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: >-
      Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower
      of the former World Trade Center in New York City. The introduction
      features Ben Stiller and Stephen Dorff mistaking Fred Durst for the valet
      and giving him the keys to their Bentley Azure. Also making a cameo is
      break dancer Mr. Wiggles. The rest of the video has several cuts to Durst
      and his bandmates hanging out of the Bentley as they drive about
      Manhattan. The song Ben Stiller is playing at the beginning is "My
      Generation" from the same album. The video also features scenes of Fred
      Durst with five girls dancing in a room. The video was filmed around the
      same time as the film Zoolander, which explains Stiller and Dorff's
      appearance. Fred Durst has a small cameo in that film.
  - text: >-
      Maze Runner: The Death Cure On April 22, 2017, the studio delayed the
      release date once again, to February 9, 2018, in order to allow more time
      for post-production; months later, on August 25, the studio moved the
      release forward two weeks.[17] The film will premiere on January 26, 2018
      in 3D, IMAX and IMAX 3D.[18][19]
  - text: who played the dj in the movie the warriors
  - text: >-
      Lionel Messi Born and raised in central Argentina, Messi was diagnosed
      with a growth hormone deficiency as a child. At age 13, he relocated to
      Spain to join Barcelona, who agreed to pay for his medical treatment.
      After a fast progression through Barcelona's youth academy, Messi made his
      competitive debut aged 17 in October 2004. Despite being injury-prone
      during his early career, he established himself as an integral player for
      the club within the next three years, finishing 2007 as a finalist for
      both the Ballon d'Or and FIFA World Player of the Year award, a feat he
      repeated the following year. His first uninterrupted campaign came in the
      2008â€“09 season, during which he helped Barcelona achieve the first
      treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and
      FIFA World Player of the Year award by record voting margins.
  - text: >-
      Send In the Clowns "Send In the Clowns" is a song written by Stephen
      Sondheim for the 1973 musical A Little Night Music, an adaptation of
      Ingmar Bergman's film Smiles of a Summer Night. It is a ballad from Act
      Two, in which the character Desirée reflects on the ironies and
      disappointments of her life. Among other things, she looks back on an
      affair years earlier with the lawyer Fredrik, who was deeply in love with
      her but whose marriage proposals she had rejected. Meeting him after so
      long, she realizes she is in love with him and finally ready to marry him,
      but now it is he who rejects her: he is in an unconsummated marriage with
      a much younger woman. Desirée proposes marriage to rescue him from this
      situation, but he declines, citing his dedication to his bride. Reacting
      to his rejection, Desirée sings this song. The song is later reprised as a
      coda after Fredrik's young wife runs away with his son, and Fredrik is
      finally free to accept Desirée's offer.[1]
datasets:
  - sentence-transformers/natural-questions
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: 2.229070129979357
  energy_consumed: 0.0397771218255029
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: AMD Ryzen 9 6900HX with Radeon Graphics
  ram_total_size: 30.6114501953125
  hours_used: 0.322
  hardware_used: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
model-index:
  - name: splade-distilbert-base-uncased trained on Natural Questions
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.28
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.28
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.16666666666666663
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.132
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.28
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.5
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.66
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.74
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.49577037509991184
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4185238095238094
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4294303031277172
            name: Dot Map@100
          - type: query_active_dims
            value: 52.560001373291016
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9982779633912164
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 106.13404846191406
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9965227033463759
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.28
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.74
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.28
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.16666666666666663
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.132
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07400000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.28
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.5
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.66
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.74
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.49577037509991184
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4185238095238094
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4294303031277172
            name: Dot Map@100
          - type: query_active_dims
            value: 52.560001373291016
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9982779633912164
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 106.13404846191406
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9965227033463759
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.46
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.52
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.31999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.292
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.236
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.0242331024704017
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.053060546044216006
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.07273890139350063
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.09593681264940912
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2784960942139155
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4393888888888889
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.11744499575471842
            name: Dot Map@100
          - type: query_active_dims
            value: 62.5
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9979522967040168
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 126.24652862548828
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9958637530756345
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.46
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.52
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.31999999999999995
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.292
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.236
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.0242331024704017
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.053060546044216006
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.07273890139350063
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.09593681264940912
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2784960942139155
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4393888888888889
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.11744499575471842
            name: Dot Map@100
          - type: query_active_dims
            value: 62.5
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9979522967040168
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 126.24652862548828
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9958637530756345
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.36
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.58
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.36
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.34
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.56
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.64
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.72
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5341909779287488
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4836666666666667
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.47986132889672267
            name: Dot Map@100
          - type: query_active_dims
            value: 45.63999938964844
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9985046851651384
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 104.37854766845703
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9965802192625498
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.36
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.58
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.36
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.34
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.56
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.64
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.72
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5341909779287488
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4836666666666667
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.47986132889672267
            name: Dot Map@100
          - type: query_active_dims
            value: 45.63999938964844
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9985046851651384
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 104.37854766845703
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9965802192625498
            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: 0.34
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.5133333333333333
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6133333333333334
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7066666666666667
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.34
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.22888888888888884
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18800000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.13
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.21474436749013393
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.3710201820147387
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.45757963379783356
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5186456042164697
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.436152482414192
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4471931216931217
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3422455425930528
            name: Dot Map@100
          - type: query_active_dims
            value: 53.56666692097982
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9982449817534571
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 110.01350571216182
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9963955997080087
            name: Corpus Sparsity Ratio
          - type: dot_accuracy@1
            value: 0.44241758241758244
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6319937205651491
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6982103610675039
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7922762951334378
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44241758241758244
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.28906331763474624
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.22349764521193094
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.1596828885400314
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.24762543099163964
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.4029567497606036
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.47356029516066417
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5593700517107145
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4973635297458972
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5535014203483591
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.41852441580546024
            name: Dot Map@100
          - type: query_active_dims
            value: 70.98613267025705
