transformers onnx bert feature-extraction mteb sparse sparsity quantized embeddings int8 deepsparse en license:mit model-index text-embeddings-inference endpoints_compatible region:us
---
language:
- en
license: mit
tags:
- mteb
- sparse
- sparsity
- quantized
- onnx
- embeddings
- int8
- deepsparse
model-index:
- name: bge-base-en-v1.5-quant
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 76.16417910447761
    - type: ap
      value: 39.62965026785565
    - type: f1
      value: 70.30041589476463
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 92.95087500000001
    - type: ap
      value: 89.92451248271642
    - type: f1
      value: 92.94162732408543
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 48.214
    - type: f1
      value: 47.57084372829096
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 48.499816497755646
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 42.006939120636034
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 62.390343953329875
    - type: mrr
      value: 75.69922613551422
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 89.03408553833623
    - type: cos_sim_spearman
      value: 86.71221676053791
    - type: euclidean_pearson
      value: 87.81477796215844
    - type: euclidean_spearman
      value: 87.28994076774481
    - type: manhattan_pearson
      value: 87.76204756059836
    - type: manhattan_spearman
      value: 87.1971675695072
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 86.35064935064935
    - type: f1
      value: 86.32782396028989
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 39.299558776859485
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 35.64603198816062
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 51.269999999999996
    - type: f1
      value: 45.9714399031315
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 89.7204
    - type: ap
      value: 85.70238397381907
    - type: f1
      value: 89.70961232185473
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.95120839033288
    - type: f1
      value: 93.70348712248138
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 75.25763793889648
    - type: f1
      value: 57.59583082574482
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 75.16476126429052
    - type: f1
      value: 73.29287381030854
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 78.9340954942838
    - type: f1
      value: 79.04036413238218
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 32.80025982143821
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 30.956464446009623
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.886626060290734
    - type: mrr
      value: 32.99813843700759
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 55.693914682185365
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 62.32723620518647
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 84.70275347034692
    - type: cos_sim_spearman
      value: 80.06126639668393
    - type: euclidean_pearson
      value: 82.18370726102707
    - type: euclidean_spearman
      value: 80.05483013524909
    - type: manhattan_pearson
      value: 82.11962032129463
    - type: manhattan_spearman
      value: 79.97174232961949
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 86.08210281025868
    - type: cos_sim_spearman
      value: 77.75002826042643
    - type: euclidean_pearson
      value: 83.06487161944293
    - type: euclidean_spearman
      value: 78.0677956304104
    - type: manhattan_pearson
      value: 83.04321232787379
    - type: manhattan_spearman
      value: 78.09582483148635
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 84.64353592106988
    - type: cos_sim_spearman
      value: 86.07934653140616
    - type: euclidean_pearson
      value: 85.21820182954883
    - type: euclidean_spearman
      value: 86.18828773665395
    - type: manhattan_pearson
      value: 85.12075207905364
    - type: manhattan_spearman
      value: 86.12061116344299
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 84.33571296969136
    - type: cos_sim_spearman
      value: 82.8868213429789
    - type: euclidean_pearson
      value: 83.65476643152161
    - type: euclidean_spearman
      value: 82.76439753890263
    - type: manhattan_pearson
      value: 83.63348951033883
    - type: manhattan_spearman
      value: 82.76176495070241
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.6337321089215
    - type: cos_sim_spearman
      value: 88.54453531860615
    - type: euclidean_pearson
      value: 87.68754116644199
    - type: euclidean_spearman
      value: 88.22610830299979
    - type: manhattan_pearson
      value: 87.62214887890859
    - type: manhattan_spearman
      value: 88.14766677391091
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 83.89742747806514
    - type: cos_sim_spearman
      value: 85.76282302560992
    - type: euclidean_pearson
      value: 84.83917251074928
    - type: euclidean_spearman
      value: 85.74354740775905
    - type: manhattan_pearson
      value: 84.91190952448616
    - type: manhattan_spearman
      value: 85.82001542154245
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 87.70974342036347
    - type: cos_sim_spearman
      value: 87.82200371351459
    - type: euclidean_pearson
      value: 88.04095125600278
    - type: euclidean_spearman
      value: 87.5069523002544
    - type: manhattan_pearson
      value: 88.03247709799281
    - type: manhattan_spearman
      value: 87.43433979175654
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 65.0349727703108
    - type: cos_sim_spearman
      value: 65.46090125254047
    - type: euclidean_pearson
      value: 66.75349075443432
    - type: euclidean_spearman
      value: 65.57576680702924
    - type: manhattan_pearson
      value: 66.72598998285412
    - type: manhattan_spearman
      value: 65.63446184311414
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.18026134463653
    - type: cos_sim_spearman
