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---
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 2008–09 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: 32.40901449048007
energy_consumed: 0.08337753469362151
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: 0.285
hardware_used: 1 x NVIDIA GeForce RTX 3090
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: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
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: 0.1733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.12000000000000002
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: 0.52
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4954197868237354
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.41905555555555546
name: Dot Mrr@10
- type: dot_map@100
value: 0.43020916049077634
name: Dot Map@100
- type: query_active_dims
value: 62.65999984741211
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9979470545885784
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 110.4578628540039
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9963810411226655
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.26
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.26
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666669
name: Dot Precision@3
- type: dot_precision@5
value: 0.128
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.26
name: Dot Recall@1
- type: dot_recall@3
value: 0.5
name: Dot Recall@3
- type: dot_recall@5
value: 0.64
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4944666703438861
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.41657936507936505
name: Dot Mrr@10
- type: dot_map@100
value: 0.42694690636460897
name: Dot Map@100
- type: query_active_dims
value: 70.22000122070312
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9976993643529027
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 125.49811553955078
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9958882735227197
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.46
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.52
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.272
name: Dot Precision@5
- type: dot_precision@10
value: 0.22399999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.01892455420216294
name: Dot Recall@1
- type: dot_recall@3
value: 0.04889990251243477
name: Dot Recall@3
- type: dot_recall@5
value: 0.0672946061870769
name: Dot Recall@5
- type: dot_recall@10
value: 0.08887922550901164
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.26311322734975795
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3882460317460318
name: Dot Mrr@10
- type: dot_map@100
value: 0.11155968685488596
name: Dot Map@100
- type: query_active_dims
value: 74.22000122070312
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9975683113419598
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 152.51846313476562
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9950029990454503
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.46
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.48
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.31999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.264
name: Dot Precision@5
- type: dot_precision@10
value: 0.23200000000000004
name: Dot Precision@10
- type: dot_recall@1
value: 0.01967175630881205
name: Dot Recall@1
- type: dot_recall@3
value: 0.04958955768484856
name: Dot Recall@3
- type: dot_recall@5
value: 0.06588472678704523
name: Dot Recall@5
- type: dot_recall@10
value: 0.08890872761034473
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2726981353115194
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.42394444444444446
name: Dot Mrr@10
- type: dot_map@100
value: 0.11062543949876841
name: Dot Map@100
- type: query_active_dims
value: 85.58000183105469
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971961207708848
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 182.97967529296875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9940049906528744
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: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
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: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
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: 0.66
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5163228308253419
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.48788888888888876
name: Dot Mrr@10
- type: dot_map@100
value: 0.4744598045833104
name: Dot Map@100
- type: query_active_dims
value: 46.939998626708984
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9984620929615783
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 96.43376159667969
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9968405162965507
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.35
name: Dot Recall@1
- type: dot_recall@3
value: 0.55
name: Dot Recall@3
- type: dot_recall@5
value: 0.65
name: Dot Recall@5
- type: dot_recall@10
value: 0.66
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5211787059288393
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.49649999999999994
name: Dot Mrr@10
- type: dot_map@100
value: 0.48018058391724333
name: Dot Map@100
- type: query_active_dims
value: 55.08000183105469
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981953999793246
