Add new SparseEncoder model

6153c7b verified 12 days ago

79.2 kB

language:
  - en
license: mit
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - generated_from_trainer
  - dataset_size:99000
  - loss:SpladeLoss
  - loss:SparseMultipleNegativesRankingLoss
  - loss:FlopsLoss
base_model: jhu-clsp/ettin-encoder-17m
widget:
  - text: >-
      In addition to proof of identity, such as passport, birth certificate or
      adoption certificate, and proof of your address, or a letter from your
      university or college confirming your place, and bring it along with your
      other identity and address verification documents to your local branch.
  - text: >-
      An abiotic factor is a non-living part of an ecosystem that shapes its
      environment. In a terrestrial ecosystem, examples might include
      temperature, light, and water. In a marine ecosystem, abiotic factors
      would include salinity and ocean currents. Abiotic and biotic factors work
      together to create a unique ecosystem.
  - text: how many 16 oz bottles of water is a gallon?
  - text: >-
      A vet assistant and vet tech both provide general animal care and assist
      with the treatment of sick and injured animals. Depending on the setting,
      a vet assistant may have more administrative responsibilities, while a vet
      tech may have more clinical responsibilities.
  - text: >-
      The two main stars are Alpha Centauri A and Alpha Centauri B, which form a
      binary pair. They are an average of 4.3 light-years from Earth. The third
      star is Proxima Centauri. It is about 4.22 light-years from Earth and is
      the closest star other than the sun.
datasets:
  - sentence-transformers/gooaq
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
  - query_active_dims
  - query_sparsity_ratio
  - corpus_active_dims
  - corpus_sparsity_ratio
  - avg_flops
model-index:
  - name: splade-ettin-encoder-17m trained on GooAQ
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO 256
          type: NanoMSMARCO_256
        metrics:
          - type: dot_accuracy@1
            value: 0.2
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.34
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.44
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.58
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.2
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.11333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08800000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.057999999999999996
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.2
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.34
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.44
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.58
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.36748190085250804
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.30210317460317454
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3224995402831516
            name: Dot Map@100
          - type: query_active_dims
            value: 252
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949968233799238
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780179
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 104.76126861572266
            name: Avg Flops
          - type: dot_accuracy@1
            value: 0.2
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.34
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.42
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.2
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.11333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.084
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.06000000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.2
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.34
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.42
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.6
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.37143757052695625
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.302047619047619
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3209443084622113
            name: Dot Map@100
          - type: query_active_dims
            value: 252.10000610351562
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949948378711977
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780179
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 104.66670989990234
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus 256
          type: NanoNFCorpus_256
        metrics:
          - type: dot_accuracy@1
            value: 0.24
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.32
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.52
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.24
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18666666666666668
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14400000000000002
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.007416693841083107
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.01728407183582485
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.03449216239510752
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.04713697363333782
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.16244101089890484
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3040714285714286
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.04634431882731895
            name: Dot Map@100
          - type: query_active_dims
            value: 255.6199951171875
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949249524476416
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780177
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 101.3379135131836
            name: Avg Flops
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.34
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.38
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.52
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.19333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.006928888963034326
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.018744633333151052
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.030603858996218224
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.04577448609594998
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.15706743654657598
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.29643650793650794
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.046568619637846746
            name: Dot Map@100
          - type: query_active_dims
            value: 255.6199951171875
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949249524476416
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780177
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 101.26585388183594
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ 256
          type: NanoNQ_256
        metrics:
          - type: dot_accuracy@1
            value: 0.1
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.32
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.42
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.52
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.1
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.10666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.084
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.05800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.09
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.29
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.38
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.51
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2892021613044471
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.2285793650793651
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.22850363025154985
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780176
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 114.13877868652344
            name: Avg Flops
          - type: dot_accuracy@1
            value: 0.1
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.28
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.54
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.1
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.09333333333333334
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08000000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.05800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.09
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.25
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.36
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.52
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.29254729188799355
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.2322698412698413
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.23046052440382125
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780176
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 114.126953125
            name: Avg Flops
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean 256
          type: NanoBEIR_mean_256
        metrics:
          - type: dot_accuracy@1
            value: 0.18000000000000002
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.32666666666666666
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.42
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.54
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.18000000000000002
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.13555555555555554
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.11733333333333335
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08666666666666667
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.0991388979470277
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.21576135727860826
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.2848307207983692
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.3790456578777793
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.27304169101861997
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.27825132275132275
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.1991158297873401
            name: Dot Map@100
          - type: query_active_dims
            value: 254.53999837239584
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949463945685276
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780177
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 105.0486068725586
            name: Avg Flops
          - type: dot_accuracy@1
            value: 0.2730612244897959
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.47042386185243334
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.556734693877551
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6553532182103611
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.2730612244897959
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.20167451596023023
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.16646153846153847
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12314913657770801
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.12970938218659442
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.26896230737703214
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.3370186758223455
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.4261361667823346
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.345879757383274
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3924966360170442
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2731986853504099
            name: Dot Map@100
          - type: query_active_dims
            value: 249.2619424828763
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9950511844329163
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 254.11144146927373
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949549030839169
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 109.02598571777344
