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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
- text: 'The term emergent literacy signals a belief that, in a literate society,
young children even one and two year olds, are in the process of becoming literate”.
... Gray (1956:21) notes: Functional literacy is used for the training of adults
to ''meet independently the reading and writing demands placed on them''.'
- text: Rey is seemingly confirmed as being The Chosen One per a quote by a Lucasfilm
production designer who worked on The Rise of Skywalker.
- text: are union gun safes fireproof?
- text: Fruit is an essential part of a healthy diet — and may aid weight loss. Most
fruits are low in calories while high in nutrients and fiber, which can boost
your fullness. Keep in mind that it's best to eat fruits whole rather than juiced.
What's more, simply eating fruit is not the key to weight loss.
- text: Treatment of suspected bacterial infection is with antibiotics, such as amoxicillin/clavulanate
or doxycycline, given for 5 to 7 days for acute sinusitis and for up to 6 weeks
for chronic sinusitis.
datasets:
- sentence-transformers/gooaq
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: 1.0881870582723092
energy_consumed: 0.019418388234485075
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: AMD Ryzen 9 6900HX with Radeon Graphics
ram_total_size: 30.6114501953125
hours_used: 0.174
hardware_used: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
model-index:
- name: splade-distilbert-base-uncased trained on GooAQ
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.22
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: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.15333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.10800000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.22
name: Dot Recall@1
- type: dot_recall@3
value: 0.46
name: Dot Recall@3
- type: dot_recall@5
value: 0.54
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.44470504856183124
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3652460317460317
name: Dot Mrr@10
- type: dot_map@100
value: 0.37928248813494486
name: Dot Map@100
- type: query_active_dims
value: 125.86000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9958764169906837
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 296.2349853515625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9902943783057611
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.24
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: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666663
name: Dot Precision@3
- type: dot_precision@5
value: 0.11600000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07200000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.24
name: Dot Recall@1
- type: dot_recall@3
value: 0.5
name: Dot Recall@3
- type: dot_recall@5
value: 0.58
name: Dot Recall@5
- type: dot_recall@10
value: 0.72
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.47847271089832977
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.40169047619047615
name: Dot Mrr@10
- type: dot_map@100
value: 0.4140044816294816
name: Dot Map@100
- type: query_active_dims
value: 109.69999694824219
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9964058712748758
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 265.6180725097656
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9912974879591847
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.56
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.276
name: Dot Precision@5
- type: dot_precision@10
value: 0.20400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.020432228546915038
name: Dot Recall@1
- type: dot_recall@3
value: 0.05966030415500706
name: Dot Recall@3
- type: dot_recall@5
value: 0.08546529551494754
name: Dot Recall@5
- type: dot_recall@10
value: 0.10325648585391117
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2586742055175529
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.444
name: Dot Mrr@10
- type: dot_map@100
value: 0.10277044614671307
name: Dot Map@100
- type: query_active_dims
value: 160.10000610351562
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9947546030370383
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 409.76904296875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.986574633281936
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.29333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.268
name: Dot Precision@5
- type: dot_precision@10
value: 0.22799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.03979891140267026
name: Dot Recall@1
- type: dot_recall@3
value: 0.05843303142773433
name: Dot Recall@3
- type: dot_recall@5
value: 0.07656018207627424
name: Dot Recall@5
- type: dot_recall@10
value: 0.10998150964383814
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.28162049888840096
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4571904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.11433559983443616
name: Dot Map@100
- type: query_active_dims
value: 140.05999755859375
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9954111789018218
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 371.9038391113281
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9878152205258068
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.5
name: Dot Recall@3
- type: dot_recall@5
value: 0.62
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5013957867971872
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4491904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.44262111936629595
name: Dot Map@100
- type: query_active_dims
value: 128.39999389648438
