---
tags:
- sparse sparsity quantized onnx embeddings int8
- mteb
model-index:
- name: gte-small-quant
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 72.88059701492537
    - type: ap
      value: 35.74239003564444
    - type: f1
      value: 66.98065758287116
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 91.031575
    - type: ap
      value: 87.60741691468986
    - type: f1
      value: 91.00983458583187
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.943999999999996
    - type: f1
      value: 46.33280307575562
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 60.75683986813218
    - type: mrr
      value: 73.51624675724399
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 89.07092347634877
    - type: cos_sim_spearman
      value: 87.80621759170344
    - type: euclidean_pearson
      value: 87.29751551472525
    - type: euclidean_spearman
      value: 87.5634409755362
    - type: manhattan_pearson
      value: 87.56100206227441
    - type: manhattan_spearman
      value: 87.45982415672536
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 83.46753246753246
    - type: f1
      value: 83.39526091362032
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 45.800000000000004
    - type: f1
      value: 40.76055487612189
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 85.0096
    - type: ap
      value: 79.91059611360778
    - type: f1
      value: 84.9738791599706
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.51025991792065
    - type: f1
      value: 92.2852224639839
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 69.61924304605563
    - type: f1
      value: 51.832892524807505
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 70.2320107599193
    - type: f1
      value: 68.03367707473218
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 75.28581035642232
    - type: f1
      value: 75.43554941058956
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.58628262329275
    - type: cos_sim_spearman
      value: 77.30534089053104
    - type: euclidean_pearson
      value: 80.86400799226335
    - type: euclidean_spearman
      value: 77.26947744139412
    - type: manhattan_pearson
      value: 80.79442484789072
    - type: manhattan_spearman
      value: 77.18043722794019
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 82.77293561742106
    - type: cos_sim_spearman
      value: 73.98616407095425
    - type: euclidean_pearson
      value: 78.7096804108132
    - type: euclidean_spearman
      value: 73.52379687387366
    - type: manhattan_pearson
      value: 78.80694876432868
    - type: manhattan_spearman
      value: 73.64907838788528
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 82.12995363427328
    - type: cos_sim_spearman
      value: 84.23345798311749
    - type: euclidean_pearson
      value: 83.94003648503143
    - type: euclidean_spearman
      value: 84.74522675669463
    - type: manhattan_pearson
      value: 83.82868963165394
    - type: manhattan_spearman
      value: 84.61059125620956
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 81.88504872832357
    - type: cos_sim_spearman
      value: 80.09345991196561
    - type: euclidean_pearson
      value: 81.99899431994811
    - type: euclidean_spearman
      value: 80.25520445997002
    - type: manhattan_pearson
      value: 81.9635758954928
    - type: manhattan_spearman
      value: 80.24335353637277
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 86.55052353126385
    - type: cos_sim_spearman
      value: 88.1950992730786
    - type: euclidean_pearson
      value: 87.83472249083056
    - type: euclidean_spearman
      value: 88.43301043636015
    - type: manhattan_pearson
      value: 87.75102815516877
    - type: manhattan_spearman
      value: 88.34719608377306
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 81.58832350766542
    - type: cos_sim_spearman
      value: 83.60857270697358
    - type: euclidean_pearson
      value: 82.9059299279255
    - type: euclidean_spearman
      value: 83.87380773329784
    - type: manhattan_pearson
      value: 82.76009241925925
    - type: manhattan_spearman
      value: 83.72876466499108
  - 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: 87.96440735880392
    - type: cos_sim_spearman
      value: 87.79655666183349
    - type: euclidean_pearson
      value: 88.47129589774806
    - type: euclidean_spearman
      value: 87.95235258398374
    - type: manhattan_pearson
      value: 88.37144209103296
    - type: manhattan_spearman
      value: 87.81869790317533
  - 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: 66.66468384683428
    - type: cos_sim_spearman
      value: 66.84275911821702
    - type: euclidean_pearson
      value: 67.73972664535547
    - type: euclidean_spearman
      value: 66.57863145583491
    - type: manhattan_pearson
      value: 67.91309920462287
    - type: manhattan_spearman
      value: 66.67487869242575
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 84.07668437020894
    - type: cos_sim_spearman
      value: 85.13186558138277
    - type: euclidean_pearson
      value: 85.28607166042313
    - type: euclidean_spearman
      value: 85.25082312265897
    - type: manhattan_pearson
      value: 85.0870328315141
    - type: manhattan_spearman