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9976742633945921
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 109.8632523295962
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.996400522497556
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.26
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.42
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.48
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.62
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.26
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.15333333333333332
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.10800000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.12499999999999999
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.20166666666666663
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.24
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.32166666666666666
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.26602915512735714
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3617857142857142
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2080211358638896
            name: Dot Map@100
          - type: query_active_dims
            value: 89.9000015258789
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.997054583529065
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 107.88761901855469
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9964652506710387
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: dot_accuracy@1
            value: 0.62
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.82
            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: 0.62
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5266666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.4640000000000001
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.43199999999999994
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.07037508003753436
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.1332476350020503
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.17834335811098734
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.3023813591870427
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5284701506717093
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7312222222222222
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.39806509167251636
            name: Dot Map@100
          - type: query_active_dims
            value: 48.779998779296875
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9984018085715453
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 112.2790756225586
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9963213722684438
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.54
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.72
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.82
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.54
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.24
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.16799999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09599999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.5266666666666666
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6866666666666665
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7666666666666666
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.8766666666666667
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.696250000763901
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6531666666666667
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6383785103785103
            name: Dot Map@100
          - type: query_active_dims
            value: 82.72000122070312
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9972898236937061
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 121.61109161376953
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9960156250699899
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.42
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.44
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.54
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.16666666666666663
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.11200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07200000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.12335714285714286
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.29043650793650794
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.3084365079365079
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.38043650793650796
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2842623648908474
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3184126984126985
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.24328509002216755
            name: Dot Map@100
          - type: query_active_dims
            value: 52.91999816894531
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9982661687252163
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 104.23889923095703
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9965847945996016
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: dot_accuracy@1
            value: 0.74
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.98
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.74
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.41999999999999993
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.284
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.16399999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.37
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.63
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.71
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.82
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.7250698177423742
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8267222222222221
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.63152024851727
            name: Dot Map@100
          - type: query_active_dims
            value: 69.81999969482422
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9977124697039897
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 134.8498992919922
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9955818786681085
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: dot_accuracy@1
            value: 0.8
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.92
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.94
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.98
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.8
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.35999999999999993
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.23599999999999993
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12999999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7240000000000001
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8613333333333333
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9059999999999999
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9633333333333333
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8826618022083887
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8673809523809524
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.8507124011593389
            name: Dot Map@100
          - type: query_active_dims
            value: 49.619998931884766
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9983742874342479
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 54.11692428588867
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.998226953532341
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: dot_accuracy@1
            value: 0.44
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.62
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2733333333333334
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.22
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.16399999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.09166666666666666
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.16866666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.22566666666666665
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.3356666666666666
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.326903742587538
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5351904761904761
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.24963439583895705
            name: Dot Map@100
          - type: query_active_dims
            value: 86.16000366210938
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9971771180243068
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 115.15058898925781
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9962272921502766
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: dot_accuracy@1
            value: 0.1
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.52
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.78
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.1
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.17333333333333337
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.132
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.1
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.52
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.66
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.78
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.44166045098306866
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3330793650793652
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.33989533146591966
            name: Dot Map@100
          - type: query_active_dims
            value: 119.26000213623047
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9960926544087468
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 117.85887145996094
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9961385600072092
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: dot_accuracy@1
            value: 0.44
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.54
            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: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14400000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07800000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.405
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.525
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.63
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.68
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5538495558550187
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5216904761904761
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5176733402786808
            name: Dot Map@100
          - type: query_active_dims
            value: 111.37999725341797
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9963508290002812
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 109.96676635742188
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9963971310413007
            name: Corpus Sparsity Ratio
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: dot_accuracy@1
            value: 0.5714285714285714
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.7959183673469388
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8367346938775511
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9795918367346939
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.5714285714285714
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5578231292517006
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.4734693877551021
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.3938775510204081
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.03883194419290252
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.10835972457173955
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.15843173631430588
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.25572265913299697
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.4521113986238841
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7052883057985098
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.33689523249457515
            name: Dot Map@100
          - type: query_active_dims
            value: 51.163265228271484
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9983237250105409
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 122.40800476074219
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9959895156031472
            name: Corpus Sparsity Ratio