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9957931985487031
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 359.4007873535156
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9882248611705158
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.078
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.54
name: Dot Recall@3
- type: dot_recall@5
value: 0.63
name: Dot Recall@5
- type: dot_recall@10
value: 0.69
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5189963924532662
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4757777777777777
name: Dot Mrr@10
- type: dot_map@100
value: 0.46913515575703424
name: Dot Map@100
- type: query_active_dims
value: 115.30000305175781
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9962223968595846
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 336.913818359375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9889616074189316
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: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5800000000000001
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6733333333333332
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666664
name: Dot Precision@3
- type: dot_precision@5
value: 0.17333333333333334
name: Dot Precision@5
- type: dot_precision@10
value: 0.11800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.180144076182305
name: Dot Recall@1
- type: dot_recall@3
value: 0.339886768051669
name: Dot Recall@3
- type: dot_recall@5
value: 0.4151550985049825
name: Dot Recall@5
- type: dot_recall@10
value: 0.501085495284637
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.40159168029219044
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.41947883597883595
name: Dot Mrr@10
- type: dot_map@100
value: 0.30822468454931795
name: Dot Map@100
- type: query_active_dims
value: 138.12000020345053
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9954747395254749
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 346.36973212643693
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9886518009263339
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.4301726844583988
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6182417582417583
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6783359497645213
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7722135007849293
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4301726844583988
name: Dot Precision@1
- type: dot_precision@3
value: 0.274160125588697
name: Dot Precision@3
- type: dot_precision@5
value: 0.21524646781789644
name: Dot Precision@5
- type: dot_precision@10
value: 0.1563861852433281
name: Dot Precision@10
- type: dot_recall@1
value: 0.24332694326060123
name: Dot Recall@1
- type: dot_recall@3
value: 0.38912806185875454
name: Dot Recall@3
- type: dot_recall@5
value: 0.4466126446755131
name: Dot Recall@5
- type: dot_recall@10
value: 0.5378480354517308
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.48091561944614786
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5383367720714658
name: Dot Mrr@10
- type: dot_map@100
value: 0.40550699373209664
name: Dot Map@100
- type: query_active_dims
value: 161.59013707612073
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9947057814993735
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 302.84806046588795
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.99007771245443
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.26
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.4
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.42
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.64
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.26
name: Dot Precision@1
- type: dot_precision@3
value: 0.14
name: Dot Precision@3
- type: dot_precision@5
value: 0.09200000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.13
name: Dot Recall@1
- type: dot_recall@3
value: 0.18
name: Dot Recall@3
- type: dot_recall@5
value: 0.19
name: Dot Recall@5
- type: dot_recall@10
value: 0.30733333333333335
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2528315611912319
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3483253968253968
name: Dot Mrr@10
- type: dot_map@100
value: 0.195000428587257
name: Dot Map@100
- type: query_active_dims
value: 215.39999389648438
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9929427955606944
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 334.818359375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9890302614712339
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.54
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.54
name: Dot Precision@1
- type: dot_precision@3
value: 0.43333333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.4
name: Dot Precision@5
- type: dot_precision@10
value: 0.35999999999999993
name: Dot Precision@10
- type: dot_recall@1
value: 0.04725330037285543
name: Dot Recall@1
- type: dot_recall@3
value: 0.09136010229983793
name: Dot Recall@3
- type: dot_recall@5
value: 0.12256470056683391
name: Dot Recall@5
- type: dot_recall@10
value: 0.24664786941021674
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.43054704834652313
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6440714285714284
name: Dot Mrr@10
- type: dot_map@100
value: 0.3239717199251123
name: Dot Map@100
- type: query_active_dims
value: 147.72000122070312
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9951602122658835
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 295.1452331542969
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9903300821324194
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.56
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.78
