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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:SparseMarginMSELoss
- loss:FlopsLoss
base_model: Luyu/co-condenser-marco
widget:
- text: how old do you have to be to have lasik
- text: when is house of cards on netflix
- text: Answer by lauryn (194). The length of time it takes a women to get her period
after giving birth varies from women to women. For many women it can take about
2 to 3 months before your period returns to normal. If you are nursing than this
time frame will last even longer.
- text: what are cys residues
- text: "You heard about fastest cars, bikes and plans but today we have world fastest\
\ bird collection. In our collection we have top 10 fastest birds of the world.\
\ Birdâ\x80\x99s flight speed is fundamentally changeable; a hunting bird speed\
\ will increase while diving-to-catch prey as compared to its gliding speeds.\
\ Here we have the top 10 fastest birds with their flight speed. 10. Teal 109\
\ km/h (68mph) This bird can fly 109 km/ h (68mph); they are 53 to 59cm long.\
\ This bird always lives in group. 09."
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: 34.21475343773813
energy_consumed: 0.0926891546467269
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: AMD EPYC 7R13 Processor
ram_total_size: 248.0
hours_used: 0.305
hardware_used: 1 x NVIDIA H100 80GB HBM3
model-index:
- name: splade-co-condenser-marco trained on MS MARCO hard negatives with distillation
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.4
name: Dot Recall@1
- type: dot_recall@3
value: 0.62
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6076647728795561
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5352777777777777
name: Dot Mrr@10
- type: dot_map@100
value: 0.5419469179877314
name: Dot Map@100
- type: query_active_dims
value: 54.119998931884766
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982268527969371
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 187.67538452148438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.993851143944647
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.4
name: Dot Recall@1
- type: dot_recall@3
value: 0.62
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6076647728795561
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5352777777777777
name: Dot Mrr@10
- type: dot_map@100
value: 0.5419469179877314
name: Dot Map@100
- type: query_active_dims
value: 54.119998931884766
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982268527969371
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 187.67538452148438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.993851143944647
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.44
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: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.34
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.06311467051346893
name: Dot Recall@1
- type: dot_recall@3
value: 0.09895898433766803
name: Dot Recall@3
- type: dot_recall@5
value: 0.1169352131561954
name: Dot Recall@5
- type: dot_recall@10
value: 0.14677603057730104
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.34523070842752446
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5258333333333334
name: Dot Mrr@10
- type: dot_map@100
value: 0.16994217536385264
name: Dot Map@100
- type: query_active_dims
value: 51.70000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9983061398085663
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 336.32476806640625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9889809066225539
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: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.34
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.27
name: Dot Precision@10
- type: dot_recall@1
value: 0.06311467051346893
name: Dot Recall@1
- type: dot_recall@3
value: 0.09895898433766803
name: Dot Recall@3
- type: dot_recall@5
value: 0.1169352131561954
name: Dot Recall@5
- type: dot_recall@10
value: 0.14677603057730104
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.34523070842752446
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5258333333333334
name: Dot Mrr@10
- type: dot_map@100
value: 0.16994217536385264
name: Dot Map@100
- type: query_active_dims
value: 51.70000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9983061398085663
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 336.32476806640625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9889809066225539
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.52
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.74
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.52
name: Dot Precision@1
- type: dot_precision@3
value: 0.2533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.08999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.48
name: Dot Recall@1
- type: dot_recall@3
value: 0.69
name: Dot Recall@3
- type: dot_recall@5
value: 0.73
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6594960548473345
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6369365079365078
name: Dot Mrr@10
- type: dot_map@100
value: 0.6105143613696246
name: Dot Map@100
- type: query_active_dims