splade-distilbert-base-uncased trained on Natural Questions

This is a SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased on the natural-questions 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:
- natural-questions
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}) with MLMTransformer model: 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("arthurbresnu/splade-distilbert-base-uncased-nq")
# Run inference
sentences = [
    'is send in the clowns from a musical',
    'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
    'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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	0.28	0.38	0.36	0.26	0.62	0.54	0.22	0.74	0.8	0.44	0.1	0.44	0.5714
dot_accuracy@3	0.5	0.46	0.58	0.42	0.82	0.72	0.42	0.9	0.92	0.62	0.52	0.54	0.7959
dot_accuracy@5	0.66	0.52	0.66	0.48	0.86	0.82	0.44	0.9	0.94	0.66	0.66	0.64	0.8367
dot_accuracy@10	0.74	0.62	0.76	0.62	0.92	0.92	0.54	0.98	0.98	0.76	0.78	0.7	0.9796
dot_precision@1	0.28	0.38	0.36	0.26	0.62	0.54	0.22	0.74	0.8	0.44	0.1	0.44	0.5714
dot_precision@3	0.1667	0.32	0.2	0.1533	0.5267	0.24	0.1667	0.42	0.36	0.2733	0.1733	0.2	0.5578
dot_precision@5	0.132	0.292	0.14	0.108	0.464	0.168	0.112	0.284	0.236	0.22	0.132	0.144	0.4735
dot_precision@10	0.074	0.236	0.08	0.078	0.432	0.096	0.072	0.164	0.13	0.164	0.078	0.078	0.3939
dot_recall@1	0.28	0.0242	0.34	0.125	0.0704	0.5267	0.1234	0.37	0.724	0.0917	0.1	0.405	0.0388
dot_recall@3	0.5	0.0531	0.56	0.2017	0.1332	0.6867	0.2904	0.63	0.8613	0.1687	0.52	0.525	0.1084
dot_recall@5	0.66	0.0727	0.64	0.24	0.1783	0.7667	0.3084	0.71	0.906	0.2257	0.66	0.63	0.1584
dot_recall@10	0.74	0.0959	0.72	0.3217	0.3024	0.8767	0.3804	0.82	0.9633	0.3357	0.78	0.68	0.2557
dot_ndcg@10	0.4958	0.2785	0.5342	0.266	0.5285	0.6963	0.2843	0.7251	0.8827	0.3269	0.4417	0.5538	0.4521
dot_mrr@10	0.4185	0.4394	0.4837	0.3618	0.7312	0.6532	0.3184	0.8267	0.8674	0.5352	0.3331	0.5217	0.7053
dot_map@100	0.4294	0.1174	0.4799	0.208	0.3981	0.6384	0.2433	0.6315	0.8507	0.2496	0.3399	0.5177	0.3369
query_active_dims	52.56	62.5	45.64	89.9	48.78	82.72	52.92	69.82	49.62	86.16	119.26	111.38	51.1633
query_sparsity_ratio	0.9983	0.998	0.9985	0.9971	0.9984	0.9973	0.9983	0.9977	0.9984	0.9972	0.9961	0.9964	0.9983
corpus_active_dims	106.134	126.2465	104.3785	107.8876	112.2791	121.6111	104.2389	134.8499	54.1169	115.1506	117.8589	109.9668	122.408
corpus_sparsity_ratio	0.9965	0.9959	0.9966	0.9965	0.9963	0.996	0.9966	0.9956	0.9982	0.9962	0.9961	0.9964	0.996

Sparse Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with SparseNanoBEIREvaluator with these parameters:

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

Metric	Value
dot_accuracy@1	0.34
dot_accuracy@3	0.5133
dot_accuracy@5	0.6133
dot_accuracy@10	0.7067
dot_precision@1	0.34
dot_precision@3	0.2289
dot_precision@5	0.188
dot_precision@10	0.13
dot_recall@1	0.2147
dot_recall@3	0.371
dot_recall@5	0.4576
dot_recall@10	0.5186
dot_ndcg@10	0.4362
dot_mrr@10	0.4472
dot_map@100	0.3422
query_active_dims	53.5667
query_sparsity_ratio	0.9982
corpus_active_dims	110.0135
corpus_sparsity_ratio	0.9964

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	0.4424
dot_accuracy@3	0.632
dot_accuracy@5	0.6982
dot_accuracy@10	0.7923
dot_precision@1	0.4424
dot_precision@3	0.2891
dot_precision@5	0.2235
dot_precision@10	0.1597
dot_recall@1	0.2476
dot_recall@3	0.403
dot_recall@5	0.4736
dot_recall@10	0.5594
dot_ndcg@10	0.4974
dot_mrr@10	0.5535
dot_map@100	0.4185
query_active_dims	70.9861
query_sparsity_ratio	0.9977
corpus_active_dims	109.8633
corpus_sparsity_ratio	0.9964

Training Details

Training Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 99,000 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 10 tokens
mean: 11.71 tokens
max: 26 tokens

min: 4 tokens
mean: 131.81 tokens
max: 450 tokens

	query	answer
type	string	string
details	min: 10 tokens mean: 11.71 tokens max: 26 tokens	min: 4 tokens mean: 131.81 tokens max: 450 tokens

Samples:

query	answer
`who played the father in papa don't preach`	`Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.`
`where was the location of the battle of hastings`	`Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.`
`how many puppies can a dog give birth to`	`Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
    "lambda_corpus": 3e-05,
    "lambda_query": 5e-05
}

Evaluation Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 1,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 10 tokens
mean: 11.69 tokens
max: 23 tokens

min: 15 tokens
mean: 134.01 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 10 tokens mean: 11.69 tokens max: 23 tokens	min: 15 tokens mean: 134.01 tokens max: 512 tokens

Samples:

query	answer
`where is the tiber river located in italy`	Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.
`what kind of car does jay gatsby drive`	`Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.`
`who sings if i can dream about you`	`I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
    "lambda_corpus": 3e-05,
    "lambda_query": 5e-05
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
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: 12
per_device_eval_batch_size: 12
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}
tp_size: 0
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
dispatch_batches: None
split_batches: 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

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
0.0242	200	6.3626	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0485	400	0.0957	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0727	600	0.0927	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0970	800	0.0588	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1212	1000	0.0408	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1455	1200	0.0515	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1697	1400	0.0517	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	1600	0.0213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2	1650	-	0.0520	0.4929	0.2618	0.4572	0.4040	-	-	-	-	-	-	-	-	-	-
0.2182	1800	0.019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2424	2000	0.0333	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2667	2200	0.0282	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2909	2400	0.0418	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3152	2600	0.0386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3394	2800	0.0289	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3636	3000	0.0242	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3879	3200	0.0335	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4	3300	-	0.0360	0.4715	0.2808	0.5340	0.4288	-	-	-	-	-	-	-	-	-	-
0.4121	3400	0.0264	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4364	3600	0.0331	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4606	3800	0.0339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4848	4000	0.0225	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5091	4200	0.0164	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5333	4400	0.0247	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5576	4600	0.0213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	4800	0.0187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6	4950	-	0.0217	0.4901	0.2930	0.5072	0.4301	-	-	-	-	-	-	-	-	-	-
0.6061	5000	0.0153	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6303	5200	0.0186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6545	5400	0.0096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6788	5600	0.0115	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7030	5800	0.0255	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7273	6000	0.0219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7515	6200	0.033	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7758	6400	0.0199	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8	6600	0.0175	0.0224	0.4700	0.2743	0.5136	0.4193	-	-	-	-	-	-	-	-	-	-
0.8242	6800	0.0236	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8485	7000	0.0145	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8727	7200	0.0372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8970	7400	0.0107	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9212	7600	0.0131	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9455	7800	0.0127	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9697	8000	0.0207	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9939	8200	0.0217	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0	8250	-	0.0219	0.4958	0.2785	0.5342	0.4362	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.4958	0.2785	0.5342	0.4974	0.2660	0.5285	0.6963	0.2843	0.7251	0.8827	0.3269	0.4417	0.5538	0.4521

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.040 kWh
Carbon Emitted: 0.002 kg of CO2
Hours Used: 0.322 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
CPU Model: AMD Ryzen 9 6900HX with Radeon Graphics
RAM Size: 30.61 GB

Framework Versions

Python: 3.12.9
Sentence Transformers: 4.2.0.dev0
Transformers: 4.50.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 3.5.0
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}
    }