      value: 86.79430055943524
    - type: euclidean_pearson
      value: 86.2668626122386
    - type: euclidean_spearman
      value: 86.72288498504841
    - type: manhattan_pearson
      value: 86.28615540445857
    - type: manhattan_spearman
      value: 86.7110630606802
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 87.05335415919195
    - type: mrr
      value: 96.27455968142243
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.84653465346534
    - type: cos_sim_ap
      value: 96.38115549823692
    - type: cos_sim_f1
      value: 92.15983813859383
    - type: cos_sim_precision
      value: 93.24462640736951
    - type: cos_sim_recall
      value: 91.10000000000001
    - type: dot_accuracy
      value: 99.81782178217821
    - type: dot_ap
      value: 95.65732630933346
    - type: dot_f1
      value: 90.68825910931176
    - type: dot_precision
      value: 91.80327868852459
    - type: dot_recall
      value: 89.60000000000001
    - type: euclidean_accuracy
      value: 99.84653465346534
    - type: euclidean_ap
      value: 96.34134720479366
    - type: euclidean_f1
      value: 92.1756688541141
    - type: euclidean_precision
      value: 93.06829765545362
    - type: euclidean_recall
      value: 91.3
    - type: manhattan_accuracy
      value: 99.84356435643565
    - type: manhattan_ap
      value: 96.38165573090185
    - type: manhattan_f1
      value: 92.07622868605819
    - type: manhattan_precision
      value: 92.35412474849095
    - type: manhattan_recall
      value: 91.8
    - type: max_accuracy
      value: 99.84653465346534
    - type: max_ap
      value: 96.38165573090185
    - type: max_f1
      value: 92.1756688541141
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 64.81205738681385
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 34.083934029129445
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 54.447346270481376
    - type: mrr
      value: 55.382382119514475
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 72.123
    - type: ap
      value: 14.396060207954983
    - type: f1
      value: 55.24344377812756
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.67176004527447
    - type: f1
      value: 59.97320225890037
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 49.50190094208029
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 86.70799308577219
    - type: cos_sim_ap
      value: 76.40980707197174
    - type: cos_sim_f1
      value: 70.64264849074976
    - type: cos_sim_precision
      value: 65.56710347943967
    - type: cos_sim_recall
      value: 76.56992084432717
    - type: dot_accuracy
      value: 85.75430649102938
    - type: dot_ap
      value: 72.68783978286282
    - type: dot_f1
      value: 67.56951102588687
    - type: dot_precision
      value: 61.90162494510321
    - type: dot_recall
      value: 74.37994722955145
    - type: euclidean_accuracy
      value: 86.70799308577219
    - type: euclidean_ap
      value: 76.43046769325314
    - type: euclidean_f1
      value: 70.84852905421832
    - type: euclidean_precision
      value: 65.68981064021641
    - type: euclidean_recall
      value: 76.88654353562005
    - type: manhattan_accuracy
      value: 86.70203254455504
    - type: manhattan_ap
      value: 76.39254562413156
    - type: manhattan_f1
      value: 70.86557059961316
    - type: manhattan_precision
      value: 65.39491298527443
    - type: manhattan_recall
      value: 77.33509234828496
    - type: max_accuracy
      value: 86.70799308577219
    - type: max_ap
      value: 76.43046769325314
    - type: max_f1
      value: 70.86557059961316
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.92381728567548
    - type: cos_sim_ap
      value: 85.92532857788025
    - type: cos_sim_f1
      value: 78.11970128792525
    - type: cos_sim_precision
      value: 73.49806530445998
    - type: cos_sim_recall
      value: 83.3615645210964
    - type: dot_accuracy
      value: 88.28540381107618
    - type: dot_ap
      value: 84.42890126108796
    - type: dot_f1
      value: 76.98401162790698
    - type: dot_precision
      value: 72.89430222956234
    - type: dot_recall
      value: 81.55990144748999
    - type: euclidean_accuracy
      value: 88.95874568246207
    - type: euclidean_ap
      value: 85.88338025133037
    - type: euclidean_f1
      value: 78.14740888593184
    - type: euclidean_precision
      value: 75.15285084601166
    - type: euclidean_recall
      value: 81.3905143209116
    - type: manhattan_accuracy
      value: 88.92769821865176
    - type: manhattan_ap
      value: 85.84824183217555
    - type: manhattan_f1
      value: 77.9830582736965
    - type: manhattan_precision
      value: 74.15972222222223
    - type: manhattan_recall
      value: 82.22205112411457
    - type: max_accuracy
      value: 88.95874568246207
    - type: max_ap
      value: 85.92532857788025
    - type: max_f1
      value: 78.14740888593184
---

# bge-base-en-v1.5-quant

<div>
  <img src="https://huggingface.co/zeroshot/bge-base-en-v1.5-quant/resolve/main/bge-base-latency.png" alt="latency" width="500" style="display:inline-block; margin-right:10px;"/>
</div>

[DeepSparse](https://github.com/neuralmagic/deepsparse) is able to improve latency performance on a 10 core laptop and a 16 core AWS instance by up to 4.5X.

## Usage

This is the quantized (INT8) ONNX variant of the [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.

```bash
pip install -U deepsparse-nightly[sentence_transformers]
```

```python
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-base-en-v1.5-quant', export=False)

# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
    'Sentences are passed as a list of string.',
    'The quick brown fox jumps over the lazy dog.']

# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)

# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding.shape)
    print("")
```

For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).