value: 53.34000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982524080940768
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 223.5908660888672
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9926744359449294
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.52
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.74
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.52
name: Dot Precision@1
- type: dot_precision@3
value: 0.2533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.08999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.48
name: Dot Recall@1
- type: dot_recall@3
value: 0.69
name: Dot Recall@3
- type: dot_recall@5
value: 0.73
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6594960548473345
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6369365079365078
name: Dot Mrr@10
- type: dot_map@100
value: 0.6105143613696246
name: Dot Map@100
- type: query_active_dims
value: 53.34000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982524080940768
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 223.5908660888672
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9926744359449294
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: 0.45333333333333337
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6533333333333333
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7000000000000001
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7866666666666666
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.45333333333333337
name: Dot Precision@1
- type: dot_precision@3
value: 0.26666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.204
name: Dot Precision@5
- type: dot_precision@10
value: 0.148
name: Dot Precision@10
- type: dot_recall@1
value: 0.314371556837823
name: Dot Recall@1
- type: dot_recall@3
value: 0.4696529947792227
name: Dot Recall@3
- type: dot_recall@5
value: 0.5089784043853984
name: Dot Recall@5
- type: dot_recall@10
value: 0.5955920101924337
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5374638453848051
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.566015873015873
name: Dot Mrr@10
- type: dot_map@100
value: 0.4408011515737362
name: Dot Map@100
- type: query_active_dims
value: 53.0533332824707
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982618002331933
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 235.2385860639544
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9922928187515905
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.5580533751962323
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7137205651491366
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7722448979591837
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8291679748822605
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5580533751962323
name: Dot Precision@1
- type: dot_precision@3
value: 0.3332705389848246
name: Dot Precision@3
- type: dot_precision@5
value: 0.26179591836734695
name: Dot Precision@5
- type: dot_precision@10
value: 0.179171114599686
name: Dot Precision@10
- type: dot_recall@1
value: 0.32499349487208484
name: Dot Recall@1
- type: dot_recall@3
value: 0.4721752731683537
name: Dot Recall@3
- type: dot_recall@5
value: 0.5337131771857326
name: Dot Recall@5
- type: dot_recall@10
value: 0.6042058945750339
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.578707182604652
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6493701377987092
name: Dot Mrr@10
- type: dot_map@100
value: 0.5041070229886567
name: Dot Map@100
- type: query_active_dims
value: 86.67950763908115
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.997160097384212
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 230.5675761418069
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.992445856230201
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.32
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: 0.62
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
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: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.165
name: Dot Recall@1
- type: dot_recall@3
value: 0.26
name: Dot Recall@3
- type: dot_recall@5
value: 0.28733333333333333
name: Dot Recall@5
- type: dot_recall@10
value: 0.32233333333333336
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.30365156381250225
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4207222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.25580876542561
name: Dot Map@100
- type: query_active_dims
value: 135.3000030517578
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.99556713180487
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 270.1291198730469
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9911496913743186
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.74
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.86
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.74
name: Dot Precision@1
- type: dot_precision@3
value: 0.6133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.588
name: Dot Precision@5
- type: dot_precision@10
value: 0.508
name: Dot Precision@10
- type: dot_recall@1
value: 0.07635143960629845
name: Dot Recall@1
- type: dot_recall@3
value: 0.1800129405239251
