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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: 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](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("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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
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### 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 | 0.28 | 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 [<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 | 0.34 |
| 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 [<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 | 0.4424 |
| 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 |
<!--
## 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}
- `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
</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 | 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](https://github.com/mlco2/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
```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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