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metadata
license: mit
language:
  - en
tags:
  - sparse sparsity quantized onnx embeddings int8
  - mteb
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)

For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.

;)