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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:90000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
- text: what is chess
- text: what is a hickman for?
- text: 'Steps. 1 1. Gather your materials. Here''s what you need to build two regulations-size
horseshoe pits that will face each other (if you only want to build one pit, halve
the materials): Two 6-foot-long treated wood 2x6s (38mm x 140mm), cut in half.
2 2. Decide where you''re going to put your pit(s).'
- text: who played at california jam
- text: "To the Citizens of St. Bernard We chose as our motto a simple but profound\
\ declaration: â\x80\x9CWelcome to your office.â\x80\x9D Those words remind us\
\ that we are no more than the caretakers of the office of Clerk of Court for\
\ the Parish of St. Bernard."
datasets:
- sentence-transformers/msmarco
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: 20.864216098626564
energy_consumed: 0.05652200756224921
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: AMD EPYC 7R13 Processor
ram_total_size: 248.0
hours_used: 0.179
hardware_used: 1 x NVIDIA H100 80GB HBM3
model-index:
- name: splade-distilbert-base-uncased trained on MS MARCO triplets
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.22
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.66
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6223979987260191
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5599444444444444
name: Dot Mrr@10
- type: dot_map@100
value: 0.5701364200315813
name: Dot Map@100
- type: query_active_dims
value: 25.260000228881836
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9991724002283965
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 89.06385040283203
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9970819785596348
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.2
name: Dot Precision@3
- type: dot_precision@5
value: 0.14800000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.74
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6241753240638171
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5571349206349206
name: Dot Mrr@10
- type: dot_map@100
value: 0.5639260419913368
name: Dot Map@100
- type: query_active_dims
value: 20.5
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9993283533189175
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 81.87666320800781
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9973174541901578
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.3666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.332
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.023282599806398227
name: Dot Recall@1
- type: dot_recall@3
value: 0.07519782108259539
name: Dot Recall@3
- type: dot_recall@5
value: 0.09254782270412643
name: Dot Recall@5
- type: dot_recall@10
value: 0.12120665375595915
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.32050254842735026
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4703888888888889
name: Dot Mrr@10
- type: dot_map@100
value: 0.13331879084552362
name: Dot Map@100
- type: query_active_dims
value: 17.639999389648438
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9994220562417387
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 165.31358337402344
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9945837892872674
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: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.34
name: Dot Precision@3
- type: dot_precision@5
value: 0.32799999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.02081925669789383
name: Dot Recall@1
- type: dot_recall@3
value: 0.07064967781220355
name: Dot Recall@3
- type: dot_recall@5
value: 0.09055307754310991
name: Dot Recall@5
- type: dot_recall@10
value: 0.14403725441385476
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3196380424829849
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4414444444444445
name: Dot Mrr@10
- type: dot_map@100
value: 0.13569627052041464
name: Dot Map@100
- type: query_active_dims
value: 18.299999237060547
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9994004324999325
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 156.04843139648438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9948873458031424
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.48
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.48
name: Dot Precision@1
- type: dot_precision@3
value: 0.22666666666666668
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.46
name: Dot Recall@1
- type: dot_recall@3
value: 0.65
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6136977374010735
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.585079365079365
name: Dot Mrr@10
- type: dot_map@100
value: 0.5730967720685111
name: Dot Map@100
- type: query_active_dims
value: 24.299999237060547
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9992038529835181
