metadata
license: mit
language:
- en
tags:
- sparse
- sparsity
- quantized
- onnx
- embeddings
- int8
- mteb
- deepsparse
model-index:
- name: bge-large-en-v1.5-quant
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.53731343283583
- type: ap
value: 38.30609312253564
- type: f1
value: 69.42802757893695
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.27346145216443
- type: cos_sim_spearman
value: 88.36526647458979
- type: euclidean_pearson
value: 86.83053354694746
- type: euclidean_spearman
value: 87.56223612880584
- type: manhattan_pearson
value: 86.59250609226758
- type: manhattan_spearman
value: 87.70681773644885
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.18998669716373
- type: cos_sim_spearman
value: 82.06129973984048
- type: euclidean_pearson
value: 83.65969509485801
- type: euclidean_spearman
value: 81.91666612708826
- type: manhattan_pearson
value: 83.6906794731384
- type: manhattan_spearman
value: 81.91752705367436
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.93407086985752
- type: cos_sim_spearman
value: 78.82992283957066
- type: euclidean_pearson
value: 83.39733473832982
- type: euclidean_spearman
value: 78.86999229850214
- type: manhattan_pearson
value: 83.39397058098533
- type: manhattan_spearman
value: 78.85397971200753
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 87.2586009863056
- type: cos_sim_spearman
value: 87.99415514558852
- type: euclidean_pearson
value: 86.98993652364359
- type: euclidean_spearman
value: 87.72725335668807
- type: manhattan_pearson
value: 86.897205761048
- type: manhattan_spearman
value: 87.65231103509018
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.41417660460755
- type: cos_sim_spearman
value: 83.50291886604928
- type: euclidean_pearson
value: 84.67758839660924
- type: euclidean_spearman
value: 83.4368059512681
- type: manhattan_pearson
value: 84.66027228213025
- type: manhattan_spearman
value: 83.43472054456252
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.02513262365703
- type: cos_sim_spearman
value: 89.00430907638267
- type: euclidean_pearson
value: 88.16290361497319
- type: euclidean_spearman
value: 88.6645154822661
- type: manhattan_pearson
value: 88.15337528825458
- type: manhattan_spearman
value: 88.66202950081507
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.10194022827035
- type: cos_sim_spearman
value: 86.45367112223394
- type: euclidean_pearson
value: 85.45292931769094
- type: euclidean_spearman
value: 86.06607589083283
- type: manhattan_pearson
value: 85.4111233047049
- type: manhattan_spearman
value: 86.04379654118996
- 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: 89.86966589113663
- type: cos_sim_spearman
value: 89.5617056243649
- type: euclidean_pearson
value: 89.018495917952
- type: euclidean_spearman
value: 88.387335721179
- type: manhattan_pearson
value: 89.07568042943448
- type: manhattan_spearman
value: 88.51733863475219
- 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: 68.38465344518238
- type: cos_sim_spearman
value: 68.15219488291783
- type: euclidean_pearson
value: 68.99169681132668
- type: euclidean_spearman
value: 68.01334641045888
- type: manhattan_pearson
value: 68.84952679202642
- type: manhattan_spearman
value: 67.85430179655137
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.60574360222778
- type: cos_sim_spearman
value: 87.8878986593873
- type: euclidean_pearson
value: 87.11557232168404
- type: euclidean_spearman
value: 87.40944677043365
- type: manhattan_pearson
value: 87.10395398212532
- type: manhattan_spearman
value: 87.35977283466168
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84752475247525
- type: cos_sim_ap
value: 96.49316696572335
- type: cos_sim_f1
value: 92.35352532274081
- type: cos_sim_precision
value: 91.71597633136095
- type: cos_sim_recall
value: 93
- type: dot_accuracy
value: 99.77326732673268
- type: dot_ap
value: 93.5497681978726
- type: dot_f1
value: 88.35582208895552
- type: dot_precision
value: 88.31168831168831
- type: dot_recall
value: 88.4
- type: euclidean_accuracy
value: 99.84653465346534
- type: euclidean_ap
value: 96.36378999360083
- type: euclidean_f1
value: 92.33052944087086
- type: euclidean_precision
value: 91.38099902056807
- type: euclidean_recall
value: 93.30000000000001
- type: manhattan_accuracy
value: 99.84455445544555
- type: manhattan_ap
value: 96.36035171233175
- type: manhattan_f1
value: 92.13260761999011
- type: manhattan_precision
value: 91.1851126346719
- type: manhattan_recall
value: 93.10000000000001
- type: max_accuracy
value: 99.84752475247525
- type: max_ap
value: 96.49316696572335
- type: max_f1
value: 92.35352532274081
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.26828396018358
- type: cos_sim_ap
value: 77.79878217023162
- type: cos_sim_f1
value: 71.0425694621463
- type: cos_sim_precision
value: 68.71301775147928
- type: cos_sim_recall
value: 73.53562005277044
- type: dot_accuracy
value: 84.01978899684092
- type: dot_ap
value: 66.12134149171163
- type: dot_f1
value: 63.283507097098365
- type: dot_precision
value: 60.393191081275475
- type: dot_recall
value: 66.46437994722955
- type: euclidean_accuracy
value: 87.24444179531503
- type: euclidean_ap
value: 77.84821131946212
- type: euclidean_f1
value: 71.30456661215247
- type: euclidean_precision
value: 68.1413801394566
- type: euclidean_recall
value: 74.77572559366754
- type: manhattan_accuracy
value: 87.19079692436074
- type: manhattan_ap
value: 77.78054941055291
- type: manhattan_f1
value: 71.13002127393318
- type: manhattan_precision
value: 67.65055939062128
- type: manhattan_recall
value: 74.9868073878628
- type: max_accuracy
value: 87.26828396018358
- type: max_ap
value: 77.84821131946212
- type: max_f1
value: 71.30456661215247
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.91023402025847
- type: cos_sim_ap
value: 85.94088151184411
- type: cos_sim_f1
value: 78.25673997223645
- type: cos_sim_precision
value: 74.45433059919367
- type: cos_sim_recall
value: 82.46843239913767
- type: dot_accuracy
value: 87.91865564481701
- type: dot_ap
value: 82.75373957440969
- type: dot_f1
value: 75.97383507276201
- type: dot_precision
value: 72.67294713160854
- type: dot_recall
value: 79.5888512473052
- type: euclidean_accuracy
value: 88.8539604921023
- type: euclidean_ap
value: 85.71590936389937
- type: euclidean_f1
value: 77.82902261742242
- type: euclidean_precision
value: 74.7219270279844
- type: euclidean_recall
value: 81.20572836464429
- type: manhattan_accuracy
value: 88.78992509799356
- type: manhattan_ap
value: 85.70200619366904
- type: manhattan_f1
value: 77.85875848203065
- type: manhattan_precision
value: 72.94315506222671
- type: manhattan_recall
value: 83.48475515860795
- type: max_accuracy
value: 88.91023402025847
- type: max_ap
value: 85.94088151184411
- type: max_f1
value: 78.25673997223645
bge-large-en-v1.5-quant
DeepSparse is able to improve latency performance on a 10 core laptop by 4.8X and up to 3.5X on a 16 core AWS instance.
Usage
This is the quantized (INT8) ONNX variant of the bge-large-en-v1.5 embeddings model accelerated with Sparsify for quantization and DeepSparseSentenceTransformers for inference.
pip install -U deepsparse-nightly[sentence_transformers]
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-large-en-v1.5-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 general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.