.734 - type: map_at_5
value: 45.362 - type: mrr_at_1
value: 34.936 - type: mrr_at_10
value: 49.571 - type: mrr_at_100
value: 50.345 - type: mrr_at_1000
value: 50.363 - type: mrr_at_3
value: 45.959 - type: mrr_at_5
value: 48.165 - type: ndcg_at_1
value: 34.936 - type: ndcg_at_10
value: 55.028999999999996 - type: ndcg_at_100
value: 59.244 - type: ndcg_at_1000
value: 59.861 - type: ndcg_at_3
value: 46.872 - type: ndcg_at_5
value: 51.217999999999996 - type: precision_at_1
value: 34.936 - type: precision_at_10
value: 9.099 - type: precision_at_100
value: 1.145 - type: precision_at_1000
value: 0.12 - type: precision_at_3
value: 21.456 - type: precision_at_5
value: 15.411 - type: recall_at_1
value: 31.108000000000004 - type: recall_at_10
value: 76.53999999999999 - type: recall_at_100
value: 94.39 - type: recall_at_1000
value: 98.947 - type: recall_at_3
value: 55.572 - type: recall_at_5
value: 65.525
- type: map_at_1
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 71.56400000000001 - type: map_at_10
value: 85.482 - type: map_at_100
value: 86.114 - type: map_at_1000
value: 86.13 - type: map_at_3
value: 82.607 - type: map_at_5
value: 84.405 - type: mrr_at_1
value: 82.42 - type: mrr_at_10
value: 88.304 - type: mrr_at_100
value: 88.399 - type: mrr_at_1000
value: 88.399 - type: mrr_at_3
value: 87.37 - type: mrr_at_5
value: 88.024 - type: ndcg_at_1
value: 82.45 - type: ndcg_at_10
value: 89.06500000000001 - type: ndcg_at_100
value: 90.232 - type: ndcg_at_1000
value: 90.305 - type: ndcg_at_3
value: 86.375 - type: ndcg_at_5
value: 87.85300000000001 - type: precision_at_1
value: 82.45 - type: precision_at_10
value: 13.486999999999998 - type: precision_at_100
value: 1.534 - type: precision_at_1000
value: 0.157 - type: precision_at_3
value: 37.813 - type: precision_at_5
value: 24.773999999999997 - type: recall_at_1
value: 71.56400000000001 - type: recall_at_10
value: 95.812 - type: recall_at_100
value: 99.7 - type: recall_at_1000
value: 99.979 - type: recall_at_3
value: 87.966 - type: recall_at_5
value: 92.268
- type: map_at_1
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:- type: v_measure
value: 57.241876648614145
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:- type: v_measure
value: 64.66212576446223
- type: v_measure
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 5.308 - type: map_at_10
value: 13.803 - type: map_at_100
value: 16.176 - type: map_at_1000
value: 16.561 - type: map_at_3
value: 9.761000000000001 - type: map_at_5
value: 11.802 - type: mrr_at_1
value: 26.200000000000003 - type: mrr_at_10
value: 37.621 - type: mrr_at_100
value: 38.767 - type: mrr_at_1000
value: 38.815 - type: mrr_at_3
value: 34.117 - type: mrr_at_5
value: 36.107 - type: ndcg_at_1
value: 26.200000000000003 - type: ndcg_at_10
value: 22.64 - type: ndcg_at_100
value: 31.567 - type: ndcg_at_1000
value: 37.623 - type: ndcg_at_3
value: 21.435000000000002 - type: ndcg_at_5
value: 18.87 - type: precision_at_1
value: 26.200000000000003 - type: precision_at_10
value: 11.74 - type: precision_at_100
value: 2.465 - type: precision_at_1000
value: 0.391 - type: precision_at_3
value: 20.033 - type: precision_at_5
value: 16.64 - type: recall_at_1
value: 5.308 - type: recall_at_10
value: 23.794999999999998 - type: recall_at_100
value: 50.015 - type: recall_at_1000
value: 79.283 - type: recall_at_3
value: 12.178 - type: recall_at_5
value: 16.882
- type: map_at_1
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:- type: cos_sim_pearson
value: 84.93231134675553 - type: cos_sim_spearman
value: 81.68319292603205 - type: euclidean_pearson
value: 81.8396814380367 - type: euclidean_spearman
value: 81.24641903349945 - type: manhattan_pearson
value: 81.84698799204274 - type: manhattan_spearman