name: Dot Recall@3
- type: dot_recall@5
value: 0.23739681193828663
name: Dot Recall@5
- type: dot_recall@10
value: 0.33976750488378327
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.622759301760137
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8137142857142856
name: Dot Mrr@10
- type: dot_map@100
value: 0.4830025510651395
name: Dot Map@100
- type: query_active_dims
value: 52.2599983215332
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982877924670227
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 219.79901123046875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9927986694439921
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.8
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: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.8
name: Dot Precision@1
- type: dot_precision@3
value: 0.31999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.204
name: Dot Precision@5
- type: dot_precision@10
value: 0.10599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7566666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.8866666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.92
name: Dot Recall@5
- type: dot_recall@10
value: 0.95
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.871923100931238
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8608333333333333
name: Dot Mrr@10
- type: dot_map@100
value: 0.8427126216077829
name: Dot Map@100
- type: query_active_dims
value: 79.13999938964844
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974071161984913
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 287.1961669921875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9905905193961015
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
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: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.11
name: Dot Precision@10
- type: dot_recall@1
value: 0.23607936507936508
name: Dot Recall@1
- type: dot_recall@3
value: 0.31813492063492066
name: Dot Recall@3
- type: dot_recall@5
value: 0.3794920634920635
name: Dot Recall@5
- type: dot_recall@10
value: 0.4829047619047619
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.41245963928815416
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4934444444444444
name: Dot Mrr@10
- type: dot_map@100
value: 0.35636809652397866
name: Dot Map@100
- type: query_active_dims
value: 54.040000915527344
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982294737921654
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 213.87989807128906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.992992598844398
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: 0.94
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.96
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: 0.5133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.3399999999999999
name: Dot Precision@5
- type: dot_precision@10
value: 0.17199999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.77
name: Dot Recall@3
- type: dot_recall@5
value: 0.85
name: Dot Recall@5
- type: dot_recall@10
value: 0.86
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8259863564109206
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9116666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.772433308579342
name: Dot Map@100
- type: query_active_dims
value: 68.36000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9977603040229883
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 223.86521911621094
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9926654472473556
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.9
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 1.0
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1.0
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9
name: Dot Precision@1
- type: dot_precision@3
value: 0.38666666666666655
name: Dot Precision@3
- type: dot_precision@5
value: 0.24799999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.12999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8073333333333333
name: Dot Recall@1
- type: dot_recall@3
value: 0.938
name: Dot Recall@3
- type: dot_recall@5
value: 0.9653333333333333
name: Dot Recall@5
- type: dot_recall@10
value: 0.98
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9411045044022702
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9466666666666665
name: Dot Mrr@10
- type: dot_map@100
value: 0.9183274196019293
name: Dot Map@100
- type: query_active_dims
value: 57.5
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981161129676954
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 58.39020919799805
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9980869468187538
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: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.28
name: Dot Precision@3
- type: dot_precision@5
value: 0.25199999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.154
name: Dot Precision@10
- type: dot_recall@1
value: 0.08766666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.17266666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.25766666666666665
name: Dot Recall@5
- type: dot_recall@10
value: 0.31566666666666665