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER 256
          type: NanoClimateFEVER_256
        metrics:
          - type: dot_accuracy@1
            value: 0.2
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.38
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.42
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.48
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.2
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.12666666666666665
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.092
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.066
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.085
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.17833333333333332
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.2
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.26
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.215122111282969
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.29888888888888887
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.1741526011558945
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780177
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 129.39976501464844
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia 256
          type: NanoDBPedia_256
        metrics:
          - type: dot_accuracy@1
            value: 0.4
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.66
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.74
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.4
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.34800000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.324
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.043815756303188326
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.08533510688544765
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.13559343564169452
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.20974054299022687
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3792423494760472
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5518571428571429
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2586818991199033
            name: Dot Map@100
          - type: query_active_dims
            value: 253.9600067138672
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949579096506935
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780179
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 108.96640014648438
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER 256
          type: NanoFEVER_256
        metrics:
          - type: dot_accuracy@1
            value: 0.34
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.78
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.15600000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.30666666666666664
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6566666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7366666666666666
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.8766666666666667
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5965342963229342
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5312936507936507
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4997229758545548
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780179
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 120.7999038696289
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFiQA2018 256
          type: NanoFiQA2018_256
        metrics:
          - type: dot_accuracy@1
            value: 0.18
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.3
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.44
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.18
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.14
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.124
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.0821904761904762
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.17335714285714285
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.24057936507936506
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.2906349206349207
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2236717246585837
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.25316666666666665
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.18633896011707374
            name: Dot Map@100
          - type: query_active_dims
            value: 240.22000122070312
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9952307020088011
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780176
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 116.01726531982422
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoHotpotQA 256
          type: NanoHotpotQA_256
        metrics:
          - type: dot_accuracy@1
            value: 0.58
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.78
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.58
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.32
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.21599999999999994
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12599999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.29
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.48
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.54
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.63
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5600780523888909
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6586666666666666
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.49395504821298336
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780176
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 114.6017074584961
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoQuoraRetrieval 256
          type: NanoQuoraRetrieval_256
        metrics:
          - type: dot_accuracy@1
            value: 0.16
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.38
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.4
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.46
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.16
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.12666666666666665
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.04800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.16
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.354
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.374
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.444
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.31465958594721516
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.2770238095238095
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2826009380599248
            name: Dot Map@100
          - type: query_active_dims
            value: 229
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9954534625158831
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 234.99563598632812
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9953344259056082
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 119.71977996826172
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSCIDOCS 256
          type: NanoSCIDOCS_256
        metrics:
          - type: dot_accuracy@1
            value: 0.34
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.58
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.72
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.22
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.13799999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.07266666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.13766666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.18566666666666667
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.2836666666666667
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2672568147859396
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4424126984126984
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.19041553956973106
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780177
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 123.3700942993164
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna 256
          type: NanoArguAna_256
        metrics:
          - type: dot_accuracy@1
            value: 0.04
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.32
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.42
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.48
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.04
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.10666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.084
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.04800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.04
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.32
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.42
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.48
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2567073862056186
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.18450000000000003
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.18984106283244218
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780177
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 150.7188262939453
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact 256
          type: NanoSciFact_256
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.44
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.64
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.1533333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.078
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.275
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.42
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.61
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.68
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4730952763241869
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.41774603174603164
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.40792257335430643
            name: Dot Map@100
          - type: query_active_dims
            value: 256
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9949174078780177
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 256
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949174078780179
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 129.66539001464844
            name: Avg Flops
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoTouche2020 256
          type: NanoTouche2020_256
        metrics:
          - type: dot_accuracy@1
            value: 0.4897959183673469
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7755102040816326
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8775510204081632
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9795918367346939
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.4897959183673469
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.4217687074829932
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.4
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.34693877551020413
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.03395351363569543
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.08240644615901016
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.12813279263988042
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.21928688511591962
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.38901694962865085
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6561467444120505
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2699778587746352
            name: Dot Map@100
          - type: query_active_dims
            value: 216.85714721679688
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9956945452029702
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 255.92410278320312
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9949189147319091
            name: Corpus Sparsity Ratio
          - type: avg_flops
            value: 86.41167449951172
            name: Avg Flops