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.86
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.92
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.56
name: Dot Precision@1
- type: dot_precision@3
value: 0.26
name: Dot Precision@3
- type: dot_precision@5
value: 0.172
name: Dot Precision@5
- type: dot_precision@10
value: 0.096
name: Dot Precision@10
- type: dot_recall@1
value: 0.5466666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.7466666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.8066666666666668
name: Dot Recall@5
- type: dot_recall@10
value: 0.8766666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7202530021492869
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6843809523809523
name: Dot Mrr@10
- type: dot_map@100
value: 0.6647642136958143
name: Dot Map@100
- type: query_active_dims
value: 201.5399932861328
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9933968942636088
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 374.9945983886719
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9877139571984578
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.36
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: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.168
name: Dot Precision@5
- type: dot_precision@10
value: 0.106
name: Dot Precision@10
- type: dot_recall@1
value: 0.18857936507936507
name: Dot Recall@1
- type: dot_recall@3
value: 0.3216825396825396
name: Dot Recall@3
- type: dot_recall@5
value: 0.3532380952380953
name: Dot Recall@5
- type: dot_recall@10
value: 0.4552380952380953
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3784249151812378
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.44319047619047613
name: Dot Mrr@10
- type: dot_map@100
value: 0.31981273776184116
name: Dot Map@100
- type: query_active_dims
value: 87.62000274658203
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971292837053083
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 275.46795654296875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9909747737191872
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.66
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.86
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.92
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.92
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.66
name: Dot Precision@1
- type: dot_precision@3
value: 0.4333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.2879999999999999
name: Dot Precision@5
- type: dot_precision@10
value: 0.15599999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.33
name: Dot Recall@1
- type: dot_recall@3
value: 0.65
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6985941766475363
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7596666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.632578269203448
name: Dot Map@100
- type: query_active_dims
value: 131.75999450683594
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9956831139995139
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 330.9889831542969
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9891557242921729
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.58
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.86
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.58
name: Dot Precision@1
- type: dot_precision@3
value: 0.26
name: Dot Precision@3
- type: dot_precision@5
value: 0.184
name: Dot Precision@5
- type: dot_precision@10
value: 0.11199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.57
name: Dot Recall@1
- type: dot_recall@3
value: 0.7233333333333334
name: Dot Recall@3
- type: dot_recall@5
value: 0.8233333333333333
name: Dot Recall@5
- type: dot_recall@10
value: 0.8953333333333333
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7379320795882585
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6864126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.6882004324782192
name: Dot Map@100
- type: query_active_dims
value: 56.70000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9981423235448876
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 63.429447174072266
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9979218449913483
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.2533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.228
name: Dot Precision@5
- type: dot_precision@10
value: 0.154
name: Dot Precision@10
- type: dot_recall@1
value: 0.08466666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.15866666666666668
name: Dot Recall@3
- type: dot_recall@5
value: 0.23566666666666666
name: Dot Recall@5
- type: dot_recall@10
value: 0.31666666666666665
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.307302076202993
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5031111111111111
name: Dot Mrr@10
- type: dot_map@100
value: 0.2314013330851555
name: Dot Map@100
- type: query_active_dims
value: 219.97999572753906
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9927927398031735
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 370.2647399902344
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.98786892274457
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: 0.38
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.46
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.54
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.1
name: Dot Precision@1
- type: dot_precision@3
value: 0.12666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.09200000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.05400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.1
name: Dot Recall@1
- type: dot_recall@3
value: 0.38
name: Dot Recall@3
- type: dot_recall@5
value: 0.46