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 103.79106140136719
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9965994672235972
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.48
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.48
name: Dot Precision@1
- type: dot_precision@3
value: 0.22666666666666668
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.47
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.7
name: Dot Recall@5
- type: dot_recall@10
value: 0.73
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6150809765850531
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5864999999999999
name: Dot Mrr@10
- type: dot_map@100
value: 0.5841443871983568
name: Dot Map@100
- type: query_active_dims
value: 22.200000762939453
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9992726557642704
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 103.72532653808594
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9966016209115365
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: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6200000000000001
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7466666666666667
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.27111111111111114
name: Dot Precision@3
- type: dot_precision@5
value: 0.2066666666666667
name: Dot Precision@5
- type: dot_precision@10
value: 0.14466666666666664
name: Dot Precision@10
- type: dot_recall@1
value: 0.30776086660213275
name: Dot Recall@1
- type: dot_recall@3
value: 0.4617326070275318
name: Dot Recall@3
- type: dot_recall@5
value: 0.49751594090137546
name: Dot Recall@5
- type: dot_recall@10
value: 0.5604022179186531
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5188660948514809
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5384708994708994
name: Dot Mrr@10
- type: dot_map@100
value: 0.42551732764853867
name: Dot Map@100
- type: query_active_dims
value: 22.399999618530273
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9992661031512178
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 112.03345893951784
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9963294194699063
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.5241130298273154
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6799372056514913
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7415070643642072
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8169230769230769
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5241130298273154
name: Dot Precision@1
- type: dot_precision@3
value: 0.3215384615384615
name: Dot Precision@3
- type: dot_precision@5
value: 0.2547566718995291
name: Dot Precision@5
- type: dot_precision@10
value: 0.17874411302982732
name: Dot Precision@10
- type: dot_recall@1
value: 0.30856930592565196
name: Dot Recall@1
- type: dot_recall@3
value: 0.4441119539769697
name: Dot Recall@3
- type: dot_recall@5
value: 0.5092929381431597
name: Dot Recall@5
- type: dot_recall@10
value: 0.5878231569460904
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5577320367017354
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6173593605940545
name: Dot Mrr@10
- type: dot_map@100
value: 0.48084758588880655
name: Dot Map@100
- type: query_active_dims
value: 38.07395960627058
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9987525732387698
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 105.05153383516846
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9965581700466821
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.56
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.64
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.14666666666666664
name: Dot Precision@3
- type: dot_precision@5
value: 0.12
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.11833333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.21166666666666664
name: Dot Recall@3
- type: dot_recall@5
value: 0.26233333333333336
name: Dot Recall@5
- type: dot_recall@10
value: 0.29966666666666664
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.25712162589613363
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.35861111111111116
name: Dot Mrr@10
- type: dot_map@100
value: 0.20460406106488077
name: Dot Map@100
- type: query_active_dims
value: 51.47999954223633
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9983133477641624
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 134.2989959716797
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9955999280528248
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.7
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.82
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.88
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.92
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7
name: Dot Precision@1
- type: dot_precision@3
value: 0.6133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.58
name: Dot Precision@5
- type: dot_precision@10