splade-ettin-encoder-17m trained on GooAQ

This is a SPLADE Sparse Encoder model finetuned from jhu-clsp/ettin-encoder-17m on the gooaq dataset using the sentence-transformers library. It maps sentences & paragraphs to a 50368-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: SPLADE Sparse Encoder
Base model: jhu-clsp/ettin-encoder-17m
Maximum Sequence Length: 256 tokens
Output Dimensionality: 50368 dimensions
Similarity Function: Dot Product
Supported Modality: Text
Training Dataset:
- gooaq
Language: en
License: mit

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

Full Model Architecture

SparseEncoder(
  (0): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ModernBertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 50368})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("capemox/splade-ettin-encoder-17m-gooaq")
# Run inference
sentences = [
    'how close is the closest star?',
    'The two main stars are Alpha Centauri A and Alpha Centauri B, which form a binary pair. They are an average of 4.3 light-years from Earth. The third star is Proxima Centauri. It is about 4.22 light-years from Earth and is the closest star other than the sun.',
    'One gallon can of paint will cover up to 400 square feet, which is enough to cover a small room like a bathroom. Two gallon cans of paint cover up to 800 square feet, which is enough to cover an average size room. This is the most common amount needed, especially when considering second coat coverage.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 50368]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[144.9772, 158.3885, 140.6785],
#         [158.3885, 496.0056, 344.2902],
#         [140.6785, 344.2902, 528.3322]])

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO_256, NanoNFCorpus_256, NanoNQ_256, NanoClimateFEVER_256, NanoDBPedia_256, NanoFEVER_256, NanoFiQA2018_256, NanoHotpotQA_256, NanoMSMARCO_256, NanoNFCorpus_256, NanoNQ_256, NanoQuoraRetrieval_256, NanoSCIDOCS_256, NanoArguAna_256, NanoSciFact_256 and NanoTouche2020_256
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
```
{
    "max_active_dims": 256
}
```

Metric	NanoMSMARCO_256	NanoNFCorpus_256	NanoNQ_256	NanoClimateFEVER_256	NanoDBPedia_256	NanoFEVER_256	NanoFiQA2018_256	NanoHotpotQA_256	NanoQuoraRetrieval_256	NanoSCIDOCS_256	NanoArguAna_256	NanoSciFact_256	NanoTouche2020_256
dot_accuracy@1	0.2	0.22	0.1	0.2	0.4	0.34	0.18	0.58	0.16	0.34	0.04	0.3	0.4898
dot_accuracy@3	0.34	0.34	0.28	0.38	0.66	0.7	0.3	0.7	0.38	0.5	0.32	0.44	0.7755
dot_accuracy@5	0.42	0.38	0.4	0.42	0.74	0.78	0.4	0.78	0.4	0.58	0.42	0.64	0.8776
dot_accuracy@10	0.6	0.52	0.54	0.48	0.84	0.92	0.44	0.84	0.46	0.72	0.48	0.7	0.9796
dot_precision@1	0.2	0.22	0.1	0.2	0.4	0.34	0.18	0.58	0.16	0.34	0.04	0.3	0.4898
dot_precision@3	0.1133	0.1933	0.0933	0.1267	0.3733	0.2333	0.14	0.32	0.1267	0.22	0.1067	0.1533	0.4218
dot_precision@5	0.084	0.18	0.08	0.092	0.348	0.156	0.124	0.216	0.08	0.18	0.084	0.14	0.4
dot_precision@10	0.06	0.14	0.058	0.066	0.324	0.094	0.074	0.126	0.048	0.138	0.048	0.078	0.3469
dot_recall@1	0.2	0.0069	0.09	0.085	0.0438	0.3067	0.0822	0.29	0.16	0.0727	0.04	0.275	0.034
dot_recall@3	0.34	0.0187	0.25	0.1783	0.0853	0.6567	0.1734	0.48	0.354	0.1377	0.32	0.42	0.0824
dot_recall@5	0.42	0.0306	0.36	0.2	0.1356	0.7367	0.2406	0.54	0.374	0.1857	0.42	0.61	0.1281
dot_recall@10	0.6	0.0458	0.52	0.26	0.2097	0.8767	0.2906	0.63	0.444	0.2837	0.48	0.68	0.2193
dot_ndcg@10	0.3714	0.1571	0.2925	0.2151	0.3792	0.5965	0.2237	0.5601	0.3147	0.2673	0.2567	0.4731	0.389
dot_mrr@10	0.302	0.2964	0.2323	0.2989	0.5519	0.5313	0.2532	0.6587	0.277	0.4424	0.1845	0.4177	0.6561
dot_map@100	0.3209	0.0466	0.2305	0.1742	0.2587	0.4997	0.1863	0.494	0.2826	0.1904	0.1898	0.4079	0.27
query_active_dims	252.1	255.62	256.0	256.0	253.96	256.0	240.22	256.0	229.0	256.0	256.0	256.0	216.8571
query_sparsity_ratio	0.995	0.9949	0.9949	0.9949	0.995	0.9949	0.9952	0.9949	0.9955	0.9949	0.9949	0.9949	0.9957
corpus_active_dims	256.0	256.0	256.0	256.0	256.0	256.0	256.0	256.0	234.9956	256.0	256.0	256.0	255.9241
corpus_sparsity_ratio	0.9949	0.9949	0.9949	0.9949	0.9949	0.9949	0.9949	0.9949	0.9953	0.9949	0.9949	0.9949	0.9949
avg_flops	104.6667	101.2659	114.127	129.3998	108.9664	120.7999	116.0173	114.6017	119.7198	123.3701	150.7188	129.6654	86.4117