value: 81.24269997904105
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:- type: cos_sim_pearson
value: 86.73241671587446 - type: cos_sim_spearman
value: 79.05091082971826 - type: euclidean_pearson
value: 83.91146869578044 - type: euclidean_spearman
value: 79.87978465370936 - type: manhattan_pearson
value: 83.90888338917678 - type: manhattan_spearman
value: 79.87482848584241
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:- type: cos_sim_pearson
value: 85.14970731146177 - type: cos_sim_spearman
value: 86.37363490084627 - type: euclidean_pearson
value: 83.02154218530433 - type: euclidean_spearman
value: 83.80258761957367 - type: manhattan_pearson
value: 83.01664495119347 - type: manhattan_spearman
value: 83.77567458007952
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:- type: cos_sim_pearson
value: 83.40474139886784 - type: cos_sim_spearman
value: 82.77768789165984 - type: euclidean_pearson
value: 80.7065877443695 - type: euclidean_spearman
value: 81.375940662505 - type: manhattan_pearson
value: 80.6507552270278 - type: manhattan_spearman
value: 81.32782179098741
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:- type: cos_sim_pearson
value: 87.08585968722274 - type: cos_sim_spearman
value: 88.03110031451399 - type: euclidean_pearson
value: 85.74012019602384 - type: euclidean_spearman
value: 86.13592849438209 - type: manhattan_pearson
value: 85.74404842369206 - type: manhattan_spearman
value: 86.14492318960154
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:- type: cos_sim_pearson
value: 84.95069052788875 - type: cos_sim_spearman
value: 86.4867991595147 - type: euclidean_pearson
value: 84.31013325754635 - type: euclidean_spearman
value: 85.01529258006482 - type: manhattan_pearson
value: 84.26995570085374 - type: manhattan_spearman
value: 84.96982104986162
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:- type: cos_sim_pearson
value: 87.54617647971897 - type: cos_sim_spearman
value: 87.49834181751034 - type: euclidean_pearson
value: 86.01015322577122 - type: euclidean_spearman
value: 84.63362652063199 - type: manhattan_pearson
value: 86.13807574475706 - type: manhattan_spearman
value: 84.7772370721132
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:- type: cos_sim_pearson
value: 67.20047755786615 - type: cos_sim_spearman
value: 67.05324077987636 - type: euclidean_pearson
value: 66.91930642976601 - type: euclidean_spearman
value: 65.21491856099105 - type: manhattan_pearson
value: 66.78756851976624 - type: manhattan_spearman
value: 65.12356257740728
- type: cos_sim_pearson
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:- type: cos_sim_pearson
value: 86.19852871539686 - type: cos_sim_spearman
value: 87.5161895296395 - type: euclidean_pearson
value: 84.59848645207485 - type: euclidean_spearman
value: 85.26427328757919 - type: manhattan_pearson
value: 84.59747366996524 - type: manhattan_spearman
value: 85.24045855146915
- type: cos_sim_pearson
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:- type: map
value: 87.63320317811032 - type: mrr
value: 96.26242947321379
- type: map
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 60.928000000000004 - type: map_at_10
value: 70.112 - type: map_at_100
value: 70.59299999999999 - type: map_at_1000
value: 70.623 - type: map_at_3
value: 66.846 - type: map_at_5
value: 68.447 - type: mrr_at_1
value: 64.0 - type: mrr_at_10
value: 71.212 - type: mrr_at_100
value: 71.616 - type: mrr_at_1000
value: 71.64500000000001 - type: mrr_at_3
value: 68.77799999999999 - type: mrr_at_5
value: 70.094 - type: ndcg_at_1
value: 64.0 - type: ndcg_at_10
value: 74.607 - type: ndcg_at_100
value: 76.416 - type: ndcg_at_1000
value: 77.102 - type: ndcg_at_3
value: 69.126 - type: ndcg_at_5
