metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - splade
  - generated_from_trainer
  - dataset_size:99000
  - loss:SpladeLoss
  - loss:SparseMultipleNegativesRankingLoss
  - loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
  - text: >-
      The term emergent literacy signals a belief that, in a literate society,
      young children even one and two year olds, are in the process of becoming
      literate”. ... Gray (1956:21) notes: Functional literacy is used for the
      training of adults to 'meet independently the reading and writing demands
      placed on them'.
  - text: >-
      Rey is seemingly confirmed as being The Chosen One per a quote by a
      Lucasfilm production designer who worked on The Rise of Skywalker.
  - text: are union gun safes fireproof?
  - text: >-
      Fruit is an essential part of a healthy diet — and may aid weight loss.
      Most fruits are low in calories while high in nutrients and fiber, which
      can boost your fullness. Keep in mind that it's best to eat fruits whole
      rather than juiced. What's more, simply eating fruit is not the key to
      weight loss.
  - text: >-
      Treatment of suspected bacterial infection is with antibiotics, such as
      amoxicillin/clavulanate or doxycycline, given for 5 to 7 days for acute
      sinusitis and for up to 6 weeks for chronic sinusitis.
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
co2_eq_emissions:
  emissions: 17.39611110898171
  energy_consumed: 0.044754364806411366
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.194
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: splade-distilbert-base-uncased trained on GooAQ
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.52
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.62
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.1733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.124
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.62
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5083136502691767
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4472222222222222
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.46301971681569204
            name: Dot Map@100
          - type: query_active_dims
            value: 94.44000244140625
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9969058383316491
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 430.5700988769531
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9858931230300454
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.52
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.62
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.1733333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.124
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.62
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5083136502691767
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4472222222222222
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.46301971681569204
            name: Dot Map@100
          - type: query_active_dims
            value: 94.44000244140625
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9969058383316491
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 430.5700988769531
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9858931230300454
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.31999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.28800000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.23399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.02092621665706462
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.05736564836154951
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.0742552886400133
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.09748508422098728
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2685885617919309
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4113015873015873
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.10465320687818563
            name: Dot Map@100
          - type: query_active_dims
            value: 117.86000061035156
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9961385230125696
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 741.1876220703125
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9757162826135143
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.31999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.28800000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.23399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.02092621665706462
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.05736564836154951
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.0742552886400133
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.09748508422098728
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2685885617919309
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4113015873015873
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.10465320687818563
            name: Dot Map@100
          - type: query_active_dims
            value: 117.86000061035156
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9961385230125696
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 741.1876220703125
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9757162826135143
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.12000000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07600000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.57
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4981713467273886
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4433809523809524
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4411203459537373
            name: Dot Map@100
          - type: query_active_dims
            value: 103.13999938964844
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9966207981328338
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 552.16943359375
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9819091332942222
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.12000000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07600000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.57
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4981713467273886
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4433809523809524
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4411203459537373
            name: Dot Map@100
          - type: query_active_dims
            value: 103.13999938964844
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9966207981328338
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 552.16943359375
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9819091332942222
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: dot_accuracy@1
            value: 0.30666666666666664
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5333333333333333
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.5933333333333334
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6933333333333334
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.30666666666666664
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.22444444444444445
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.17733333333333334
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12666666666666668
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.21364207221902154
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.3657885494538499
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.421418429546671
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.49916169474032906
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.42502451959616544
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.43396825396825395
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.33626442321587163
            name: Dot Map@100
          - type: query_active_dims
            value: 105.14666748046875
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9965550531590175
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 547.9445287970715
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9820475549178602
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.3655886970172685
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5519937205651492
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6337833594976452
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7169230769230768
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3655886970172685
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2505494505494505
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.20602825745682887
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.14839246467817896
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.19010384074919356
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.32704484579274
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.39456504537988873
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.47685273851096455
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4222423106928735
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.47784013605442177
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.34926499736508426
            name: Dot Map@100
          - type: query_active_dims
            value: 210.38829069916383
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9931069952591848
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 515.9201662327877
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9830967772022544
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.2
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.32
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.44
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.52
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.2
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.10666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.08800000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.06
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.08833333333333332
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.1383333333333333
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.18899999999999997
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.24733333333333335
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.1989737274366133
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.28883333333333333
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.1570276263159355
            name: Dot Map@100
          - type: query_active_dims
            value: 322.94000244140625
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9894194350815344
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 559.904052734375
