metadata
license: mit
language:
- en
tags:
- sparse sparsity quantized onnx embeddings int8
model-index:
- name: bge-base-en-v1.5-sparse
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.38805970149254
- type: ap
value: 38.80643435437097
- type: f1
value: 69.52906891019036
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.72759999999998
- type: ap
value: 87.07910150764239
- type: f1
value: 90.71025910882096
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.494
- type: f1
value: 44.917953161904805
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.50495921726095
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.080055890804836
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.22880715757138
- type: mrr
value: 73.11227630479708
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.9542549153515
- type: cos_sim_spearman
value: 83.93865958725257
- type: euclidean_pearson
value: 86.00372707912037
- type: euclidean_spearman
value: 84.97302050526537
- type: manhattan_pearson
value: 85.63207676453459
- type: manhattan_spearman
value: 84.82542678079645
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.29545454545455
- type: f1
value: 84.26780483160312
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.78678386185847
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.42462869304013
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.705
- type: f1
value: 41.82618717355017
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 83.14760000000001
- type: ap
value: 77.40813245635195
- type: f1
value: 83.08648833100911
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.0519835841313
- type: f1
value: 91.73392170858916
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.48974008207935
- type: f1
value: 54.812872972777505
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.17753866846
- type: f1
value: 71.51091282373878
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.5353059852051
- type: f1
value: 77.42427561340143
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.00163251745748
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.37879992380756
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.99679402527969
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 59.28024721612242
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.54645068673153
- type: cos_sim_spearman
value: 78.64401947043316
- type: euclidean_pearson
value: 82.36873285307261
- type: euclidean_spearman
value: 78.57406974337181
- type: manhattan_pearson
value: 82.33000263843067
- type: manhattan_spearman
value: 78.51127629983256
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.3001843293691
- type: cos_sim_spearman
value: 74.87989254109124
- type: euclidean_pearson
value: 80.88523322810525
- type: euclidean_spearman
value: 75.6469299496058
- type: manhattan_pearson
value: 80.8921104008781
- type: manhattan_spearman
value: 75.65942956132456
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.40319855455617
- type: cos_sim_spearman
value: 83.63807375781141
- type: euclidean_pearson
value: 83.28557187260904
- type: euclidean_spearman
value: 83.65223617817439
- type: manhattan_pearson
value: 83.30411918680012
- type: manhattan_spearman
value: 83.69204806663276
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.08942420708404
- type: cos_sim_spearman
value: 80.39991846857053
- type: euclidean_pearson
value: 82.68275416568997
- type: euclidean_spearman
value: 80.49626214786178
- type: manhattan_pearson
value: 82.62993414444689
- type: manhattan_spearman
value: 80.44148684748403
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.70365000096972
- type: cos_sim_spearman
value: 88.00515486253518
- type: euclidean_pearson
value: 87.65142168651604
- type: euclidean_spearman
value: 88.05834854642737
- type: manhattan_pearson
value: 87.59548659661925
- type: manhattan_spearman
value: 88.00573237576926
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.47886818876728
- type: cos_sim_spearman
value: 84.30874770680975
- type: euclidean_pearson
value: 83.74580951498133
- type: euclidean_spearman
value: 84.60595431454789
- type: manhattan_pearson
value: 83.74122023121615
- type: manhattan_spearman
value: 84.60549899361064
- 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: 87.60257252565631
- type: cos_sim_spearman
value: 88.29577246271319
- type: euclidean_pearson
value: 88.25434138634807
- type: euclidean_spearman
value: 88.06678743723845
- type: manhattan_pearson
value: 88.3651048848073
- type: manhattan_spearman
value: 88.23688291108866
- 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: 61.666254720687206
- type: cos_sim_spearman
value: 63.83700525419119
- type: euclidean_pearson
value: 64.36325040161177
- type: euclidean_spearman
value: 63.99833771224718
- type: manhattan_pearson
value: 64.01356576965371
- type: manhattan_spearman
value: 63.7201674202641
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.14584232139909
- type: cos_sim_spearman
value: 85.92570762612142
- type: euclidean_pearson
value: 86.34291503630607
- type: euclidean_spearman
value: 86.12670269109282