value: 71.41300000000001 - type: precision_at_1
value: 64.0 - type: precision_at_10
value: 9.933 - type: precision_at_100
value: 1.077 - type: precision_at_1000
value: 0.11299999999999999 - type: precision_at_3
value: 26.556 - type: precision_at_5
value: 17.467 - type: recall_at_1
value: 60.928000000000004 - type: recall_at_10
value: 87.322 - type: recall_at_100
value: 94.833 - type: recall_at_1000
value: 100.0 - type: recall_at_3
value: 72.628 - type: recall_at_5
value: 78.428
- type: map_at_1
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:- type: cos_sim_accuracy
value: 99.86237623762376 - type: cos_sim_ap
value: 96.72586477206649 - type: cos_sim_f1
value: 93.01858362631845 - type: cos_sim_precision
value: 93.4409687184662 - type: cos_sim_recall
value: 92.60000000000001 - type: dot_accuracy
value: 99.78019801980199 - type: dot_ap
value: 93.72748205246228 - type: dot_f1
value: 89.04109589041096 - type: dot_precision
value: 87.16475095785441 - type: dot_recall
value: 91.0 - type: euclidean_accuracy
value: 99.85445544554456 - type: euclidean_ap
value: 96.6661459876145 - type: euclidean_f1
value: 92.58337481333997 - type: euclidean_precision
value: 92.17046580773042 - type: euclidean_recall
value: 93.0 - type: manhattan_accuracy
value: 99.85445544554456 - type: manhattan_ap
value: 96.6883549244056 - type: manhattan_f1
value: 92.57598405580468 - type: manhattan_precision
value: 92.25422045680239 - type: manhattan_recall
value: 92.9 - type: max_accuracy
value: 99.86237623762376 - type: max_ap
value: 96.72586477206649 - type: max_f1
value: 93.01858362631845
- type: cos_sim_accuracy
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:- type: v_measure
value: 66.39930057069995
- type: v_measure
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:- type: v_measure
value: 34.96398659903402
- type: v_measure
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:- type: map
value: 55.946944700355395 - type: mrr
value: 56.97151398438164
- type: map
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:- type: cos_sim_pearson
value: 31.541657650692905 - type: cos_sim_spearman
value: 31.605804192286303 - type: dot_pearson
value: 28.26905996736398 - type: dot_spearman
value: 27.864801765851187
- type: cos_sim_pearson
- 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: 1.8870000000000002 - type: map_at_100
value: 9.78 - type: map_at_1000
value: 22.514 - type: map_at_3
value: 0.6669999999999999 - type: map_at_5
value: 1.077 - type: mrr_at_1
value: 82.0 - type: mrr_at_10
value: 89.86699999999999 - type: mrr_at_100
value: 89.86699999999999 - type: mrr_at_1000
value: 89.86699999999999 - type: mrr_at_3
value: 89.667 - type: mrr_at_5
value: 89.667 - type: ndcg_at_1
value: 79.0 - type: ndcg_at_10
value: 74.818 - type: ndcg_at_100
value: 53.715999999999994 - type: ndcg_at_1000
value: 47.082 - type: ndcg_at_3
value: 82.134 - type: ndcg_at_5
value: 79.81899999999999 - type: precision_at_1
value: 82.0 - type: precision_at_10
value: 78.0 - type: precision_at_100
value: 54.48 - type: precision_at_1000
value: 20.518 - type: precision_at_3
value: 87.333 - type: precision_at_5
value: 85.2 - type: recall_at_1
value: 0.22599999999999998 - type: recall_at_10
value: 2.072 - type: recall_at_100
value: 13.013 - type: recall_at_1000
value: 43.462 - type: recall_at_3
value: 0.695 - type: recall_at_5
value: 1.139
- type: map_at_1
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:- type: map_at_1
value: 2.328 - type: map_at_10
value: 9.795 - type: map_at_100
value: 15.801000000000002 - type: map_at_1000
value: 17.23 - type: map_at_3
value: 4.734 - type: map_at_5
value: 6.644 - type: mrr_at_1
value: 30.612000000000002 - type: mrr_at_10
value: 46.902 - type: mrr_at_100