      value: 85.10612962221282
  - task:
      type: Reranking
    dataset:
      type: mteb/scidocs-reranking
      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 84.33835340608282
    - type: mrr
      value: 95.54063220729888
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.81386138613861
    - type: cos_sim_ap
      value: 95.49398397880566
    - type: cos_sim_f1
      value: 90.5050505050505
    - type: cos_sim_precision
      value: 91.42857142857143
    - type: cos_sim_recall
      value: 89.60000000000001
    - type: dot_accuracy
      value: 99.75742574257426
    - type: dot_ap
      value: 93.40675781804289
    - type: dot_f1
      value: 87.45519713261648
    - type: dot_precision
      value: 89.61175236096537
    - type: dot_recall
      value: 85.39999999999999
    - type: euclidean_accuracy
      value: 99.81485148514851
    - type: euclidean_ap
      value: 95.39724876386569
    - type: euclidean_f1
      value: 90.5793450881612
    - type: euclidean_precision
      value: 91.26903553299492
    - type: euclidean_recall
      value: 89.9
    - type: manhattan_accuracy
      value: 99.81485148514851
    - type: manhattan_ap
      value: 95.46515830873487
    - type: manhattan_f1
      value: 90.56974459724951
    - type: manhattan_precision
      value: 88.996138996139
    - type: manhattan_recall
      value: 92.2
    - type: max_accuracy
      value: 99.81485148514851
    - type: max_ap
      value: 95.49398397880566
    - type: max_f1
      value: 90.5793450881612
  - task:
      type: Reranking
    dataset:
      type: mteb/stackoverflowdupquestions-reranking
      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 51.68384236354744
    - type: mrr
      value: 52.52933749257278
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 69.7972
    - type: ap
      value: 13.790209566654962
    - type: f1
      value: 53.73625700975159
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 57.81550650820599
    - type: f1
      value: 58.22494506904567
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 84.30589497526375
    - type: cos_sim_ap
      value: 68.60854966172107
    - type: cos_sim_f1
      value: 65.06926244852113
    - type: cos_sim_precision
      value: 61.733364906464594
    - type: cos_sim_recall
      value: 68.7862796833773
    - type: dot_accuracy
      value: 81.63557250998392
    - type: dot_ap
      value: 58.80135920860792
    - type: dot_f1
      value: 57.39889705882353
    - type: dot_precision
      value: 50.834350834350836
    - type: dot_recall
      value: 65.91029023746702
    - type: euclidean_accuracy
      value: 84.37742146986946
    - type: euclidean_ap
      value: 68.88494996210581
    - type: euclidean_f1
      value: 65.23647001462702
    - type: euclidean_precision
      value: 60.62528318985048
    - type: euclidean_recall
      value: 70.60686015831135
    - type: manhattan_accuracy
      value: 84.21648685700661
    - type: manhattan_ap
      value: 68.54917405273397
    - type: manhattan_f1
      value: 64.97045701193778
    - type: manhattan_precision
      value: 59.826782145236514
    - type: manhattan_recall
      value: 71.08179419525065
    - type: max_accuracy
      value: 84.37742146986946
    - type: max_ap
      value: 68.88494996210581
    - type: max_f1
      value: 65.23647001462702
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.60752124810804
    - type: cos_sim_ap
      value: 85.16030341274225
    - type: cos_sim_f1
      value: 77.50186985789081
    - type: cos_sim_precision
      value: 75.34904013961605
    - type: cos_sim_recall
      value: 79.781336618417
    - type: dot_accuracy
      value: 86.00147475453099
    - type: dot_ap
      value: 79.24446611557556
    - type: dot_f1
      value: 72.34317740892433
    - type: dot_precision
      value: 67.81624680048498
    - type: dot_recall
      value: 77.51770865414228
    - type: euclidean_accuracy
      value: 88.7026041060271
    - type: euclidean_ap
      value: 85.30879801684605
    - type: euclidean_f1
      value: 77.60992108229988
    - type: euclidean_precision
      value: 75.80384671854354
    - type: euclidean_recall
      value: 79.50415768401602
    - type: manhattan_accuracy
      value: 88.75305623471883
    - type: manhattan_ap
      value: 85.24656615741652
    - type: manhattan_f1
      value: 77.5542141739325
    - type: manhattan_precision
      value: 75.14079422382672
    - type: manhattan_recall
      value: 80.12781028641824
    - type: max_accuracy
      value: 88.75305623471883
    - type: max_ap
      value: 85.30879801684605
    - type: max_f1
      value: 77.60992108229988
license: mit
language:
- en
---

# gte-small-quant

This is the quantized (INT8) ONNX variant of the [gte-small](https://huggingface.co/thenlper/gte-small) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization.

Current list of sparse and quantized gte ONNX models:

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

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

```python
from deepsparse.sentence_transformers import SentenceTransformer
model = SentenceTransformer('zeroshot/gte-small-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 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)