---
license: mit
language:
- en
tags:
- sparse sparsity quantized onnx embeddings int8
- mteb
model-index:
- name: bge-large-en-v1.5-sparse
  results:
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 87.73305831153709
    - type: cos_sim_spearman
      value: 85.64351771070989
    - type: euclidean_pearson
      value: 86.06880877736519
    - type: euclidean_spearman
      value: 85.60676988543395
    - type: manhattan_pearson
      value: 85.69108036145253
    - type: manhattan_spearman
      value: 85.05314281283421
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 85.61833776000717
    - type: cos_sim_spearman
      value: 80.73718686921521
    - type: euclidean_pearson
      value: 83.9368704709159
    - type: euclidean_spearman
      value: 80.64477415487963
    - type: manhattan_pearson
      value: 83.92383757341743
    - type: manhattan_spearman
      value: 80.59625506933862
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 83.81272888013494
    - type: cos_sim_spearman
      value: 76.07038564455931
    - type: euclidean_pearson
      value: 80.33676600912023
    - type: euclidean_spearman
      value: 75.86575335744111
    - type: manhattan_pearson
      value: 80.36973770593211
    - type: manhattan_spearman
      value: 75.88787860200954
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 85.58781524090651
    - type: cos_sim_spearman
      value: 86.80508359626748
    - type: euclidean_pearson
      value: 85.22891409219575
    - type: euclidean_spearman
      value: 85.78295876926319
    - type: manhattan_pearson
      value: 85.2193177032458
    - type: manhattan_spearman
      value: 85.74049940198427
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 84.0862821699066
    - type: cos_sim_spearman
      value: 81.67856196476185
    - type: euclidean_pearson
      value: 83.38475353138897
    - type: euclidean_spearman
      value: 81.45279784228292
    - type: manhattan_pearson
      value: 83.29235221714131
    - type: manhattan_spearman
      value: 81.3971683104493
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.44459051393112
    - type: cos_sim_spearman
      value: 88.74673154561383
    - type: euclidean_pearson
      value: 88.13112382236628
    - type: euclidean_spearman
      value: 88.56241954487271
    - type: manhattan_pearson
      value: 88.11098632041256
    - type: manhattan_spearman
      value: 88.55607051247829
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 82.8825746257794
    - type: cos_sim_spearman
      value: 84.6066555379785
    - type: euclidean_pearson
      value: 84.12438131112606
    - type: euclidean_spearman
      value: 84.75862802179907
    - type: manhattan_pearson
      value: 84.12791217960807
    - type: manhattan_spearman
      value: 84.7739597139034
  - 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: 89.19971502207773
    - type: cos_sim_spearman
      value: 89.75109780507901
    - type: euclidean_pearson
      value: 89.5913898113725
    - type: euclidean_spearman
      value: 89.20244860773123
    - type: manhattan_pearson
      value: 89.68755363801112
    - type: manhattan_spearman
      value: 89.3105024782381
  - 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: 61.73885819503523
    - type: cos_sim_spearman
      value: 64.09521607825829
    - type: euclidean_pearson
      value: 64.22116001518724
    - type: euclidean_spearman
      value: 63.84189650719827
    - type: manhattan_pearson
      value: 64.23930191730729
    - type: manhattan_spearman
      value: 63.7536172795383
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.68505574064375
    - type: cos_sim_spearman
      value: 86.87614324154406
    - type: euclidean_pearson
      value: 86.96751967489614
    - type: euclidean_spearman
      value: 86.78979082790067
    - type: manhattan_pearson
      value: 86.92578795715433
    - type: manhattan_spearman
      value: 86.74076104131726
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.80990099009901
    - type: cos_sim_ap
      value: 95.00187845875503
    - type: cos_sim_f1
      value: 90.37698412698413
    - type: cos_sim_precision
      value: 89.66535433070865
    - type: cos_sim_recall
      value: 91.10000000000001
    - type: dot_accuracy
      value: 99.63366336633663
    - type: dot_ap
      value: 87.6642728041652
    - type: dot_f1
      value: 81.40803173029252
    - type: dot_precision
      value: 80.7276302851524
    - type: dot_recall
      value: 82.1
    - type: euclidean_accuracy
      value: 99.8079207920792
    - type: euclidean_ap
      value: 94.88531851782375
    - type: euclidean_f1
      value: 90.49019607843137
    - type: euclidean_precision
      value: 88.75
    - type: euclidean_recall
      value: 92.30000000000001
    - type: manhattan_accuracy
      value: 99.81188118811882
    - type: manhattan_ap
      value: 94.87944331919043
    - type: manhattan_f1
      value: 90.5
    - type: manhattan_precision
      value: 90.5
    - type: manhattan_recall
      value: 90.5
    - type: max_accuracy
      value: 99.81188118811882
    - type: max_ap
      value: 95.00187845875503
    - type: max_f1
      value: 90.5
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 86.3861238600465
    - type: cos_sim_ap
      value: 74.50058066578084
    - type: cos_sim_f1
      value: 69.25949774629748
    - type: cos_sim_precision
      value: 67.64779874213836
    - type: cos_sim_recall
      value: 70.94986807387863
    - type: dot_accuracy
      value: 81.57000655659535
    - type: dot_ap
      value: 59.10193583653485
    - type: dot_f1
      value: 58.39352155832786
    - type: dot_precision
      value: 49.88780852655198
    - type: dot_recall
      value: 70.3957783641161
    - type: euclidean_accuracy
      value: 86.37420277761221
    - type: euclidean_ap
      value: 74.41671247141966
    - type: euclidean_f1
      value: 69.43907156673114
    - type: euclidean_precision
      value: 64.07853636769299
    - type: euclidean_recall
      value: 75.77836411609499
    - type: manhattan_accuracy
      value: 86.30267628300649
    - type: manhattan_ap
      value: 74.34438603336339
    - type: manhattan_f1
      value: 69.41888619854721
    - type: manhattan_precision
      value: 64.13870246085011
    - type: manhattan_recall
      value: 75.64643799472296
    - type: max_accuracy
      value: 86.3861238600465
    - type: max_ap
      value: 74.50058066578084
    - type: max_f1
      value: 69.43907156673114
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.87530562347187
    - type: cos_sim_ap
      value: 85.69496469410068
    - type: cos_sim_f1
      value: 77.96973052787007
    - type: cos_sim_precision
      value: 74.8900865125514
    - type: cos_sim_recall
      value: 81.3135201724669
    - type: dot_accuracy
      value: 86.70780455621532
    - type: dot_ap
      value: 80.03489678512908
    - type: dot_f1
      value: 73.26376129933124
    - type: dot_precision
      value: 70.07591733445804
    - type: dot_recall
      value: 76.75546658453958
    - type: euclidean_accuracy
      value: 88.85978189156674
    - type: euclidean_ap
      value: 85.67894953317325
    - type: euclidean_f1
      value: 78.04295942720763
    - type: euclidean_precision
      value: 75.67254845241538
    - type: euclidean_recall
      value: 80.56667693255312
    - type: manhattan_accuracy
      value: 88.88306748942446
    - type: manhattan_ap
      value: 85.66556510677526
    - type: manhattan_f1
      value: 78.06278290950576
    - type: manhattan_precision
      value: 74.76912231230173
    - type: manhattan_recall
      value: 81.65999384046813
    - type: max_accuracy
      value: 88.88306748942446
    - type: max_ap
      value: 85.69496469410068
    - type: max_f1
      value: 78.06278290950576
---

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

## Usage

This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization/pruning 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-large-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 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).