value: 47.495 - type: mrr_at_1000
value: 47.495 - type: mrr_at_3
value: 41.156 - type: mrr_at_5
value: 44.218 - type: ndcg_at_1
value: 28.571 - type: ndcg_at_10
value: 24.806 - type: ndcg_at_100
value: 36.419000000000004 - type: ndcg_at_1000
value: 47.272999999999996 - type: ndcg_at_3
value: 25.666 - type: ndcg_at_5
value: 25.448999999999998 - type: precision_at_1
value: 30.612000000000002 - type: precision_at_10
value: 23.061 - type: precision_at_100
value: 7.714 - type: precision_at_1000
value: 1.484 - type: precision_at_3
value: 26.531 - type: precision_at_5
value: 26.122 - type: recall_at_1
value: 2.328 - type: recall_at_10
value: 16.524 - type: recall_at_100
value: 47.179 - type: recall_at_1000
value: 81.22200000000001 - type: recall_at_3
value: 5.745 - type: recall_at_5
value: 9.339
- type: map_at_1
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:- type: accuracy
value: 70.9142 - type: ap
value: 14.335574772555415 - type: f1
value: 54.62839595194111
- type: accuracy
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:- type: accuracy
value: 59.94340690435768 - type: f1
value: 60.286487936731916
- type: accuracy
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:- type: v_measure
value: 51.26597708987974
- type: v_measure
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:- type: cos_sim_accuracy
value: 87.48882398521786 - type: cos_sim_ap
value: 79.04326607602204 - type: cos_sim_f1
value: 71.64566826860633 - type: cos_sim_precision
value: 70.55512918905092 - type: cos_sim_recall
value: 72.77044854881267 - type: dot_accuracy
value: 84.19264469213805 - type: dot_ap
value: 67.96360043562528 - type: dot_f1
value: 64.06418393006827 - type: dot_precision
value: 58.64941898706424 - type: dot_recall
value: 70.58047493403694 - type: euclidean_accuracy
value: 87.45902127913214 - type: euclidean_ap
value: 78.9742237648272 - type: euclidean_f1
value: 71.5553235908142 - type: euclidean_precision
value: 70.77955601445535 - type: euclidean_recall
value: 72.34828496042216 - type: manhattan_accuracy
value: 87.41729749061214 - type: manhattan_ap
value: 78.90073137580596 - type: manhattan_f1
value: 71.3942611553533 - type: manhattan_precision
value: 68.52705653967483 - type: manhattan_recall
value: 74.51187335092348 - type: max_accuracy
value: 87.48882398521786 - type: max_ap
value: 79.04326607602204 - type: max_f1
value: 71.64566826860633
- type: cos_sim_accuracy
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:- type: cos_sim_accuracy
value: 88.68125897465751 - type: cos_sim_ap
value: 85.6003454431979 - type: cos_sim_f1
value: 77.6957163958641 - type: cos_sim_precision
value: 73.0110366307807 - type: cos_sim_recall
value: 83.02279026793964 - type: dot_accuracy
value: 87.7672992587418 - type: dot_ap
value: 82.4971301112899 - type: dot_f1
value: 75.90528233151184 - type: dot_precision
value: 72.0370626469368 - type: dot_recall
value: 80.21250384970742 - type: euclidean_accuracy
value: 88.4503434625684 - type: euclidean_ap
value: 84.91949884748384 - type: euclidean_f1
value: 76.92365018444684 - type: euclidean_precision
value: 74.53245721712759 - type: euclidean_recall
value: 79.47336002463813 - type: manhattan_accuracy
value: 88.47556952691427 - type: manhattan_ap
value: 84.8963689101517 - type: manhattan_f1
value: 76.85901249256395 - type: manhattan_precision
value: 74.31693989071039 - type: manhattan_recall
value: 79.58115183246073 - type: max_accuracy
value: 88.68125897465751 - type: max_ap
value: 85.6003454431979 - type: max_f1
value: 77.6957163958641
- type: cos_sim_accuracy
- task:
license: mit
language:
- en
FlagEmbedding
Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License
For more details please refer to our Github: FlagEmbedding.
