---
tags:
- sparse sparsity quantized onnx embeddings int8
- mteb
model-index:
- name: gte-large-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: 88.64253410928214
    - type: cos_sim_spearman
      value: 85.83388349410652
    - type: euclidean_pearson
      value: 86.86126159318735
    - type: euclidean_spearman
      value: 85.61580623591163
    - type: manhattan_pearson
      value: 86.6901132883383
    - type: manhattan_spearman
      value: 85.60255292187769
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 85.23314640591607
    - type: cos_sim_spearman
      value: 79.00078545104338
    - type: euclidean_pearson
      value: 83.48009254500714
    - type: euclidean_spearman
      value: 78.95413001389939
    - type: manhattan_pearson
      value: 83.46945566025941
    - type: manhattan_spearman
      value: 78.9241707208135
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 81.77526666043804
    - type: cos_sim_spearman
      value: 73.4849063285867
    - type: euclidean_pearson
      value: 78.04477932740524
    - type: euclidean_spearman
      value: 73.01394205771743
    - type: manhattan_pearson
      value: 78.08836684503294
    - type: manhattan_spearman
      value: 73.05074711098149
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 84.57839215661352
    - type: cos_sim_spearman
      value: 86.13854767345153
    - type: euclidean_pearson
      value: 85.12712609946449
    - type: euclidean_spearman
      value: 85.52497994789026
    - type: manhattan_pearson
      value: 85.06833141611173
    - type: manhattan_spearman
      value: 85.45003068636466
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.30485126978374
    - type: cos_sim_spearman
      value: 80.36497172462357
    - type: euclidean_pearson
      value: 82.91977909424605
    - type: euclidean_spearman
      value: 80.16995106297438
    - type: manhattan_pearson
      value: 82.88200991402184
    - type: manhattan_spearman
      value: 80.14259757215227
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 86.99883111314007
    - type: cos_sim_spearman
      value: 88.531352572377
    - type: euclidean_pearson
      value: 87.96834578059067
    - type: euclidean_spearman
      value: 88.44800718542935
    - type: manhattan_pearson
      value: 87.94889391725033
    - type: manhattan_spearman
      value: 88.45467695837115
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 82.4636984892402
    - type: cos_sim_spearman
      value: 84.0808920789148
    - type: euclidean_pearson
      value: 83.70613486028309
    - type: euclidean_spearman
      value: 84.35941626905009
    - type: manhattan_pearson
      value: 83.70259457073782
    - type: manhattan_spearman
      value: 84.35496521501604
  - 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: 88.76172944971023
    - type: cos_sim_spearman
      value: 89.4190945039165
    - type: euclidean_pearson
      value: 89.47263005347381
    - type: euclidean_spearman
      value: 89.49228360724095
    - type: manhattan_pearson
      value: 89.49959868816694
    - type: manhattan_spearman
      value: 89.5314536157954
  - 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: 64.57158223787549
    - type: cos_sim_spearman
      value: 66.75053533168037
    - type: euclidean_pearson
      value: 66.45526604831747
    - type: euclidean_spearman
      value: 66.14567667353113
    - type: manhattan_pearson
      value: 66.47352000151176
    - type: manhattan_spearman
      value: 66.21099856852885
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.055653571006
    - type: cos_sim_spearman
      value: 85.45387832634702
    - type: euclidean_pearson
      value: 86.31667154906651
    - type: euclidean_spearman
      value: 85.66079590537946
    - type: manhattan_pearson
      value: 86.2806853257308
    - type: manhattan_spearman
      value: 85.63700636713952
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.78811881188119
    - type: cos_sim_ap
      value: 94.67027715905307
    - type: cos_sim_f1
      value: 89.33074684772066