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9816557220125033
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: dot_accuracy@1
            value: 0.58
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.76
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.84
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.58
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.4666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.44799999999999995
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.38000000000000006
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.057023602844467905
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.1261845127382798
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.19343214279368734
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.2774143036317426
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.476174156780402
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6897777777777777
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3606146481942123
            name: Dot Map@100
          - type: query_active_dims
            value: 127.16000366210938
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9958338246621418
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 502.05657958984375
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.983550993395261
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.56
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18666666666666668
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.132
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08199999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3666666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.5466666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.6266666666666666
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.7666666666666666
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.558322903157265
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5037619047619047
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.49508652136623893
            name: Dot Map@100
          - type: query_active_dims
            value: 341.5
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9888113491907476
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 675.0518188476562
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9778831066493788
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.48
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.6
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.22
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.172
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.1
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.16691269841269843
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.28874603174603175
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.3914920634920635
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.45304761904761903
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.3614112634088202
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4161904761904761
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2967904293410732
            name: Dot Map@100
          - type: query_active_dims
            value: 83.80000305175781
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9972544393207602
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 415.08380126953125
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9864005045124982
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: dot_accuracy@1
            value: 0.64
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.76
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.86
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.64
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.38666666666666655
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.26399999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.144
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.58
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.66
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.72
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6440206432819057
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7276111111111111
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5728876536286595
            name: Dot Map@100
          - type: query_active_dims
            value: 181.72000122070312
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9940462616728687
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 580.4143676757812
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9809837373803886
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.56
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.64
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.72
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18666666666666668
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.136
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07600000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3066666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.5106666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.6106666666666667
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.6906666666666667
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5024131501177981
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.45416666666666666
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.45581081139277496
            name: Dot Map@100
          - type: query_active_dims
            value: 68.72000122070312
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9977485092320063
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 79.2294921875
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9974041841233373
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.68
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.25333333333333335
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.20799999999999996
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.146
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.06866666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.15766666666666668
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.21466666666666662
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.30066666666666664
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2836948178391744
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4724047619047619
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.20698613017114634
            name: Dot Map@100
          - type: query_active_dims
            value: 210.05999755859375
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9931177512103206
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 610.8638916015625
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9799861119323255
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: dot_accuracy@1
            value: 0.08
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.26
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.28
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.34
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.08
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.08666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.056000000000000015
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.034
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.08
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.26
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.28
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.34
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.21332166570570366
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.17257936507936505
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.18781587309924735
            name: Dot Map@100
          - type: query_active_dims
            value: 664
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9782452001834742
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 613.4024658203125
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.979902939983608
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: dot_accuracy@1
            value: 0.36
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.48
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.54
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.36
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.16666666666666669
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.124
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07200000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.335
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.44
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.525
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.61
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4739942979888677
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4374126984126983
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4391076754874009
            name: Dot Map@100
          - type: query_active_dims
            value: 371
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9878448332350436
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 683.730712890625
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9775987578503826
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: dot_accuracy@1
            value: 0.6326530612244898
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7959183673469388
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9591836734693877
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6326530612244898
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5238095238095237
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.5183673469387755
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.4551020408163265
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.0411540784919523
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.10595346912642524
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.17416609501278957
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.2958052604088571
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5017498545023089
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.747278911564626
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.35952432710179216
            name: Dot Map@100
          - type: query_active_dims
            value: 45.408164978027344
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9985122808145591
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 522.2584838867188
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9828891132990394
            name: Corpus Sparsity Ratio