Sparse Nano BEIR

Dataset: NanoBEIR_mean_256

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ],
    "dataset_id": "sentence-transformers/NanoBEIR-en",
    "max_active_dims": 256
}

Metric	Value
dot_accuracy@1	0.18
dot_accuracy@3	0.3267
dot_accuracy@5	0.42
dot_accuracy@10	0.54
dot_precision@1	0.18
dot_precision@3	0.1356
dot_precision@5	0.1173
dot_precision@10	0.0867
dot_recall@1	0.0991
dot_recall@3	0.2158
dot_recall@5	0.2848
dot_recall@10	0.379
dot_ndcg@10	0.273
dot_mrr@10	0.2783
dot_map@100	0.1991
query_active_dims	254.54
query_sparsity_ratio	0.9949
corpus_active_dims	256.0
corpus_sparsity_ratio	0.9949
avg_flops	105.0486

Sparse Nano BEIR

Dataset: NanoBEIR_mean_256

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "climatefever",
        "dbpedia",
        "fever",
        "fiqa2018",
        "hotpotqa",
        "msmarco",
        "nfcorpus",
        "nq",
        "quoraretrieval",
        "scidocs",
        "arguana",
        "scifact",
        "touche2020"
    ],
    "dataset_id": "sentence-transformers/NanoBEIR-en",
    "max_active_dims": 256
}

Metric	Value
dot_accuracy@1	0.2731
dot_accuracy@3	0.4704
dot_accuracy@5	0.5567
dot_accuracy@10	0.6554
dot_precision@1	0.2731
dot_precision@3	0.2017
dot_precision@5	0.1665
dot_precision@10	0.1231
dot_recall@1	0.1297
dot_recall@3	0.269
dot_recall@5	0.337
dot_recall@10	0.4261
dot_ndcg@10	0.3459
dot_mrr@10	0.3925
dot_map@100	0.2732
query_active_dims	249.2619
query_sparsity_ratio	0.9951
corpus_active_dims	254.1114
corpus_sparsity_ratio	0.995
avg_flops	109.026

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 99,000 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.93 tokens
max: 21 tokens

min: 15 tokens
mean: 58.27 tokens
max: 137 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.93 tokens max: 21 tokens	min: 15 tokens mean: 58.27 tokens max: 137 tokens

Samples:

question	answer
`what are the 5 characteristics of a star?`	`Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness.`
`are copic markers alcohol ink?`	`Copic Ink is alcohol-based and flammable. Keep away from direct sunlight and extreme temperatures.`
`what is the difference between appellate term and appellate division?`	`Appellate terms An appellate term is an intermediate appellate court that hears appeals from the inferior courts within their designated counties or judicial districts, and are intended to ease the workload on the Appellate Division and provide a less expensive forum closer to the people.`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)",
    "document_regularizer_weight": 3e-05,
    "query_regularizer_weight": 5e-05
}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 1,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 12.05 tokens
max: 23 tokens

min: 15 tokens
mean: 58.98 tokens
max: 186 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 12.05 tokens max: 23 tokens	min: 15 tokens mean: 58.98 tokens max: 186 tokens

