---
tags:
- mteb
model-index:
- name: bge-small-en-v1.5-quant
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 74.19402985074626
    - type: ap
      value: 37.562368912364036
    - type: f1
      value: 68.47046663470138
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 91.89432499999998
    - type: ap
      value: 88.64572979375352
    - type: f1
      value: 91.87171177424113
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.71799999999999
    - type: f1
      value: 46.25791412217894
  - task:
      type: Retrieval
    dataset:
      type: arguana
      name: MTEB ArguAna
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 34.424
    - type: map_at_10
      value: 49.63
    - type: map_at_100
      value: 50.477000000000004
    - type: map_at_1000
      value: 50.483
    - type: map_at_3
      value: 45.389
    - type: map_at_5
      value: 47.888999999999996
    - type: mrr_at_1
      value: 34.78
    - type: mrr_at_10
      value: 49.793
    - type: mrr_at_100
      value: 50.632999999999996
    - type: mrr_at_1000
      value: 50.638000000000005
    - type: mrr_at_3
      value: 45.531
    - type: mrr_at_5
      value: 48.010000000000005
    - type: ndcg_at_1
      value: 34.424
    - type: ndcg_at_10
      value: 57.774
    - type: ndcg_at_100
      value: 61.248000000000005
    - type: ndcg_at_1000
      value: 61.378
    - type: ndcg_at_3
      value: 49.067
    - type: ndcg_at_5
      value: 53.561
    - type: precision_at_1
      value: 34.424
    - type: precision_at_10
      value: 8.364
    - type: precision_at_100
      value: 0.985
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 19.915
    - type: precision_at_5
      value: 14.124999999999998
    - type: recall_at_1
      value: 34.424
    - type: recall_at_10
      value: 83.64200000000001
    - type: recall_at_100
      value: 98.506
    - type: recall_at_1000
      value: 99.502
    - type: recall_at_3
      value: 59.744
    - type: recall_at_5
      value: 70.626
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 62.40334669601722
    - type: mrr
      value: 75.33175042870333
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 88.00433892980047
    - type: cos_sim_spearman
      value: 86.65558896421105
    - type: euclidean_pearson
      value: 85.98927300398377
    - type: euclidean_spearman
      value: 86.0905158476729
    - type: manhattan_pearson
      value: 86.0272425017433
    - type: manhattan_spearman
      value: 85.8929209838941
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 85.1038961038961
    - type: f1
      value: 85.06851570045757
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 46.845
    - type: f1
      value: 41.70045120106269
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 89.3476
    - type: ap
      value: 85.26891728027032
    - type: f1
      value: 89.33488973832894
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.67441860465115
    - type: f1
      value: 92.48821366022861
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 74.02872777017784
    - type: f1
      value: 57.28822860484337
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 74.01479488903833
    - type: f1
      value: 71.83716204573571
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.95897780766644
    - type: f1
      value: 77.80380046125542
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.86793477948164
    - type: cos_sim_spearman
      value: 79.43675709317894
    - type: euclidean_pearson
      value: 81.42564463337872
    - type: euclidean_spearman
      value: 79.39138648510273
    - type: manhattan_pearson
      value: 81.31167449689285
    - type: manhattan_spearman
      value: 79.28411420758785
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 84.43490408077298
    - type: cos_sim_spearman
      value: 76.16878340109265
    - type: euclidean_pearson
      value: 80.6016219080782
    - type: euclidean_spearman
      value: 75.67063072565917
    - type: manhattan_pearson
      value: 80.7238920179759
    - type: manhattan_spearman
      value: 75.85631683403953
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 83.03882477767792
    - type: cos_sim_spearman
      value: 84.15171505206217
    - type: euclidean_pearson
      value: 84.11692506470922
    - type: euclidean_spearman
      value: 84.78589046217311
    - type: manhattan_pearson
      value: 83.98651139454486
    - type: manhattan_spearman
      value: 84.64928563751276
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.11158600428418
    - type: cos_sim_spearman
      value: 81.48561519933875
    - type: euclidean_pearson
      value: 83.21025907155807
    - type: euclidean_spearman
      value: 81.68699235487654
    - type: manhattan_pearson
      value: 83.16704771658094
    - type: manhattan_spearman
      value: 81.7133110412898
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.1514510686502
    - type: cos_sim_spearman
      value: 88.11449450494452
    - type: euclidean_pearson
      value: 87.75854949349939
    - type: euclidean_spearman
      value: 88.4055148221637
    - type: manhattan_pearson
      value: 87.71487828059706
    - type: manhattan_spearman
      value: 88.35301381116254
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 83.36838640113687
    - type: cos_sim_spearman
      value: 84.98776974283366
    - type: euclidean_pearson
      value: 84.0617526427129
    - type: euclidean_spearman
      value: 85.04234805662242
    - type: manhattan_pearson
      value: 83.87433162971784
    - type: manhattan_spearman
      value: 84.87174280390242
  - 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: 87.72465270691285
    - type: cos_sim_spearman
      value: 87.97672332532184
    - type: euclidean_pearson
      value: 88.78764701492182
    - type: euclidean_spearman
      value: 88.3509718074474
    - type: manhattan_pearson