value: 0.52
name: Dot Precision@10
- type: dot_recall@1
value: 0.05306233623739282
name: Dot Recall@1
- type: dot_recall@3
value: 0.16391544714816778
name: Dot Recall@3
- type: dot_recall@5
value: 0.23662708539883293
name: Dot Recall@5
- type: dot_recall@10
value: 0.3543605851621492
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6137764330075132
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.771888888888889
name: Dot Mrr@10
- type: dot_map@100
value: 0.4604772150699302
name: Dot Map@100
- type: query_active_dims
value: 20.520000457763672
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999327698038865
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 111.07841491699219
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9963607098185902
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.74
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.92
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: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.19599999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.10399999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7066666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.8666666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.8933333333333333
name: Dot Recall@5
- type: dot_recall@10
value: 0.9433333333333332
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8368149756149829
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8170000000000001
name: Dot Mrr@10
- type: dot_map@100
value: 0.7993556466302367
name: Dot Map@100
- type: query_active_dims
value: 44.84000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9985308957423306
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 154.09767150878906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9949512590423697
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.17600000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.11199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.1770793650793651
name: Dot Recall@1
- type: dot_recall@3
value: 0.3069920634920635
name: Dot Recall@3
- type: dot_recall@5
value: 0.3936825396825397
name: Dot Recall@5
- type: dot_recall@10
value: 0.48673809523809525
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3901649596140352
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4438809523809523
name: Dot Mrr@10
- type: dot_map@100
value: 0.32670074884185174
name: Dot Map@100
- type: query_active_dims
value: 18.920000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9993801192557403
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 75.49989318847656
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9975263779179453
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.88
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: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.88
name: Dot Precision@1
- type: dot_precision@3
value: 0.4866666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.324
name: Dot Precision@5
- type: dot_precision@10
value: 0.16999999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.73
name: Dot Recall@3
- type: dot_recall@5
value: 0.81
name: Dot Recall@5
- type: dot_recall@10
value: 0.85
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8077539978128343
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9041666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.74474463747389
name: Dot Map@100
- type: query_active_dims
value: 43.880001068115234
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9985623484349612
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 120.78840637207031
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9960425789144856
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.84
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: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.84
name: Dot Precision@1
- type: dot_precision@3
value: 0.32666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.22
name: Dot Precision@5
- type: dot_precision@10
value: 0.11999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7873333333333333
name: Dot Recall@1
- type: dot_recall@3
value: 0.8540000000000001
name: Dot Recall@3
- type: dot_recall@5
value: 0.898
name: Dot Recall@5
- type: dot_recall@10
value: 0.9313333333333332
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8841170132005264
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8805555555555554
name: Dot Mrr@10
- type: dot_map@100
value: 0.8625873756339163
name: Dot Map@100
- type: query_active_dims
value: 18.760000228881836
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9993853613711787
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 20.381887435913086
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9993322230707059
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: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.21999999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.152