Samples:

question	answer
`should you take ibuprofen with high blood pressure?`	`In general, people with high blood pressure should use acetaminophen or possibly aspirin for over-the-counter pain relief. Unless your health care provider has said it's OK, you should not use ibuprofen, ketoprofen, or naproxen sodium. If aspirin or acetaminophen doesn't help with your pain, call your doctor.`
`how old do you have to be to work in sc?`	`The general minimum age of employment for South Carolina youth is 14, although the state allows younger children who are performers to work in show business. If their families are agricultural workers, children younger than age 14 may also participate in farm labor.`
`how to write a topic proposal for a research paper?`	`['Write down the main topic of your paper. ... ', 'Write two or three short sentences under the main topic that explain why you chose that topic. ... ', 'Write a thesis sentence that states the angle and purpose of your research paper. ... ', 'List the items you will cover in the body of the paper that support your thesis statement.']`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)",
    "document_regularizer_weight": 3e-05,
    "query_regularizer_weight": 5e-05
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
num_train_epochs: 1
learning_rate: 2e-05
bf16: True
per_device_eval_batch_size: 32
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 32
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
per_device_eval_batch_size: 32
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_256_dot_ndcg@10	NanoNFCorpus_256_dot_ndcg@10	NanoNQ_256_dot_ndcg@10	NanoBEIR_mean_256_dot_ndcg@10	NanoClimateFEVER_256_dot_ndcg@10	NanoDBPedia_256_dot_ndcg@10	NanoFEVER_256_dot_ndcg@10	NanoFiQA2018_256_dot_ndcg@10	NanoHotpotQA_256_dot_ndcg@10	NanoQuoraRetrieval_256_dot_ndcg@10	NanoSCIDOCS_256_dot_ndcg@10	NanoArguAna_256_dot_ndcg@10	NanoSciFact_256_dot_ndcg@10	NanoTouche2020_256_dot_ndcg@10
0.0323	100	954.7106	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	200	17.8241	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0970	300	4.5136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1293	400	2.5427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1616	500	1.4733	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	600	1.0940	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1972	610	-	0.8522	0.1440	0.0453	0.0771	0.0888	-	-	-	-	-	-	-	-	-	-
0.2262	700	0.7541	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2586	800	0.7425	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2909	900	0.5966	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3232	1000	0.5606	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3555	1100	0.5440	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3878	1200	0.4032	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3943	1220	-	0.4165	0.2494	0.0613	0.2065	0.1724	-	-	-	-	-	-	-	-	-	-
0.4202	1300	0.3995	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4525	1400	0.2976	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4848	1500	0.2971	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5171	1600	0.2716	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5495	1700	0.2577	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	1800	0.2370	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5915	1830	-	0.2104	0.3406	0.1173	0.2414	0.2331	-	-	-	-	-	-	-	-	-	-
0.6141	1900	0.2360	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	2000	0.2238	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6787	2100	0.2237	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7111	2200	0.2162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7434	2300	0.2044	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7757	2400	0.2202	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7886	2440	-	0.1736	0.3932	0.1545	0.2717	0.2731	-	-	-	-	-	-	-	-	-	-
0.8080	2500	0.1672	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	2600	0.2122	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8727	2700	0.1704	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	2800	0.1870	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9373	2900	0.1671	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9696	3000	0.1386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9858	3050	-	0.17	0.3714	0.1571	0.2925	0.2737	-	-	-	-	-	-	-	-	-	-
1.0	3094	-	0.1706	0.3675	0.1624	0.2892	0.2730	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.3714	0.1571	0.2925	0.3459	0.2151	0.3792	0.5965	0.2237	0.5601	0.3147	0.2673	0.2567	0.4731	0.3890

The bold row denotes the saved checkpoint.

Training Time

Training: 7.7 minutes
Evaluation: 2.0 minutes
Total: 9.7 minutes

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.4.1
Transformers: 5.5.4
PyTorch: 2.11.0+cu130
Accelerate: 1.13.0
Datasets: 4.8.4
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

SpladeLoss

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}

SparseMultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

FlopsLoss

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
}