splade-distilbert-base-uncased trained on GooAQ

This is a SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased on the gooaq dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-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: distilbert/distilbert-base-uncased
Maximum Sequence Length: 256 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- gooaq
Language: en
License: apache-2.0

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): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

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("tomaarsen/splade-distilbert-base-uncased-gooaq-peft-r512")
# Run inference
queries = [
    "how many days for doxycycline to work on sinus infection?",
]
documents = [
    'Treatment of suspected bacterial infection is with antibiotics, such as amoxicillin/clavulanate or doxycycline, given for 5 to 7 days for acute sinusitis and for up to 6 weeks for chronic sinusitis.',
    'Most engagements typically have a cocktail dress code, calling for dresses at, or slightly above, knee-length and high heels. If your party states a different dress code, however, such as semi-formal or dressy-casual, you may need to dress up or down accordingly.',
    'The average service life of a gas furnace is about 15 years, but the actual life span of an individual unit can vary greatly. There are a number of contributing factors that determine the age a furnace reaches: The quality of the equipment.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[95.1919, 19.8228, 34.4896]])

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
dot_accuracy@1	0.32	0.3	0.3	0.2	0.58	0.38	0.32	0.64	0.32	0.32	0.08	0.36	0.6327
dot_accuracy@3	0.52	0.54	0.54	0.32	0.76	0.56	0.48	0.76	0.56	0.6	0.26	0.48	0.7959
dot_accuracy@5	0.62	0.56	0.6	0.44	0.84	0.66	0.56	0.86	0.64	0.68	0.28	0.54	0.9592
dot_accuracy@10	0.7	0.62	0.76	0.52	0.92	0.8	0.6	0.92	0.72	0.8	0.34	0.62	1.0
dot_precision@1	0.32	0.3	0.3	0.2	0.58	0.38	0.32	0.64	0.32	0.32	0.08	0.36	0.6327
dot_precision@3	0.1733	0.32	0.18	0.1067	0.4667	0.1867	0.22	0.3867	0.1867	0.2533	0.0867	0.1667	0.5238
dot_precision@5	0.124	0.288	0.12	0.088	0.448	0.132	0.172	0.264	0.136	0.208	0.056	0.124	0.5184
dot_precision@10	0.07	0.234	0.076	0.06	0.38	0.082	0.1	0.144	0.076	0.146	0.034	0.072	0.4551
dot_recall@1	0.32	0.0209	0.3	0.0883	0.057	0.3667	0.1669	0.32	0.3067	0.0687	0.08	0.335	0.0412
dot_recall@3	0.52	0.0574	0.52	0.1383	0.1262	0.5467	0.2887	0.58	0.5107	0.1577	0.26	0.44	0.106
dot_recall@5	0.62	0.0743	0.57	0.189	0.1934	0.6267	0.3915	0.66	0.6107	0.2147	0.28	0.525	0.1742
dot_recall@10	0.7	0.0975	0.7	0.2473	0.2774	0.7667	0.453	0.72	0.6907	0.3007	0.34	0.61	0.2958
dot_ndcg@10	0.5083	0.2686	0.4982	0.199	0.4762	0.5583	0.3614	0.644	0.5024	0.2837	0.2133	0.474	0.5017
dot_mrr@10	0.4472	0.4113	0.4434	0.2888	0.6898	0.5038	0.4162	0.7276	0.4542	0.4724	0.1726	0.4374	0.7473
dot_map@100	0.463	0.1047	0.4411	0.157	0.3606	0.4951	0.2968	0.5729	0.4558	0.207	0.1878	0.4391	0.3595
query_active_dims	94.44	117.86	103.14	322.94	127.16	341.5	83.8	181.72	68.72	210.06	664.0	371.0	45.4082
query_sparsity_ratio	0.9969	0.9961	0.9966	0.9894	0.9958	0.9888	0.9973	0.994	0.9977	0.9931	0.9782	0.9878	0.9985
corpus_active_dims	430.5701	741.1876	552.1694	559.9041	502.0566	675.0518	415.0838	580.4144	79.2295	610.8639	613.4025	683.7307	522.2585
corpus_sparsity_ratio	0.9859	0.9757	0.9819	0.9817	0.9836	0.9779	0.9864	0.981	0.9974	0.98	0.9799	0.9776	0.9829

Sparse Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ]
}

Metric	Value
dot_accuracy@1	0.3067
dot_accuracy@3	0.5333
dot_accuracy@5	0.5933
dot_accuracy@10	0.6933
dot_precision@1	0.3067
dot_precision@3	0.2244
dot_precision@5	0.1773
dot_precision@10	0.1267
dot_recall@1	0.2136
dot_recall@3	0.3658
dot_recall@5	0.4214
dot_recall@10	0.4992
dot_ndcg@10	0.425
dot_mrr@10	0.434
dot_map@100	0.3363
query_active_dims	105.1467
query_sparsity_ratio	0.9966
corpus_active_dims	547.9445
corpus_sparsity_ratio	0.982