    - type: cos_sim_precision
      value: 86.7231638418079
    - type: cos_sim_recall
      value: 92.10000000000001
    - type: dot_accuracy
      value: 99.47128712871287
    - type: dot_ap
      value: 78.41478815918727
    - type: dot_f1
      value: 73.30049261083744
    - type: dot_precision
      value: 72.23300970873787
    - type: dot_recall
      value: 74.4
    - type: euclidean_accuracy
      value: 99.78415841584159
    - type: euclidean_ap
      value: 94.60075930867181
    - type: euclidean_f1
      value: 89.12175648702593
    - type: euclidean_precision
      value: 88.94422310756973
    - type: euclidean_recall
      value: 89.3
    - type: manhattan_accuracy
      value: 99.78415841584159
    - type: manhattan_ap
      value: 94.62867439278095
    - type: manhattan_f1
      value: 89.2337536372454
    - type: manhattan_precision
      value: 86.62900188323917
    - type: manhattan_recall
      value: 92.0
    - type: max_accuracy
      value: 99.78811881188119
    - type: max_ap
      value: 94.67027715905307
    - type: max_f1
      value: 89.33074684772066
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.09864695714371
    - type: cos_sim_ap
      value: 70.33704198164713
    - type: cos_sim_f1
      value: 66.22893954410307
    - type: cos_sim_precision
      value: 62.42410088743577
    - type: cos_sim_recall
      value: 70.52770448548813
    - type: dot_accuracy
      value: 79.11426357513263
    - type: dot_ap
      value: 49.15484584572233
    - type: dot_f1
      value: 51.12580243364951
    - type: dot_precision
      value: 40.13840830449827
    - type: dot_recall
      value: 70.3957783641161
    - type: euclidean_accuracy
      value: 85.15825236931514
    - type: euclidean_ap
      value: 70.51017350854076
    - type: euclidean_f1
      value: 66.45416294785159
    - type: euclidean_precision
      value: 64.29805082654823
    - type: euclidean_recall
      value: 68.7598944591029
    - type: manhattan_accuracy
      value: 85.1403707456637
    - type: manhattan_ap
      value: 70.47587863399994
    - type: manhattan_f1
      value: 66.4576802507837
    - type: manhattan_precision
      value: 63.32138590203107
    - type: manhattan_recall
      value: 69.92084432717678
    - type: max_accuracy
      value: 85.15825236931514
    - type: max_ap
      value: 70.51017350854076
    - type: max_f1
      value: 66.4576802507837
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.8539604921023
    - type: cos_sim_ap
      value: 85.71869912577101
    - type: cos_sim_f1
      value: 78.00535626720983
    - type: cos_sim_precision
      value: 76.46232344893885
    - type: cos_sim_recall
      value: 79.61194949183862
    - type: dot_accuracy
      value: 84.57717235223348
    - type: dot_ap
      value: 74.89496650237145
    - type: dot_f1
      value: 69.05327823892932
    - type: dot_precision
      value: 65.75666829166377
    - type: dot_recall
      value: 72.69787496150293
    - type: euclidean_accuracy
      value: 88.89471028835332
    - type: euclidean_ap
      value: 85.75169460500409
    - type: euclidean_f1
      value: 78.17055393586006
    - type: euclidean_precision
      value: 74.21118184334348
    - type: euclidean_recall
      value: 82.57622420696026
    - type: manhattan_accuracy
      value: 88.92187681918733
    - type: manhattan_ap
      value: 85.7496679471825
    - type: manhattan_f1
      value: 78.11088295687884
    - type: manhattan_precision
      value: 75.82083061535117
    - type: manhattan_recall
      value: 80.5435786880197
    - type: max_accuracy
      value: 88.92187681918733
    - type: max_ap
      value: 85.75169460500409
    - type: max_f1
      value: 78.17055393586006
license: mit
language:
- en
---

# gte-large-sparse

This is the sparse ONNX variant of the [gte-large](https://huggingface.co/thenlper/gte-large) 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 (INT8) and unstructured pruning 50%.

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-large-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)