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3183178982652113
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5296904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.24557421391176226
name: Dot Map@100
- type: query_active_dims
value: 73.30000305175781
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9975984534744854
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 293.607177734375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9903804738308638
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.14
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.14
name: Dot Precision@1
- type: dot_precision@3
value: 0.13999999999999999
name: Dot Precision@3
- type: dot_precision@5
value: 0.11600000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.14
name: Dot Recall@1
- type: dot_recall@3
value: 0.42
name: Dot Recall@3
- type: dot_recall@5
value: 0.58
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.40946212538272647
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.317547619047619
name: Dot Mrr@10
- type: dot_map@100
value: 0.3292918677514585
name: Dot Map@100
- type: query_active_dims
value: 281.1600036621094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.990788283740839
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 268.114990234375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.991215680812713
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.54
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
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: 0.092
name: Dot Precision@10
- type: dot_recall@1
value: 0.52
name: Dot Recall@1
- type: dot_recall@3
value: 0.65
name: Dot Recall@3
- type: dot_recall@5
value: 0.74
name: Dot Recall@5
- type: dot_recall@10
value: 0.81
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.668993132237426
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.623968253968254
name: Dot Mrr@10
- type: dot_map@100
value: 0.6278823742890459
name: Dot Map@100
- type: query_active_dims
value: 109.4000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9964157001007182
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 348.5179748535156
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9885814175069289
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.7346938775510204
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9183673469387755
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9591836734693877
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7346938775510204
name: Dot Precision@1
- type: dot_precision@3
value: 0.6258503401360545
name: Dot Precision@3
- type: dot_precision@5
value: 0.5673469387755103
name: Dot Precision@5
- type: dot_precision@10
value: 0.4612244897959184
name: Dot Precision@10
- type: dot_recall@1
value: 0.052703291471304
name: Dot Recall@1
- type: dot_recall@3
value: 0.1338383723587515
name: Dot Recall@3
- type: dot_recall@5
value: 0.19411388149464573
name: Dot Recall@5
- type: dot_recall@10
value: 0.30722833210959427
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5361442152154757
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8255102040816327
name: Dot Mrr@10
- type: dot_map@100
value: 0.3995866253752792
name: Dot Map@100
- type: query_active_dims
value: 56.61224365234375
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981451987532814
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 224.8710174560547
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9926324940221462
name: Corpus Sparsity Ratio
---
# splade-co-condenser-marco trained on MS MARCO hard negatives with distillation
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) 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:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) <!-- at revision e0cef0ab2410aae0f0994366ddefb5649a266709 -->
- **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: BertForMaskedLM
(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/co-condenser-marco-msmarco-hard-negatives")
# Run inference
queries = [
"fastest super cars in the world",
]
documents = [
'The McLaren F1 is amongst the fastest cars in the McLaren series and also the fastest car in the world. The McLaren F1 can clock a maximum speed of 240 miles per hour, or an equivalent of 386 km per hour.',
'You heard about fastest cars, bikes and plans but today we have world fastest bird collection. In our collection we have top 10 fastest birds of the world. Birdâ\x80\x99s flight speed is fundamentally changeable; a hunting bird speed will increase while diving-to-catch prey as compared to its gliding speeds. Here we have the top 10 fastest birds with their flight speed. 10. Teal 109 km/h (68mph) This bird can fly 109 km/ h (68mph); they are 53 to 59cm long. This bird always lives in group. 09.',
'Where is Langley, BC? Location of Langley on a map. Langley is a city found in British Columbia, Canada. It is located 49.08 latitude and -122.59 longitude and it is situated at elevation 78 meters above sea level. Langley has a population of 93,726 making it the 13th biggest city in British Columbia.',
]
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([[35.7080, 24.5349, 3.8619]])
```
<!--