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 114.79106140136719
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9962390714435041
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: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5800000000000001
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6533333333333333
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.22888888888888884
name: Dot Precision@3
- type: dot_precision@5
value: 0.17600000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.12266666666666666
name: Dot Precision@10
- type: dot_recall@1
value: 0.212974851400721
name: Dot Recall@1
- type: dot_recall@3
value: 0.37629996750414496
name: Dot Recall@3
- type: dot_recall@5
value: 0.43576486872902565
name: Dot Recall@5
- type: dot_recall@10
value: 0.4962930751696706
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.42495194833294514
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4317301587301587
name: Dot Mrr@10
- type: dot_map@100
value: 0.33874288397632424
name: Dot Map@100
- type: query_active_dims
value: 61.27333323160807
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9979924862973721
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 119.80336252848308
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9960748521548889
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.4594348508634223
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6475039246467816
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7245525902668759
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7984615384615383
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4594348508634223
name: Dot Precision@1
- type: dot_precision@3
value: 0.29682888540031394
name: Dot Precision@3
- type: dot_precision@5
value: 0.23129042386185245
name: Dot Precision@5
- type: dot_precision@10
value: 0.1639026687598116
name: Dot Precision@10
- type: dot_recall@1
value: 0.2607784592309238
name: Dot Recall@1
- type: dot_recall@3
value: 0.41707679910314266
name: Dot Recall@3
- type: dot_recall@5
value: 0.4815762664814047
name: Dot Recall@5
- type: dot_recall@10
value: 0.5608540436995575
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5109231200022869
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5709977698038922
name: Dot Mrr@10
- type: dot_map@100
value: 0.4325529585492243
name: Dot Map@100
- type: query_active_dims
value: 86.22103676429161
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971751183813548
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 137.4154827411358
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9954978218091496
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: 0.36
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.48
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.26
name: Dot Precision@1
- type: dot_precision@3
value: 0.13333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.10800000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.074
name: Dot Precision@10
- type: dot_recall@1
value: 0.12833333333333333
name: Dot Recall@1
- type: dot_recall@3
value: 0.18833333333333332
name: Dot Recall@3
- type: dot_recall@5
value: 0.24666666666666665
name: Dot Recall@5
- type: dot_recall@10
value: 0.30333333333333334
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2564995235608964
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3453015873015872
name: Dot Mrr@10
- type: dot_map@100
value: 0.2062826189577625
name: Dot Map@100
- type: query_active_dims
value: 86.26000213623047
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971738417490259
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0489959716797
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9958046983824231
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: 0.84
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9
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: 0.54
name: Dot Precision@3
- type: dot_precision@5
value: 0.49200000000000005
name: Dot Precision@5
- type: dot_precision@10
value: 0.43400000000000005
name: Dot Precision@10
- type: dot_recall@1
value: 0.08260659025654458
name: Dot Recall@1
- type: dot_recall@3
value: 0.14565005878146683
name: Dot Recall@3
- type: dot_recall@5
value: 0.1854201572717294
name: Dot Recall@5
- type: dot_recall@10
value: 0.2804326420122478
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.534178112145825
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7352222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.41480896090579994
name: Dot Map@100
- type: query_active_dims
value: 55.939998626708984
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981672236869567
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 125.40165710449219
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9958914338148059
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.64
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.84
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: 0.64
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.18799999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.10399999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.6166666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.8166666666666668
name: Dot Recall@3
- type: dot_recall@5
value: 0.8766666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.9433333333333332