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3.
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- Long-Context LLM: Activation Beacon
- Fine-tuning of LM : LM-Cocktail
- Dense Retrieval: BGE-M3, LLM Embedder, BGE Embedding
- Reranker Model: BGE Reranker
- Benchmark: C-MTEB
News
- 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
Technical Report and Code. :fire: - 1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. Technical Report :fire:
- 12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. Technical Report :fire:
- 11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report :fire:
- 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report
- 09/15/2023: The technical report and massive training data of BGE has been released
- 09/12/2023: New models:
- New reranker model: release cross-encoder models
BAAI/bge-reranker-baseandBAAI/bge-reranker-large, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - update embedding model: release
bge-*-v1.5embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
- New reranker model: release cross-encoder models
More
- 09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is available.
- 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! :tada: :tada: - 08/01/2023: We release the Chinese Massive Text Embedding Benchmark (C-MTEB), consisting of 31 test dataset.
Model List
bge is short for BAAI general embedding.
| Model | Language | Description | query instruction for retrieval [1] | |
|---|---|---|---|---|
| BAAI/bge-m3 | Multilingual | Inference Fine-tune | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| BAAI/llm-embedder | English | Inference Fine-tune | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See README |
| BAAI/bge-reranker-large | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | |
| BAAI/bge-reranker-base | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | |
| BAAI/bge-large-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | Represent this sentence for searching relevant passages: |
| BAAI/bge-base-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | Represent this sentence for searching relevant passages: |
| BAAI/bge-small-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | Represent this sentence for searching relevant passages: |
| BAAI/bge-large-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | 为这个句子生成表示以用于检索相关文章： |
| BAAI/bge-base-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | 为这个句子生成表示以用于检索相关文章： |
| BAAI/bge-small-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | 为这个句子生成表示以用于检索相关文章： |
| BAAI/bge-large-en | English | Inference Fine-tune | :trophy: rank 1st in MTEB leaderboard | Represent this sentence for searching relevant passages: |
| BAAI/bge-base-en | English | Inference Fine-tune | a base-scale model but with similar ability to bge-large-en |
Represent this sentence for searching relevant passages: |
| BAAI/bge-small-en | English | Inference Fine-tune | a small-scale model but with competitive performance | Represent this sentence for searching relevant passages: |
| BAAI/bge-large-zh | Chinese | Inference Fine-tune | :trophy: rank 1st in C-MTEB benchmark | 为这个句子生成表示以用于检索相关文章： |
| BAAI/bge-base-zh | Chinese | Inference Fine-tune | a base-scale model but with similar ability to 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 | Chinese | Inference Fine-tune | 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： |
[1]: If you need to search the relevant passages to a query, we suggest 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 needs to be added to passages.
[2]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
Frequently asked questions
1. How to fine-tune bge embedding model?
Following this example to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this example, which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
2. The similarity score between two dissimilar sentences is higher than 0.5
Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval [0.6, 1].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
what matters is the relative order of the scores, not the absolute value.
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
3. When does the query instruction need to be used
For the bge-*-v1.5, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.
In all cases, the documents/passages do not need to add the instruction.
