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---
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).
 |