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7890721601412974
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7516904761904764
name: Dot Mrr@10
- type: dot_map@100
value: 0.7337194522253345
name: Dot Map@100
- type: query_active_dims
value: 84.86000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9972197103528487
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 142.34327697753906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9953363712411526
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666669
name: Dot Precision@3
- type: dot_precision@5
value: 0.128
name: Dot Precision@5
- type: dot_precision@10
value: 0.09199999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.13585714285714284
name: Dot Recall@1
- type: dot_recall@3
value: 0.28919047619047616
name: Dot Recall@3
- type: dot_recall@5
value: 0.33274603174603173
name: Dot Recall@5
- type: dot_recall@10
value: 0.4233015873015873
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.32546154855128656
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3704365079365079
name: Dot Mrr@10
- type: dot_map@100
value: 0.26492479686181064
name: Dot Map@100
- type: query_active_dims
value: 65.44000244140625
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9978559726609854
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 132.02734375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9956743547686915
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: 0.94
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.96
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: 0.27599999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.156
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: 0.69
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7118024522387334
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8268888888888888
name: Dot Mrr@10
- type: dot_map@100
value: 0.6307915421731377
name: Dot Map@100
- type: query_active_dims
value: 81.9000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9973166895509509
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 142.991943359375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9953151188205434
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.86
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.94
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.86
name: Dot Precision@1
- type: dot_precision@3
value: 0.3666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.24799999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.132
name: Dot Precision@10
- type: dot_recall@1
value: 0.7706666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.8846666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.9359999999999999
name: Dot Recall@5
- type: dot_recall@10
value: 0.9733333333333333
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.909591417031897
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.904190476190476
name: Dot Mrr@10
- type: dot_map@100
value: 0.8825369408369409
name: Dot Map@100
- type: query_active_dims
value: 58.36000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9980879365503456
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 64.70333099365234
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9978801084138113
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.28
name: Dot Precision@3
- type: dot_precision@5
value: 0.236
name: Dot Precision@5
- type: dot_precision@10
value: 0.16799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.086
name: Dot Recall@1
- type: dot_recall@3
value: 0.17566666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.24466666666666664
name: Dot Recall@5
- type: dot_recall@10
value: 0.3446666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3317925768694159
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.533222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.25209583120462153
name: Dot Map@100
- type: query_active_dims
value: 134.1999969482422
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9956031715828503
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 164.88478088378906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9945978382516287
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: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.1
name: Dot Precision@1
- type: dot_precision@3
value: 0.1733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.12400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
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: 0.62
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.44172833183312293
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.32852380952380955
name: Dot Mrr@10
- type: dot_map@100
value: 0.3339302930314127
name: Dot Map@100
- type: query_active_dims
value: 152.0399932861328
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9950186752740275
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 149.56478881835938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9950997710235777
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.14
name: Dot Precision@5
- type: dot_precision@10
value: 0.07800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.425
name: Dot Recall@1
- type: dot_recall@3
value: 0.545
name: Dot Recall@3
- type: dot_recall@5
value: 0.59
name: Dot Recall@5
- type: dot_recall@10
value: 0.67