- type: manhattan_pearson
value: 86.26109450032494
- type: manhattan_spearman
value: 86.07665628498633
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.46430478723548
- type: mrr
value: 95.63907044299201
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82178217821782
- type: cos_sim_ap
value: 95.49612561375889
- type: cos_sim_f1
value: 91.02691924227318
- type: cos_sim_precision
value: 90.75546719681908
- type: cos_sim_recall
value: 91.3
- type: dot_accuracy
value: 99.67821782178218
- type: dot_ap
value: 90.55740832326241
- type: dot_f1
value: 83.30765279917823
- type: dot_precision
value: 85.6388595564942
- type: dot_recall
value: 81.10000000000001
- type: euclidean_accuracy
value: 99.82475247524752
- type: euclidean_ap
value: 95.4739426775874
- type: euclidean_f1
value: 91.07413010590017
- type: euclidean_precision
value: 91.8616480162767
- type: euclidean_recall
value: 90.3
- type: manhattan_accuracy
value: 99.82376237623762
- type: manhattan_ap
value: 95.48506891694475
- type: manhattan_f1
value: 91.02822580645163
- type: manhattan_precision
value: 91.76829268292683
- type: manhattan_recall
value: 90.3
- type: max_accuracy
value: 99.82475247524752
- type: max_ap
value: 95.49612561375889
- type: max_f1
value: 91.07413010590017
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.92486258951404
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.97511013092965
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.31647363355174
- type: mrr
value: 53.26469792462439
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.917
- type: ap
value: 13.760770628090576
- type: f1
value: 54.23887489664618
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.49349179400113
- type: f1
value: 59.815392064510775
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 47.29662657485732
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.74834594981225
- type: cos_sim_ap
value: 72.92449226447182
- type: cos_sim_f1
value: 68.14611644433363
- type: cos_sim_precision
value: 64.59465847317419
- type: cos_sim_recall
value: 72.1108179419525
- type: dot_accuracy
value: 82.73827263515527
- type: dot_ap
value: 63.27505594570806
- type: dot_f1
value: 61.717543651265
- type: dot_precision
value: 56.12443292287751
- type: dot_recall
value: 68.54881266490766
- type: euclidean_accuracy
value: 85.90332002145796
- type: euclidean_ap
value: 73.08299660990401
- type: euclidean_f1
value: 67.9050313691721
- type: euclidean_precision
value: 63.6091265268495
- type: euclidean_recall
value: 72.82321899736148
- type: manhattan_accuracy
value: 85.87351731537224
- type: manhattan_ap
value: 73.02205874497865
- type: manhattan_f1
value: 67.87532596547871
- type: manhattan_precision
value: 64.109781843772
- type: manhattan_recall
value: 72.1108179419525
- type: max_accuracy
value: 85.90332002145796
- type: max_ap
value: 73.08299660990401
- type: max_f1
value: 68.14611644433363
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.84231769317343
- type: cos_sim_ap
value: 85.65683184516553
- type: cos_sim_f1
value: 77.60567077973222
- type: cos_sim_precision
value: 75.6563071297989
- type: cos_sim_recall
value: 79.65814598090545
- type: dot_accuracy
value: 86.85333954282609
- type: dot_ap
value: 80.79899186896125
- type: dot_f1
value: 74.15220098146928
- type: dot_precision
value: 70.70819946919961
- type: dot_recall
value: 77.94887588543271
- type: euclidean_accuracy
value: 88.77634183257655
- type: euclidean_ap
value: 85.67411484805298
- type: euclidean_f1
value: 77.61566374357423
- type: euclidean_precision
value: 76.23255123255123
- type: euclidean_recall
value: 79.04989220819218
- type: manhattan_accuracy
value: 88.79962743043428
- type: manhattan_ap
value: 85.6494795781639
- type: manhattan_f1
value: 77.54222877224805
- type: manhattan_precision
value: 76.14100185528757
- type: manhattan_recall
value: 78.99599630428088
- type: max_accuracy
value: 88.84231769317343
- type: max_ap
value: 85.67411484805298
- type: max_f1
value: 77.61566374357423
This is the sparsified ONNX variant of the bge-base-en-v1.5 embeddings model created with DeepSparse Optimum for ONNX export/inference pipeline and Neural Magic's Sparsify for one-shot quantization (INT8) and unstructured pruning (50%).
Current list of sparse and quantized bge ONNX models:
| Links | Sparsification Method |
|---|---|
| zeroshot/bge-large-en-v1.5-sparse | Quantization (INT8) & 50% Pruning |
| zeroshot/bge-large-en-v1.5-quant | Quantization (INT8) |
| zeroshot/bge-base-en-v1.5-sparse | Quantization (INT8) & 50% Pruning |
| zeroshot/bge-base-en-v1.5-quant | Quantization (INT8) |
| zeroshot/bge-small-en-v1.5-sparse | Quantization (INT8) & 50% Pruning |
| zeroshot/bge-small-en-v1.5-quant | Quantization (INT8) |
pip install -U deepsparse-nightly[sentence_transformers]
from deepsparse.sentence_transformers import SentenceTransformer
model = SentenceTransformer('zeroshot/bge-base-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 further details regarding DeepSparse & Sentence Transformers integration, refer to the DeepSparse README.
For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.
