---
license: mit
language:
- en
tags:
- mteb
- sparse sparsity quantized onnx embeddings int8
model-index:
- name: bge-base-en-v1.5-sparse
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 75.38805970149254
    - type: ap
      value: 38.80643435437097
    - type: f1
      value: 69.52906891019036
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 90.72759999999998
    - type: ap
      value: 87.07910150764239
    - type: f1
      value: 90.71025910882096
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 45.494
    - type: f1
      value: 44.917953161904805
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 46.50495921726095
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 40.080055890804836
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 60.22880715757138
    - type: mrr
      value: 73.11227630479708
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 86.9542549153515
    - type: cos_sim_spearman
      value: 83.93865958725257
    - type: euclidean_pearson
      value: 86.00372707912037
    - type: euclidean_spearman
      value: 84.97302050526537
    - type: manhattan_pearson
      value: 85.63207676453459
    - type: manhattan_spearman
      value: 84.82542678079645
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 84.29545454545455
    - type: f1
      value: 84.26780483160312
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 36.78678386185847
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 34.42462869304013
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 46.705
    - type: f1
      value: 41.82618717355017
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 83.14760000000001
    - type: ap
      value: 77.40813245635195
    - type: f1
      value: 83.08648833100911
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.0519835841313
    - type: f1
      value: 91.73392170858916
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 72.48974008207935
    - type: f1
      value: 54.812872972777505
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.17753866846
    - type: f1
      value: 71.51091282373878
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.5353059852051
    - type: f1
      value: 77.42427561340143
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 32.00163251745748
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 30.37879992380756
  - task:
      type: Reranking
    dataset:
      type: mteb/mind_small
      name: MTEB MindSmallReranking
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.714215488161983
    - type: mrr
      value: 32.857362140961904
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 50.99679402527969
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 59.28024721612242
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 84.54645068673153
    - type: cos_sim_spearman
      value: 78.64401947043316
    - type: euclidean_pearson
      value: 82.36873285307261
    - type: euclidean_spearman
      value: 78.57406974337181
    - type: manhattan_pearson
      value: 82.33000263843067
    - type: manhattan_spearman
      value: 78.51127629983256
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 83.3001843293691
    - type: cos_sim_spearman
      value: 74.87989254109124
    - type: euclidean_pearson
      value: 80.88523322810525
    - type: euclidean_spearman
      value: 75.6469299496058
    - type: manhattan_pearson
      value: 80.8921104008781
    - type: manhattan_spearman
      value: 75.65942956132456
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 82.40319855455617
    - type: cos_sim_spearman
      value: 83.63807375781141
    - type: euclidean_pearson
      value: 83.28557187260904
    - type: euclidean_spearman
      value: 83.65223617817439
    - type: manhattan_pearson
      value: 83.30411918680012
    - type: manhattan_spearman
      value: 83.69204806663276
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.08942420708404
    - type: cos_sim_spearman
      value: 80.39991846857053
    - type: euclidean_pearson
      value: 82.68275416568997
    - type: euclidean_spearman
      value: 80.49626214786178
    - type: manhattan_pearson
      value: 82.62993414444689
    - type: manhattan_spearman
      value: 80.44148684748403
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 86.70365000096972
    - type: cos_sim_spearman
      value: 88.00515486253518
    - type: euclidean_pearson
      value: 87.65142168651604
    - type: euclidean_spearman
      value: 88.05834854642737
    - type: manhattan_pearson
      value: 87.59548659661925
    - type: manhattan_spearman
      value: 88.00573237576926
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 82.47886818876728
    - type: cos_sim_spearman
      value: 84.30874770680975
    - type: euclidean_pearson
      value: 83.74580951498133
    - type: euclidean_spearman
      value: 84.60595431454789
    - type: manhattan_pearson
      value: 83.74122023121615
    - type: manhattan_spearman
      value: 84.60549899361064
  - 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: 87.60257252565631
    - type: cos_sim_spearman
      value: 88.29577246271319
    - type: euclidean_pearson
      value: 88.25434138634807
    - type: euclidean_spearman
      value: 88.06678743723845
    - type: manhattan_pearson
      value: 88.3651048848073
    - type: manhattan_spearman
      value: 88.23688291108866
  - 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: 61.666254720687206
    - type: cos_sim_spearman
      value: 63.83700525419119
    - type: euclidean_pearson
      value: 64.36325040161177
    - type: euclidean_spearman
      value: 63.99833771224718
    - type: manhattan_pearson
      value: 64.01356576965371
    - type: manhattan_spearman
      value: 63.7201674202641
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.14584232139909
    - type: cos_sim_spearman
      value: 85.92570762612142
    - type: euclidean_pearson
      value: 86.34291503630607
    - type: euclidean_spearman
      value: 86.12670269109282
    - type: manhattan_pearson
      value: 86.26109450032494
    - type: manhattan_spearman
      value: 86.07665628498633
  - task:
      type: Reranking
    dataset:
      type: mteb/scidocs-reranking
      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 84.46430478723548
    - type: mrr
      value: 95.63907044299201
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.82178217821782
    - type: cos_sim_ap
      value: 95.49612561375889
    - type: cos_sim_f1
      value: 91.02691924227318
    - type: cos_sim_precision
      value: 90.75546719681908
    - type: cos_sim_recall
      value: 91.3
    - type: dot_accuracy
      value: 99.67821782178218
    - type: dot_ap
      value: 90.55740832326241
    - type: dot_f1
      value: 83.30765279917823
    - type: dot_precision
      value: 85.6388595564942
    - type: dot_recall
      value: 81.10000000000001
    - type: euclidean_accuracy
      value: 99.82475247524752
    - type: euclidean_ap
      value: 95.4739426775874
    - type: euclidean_f1
      value: 91.07413010590017
    - type: euclidean_precision
      value: 91.8616480162767
    - type: euclidean_recall
      value: 90.3
    - type: manhattan_accuracy
      value: 99.82376237623762
    - type: manhattan_ap
      value: 95.48506891694475
    - type: manhattan_f1
      value: 91.02822580645163
    - type: manhattan_precision
      value: 91.76829268292683
    - type: manhattan_recall
      value: 90.3
    - type: max_accuracy
      value: 99.82475247524752
    - type: max_ap
      value: 95.49612561375889
    - type: max_f1
      value: 91.07413010590017
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 60.92486258951404
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 32.97511013092965
  - task:
      type: Reranking
    dataset:
      type: mteb/stackoverflowdupquestions-reranking
      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 52.31647363355174
    - type: mrr
      value: 53.26469792462439
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 70.917
    - type: ap
      value: 13.760770628090576
    - type: f1
      value: 54.23887489664618
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.49349179400113
    - type: f1
      value: 59.815392064510775
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 47.29662657485732
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.74834594981225
    - type: cos_sim_ap
      value: 72.92449226447182
    - type: cos_sim_f1
      value: 68.14611644433363
    - type: cos_sim_precision
      value: 64.59465847317419
    - type: cos_sim_recall
      value: 72.1108179419525
    - type: dot_accuracy
      value: 82.73827263515527
    - type: dot_ap
      value: 63.27505594570806
    - type: dot_f1
      value: 61.717543651265
    - type: dot_precision
      value: 56.12443292287751
    - type: dot_recall
      value: 68.54881266490766
    - type: euclidean_accuracy
      value: 85.90332002145796
    - type: euclidean_ap
      value: 73.08299660990401
    - type: euclidean_f1
      value: 67.9050313691721
    - type: euclidean_precision
      value: 63.6091265268495
    - type: euclidean_recall
      value: 72.82321899736148
    - type: manhattan_accuracy
      value: 85.87351731537224
    - type: manhattan_ap
      value: 73.02205874497865
    - type: manhattan_f1
      value: 67.87532596547871
    - type: manhattan_precision
      value: 64.109781843772
    - type: manhattan_recall
      value: 72.1108179419525
    - type: max_accuracy
      value: 85.90332002145796
    - type: max_ap
      value: 73.08299660990401
    - type: max_f1
      value: 68.14611644433363
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.84231769317343
    - type: cos_sim_ap
      value: 85.65683184516553
    - type: cos_sim_f1
      value: 77.60567077973222
    - type: cos_sim_precision
      value: 75.6563071297989
    - type: cos_sim_recall
      value: 79.65814598090545
    - type: dot_accuracy
      value: 86.85333954282609
    - type: dot_ap
      value: 80.79899186896125
    - type: dot_f1
      value: 74.15220098146928
    - type: dot_precision
      value: 70.70819946919961
    - type: dot_recall
      value: 77.94887588543271
    - type: euclidean_accuracy
      value: 88.77634183257655
    - type: euclidean_ap
      value: 85.67411484805298
    - type: euclidean_f1
      value: 77.61566374357423
    - type: euclidean_precision
      value: 76.23255123255123
    - type: euclidean_recall
      value: 79.04989220819218
    - type: manhattan_accuracy
      value: 88.79962743043428
    - type: manhattan_ap
      value: 85.6494795781639
    - type: manhattan_f1
      value: 77.54222877224805
    - type: manhattan_precision
      value: 76.14100185528757
    - type: manhattan_recall
      value: 78.99599630428088
    - type: max_accuracy
      value: 88.84231769317343
    - type: max_ap
      value: 85.67411484805298
    - type: max_f1
      value: 77.61566374357423
---