### 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 | 0.4 | 0.44 | 0.52 | 0.32 | 0.74 | 0.8 | 0.42 | 0.88 | 0.9 | 0.42 | 0.14 | 0.54 | 0.7347 |
| dot_accuracy@3 | 0.62 | 0.6 | 0.74 | 0.52 | 0.86 | 0.92 | 0.52 | 0.94 | 1.0 | 0.56 | 0.42 | 0.66 | 0.9184 |
| dot_accuracy@5 | 0.68 | 0.64 | 0.78 | 0.54 | 0.9 | 0.94 | 0.58 | 0.96 | 1.0 | 0.74 | 0.58 | 0.74 | 0.9592 |
| dot_accuracy@10 | 0.84 | 0.68 | 0.84 | 0.62 | 0.94 | 0.96 | 0.68 | 0.96 | 1.0 | 0.78 | 0.7 | 0.82 | 0.9592 |
| dot_precision@1 | 0.4 | 0.44 | 0.52 | 0.32 | 0.74 | 0.8 | 0.42 | 0.88 | 0.9 | 0.42 | 0.14 | 0.54 | 0.7347 |
| dot_precision@3 | 0.2067 | 0.34 | 0.2533 | 0.2 | 0.6133 | 0.32 | 0.2133 | 0.5133 | 0.3867 | 0.28 | 0.14 | 0.24 | 0.6259 |
| dot_precision@5 | 0.136 | 0.316 | 0.16 | 0.14 | 0.588 | 0.204 | 0.168 | 0.34 | 0.248 | 0.252 | 0.116 | 0.168 | 0.5673 |
| dot_precision@10 | 0.084 | 0.27 | 0.09 | 0.082 | 0.508 | 0.106 | 0.11 | 0.172 | 0.13 | 0.154 | 0.07 | 0.092 | 0.4612 |
| dot_recall@1 | 0.4 | 0.0631 | 0.48 | 0.165 | 0.0764 | 0.7567 | 0.2361 | 0.44 | 0.8073 | 0.0877 | 0.14 | 0.52 | 0.0527 |
| dot_recall@3 | 0.62 | 0.099 | 0.69 | 0.26 | 0.18 | 0.8867 | 0.3181 | 0.77 | 0.938 | 0.1727 | 0.42 | 0.65 | 0.1338 |
| dot_recall@5 | 0.68 | 0.1169 | 0.73 | 0.2873 | 0.2374 | 0.92 | 0.3795 | 0.85 | 0.9653 | 0.2577 | 0.58 | 0.74 | 0.1941 |
| dot_recall@10 | 0.84 | 0.1468 | 0.8 | 0.3223 | 0.3398 | 0.95 | 0.4829 | 0.86 | 0.98 | 0.3157 | 0.7 | 0.81 | 0.3072 |
| **dot_ndcg@10** | **0.6077** | **0.3452** | **0.6595** | **0.3037** | **0.6228** | **0.8719** | **0.4125** | **0.826** | **0.9411** | **0.3183** | **0.4095** | **0.669** | **0.5361** |
| dot_mrr@10 | 0.5353 | 0.5258 | 0.6369 | 0.4207 | 0.8137 | 0.8608 | 0.4934 | 0.9117 | 0.9467 | 0.5297 | 0.3175 | 0.624 | 0.8255 |
| dot_map@100 | 0.5419 | 0.1699 | 0.6105 | 0.2558 | 0.483 | 0.8427 | 0.3564 | 0.7724 | 0.9183 | 0.2456 | 0.3293 | 0.6279 | 0.3996 |
| query_active_dims | 54.12 | 51.7 | 53.34 | 135.3 | 52.26 | 79.14 | 54.04 | 68.36 | 57.5 | 73.3 | 281.16 | 109.4 | 56.6122 |
| query_sparsity_ratio | 0.9982 | 0.9983 | 0.9983 | 0.9956 | 0.9983 | 0.9974 | 0.9982 | 0.9978 | 0.9981 | 0.9976 | 0.9908 | 0.9964 | 0.9981 |
| corpus_active_dims | 187.6754 | 336.3248 | 223.5909 | 270.1291 | 219.799 | 287.1962 | 213.8799 | 223.8652 | 58.3902 | 293.6072 | 268.115 | 348.518 | 224.871 |
| corpus_sparsity_ratio | 0.9939 | 0.989 | 0.9927 | 0.9911 | 0.9928 | 0.9906 | 0.993 | 0.9927 | 0.9981 | 0.9904 | 0.9912 | 0.9886 | 0.9926 |
#### 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 | 0.4533 |
| dot_accuracy@3 | 0.6533 |
| dot_accuracy@5 | 0.7 |
| dot_accuracy@10 | 0.7867 |
| dot_precision@1 | 0.4533 |
| dot_precision@3 | 0.2667 |
| dot_precision@5 | 0.204 |
| dot_precision@10 | 0.148 |
| dot_recall@1 | 0.3144 |
| dot_recall@3 | 0.4697 |
| dot_recall@5 | 0.509 |
| dot_recall@10 | 0.5956 |
| **dot_ndcg@10** | **0.5375** |
| dot_mrr@10 | 0.566 |
| dot_map@100 | 0.4408 |
| query_active_dims | 53.0533 |
| query_sparsity_ratio | 0.9983 |
| corpus_active_dims | 235.2386 |
| corpus_sparsity_ratio | 0.9923 |
#### 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 | 0.5581 |
| dot_accuracy@3 | 0.7137 |
| dot_accuracy@5 | 0.7722 |
| dot_accuracy@10 | 0.8292 |
| dot_precision@1 | 0.5581 |
| dot_precision@3 | 0.3333 |
| dot_precision@5 | 0.2618 |
| dot_precision@10 | 0.1792 |
| dot_recall@1 | 0.325 |
| dot_recall@3 | 0.4722 |
| dot_recall@5 | 0.5337 |
| dot_recall@10 | 0.6042 |
| **dot_ndcg@10** | **0.5787** |
| dot_mrr@10 | 0.6494 |
| dot_map@100 | 0.5041 |
| query_active_dims | 86.6795 |
| query_sparsity_ratio | 0.9972 |
| corpus_active_dims | 230.5676 |
| corpus_sparsity_ratio | 0.9924 |
<!--
## Bias, Risks and Limitations
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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>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | score | query | positive | negative |
|:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | float | string | string | string |
| details | <ul><li>min: -3.66</li><li>mean: 12.97</li><li>max: 22.48</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.89 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 80.61 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 78.92 tokens</li><li>max: 250 tokens</li></ul> |
* Samples:
| score | query | positive | negative |
|:--------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>2.1688317457834883</code> | <code>what is ast test used for</code> | <code>The AST test is commonly used to check for liver diseases. It is usually measured together with alanine aminotransferase (ALT). The AST to ALT ratio can help your doctor diagnose liver disease. Symptoms of liver disease that may cause your doctor to order an AST test include: 1 fatigue. 2 weakness.3 loss of appetite.t is usually measured together with alanine aminotransferase (ALT). The AST to ALT ratio can help your doctor diagnose liver disease. Symptoms of liver disease that may cause your doctor to order an AST test include: 1 fatigue. 2 weakness. 3 loss of appetite.</code> | <code>An aspartate aminotransferase (AST) test measures the amount of this enzyme in the blood. AST is normally found in red blood cells, liver, heart, muscle tissue, pancreas, and kidneys. AST formerly was called serum glutamic oxaloacetic transaminase (SGOT).he amount of AST in the blood is directly related to the extent of the tissue damage. After severe damage, AST levels rise in 6 to 10 hours and remain high for about 4 days. The AST test may be done at the same time as a test for alanine aminotransferase, or ALT.</code> |