name: Dot Precision@10
- type: dot_recall@1
value: 0.086
name: Dot Recall@1
- type: dot_recall@3
value: 0.17666666666666664
name: Dot Recall@3
- type: dot_recall@5
value: 0.22466666666666665
name: Dot Recall@5
- type: dot_recall@10
value: 0.3116666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.31329169156104253
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5258253968253969
name: Dot Mrr@10
- type: dot_map@100
value: 0.24015404586272074
name: Dot Map@100
- type: query_active_dims
value: 38.599998474121094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9987353384943936
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 120.28081512451172
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9960592092548158
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: 0.34
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.46
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.1
name: Dot Precision@1
- type: dot_precision@3
value: 0.11333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.09200000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.06600000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.1
name: Dot Recall@1
- type: dot_recall@3
value: 0.34
name: Dot Recall@3
- type: dot_recall@5
value: 0.46
name: Dot Recall@5
- type: dot_recall@10
value: 0.66
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.35624387960476495
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2620238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.27408886435627244
name: Dot Map@100
- type: query_active_dims
value: 121.0199966430664
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9960349912639058
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 107.16836547851562
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9964888157565521
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.6
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.72
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: 0.6
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.16399999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.565
name: Dot Recall@1
- type: dot_recall@3
value: 0.68
name: Dot Recall@3
- type: dot_recall@5
value: 0.71
name: Dot Recall@5
- type: dot_recall@10
value: 0.77
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6798182226611048
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6625
name: Dot Mrr@10
- type: dot_map@100
value: 0.6532896014216637
name: Dot Map@100
- type: query_active_dims
value: 57.41999816894531
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981187340879056
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 158.03323364257812
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9948223172255234
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.673469387755102
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9591836734693877
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9795918367346939
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.673469387755102
name: Dot Precision@1
- type: dot_precision@3
value: 0.6666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.5918367346938777
name: Dot Precision@5
- type: dot_precision@10
value: 0.4836734693877551
name: Dot Precision@10
- type: dot_recall@1
value: 0.04710668568549065
name: Dot Recall@1
- type: dot_recall@3
value: 0.13289821324817133
name: Dot Recall@3
- type: dot_recall@5
value: 0.20161215990326012
name: Dot Recall@5
- type: dot_recall@10
value: 0.3205651054850781
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5525193350177682
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.814139941690962
name: Dot Mrr@10
- type: dot_map@100
value: 0.40124972048901353
name: Dot Map@100
- type: query_active_dims
value: 18.12244987487793
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9994062495945587
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 84.7328109741211
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9972238774990461
name: Corpus Sparsity Ratio
---
# splade-distilbert-base-uncased trained on MS MARCO triplets
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 [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) 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:**
- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco)
- **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-msmarco-mrl")
# Run inference
queries = [
"meaning of the name bernard",
]
documents = [
'English Meaning: The name Bernard is an English baby name. In English the meaning of the name Bernard is: Strong as a bear. See also Bjorn. American Meaning: The name Bernard is an American baby name. In American the meaning of the name Bernard is: Strong as a bear.',
'To the Citizens of St. Bernard We chose as our motto a simple but profound declaration: â\x80\x9cWelcome to your office.â\x80\x9d Those words remind us that we are no more than the caretakers of the office of Clerk of Court for the Parish of St. Bernard.',