Sparse Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "climatefever",
        "dbpedia",
        "fever",
        "fiqa2018",
        "hotpotqa",
        "msmarco",
        "nfcorpus",
        "nq",
        "quoraretrieval",
        "scidocs",
        "arguana",
        "scifact",
        "touche2020"
    ]
}

Metric	Value
dot_accuracy@1	0.3656
dot_accuracy@3	0.552
dot_accuracy@5	0.6338
dot_accuracy@10	0.7169
dot_precision@1	0.3656
dot_precision@3	0.2505
dot_precision@5	0.206
dot_precision@10	0.1484
dot_recall@1	0.1901
dot_recall@3	0.327
dot_recall@5	0.3946
dot_recall@10	0.4769
dot_ndcg@10	0.4222
dot_mrr@10	0.4778
dot_map@100	0.3493
query_active_dims	210.3883
query_sparsity_ratio	0.9931
corpus_active_dims	515.9202
corpus_sparsity_ratio	0.9831

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: 11.79 tokens
max: 24 tokens

min: 14 tokens
mean: 60.02 tokens
max: 153 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.79 tokens max: 24 tokens	min: 14 tokens mean: 60.02 tokens max: 153 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')",
    "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: 11.93 tokens
max: 25 tokens

min: 14 tokens
mean: 60.84 tokens
max: 127 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.93 tokens max: 25 tokens	min: 14 tokens mean: 60.84 tokens max: 127 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')",
    "document_regularizer_weight": 3e-05,
    "query_regularizer_weight": 5e-05
}

Training Hyperparameters

Non-Default Hyperparameters

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

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
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_dot_ndcg@10	NanoNFCorpus_dot_ndcg@10	NanoNQ_dot_ndcg@10	NanoBEIR_mean_dot_ndcg@10	NanoClimateFEVER_dot_ndcg@10	NanoDBPedia_dot_ndcg@10	NanoFEVER_dot_ndcg@10	NanoFiQA2018_dot_ndcg@10	NanoHotpotQA_dot_ndcg@10	NanoQuoraRetrieval_dot_ndcg@10	NanoSCIDOCS_dot_ndcg@10	NanoArguAna_dot_ndcg@10	NanoSciFact_dot_ndcg@10	NanoTouche2020_dot_ndcg@10
0.0323	100	28.28	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	200	0.4861	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0970	300	0.2953	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1293	400	0.2281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1616	500	0.2219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	600	0.1677	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1972	610	-	0.1561	0.5037	0.2269	0.4528	0.3945	-	-	-	-	-	-	-	-	-	-
0.2262	700	0.1841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2586	800	0.1578	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2909	900	0.1403	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3232	1000	0.1738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3555	1100	0.1453	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3878	1200	0.138	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3943	1220	-	0.1417	0.5052	0.2372	0.4614	0.4013	-	-	-	-	-	-	-	-	-	-
0.4202	1300	0.1276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4525	1400	0.1376	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4848	1500	0.1364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5171	1600	0.1174	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5495	1700	0.1107	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	1800	0.1219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5915	1830	-	0.0817	0.5298	0.2767	0.4649	0.4238	-	-	-	-	-	-	-	-	-	-
0.6141	1900	0.1012	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	2000	0.1279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6787	2100	0.1057	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7111	2200	0.1276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7434	2300	0.1154	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7757	2400	0.0964	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7886	2440	-	0.0926	0.5132	0.2587	0.4951	0.4223	-	-	-	-	-	-	-	-	-	-
0.8080	2500	0.0963	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	2600	0.1056	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8727	2700	0.0969	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	2800	0.0988	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9373	2900	0.0927	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9696	3000	0.0856	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9858	3050	-	0.0783	0.5083	0.2686	0.4982	0.425	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.5083	0.2686	0.4982	0.4222	0.1990	0.4762	0.5583	0.3614	0.6440	0.5024	0.2837	0.2133	0.4740	0.5017

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.045 kWh
Carbon Emitted: 0.017 kg of CO2
Hours Used: 0.193 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 4.2.0.dev0
Transformers: 4.52.4
PyTorch: 2.7.1+cu126
Accelerate: 1.5.1
Datasets: 2.21.0
Tokenizers: 0.21.1

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{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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}
}