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5519450641329704
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5232698412698412
name: Dot Mrr@10
- type: dot_map@100
value: 0.5187507919958133
name: Dot Map@100
- type: query_active_dims
value: 138.32000732421875
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9954681866416284
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 166.03871154785156
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9945600317296425
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: 0.8775510204081632
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9591836734693877
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6326530612244898
name: Dot Precision@1
- type: dot_precision@3
value: 0.6054421768707483
name: Dot Precision@3
- type: dot_precision@5
value: 0.5387755102040817
name: Dot Precision@5
- type: dot_precision@10
value: 0.43673469387755104
name: Dot Precision@10
- type: dot_recall@1
value: 0.04531781391284345
name: Dot Recall@1
- type: dot_recall@3
value: 0.12723496235073023
name: Dot Recall@3
- type: dot_recall@5
value: 0.18244054845345592
name: Dot Recall@5
- type: dot_recall@10
value: 0.2837929445033988
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5015858619400403
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.767201166180758
name: Dot Mrr@10
- type: dot_map@100
value: 0.3675943031666616
name: Dot Map@100
- type: query_active_dims
value: 52.67346954345703
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982742458048799
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 147.12759399414062
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9951796214535699
name: Corpus Sparsity Ratio
---
# splade-distilbert-base-uncased trained on Natural Questions
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-nq-updated-sparsity")
# 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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## 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 [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:----------------------|:------------|:-------------|:-----------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------|
| dot_accuracy@1 | 0.26 | 0.36 | 0.38 | 0.26 | 0.62 | 0.64 | 0.24 | 0.74 | 0.86 | 0.42 | 0.1 | 0.46 | 0.6327 |
| dot_accuracy@3 | 0.5 | 0.46 | 0.58 | 0.36 | 0.84 | 0.84 | 0.44 | 0.9 | 0.94 | 0.6 | 0.52 | 0.56 | 0.8776 |
| dot_accuracy@5 | 0.64 | 0.48 | 0.68 | 0.48 | 0.9 | 0.9 | 0.52 | 0.94 | 0.98 | 0.72 | 0.62 | 0.6 | 0.9592 |
| dot_accuracy@10 | 0.74 | 0.58 | 0.7 | 0.58 | 0.92 | 0.98 | 0.66 | 0.96 | 1.0 | 0.78 | 0.8 | 0.68 | 1.0 |
| dot_precision@1 | 0.26 | 0.36 | 0.38 | 0.26 | 0.62 | 0.64 | 0.24 | 0.74 | 0.86 | 0.42 | 0.1 | 0.46 | 0.6327 |
| dot_precision@3 | 0.1667 | 0.32 | 0.1933 | 0.1333 | 0.54 | 0.2867 | 0.1667 | 0.42 | 0.3667 | 0.28 | 0.1733 | 0.2067 | 0.6054 |
| dot_precision@5 | 0.128 | 0.264 | 0.136 | 0.108 | 0.492 | 0.188 | 0.128 | 0.276 | 0.248 | 0.236 | 0.124 | 0.14 | 0.5388 |
| dot_precision@10 | 0.074 | 0.232 | 0.07 | 0.074 | 0.434 | 0.104 | 0.092 | 0.156 | 0.132 | 0.168 | 0.08 | 0.078 | 0.4367 |
| dot_recall@1 | 0.26 | 0.0197 | 0.35 | 0.1283 | 0.0826 | 0.6167 | 0.1359 | 0.37 | 0.7707 | 0.086 | 0.1 | 0.425 | 0.0453 |
| dot_recall@3 | 0.5 | 0.0496 | 0.55 | 0.1883 | 0.1457 | 0.8167 | 0.2892 | 0.63 | 0.8847 | 0.1757 | 0.52 | 0.545 | 0.1272 |
| dot_recall@5 | 0.64 | 0.0659 | 0.65 | 0.2467 | 0.1854 | 0.8767 | 0.3327 | 0.69 | 0.936 | 0.2447 | 0.62 | 0.59 | 0.1824 |
| dot_recall@10 | 0.74 | 0.0889 | 0.66 | 0.3033 | 0.2804 | 0.9433 | 0.4233 | 0.78 | 0.9733 | 0.3447 | 0.8 | 0.67 | 0.2838 |
| **dot_ndcg@10** | **0.4945** | **0.2727** | **0.5212** | **0.2565** | **0.5342** | **0.7891** | **0.3255** | **0.7118** | **0.9096** | **0.3318** | **0.4417** | **0.5519** | **0.5016** |
| dot_mrr@10 | 0.4166 | 0.4239 | 0.4965 | 0.3453 | 0.7352 | 0.7517 | 0.3704 | 0.8269 | 0.9042 | 0.5332 | 0.3285 | 0.5233 | 0.7672 |
| dot_map@100 | 0.4269 | 0.1106 | 0.4802 | 0.2063 | 0.4148 | 0.7337 | 0.2649 | 0.6308 | 0.8825 | 0.2521 | 0.3339 | 0.5188 | 0.3676 |
| query_active_dims | 70.22 | 85.58 | 55.08 | 86.26 | 55.94 | 84.86 | 65.44 | 81.9 | 58.36 | 134.2 | 152.04 | 138.32 | 52.6735 |
| query_sparsity_ratio | 0.9977 | 0.9972 | 0.9982 | 0.9972 | 0.9982 | 0.9972 | 0.9979 | 0.9973 | 0.9981 | 0.9956 | 0.995 | 0.9955 | 0.9983 |
| corpus_active_dims | 125.4981 | 182.9797 | 114.7911 | 128.049 | 125.4017 | 142.3433 | 132.0273 | 142.9919 | 64.7033 | 164.8848 | 149.5648 | 166.0387 | 147.1276 |
| corpus_sparsity_ratio | 0.9959 | 0.994 | 0.9962 | 0.9958 | 0.9959 | 0.9953 | 0.9957 | 0.9953 | 0.9979 | 0.9946 | 0.9951 | 0.9946 | 0.9952 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
]
}
```
| Metric | Value |
|:----------------------|:----------|
| dot_accuracy@1 | 0.32 |
| dot_accuracy@3 | 0.52 |
| dot_accuracy@5 | 0.58 |
| dot_accuracy@10 | 0.6533 |
| dot_precision@1 | 0.32 |
| dot_precision@3 | 0.2289 |
| dot_precision@5 | 0.176 |
| dot_precision@10 | 0.1227 |
| dot_recall@1 | 0.213 |
| dot_recall@3 | 0.3763 |
| dot_recall@5 | 0.4358 |
| dot_recall@10 | 0.4963 |
| **dot_ndcg@10** | **0.425** |
| dot_mrr@10 | 0.4317 |
| dot_map@100 | 0.3387 |
| query_active_dims | 61.2733 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 119.8034 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
"msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.4594 |