"Get Your Prior Years Tax Information from the IRS. IRS Tax Tip 2012-18, January 27, 2012. Sometimes taxpayers need a copy of an old tax return, but can't find or don't have their own records. There are three easy and convenient options for getting tax return transcripts and tax account transcripts from the IRS: on the web, by phone or by mail.",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[18.6221, 10.0646, 0.0000]])
```
<!--
### 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>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## 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 | 0.44 | 0.36 | 0.48 | 0.24 | 0.7 | 0.74 | 0.34 | 0.88 | 0.84 | 0.42 | 0.1 | 0.6 | 0.6735 |
| dot_accuracy@3 | 0.6 | 0.46 | 0.68 | 0.42 | 0.82 | 0.9 | 0.5 | 0.92 | 0.92 | 0.6 | 0.34 | 0.72 | 0.9592 |
| dot_accuracy@5 | 0.74 | 0.54 | 0.74 | 0.56 | 0.88 | 0.92 | 0.58 | 0.94 | 0.94 | 0.64 | 0.46 | 0.72 | 0.9796 |
| dot_accuracy@10 | 0.84 | 0.68 | 0.76 | 0.64 | 0.92 | 0.98 | 0.68 | 0.96 | 0.96 | 0.76 | 0.66 | 0.78 | 1.0 |
| dot_precision@1 | 0.44 | 0.36 | 0.48 | 0.24 | 0.7 | 0.74 | 0.34 | 0.88 | 0.84 | 0.42 | 0.1 | 0.6 | 0.6735 |
| dot_precision@3 | 0.2 | 0.34 | 0.2267 | 0.1467 | 0.6133 | 0.3133 | 0.2133 | 0.4867 | 0.3267 | 0.2867 | 0.1133 | 0.2467 | 0.6667 |
| dot_precision@5 | 0.148 | 0.328 | 0.152 | 0.12 | 0.58 | 0.196 | 0.176 | 0.324 | 0.22 | 0.22 | 0.092 | 0.164 | 0.5918 |
| dot_precision@10 | 0.084 | 0.27 | 0.08 | 0.074 | 0.52 | 0.104 | 0.112 | 0.17 | 0.12 | 0.152 | 0.066 | 0.088 | 0.4837 |
| dot_recall@1 | 0.44 | 0.0208 | 0.47 | 0.1183 | 0.0531 | 0.7067 | 0.1771 | 0.44 | 0.7873 | 0.086 | 0.1 | 0.565 | 0.0471 |
| dot_recall@3 | 0.6 | 0.0706 | 0.64 | 0.2117 | 0.1639 | 0.8667 | 0.307 | 0.73 | 0.854 | 0.1767 | 0.34 | 0.68 | 0.1329 |
| dot_recall@5 | 0.74 | 0.0906 | 0.7 | 0.2623 | 0.2366 | 0.8933 | 0.3937 | 0.81 | 0.898 | 0.2247 | 0.46 | 0.71 | 0.2016 |
| dot_recall@10 | 0.84 | 0.144 | 0.73 | 0.2997 | 0.3544 | 0.9433 | 0.4867 | 0.85 | 0.9313 | 0.3117 | 0.66 | 0.77 | 0.3206 |
| **dot_ndcg@10** | **0.6242** | **0.3196** | **0.6151** | **0.2571** | **0.6138** | **0.8368** | **0.3902** | **0.8078** | **0.8841** | **0.3133** | **0.3562** | **0.6798** | **0.5525** |
| dot_mrr@10 | 0.5571 | 0.4414 | 0.5865 | 0.3586 | 0.7719 | 0.817 | 0.4439 | 0.9042 | 0.8806 | 0.5258 | 0.262 | 0.6625 | 0.8141 |
| dot_map@100 | 0.5639 | 0.1357 | 0.5841 | 0.2046 | 0.4605 | 0.7994 | 0.3267 | 0.7447 | 0.8626 | 0.2402 | 0.2741 | 0.6533 | 0.4012 |
| query_active_dims | 20.5 | 18.3 | 22.2 | 51.48 | 20.52 | 44.84 | 18.92 | 43.88 | 18.76 | 38.6 | 121.02 | 57.42 | 18.1224 |
| query_sparsity_ratio | 0.9993 | 0.9994 | 0.9993 | 0.9983 | 0.9993 | 0.9985 | 0.9994 | 0.9986 | 0.9994 | 0.9987 | 0.996 | 0.9981 | 0.9994 |
| corpus_active_dims | 81.8767 | 156.0484 | 103.7253 | 134.299 | 111.0784 | 154.0977 | 75.4999 | 120.7884 | 20.3819 | 120.2808 | 107.1684 | 158.0332 | 84.7328 |
| corpus_sparsity_ratio | 0.9973 | 0.9949 | 0.9966 | 0.9956 | 0.9964 | 0.995 | 0.9975 | 0.996 | 0.9993 | 0.9961 | 0.9965 | 0.9948 | 0.9972 |
#### 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 | 0.44 |
| dot_accuracy@3 | 0.62 |
| dot_accuracy@5 | 0.66 |
| dot_accuracy@10 | 0.7467 |
| dot_precision@1 | 0.44 |
| dot_precision@3 | 0.2711 |
| dot_precision@5 | 0.2067 |
| dot_precision@10 | 0.1447 |
| dot_recall@1 | 0.3078 |
| dot_recall@3 | 0.4617 |
| dot_recall@5 | 0.4975 |
| dot_recall@10 | 0.5604 |
| **dot_ndcg@10** | **0.5189** |
| dot_mrr@10 | 0.5385 |
| dot_map@100 | 0.4255 |
| query_active_dims | 22.4 |
| query_sparsity_ratio | 0.9993 |
| corpus_active_dims | 112.0335 |
| corpus_sparsity_ratio | 0.9963 |
#### 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 | 0.5241 |
| dot_accuracy@3 | 0.6799 |
| dot_accuracy@5 | 0.7415 |
| dot_accuracy@10 | 0.8169 |
| dot_precision@1 | 0.5241 |
| dot_precision@3 | 0.3215 |
| dot_precision@5 | 0.2548 |
| dot_precision@10 | 0.1787 |
| dot_recall@1 | 0.3086 |
| dot_recall@3 | 0.4441 |
| dot_recall@5 | 0.5093 |
| dot_recall@10 | 0.5878 |
| **dot_ndcg@10** | **0.5577** |
| dot_mrr@10 | 0.6174 |
| dot_map@100 | 0.4808 |
| query_active_dims | 38.074 |
| query_sparsity_ratio | 0.9988 |
| corpus_active_dims | 105.0515 |
| corpus_sparsity_ratio | 0.9966 |
<!--
## Bias, Risks and Limitations
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### Recommendations
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## Training Details