| <code>12.405409197012585</code> | <code>what does the suspensory ligament do when the cillary muscles contract</code> | <code>Suspensory Ligaments of the Ciliary Body: The suspensory ligaments of the ciliary body are ligaments that attach the ciliary body to the lens of the eye. Suspensory ligaments enable the ciliary body to change the shape of the lens as needed to focus light reflected from objects at different distances from the eye.</code> | <code>Ossification of the posterior longitudinal ligament of the spine: Introduction. Ossification of the posterior longitudinal ligament of the spine: Abnormal calcification of a spinal ligament. The progressive calcification can starts within months of birth and affects the ability to move arms and legs.ssification of the posterior longitudinal ligament of the spine: Introduction. Ossification of the posterior longitudinal ligament of the spine: Abnormal calcification of a spinal ligament. The progressive calcification can starts within months of birth and affects the ability to move arms and legs.</code> |
| <code>19.407212177912392</code> | <code>how many kids does trump have</code> | <code>Donald Trump has 5 children: Donald Jr., Eric, and Ivanka- mother Ivana Trump Tiffany -mother Marla Maples Barron-mother Malania Trump Donald Trump Jr. has 2 children: ⦠Kai Madison Trump and Donald Trump III.</code> | <code>Copyright © 2018, Trump Make America Great Again Committee. Paid for by Trump Make America Great Again Committee, a joint fundraising committee authorized by and composed of Donald J. Trump for President, Inc. and the Republican National Committee. x Close</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### 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>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | score | query | positive | negative |
|:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | float | string | string | string |
| details | <ul><li>min: -4.07</li><li>mean: 13.12</li><li>max: 22.25</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.96 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 80.54 tokens</li><li>max: 220 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 78.41 tokens</li><li>max: 242 tokens</li></ul> |
* Samples:
| score | query | positive | negative |
|:--------------------------------|:-------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>11.227776050567627</code> | <code>tabernacle definition</code> | <code>Wiktionary(0.00 / 0 votes)Rate this definition: tabernacle(Noun) any temporary dwelling, a hut, tent, booth. tabernacle(Noun) (Old Testament) The portable tent used before the construction of the temple, where the shekinah (presence of God) was believed to dwell. 1611 ... So Moses finished the work. Then a cloud covered the tent of the congregation, and the glory of the LORD filled the tabernacle.</code> | <code>Both the Annunciation tabernacle in Santa Croce and the Cantoria (the singer's pulpit) in the Duomo (now in the Museo dell'Opera del Duomo) show a vastly increased repertory of forms derived from ancient art, the harvest of Donatello's long stay in Rome (1430-33).</code> |
| <code>12.354041655858357</code> | <code>what scientist discovered radiation</code> | <code>Becquerel used an apparatus similar to that displayed below to show that the radiation he discovered could not be x-rays. X-rays are neutral and cannot be bent in a magnetic field. The new radiation was bent by the magnetic field so that the radiation must be charged and different than x-rays.</code> | <code>5a-Hydroxy Laxogenin. 5a-Hydroxy Laxogenin was discovered by a American scientist in 1996. It was shown to possess an anabolic/androgenic ratio similar to one of the most efficient anabolic substances, in particular Anavar but without the side effects of liver toxicity or testing positive for steroidal therapy.</code> |
| <code>11.721514344215393</code> | <code>are horses primates</code> | <code>Primates still do, but many, if not most, mammals do not. Horses, deer, cows and many other mammals have a reduced number of digits on their forelimbs and hindlimbs. Primates also retain other generalized skeletal features like the clavicle or collar bone.</code> | <code>The only primates that live in Canada are humans. The species originated in east Africa and is unrelated to South American primates. Humans first arrived in large numbers to Canada around 15,000 years ago from North Asia, and surged in migration starting 400 years ago from around the world, especially from Europe.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMarginMSELoss",
"lambda_corpus": 0.08,
"lambda_query": 0.1
}
```