| dot_accuracy@3 | 0.6475 |
| dot_accuracy@5 | 0.7246 |
| dot_accuracy@10 | 0.7985 |
| dot_precision@1 | 0.4594 |
| dot_precision@3 | 0.2968 |
| dot_precision@5 | 0.2313 |
| dot_precision@10 | 0.1639 |
| dot_recall@1 | 0.2608 |
| dot_recall@3 | 0.4171 |
| dot_recall@5 | 0.4816 |
| dot_recall@10 | 0.5609 |
| **dot_ndcg@10** | **0.5109** |
| dot_mrr@10 | 0.571 |
| dot_map@100 | 0.4326 |
| query_active_dims | 86.221 |
| query_sparsity_ratio | 0.9972 |
| corpus_active_dims | 137.4155 |
| corpus_sparsity_ratio | 0.9955 |
<!--
## Bias, Risks and Limitations
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>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.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>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]</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>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.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>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.</code> |
| <code>who sings if i can dream about you</code> | <code>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]</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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}
- `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
</details>
### 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 | 4.7655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0485 | 400 | 0.168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0727 | 600 | 0.0672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0970 | 800 | 0.0533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1212 | 1000 | 0.0605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1455 | 1200 | 0.051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1697 | 1400 | 0.0244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 1600 | 0.0306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2 | 1650 | - | 0.0220 | 0.4946 | 0.2654 | 0.4801 | 0.4134 | - | - | - | - | - | - | - | - | - | - |
| 0.2182 | 1800 | 0.0246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2424 | 2000 | 0.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2667 | 2200 | 0.0322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2909 | 2400 | 0.0316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3152 | 2600 | 0.029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3394 | 2800 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3636 | 3000 | 0.0312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3879 | 3200 | 0.0232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4 | 3300 | - | 0.0155 | 0.4420 | 0.2753 | 0.5112 | 0.4095 | - | - | - | - | - | - | - | - | - | - |
| 0.4121 | 3400 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4364 | 3600 | 0.0233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 3800 | 0.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4848 | 4000 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5091 | 4200 | 0.0269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5333 | 4400 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5576 | 4600 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 4800 | 0.0276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 4950 | - | 0.0098 | 0.4901 | 0.2460 | 0.5124 | 0.4162 | - | - | - | - | - | - | - | - | - | - |
| 0.6061 | 5000 | 0.0193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6303 | 5200 | 0.0223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6545 | 5400 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6788 | 5600 | 0.0254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7030 | 5800 | 0.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7273 | 6000 | 0.0271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7515 | 6200 | 0.02 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7758 | 6400 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.8** | **6600** | **0.0125** | **0.0233** | **0.4945** | **0.2727** | **0.5212** | **0.4294** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.8242 | 6800 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8485 | 7000 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8727 | 7200 | 0.0192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8970 | 7400 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9212 | 7600 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9455 | 7800 | 0.0205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9697 | 8000 | 0.0267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9939 | 8200 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 8250 | - | 0.0174 | 0.4954 | 0.2631 | 0.5163 | 0.4250 | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.4945 | 0.2727 | 0.5212 | 0.5109 | 0.2565 | 0.5342 | 0.7891 | 0.3255 | 0.7118 | 0.9096 | 0.3318 | 0.4417 | 0.5519 | 0.5016 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.083 kWh
- **Carbon Emitted**: 0.032 kg of CO2
- **Hours Used**: 0.285 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: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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
```bibtex
@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
```bibtex
@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
```bibtex
@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}
}
```
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