### Training Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 90,000 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.02 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 79.88 tokens</li><li>max: 203 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 77.8 tokens</li><li>max: 201 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>yosemite temperature in september</code> | <code>Here are the average temp in Yosemite Valley (where CV is located) by month: www.nps.gov/yose/planyourvisit/climate.htm. Also beginning of September is usually still quite warm. Nights can have a bit of a chill, but nothing a couple of blankets can't handle.</code> | <code>Guide to Switzerland weather in September. The average maximum daytime temperature in Switzerland in September is a comfortable 18°C (64°F). The average night-time temperature is usually a cool 9°C (48°F). There are usually 6 hours of bright sunshine each day, which represents 45% of the 13 hours of daylight.</code> |
| <code>what is genus</code> | <code>Intermediate minor rankings are not shown. A genus (/ËdÊiËnÉs/, pl. genera) is a taxonomic rank used in the biological classification of living and fossil organisms in biology. In the hierarchy of biological classification, genus comes above species and below family. In binomial nomenclature, the genus name forms the first part of the binomial species name for each species within the genus. The composition of a genus is determined by a taxonomist.</code> | <code>The genus is the first part of a scientific name. Note that the genus is always capitalised. An example: Lemur catta is the scientific name of the Ringtailed lemur and Lemur ⦠is the genus.Another example: Sphyrna zygaena is the scientific name of one species of Hammerhead shark and Sphyrna is the genus. name used all around the world to classify a living organism. It is composed of a genus and species name. A sceintific name can also be considered for non living things, the ⦠se are usually called scientific jargon, or very simply 'proper names for the things around you'. 4 people found this useful.</code> |
| <code>what did johannes kepler discover about the motion of the planets?</code> | <code>Johannes Kepler devised his three laws of motion from his observations of planets that are fundamental to our understanding of orbital motions.</code> | <code>Little Street, Johannes Vermeer, c. 1658. New stop on Delft tourist trail after Vermeer's Little Street identified. Few artists have left such a deep imprint on their birthplace as Johannes Vermeer on Delft. In the summer, tour parties weave through the Dutch townâs cobbled streets ticking off Vermeer landmarks.</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": 0.001,
"lambda_query": 5e-05
}
```
### Evaluation Dataset
#### msmarco
* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83)
* Size: 10,000 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.16 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 79.89 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 76.95 tokens</li><li>max: 220 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>scarehouse cast</code> | <code>The Scarehouse. The Scarehouse is a 2014 Canadian horror film directed by Gavin Michael Booth. It stars Sarah Booth and Kimberly-Sue Murray as two women who seek revenge against their former sorority.</code> | <code>Nathalie Emmanuel joined the TV series as a recurring cast member in Season 3, and continued as a recurring cast member into Season 4. Emmanuel was later promoted to a starring cast member for seasons 5 and 6.</code> |
| <code>population of bellemont arizona</code> | <code>The 2016 Bellemont (zip 86015), Arizona, population is 300. There are 55 people per square mile (population density). The median age is 29.9. The US median is 37.4. 38.19% of people in Bellemont (zip 86015), Arizona, are married.</code> | <code>⢠Arizona: A 2010 University of Arizona report estimates that 40% of the state's kissing bugs carry a parasite strain related to the Chagas disease but rarely transmit the disease to humans. The Arizona Department of Health Services reported one Chagas disease-related death in 2013, reports The Arizona Republic.</code> |
| <code>does air transat check bag size</code> | <code>⢠Weight must be 10kg (22 lb) in Economy class and in Option Plus and 15 kg (33lb) in Club Class. Checked Baggage Air Transat allows for multiple pieces, as long as the combined weight does not exceed weight limitations. ⢠Length + width + height must not exceed 158cm (62 in).</code> | <code>Bag-valve masks come in different sizes to fit infants, children, and adults. The face mask size may be independent of the bag size; for example, a single pediatric-sized bag might be used with different masks for multiple face sizes, or a pediatric mask might be used with an adult bag for patients with small faces.</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": 0.001,
"lambda_query": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 0.1
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</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 |
|:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
| 0.0178 | 100 | 199.0423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0356 | 200 | 11.3558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0533 | 300 | 0.9845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0711 | 400 | 0.4726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0889 | 500 | 0.2639 | 0.2407 | 0.5514 | 0.3061 | 0.5649 | 0.4741 | - | - | - | - | - | - | - | - | - | - |