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

This is the sparsified ONNX variant of the [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference pipeline and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization (INT8) and unstructured pruning (50%).

Current list of sparse and quantized bge ONNX models:

| Links                                                                                               | Sparsification Method |
| --------------------------------------------------------------------------------------------------- | ---------------------- |
| [zeroshot/bge-large-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-large-en-v1.5-sparse)     |    Quantization (INT8) & 50% Pruning                    |
| [zeroshot/bge-large-en-v1.5-quant](https://huggingface.co/zeroshot/bge-large-en-v1.5-quant)     |   Quantization (INT8)                     |
| [zeroshot/bge-base-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-base-en-v1.5-sparse)     |   Quantization (INT8) & 50% Pruning                     |
| [zeroshot/bge-base-en-v1.5-quant](https://huggingface.co/zeroshot/bge-base-en-v1.5-quant)     |     Quantization (INT8)                    |
| [zeroshot/bge-small-en-v1.5-sparse](https://huggingface.co/zeroshot/bge-small-en-v1.5-sparse) |    Quantization (INT8) & 50% Pruning                    |
| [zeroshot/bge-small-en-v1.5-quant](https://huggingface.co/zeroshot/bge-small-en-v1.5-quant) |     Quantization (INT8)                    |

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

```python
from deepsparse.sentence_transformers import SentenceTransformer
model = SentenceTransformer('zeroshot/bge-base-en-v1.5-sparse', 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 further details regarding DeepSparse & Sentence Transformers integration, refer to the [DeepSparse README](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers).


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).

![;)](https://media.giphy.com/media/bYg33GbNbNIVzSrr84/giphy-downsized-large.gif)