### 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
#### 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`: batch_sampler
- `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 | 664548.88 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0356 | 200 | 1912.7461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0533 | 300 | 89.4823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0711 | 400 | 57.4213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0889 | 500 | 43.5322 | 37.8169 | 0.5271 | 0.2411 | 0.5761 | 0.4481 | - | - | - | - | - | - | - | - | - | - |
| 0.1067 | 600 | 38.8042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1244 | 700 | 34.1112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1422 | 800 | 30.3487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.16 | 900 | 30.4368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1778 | 1000 | 30.9444 | 27.4550 | 0.5513 | 0.3375 | 0.6122 | 0.5003 | - | - | - | - | - | - | - | - | - | - |
| 0.1956 | 1100 | 27.7082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2133 | 1200 | 28.6251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2311 | 1300 | 27.6298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2489 | 1400 | 24.1523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2667 | 1500 | 25.3053 | 23.4952 | 0.5898 | 0.3416 | 0.6296 | 0.5203 | - | - | - | - | - | - | - | - | - | - |
| 0.2844 | 1600 | 24.8645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3022 | 1700 | 25.9037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.32 | 1800 | 25.255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3378 | 1900 | 24.4475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3556 | 2000 | 22.8183 | 26.7798 | 0.5579 | 0.3407 | 0.6160 | 0.5049 | - | - | - | - | - | - | - | - | - | - |
| 0.3733 | 2100 | 22.0948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3911 | 2200 | 22.9483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4089 | 2300 | 20.8408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4267 | 2400 | 19.5543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4444 | 2500 | 20.9379 | 18.6976 | 0.6327 | 0.3216 | 0.6255 | 0.5266 | - | - | - | - | - | - | - | - | - | - |
| 0.4622 | 2600 | 20.2078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.48 | 2700 | 20.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4978 | 2800 | 19.1764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5156 | 2900 | 19.4603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5333 | 3000 | 20.3068 | 18.4043 | 0.6081 | 0.3220 | 0.6515 | 0.5272 | - | - | - | - | - | - | - | - | - | - |
| 0.5511 | 3100 | 19.1402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5689 | 3200 | 18.0542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5867 | 3300 | 17.9658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6044 | 3400 | 18.4345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6222 | 3500 | 19.4609 | 17.0769 | 0.6155 | 0.3219 | 0.6545 | 0.5306 | - | - | - | - | - | - | - | - | - | - |
| 0.64 | 3600 | 17.4228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6578 | 3700 | 17.8939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6756 | 3800 | 16.2358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6933 | 3900 | 16.6908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 4000 | 15.9995 | 17.7298 | 0.6022 | 0.3555 | 0.6525 | 0.5367 | - | - | - | - | - | - | - | - | - | - |
| 0.7289 | 4100 | 16.3495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7467 | 4200 | 15.559 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7644 | 4300 | 17.4544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7822 | 4400 | 15.8666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8 | 4500 | 16.3616 | 18.8307 | 0.6036 | 0.3472 | 0.6112 | 0.5207 | - | - | - | - | - | - | - | - | - | - |
| 0.8178 | 4600 | 15.276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8356 | 4700 | 15.2697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8533 | 4800 | 16.6727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8711 | 4900 | 15.2223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8889 | 5000 | 15.7583 | 16.2949 | 0.6177 | 0.3438 | 0.6505 | 0.5373 | - | - | - | - | - | - | - | - | - | - |
| 0.9067 | 5100 | 15.3164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9244 | 5200 | 14.9429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9422 | 5300 | 15.5992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.96 | 5400 | 14.8593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.9778** | **5500** | **14.7565** | **16.423** | **0.6077** | **0.3452** | **0.6595** | **0.5375** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.9956 | 5600 | 14.5115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.6077 | 0.3452 | 0.6595 | 0.5787 | 0.3037 | 0.6228 | 0.8719 | 0.4125 | 0.8260 | 0.9411 | 0.3183 | 0.4095 | 0.6690 | 0.5361 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.093 kWh
- **Carbon Emitted**: 0.034 kg of CO2
- **Hours Used**: 0.305 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},
}
```
#### SparseMarginMSELoss
```bibtex
@misc{hofstätter2021improving,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
year={2021},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
#### 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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