| 0.1067 | 600 | 0.2931 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1244 | 700 | 0.2301 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1422 | 800 | 0.2168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.16 | 900 | 0.1741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1778 | 1000 | 0.1852 | 0.1878 | 0.5868 | 0.2975 | 0.5648 | 0.4830 | - | - | - | - | - | - | - | - | - | - |
| 0.1956 | 1100 | 0.1684 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2133 | 1200 | 0.1629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2311 | 1300 | 0.1736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2489 | 1400 | 0.1813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2667 | 1500 | 0.1826 | 0.1382 | 0.5941 | 0.3251 | 0.5911 | 0.5035 | - | - | - | - | - | - | - | - | - | - |
| 0.2844 | 1600 | 0.177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3022 | 1700 | 0.1568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.32 | 1800 | 0.1707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3378 | 1900 | 0.1554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3556 | 2000 | 0.1643 | 0.1553 | 0.6157 | 0.2997 | 0.5807 | 0.4987 | - | - | - | - | - | - | - | - | - | - |
| 0.3733 | 2100 | 0.1564 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3911 | 2200 | 0.1334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4089 | 2300 | 0.1349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4267 | 2400 | 0.1228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.4444** | **2500** | **0.1473** | **0.1239** | **0.6242** | **0.3196** | **0.6151** | **0.5196** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.4622 | 2600 | 0.1506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.48 | 2700 | 0.1436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4978 | 2800 | 0.1471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5156 | 2900 | 0.1378 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5333 | 3000 | 0.1248 | 0.1328 | 0.6077 | 0.3073 | 0.6022 | 0.5057 | - | - | - | - | - | - | - | - | - | - |
| 0.5511 | 3100 | 0.1672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5689 | 3200 | 0.1301 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5867 | 3300 | 0.1325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6044 | 3400 | 0.1335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6222 | 3500 | 0.122 | 0.1163 | 0.6081 | 0.3302 | 0.6190 | 0.5191 | - | - | - | - | - | - | - | - | - | - |
| 0.64 | 3600 | 0.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6578 | 3700 | 0.1651 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6756 | 3800 | 0.1243 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6933 | 3900 | 0.1122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 4000 | 0.1308 | 0.1307 | 0.6013 | 0.3232 | 0.5981 | 0.5075 | - | - | - | - | - | - | - | - | - | - |
| 0.7289 | 4100 | 0.1708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7467 | 4200 | 0.1143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7644 | 4300 | 0.167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7822 | 4400 | 0.1119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8 | 4500 | 0.1128 | 0.1177 | 0.6082 | 0.3228 | 0.5866 | 0.5058 | - | - | - | - | - | - | - | - | - | - |
| 0.8178 | 4600 | 0.125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8356 | 4700 | 0.1252 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8533 | 4800 | 0.1066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8711 | 4900 | 0.1196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8889 | 5000 | 0.1291 | 0.1120 | 0.6134 | 0.3230 | 0.6115 | 0.5160 | - | - | - | - | - | - | - | - | - | - |
| 0.9067 | 5100 | 0.1219 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9244 | 5200 | 0.1492 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9422 | 5300 | 0.1138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.96 | 5400 | 0.1583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9778 | 5500 | 0.1516 | 0.1125 | 0.6224 | 0.3205 | 0.6137 | 0.5189 | - | - | - | - | - | - | - | - | - | - |
| 0.9956 | 5600 | 0.1227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.6242 | 0.3196 | 0.6151 | 0.5577 | 0.2571 | 0.6138 | 0.8368 | 0.3902 | 0.8078 | 0.8841 | 0.3133 | 0.3562 | 0.6798 | 0.5525 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.057 kWh
- **Carbon Emitted**: 0.021 kg of CO2
- **Hours Used**: 0.179 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA H100 80GB HBM3
- **CPU Model**: AMD EPYC 7R13 Processor
- **RAM Size**: 248.00 GB
### Framework Versions
- Python: 3.13.3
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.51.3
- PyTorch: 2.7.1+cu126
- Accelerate: 0.26.0
- 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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