      value: 88.73024739256215
    - type: manhattan_spearman
      value: 88.24149566970154
  - 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: 64.65195562203238
    - type: cos_sim_spearman
      value: 65.0726777678982
    - type: euclidean_pearson
      value: 65.84698245675273
    - type: euclidean_spearman
      value: 65.13121502162804
    - type: manhattan_pearson
      value: 65.96149904857049
    - type: manhattan_spearman
      value: 65.39983948112955
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.2642818050049
    - type: cos_sim_spearman
      value: 86.30633382439257
    - type: euclidean_pearson
      value: 86.46510435905633
    - type: euclidean_spearman
      value: 86.62650496446
    - type: manhattan_pearson
      value: 86.2546330637872
    - type: manhattan_spearman
      value: 86.46309860938591
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.84257425742574
    - type: cos_sim_ap
      value: 96.25445889914926
    - type: cos_sim_f1
      value: 92.03805708562844
    - type: cos_sim_precision
      value: 92.1765295887663
    - type: cos_sim_recall
      value: 91.9
    - type: dot_accuracy
      value: 99.83069306930693
    - type: dot_ap
      value: 96.00517778550396
    - type: dot_f1
      value: 91.27995920448751
    - type: dot_precision
      value: 93.1321540062435
    - type: dot_recall
      value: 89.5
    - type: euclidean_accuracy
      value: 99.84455445544555
    - type: euclidean_ap
      value: 96.14761524546034
    - type: euclidean_f1
      value: 91.97751660705163
    - type: euclidean_precision
      value: 94.04388714733543
    - type: euclidean_recall
      value: 90
    - type: manhattan_accuracy
      value: 99.84158415841584
    - type: manhattan_ap
      value: 96.17014673429341
    - type: manhattan_f1
      value: 91.93790686029043
    - type: manhattan_precision
      value: 92.07622868605817
    - type: manhattan_recall
      value: 91.8
    - type: max_accuracy
      value: 99.84455445544555
    - type: max_ap
      value: 96.25445889914926
    - type: max_f1
      value: 92.03805708562844
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 69.5008
    - type: ap
      value: 13.64158304183089
    - type: f1
      value: 53.50073331072236
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 60.01980758347483
    - type: f1
      value: 60.35679678249753
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.68874053764081
    - type: cos_sim_ap
      value: 73.26334732095694
    - type: cos_sim_f1
      value: 68.01558376272465
    - type: cos_sim_precision
      value: 64.93880489560834
    - type: cos_sim_recall
      value: 71.39841688654354
    - type: dot_accuracy
      value: 84.71121177802945
    - type: dot_ap
      value: 70.33606362522605
    - type: dot_f1
      value: 65.0887573964497
    - type: dot_precision
      value: 63.50401606425703
    - type: dot_recall
      value: 66.75461741424802
    - type: euclidean_accuracy
      value: 85.80795136198367
    - type: euclidean_ap
      value: 73.43201285001163
    - type: euclidean_f1
      value: 68.33166833166834
    - type: euclidean_precision
      value: 64.86486486486487
    - type: euclidean_recall
      value: 72.18997361477572
    - type: manhattan_accuracy
      value: 85.62317458425225
    - type: manhattan_ap
      value: 73.21212085536185
    - type: manhattan_f1
      value: 68.01681314482232
    - type: manhattan_precision
      value: 65.74735286875153
    - type: manhattan_recall
      value: 70.44854881266491
    - type: max_accuracy
      value: 85.80795136198367
    - type: max_ap
      value: 73.43201285001163
    - type: max_f1
      value: 68.33166833166834
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.81709162882757
    - type: cos_sim_ap
      value: 85.63540257309367
    - type: cos_sim_f1
      value: 77.9091382258904
    - type: cos_sim_precision
      value: 75.32710280373833
    - type: cos_sim_recall
      value: 80.67446874037573
    - type: dot_accuracy
      value: 88.04478596654636
    - type: dot_ap
      value: 84.16371725220706
    - type: dot_f1
      value: 76.45949643213666
    - type: dot_precision
      value: 73.54719396827655
    - type: dot_recall
      value: 79.61194949183862
    - type: euclidean_accuracy
      value: 88.9296386851399
    - type: euclidean_ap
      value: 85.71894615274715
    - type: euclidean_f1
      value: 78.12952767313823
    - type: euclidean_precision
      value: 73.7688098495212
    - type: euclidean_recall
      value: 83.03818909762857
    - type: manhattan_accuracy
      value: 88.89276982186519
    - type: manhattan_ap
      value: 85.6838514059479
    - type: manhattan_f1
      value: 78.06861875184856
    - type: manhattan_precision
      value: 75.09246088193457
    - type: manhattan_recall
      value: 81.29042192793348
    - type: max_accuracy
      value: 88.9296386851399
    - type: max_ap
      value: 85.71894615274715
    - type: max_f1
      value: 78.12952767313823
license: mit
language:
- en
---

---
license: mit
---
This is the quantized ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model for embeddings created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export and Neural Magic's [Sparsify](https://account.neuralmagic.com/signin?client_id=d04a5f0c-983d-11ed-88a6-971073f187d3&return_to=https%3A//accounts.neuralmagic.com/v1/connect/authorize%3Fscope%3Dsparsify%3Aread%2Bsparsify%3Awrite%2Buser%3Aapi-key%3Aread%2Buser%3Aprofile%3Awrite%2Buser%3Aprofile%3Aread%26response_type%3Dcode%26code_challenge_method%3DS256%26redirect_uri%3Dhttps%3A//apps.neuralmagic.com/sparsify/oidc/callback.html%26state%3Da9b466a6193c4a7b92cba469408d2495%26client_id%3Dd04a5f0c-983d-11ed-88a6-971073f187d3%26code_challenge%3DP0EkmKBpplTb7crJOGS8YLSwT8UH-BeuD0wuE4JTORQ%26response_mode%3Dquery) for One-Shot INT8 quantization.