Usage
Usage for Embedding Model
Here are some examples for using bge models with
FlagEmbedding, Sentence-Transformers, Langchain, or Huggingface Transformers.
Using FlagEmbedding
pip install -U FlagEmbedding
If it doesn't work for you, you can see FlagEmbedding for more methods to install FlagEmbedding.
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
for s2p(short query to long passage) retrieval task, suggest to 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
For the value of the argument query_instruction_for_retrieval, see Model List.
By default, FlagModel will use all available GPUs when encoding. Please set os.environ["CUDA_VISIBLE_DEVICES"] to select specific GPUs.
You also can set os.environ["CUDA_VISIBLE_DEVICES"]="" to make all GPUs unavailable.
Using Sentence-Transformers
You can also use the bge models with sentence-transformers:
pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, 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).
But the instruction is not needed for passages.
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-v1.5')
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:
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
Using HuggingFace Transformers
With the 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 the first token (i.e., [CLS]) as the sentence embedding.
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-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
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)
Usage of the ONNX files
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")
Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
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')
model_output_ort = model_ort(**encoded_input)
Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
model_output and model_output_ort are identical
Its also possible to deploy the onnx files with the infinity_emb pip package.
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
Using FlagEmbedding
pip install -U FlagEmbedding
Get relevance scores (higher scores indicate more relevance):
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
Using Huggingface transformers
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
Evaluation
baai-general-embedding models achieve state-of-the-art performance on both MTEB and C-MTEB leaderboard!
For more details and evaluation tools see our scripts.
- MTEB:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) | Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|---|---|---|---|---|---|---|---|---|---|---|
| BAAI/bge-large-en-v1.5 | 1024 | 512 | 64.23 | 54.29 | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| BAAI/bge-base-en-v1.5 | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| BAAI/bge-small-en-v1.5 | 384 | 512 | 62.17 | 51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| bge-large-en | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| bge-base-en | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| gte-large | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| gte-base | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| e5-large-v2 | 1024 | 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| bge-small-en | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| instructor-xl | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| e5-base-v2 | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| gte-small | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| text-embedding-ada-002 | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| e5-small-v2 | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| 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 | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| sgpt-bloom-7b1-msmarco | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- C-MTEB:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to C_MTEB for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|---|---|---|---|---|---|---|---|---|
| BAAI/bge-large-zh-v1.5 | 1024 | 64.53 | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| BAAI/bge-base-zh-v1.5 | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| BAAI/bge-small-zh-v1.5 | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| BAAI/bge-large-zh | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| bge-large-zh-noinstruct | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| BAAI/bge-base-zh | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| multilingual-e5-large | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| BAAI/bge-small-zh | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| m3e-base | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| m3e-large | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| multilingual-e5-base | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| multilingual-e5-small | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| text-embedding-ada-002(OpenAI) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| luotuo | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| text2vec-base | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| text2vec-large | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- Reranking:
See C_MTEB for evaluation script.
| Model | T2Reranking | T2RerankingZh2En* | T2RerankingEn2Zh* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|---|---|---|---|---|---|---|---|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| BAAI/bge-reranker-base | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| BAAI/bge-reranker-large | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
Train
BAAI Embedding
We pre-train the models using retromae and train them on large-scale pairs data using contrastive learning.
You can fine-tune the embedding model on your data following our examples.
We also provide a pre-train example.
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see baai_general_embedding.
BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our example.
More details please refer to ./FlagEmbedding/reranker/README.md
Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn).
Citation
If you find this repository useful, please consider giving a star :star: and citation
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
License
FlagEmbedding is licensed under the MIT License. The released models can be used for commercial purposes free of charge.
📝 Limitations & Considerations
- • Benchmark scores may vary based on evaluation methodology and hardware configuration.
- • VRAM requirements are estimates; actual usage depends on quantization and batch size.
- • FNI scores are relative rankings and may change as new models are added.
- • Source: Unknown
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