name: Dot Recall@5
- type: dot_recall@10
value: 0.54
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.314067080699688
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.24191269841269844
name: Dot Mrr@10
- type: dot_map@100
value: 0.2544871127158089
name: Dot Map@100
- type: query_active_dims
value: 392.3999938964844
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.98714369982647
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 371.9895324707031
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9878124129326157
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: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.54
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: 0.08599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.505
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.635
name: Dot Recall@5
- type: dot_recall@10
value: 0.76
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6330847757650383
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6099365079365079
name: Dot Mrr@10
- type: dot_map@100
value: 0.5921039809068559
name: Dot Map@100
- type: query_active_dims
value: 239.02000427246094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9921689271911257
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 362.61492919921875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9881195554288966
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.6122448979591837
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8571428571428571
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9183673469387755
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9387755102040817
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6122448979591837
name: Dot Precision@1
- type: dot_precision@3
value: 0.5374149659863945
name: Dot Precision@3
- type: dot_precision@5
value: 0.5102040816326532
name: Dot Precision@5
- type: dot_precision@10
value: 0.4510204081632653
name: Dot Precision@10
- type: dot_recall@1
value: 0.04128535219959204
name: Dot Recall@1
- type: dot_recall@3
value: 0.10852246408702973
name: Dot Recall@3
- type: dot_recall@5
value: 0.17293473623380118
name: Dot Recall@5
- type: dot_recall@10
value: 0.29415698658034994
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4997767347881314
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7427113702623908
name: Dot Mrr@10
- type: dot_map@100
value: 0.37179545293679184
name: Dot Map@100
- type: query_active_dims
value: 41.06122589111328
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9986547006784905
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 307.7058410644531
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9899185557609445
name: Corpus Sparsity Ratio
---
# splade-distilbert-base-uncased trained on GooAQ
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 [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) 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:**
- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **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-gooaq")
# Run inference
sentences = [
'how many days for doxycycline to work on sinus infection?',
'Treatment of suspected bacterial infection is with antibiotics, such as amoxicillin/clavulanate or doxycycline, given for 5 to 7 days for acute sinusitis and for up to 6 weeks for chronic sinusitis.',
'Most engagements typically have a cocktail dress code, calling for dresses at, or slightly above, knee-length and high heels. If your party states a different dress code, however, such as semi-formal or dressy-casual, you may need to dress up or down accordingly.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### 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 | 0.24 | 0.38 | 0.36 | 0.26 | 0.54 | 0.56 | 0.36 | 0.66 | 0.58 | 0.4 | 0.1 | 0.54 | 0.6122 |
| dot_accuracy@3 | 0.5 | 0.5 | 0.58 | 0.4 | 0.68 | 0.78 | 0.52 | 0.86 | 0.76 | 0.58 | 0.38 | 0.64 | 0.8571 |
| dot_accuracy@5 | 0.58 | 0.52 | 0.66 | 0.42 | 0.76 | 0.86 | 0.54 | 0.92 | 0.86 | 0.66 | 0.46 | 0.66 | 0.9184 |
| dot_accuracy@10 | 0.72 | 0.66 | 0.72 | 0.64 | 0.9 | 0.92 | 0.62 | 0.92 | 0.94 | 0.74 | 0.54 | 0.78 | 0.9388 |
| dot_precision@1 | 0.24 | 0.38 | 0.36 | 0.26 | 0.54 | 0.56 | 0.36 | 0.66 | 0.58 | 0.4 | 0.1 | 0.54 | 0.6122 |
| dot_precision@3 | 0.1667 | 0.2933 | 0.1933 | 0.14 | 0.4333 | 0.26 | 0.2467 | 0.4333 | 0.26 | 0.2533 | 0.1267 | 0.22 | 0.5374 |
| dot_precision@5 | 0.116 | 0.268 | 0.136 | 0.092 | 0.4 | 0.172 | 0.168 | 0.288 | 0.184 | 0.228 | 0.092 | 0.144 | 0.5102 |
| dot_precision@10 | 0.072 | 0.228 | 0.078 | 0.08 | 0.36 | 0.096 | 0.106 | 0.156 | 0.112 | 0.154 | 0.054 | 0.086 | 0.451 |
| dot_recall@1 | 0.24 | 0.0398 | 0.34 | 0.13 | 0.0473 | 0.5467 | 0.1886 | 0.33 | 0.57 | 0.0847 | 0.1 | 0.505 | 0.0413 |
| dot_recall@3 | 0.5 | 0.0584 | 0.54 | 0.18 | 0.0914 | 0.7467 | 0.3217 | 0.65 | 0.7233 | 0.1587 | 0.38 | 0.6 | 0.1085 |
| dot_recall@5 | 0.58 | 0.0766 | 0.63 | 0.19 | 0.1226 | 0.8067 | 0.3532 | 0.72 | 0.8233 | 0.2357 | 0.46 | 0.635 | 0.1729 |
| dot_recall@10 | 0.72 | 0.11 | 0.69 | 0.3073 | 0.2466 | 0.8767 | 0.4552 | 0.78 | 0.8953 | 0.3167 | 0.54 | 0.76 | 0.2942 |
| **dot_ndcg@10** | **0.4785** | **0.2816** | **0.519** | **0.2528** | **0.4305** | **0.7203** | **0.3784** | **0.6986** | **0.7379** | **0.3073** | **0.3141** | **0.6331** | **0.4998** |
| dot_mrr@10 | 0.4017 | 0.4572 | 0.4758 | 0.3483 | 0.6441 | 0.6844 | 0.4432 | 0.7597 | 0.6864 | 0.5031 | 0.2419 | 0.6099 | 0.7427 |
| dot_map@100 | 0.414 | 0.1143 | 0.4691 | 0.195 | 0.324 | 0.6648 | 0.3198 | 0.6326 | 0.6882 | 0.2314 | 0.2545 | 0.5921 | 0.3718 |
| query_active_dims | 109.7 | 140.06 | 115.3 | 215.4 | 147.72 | 201.54 | 87.62 | 131.76 | 56.7 | 219.98 | 392.4 | 239.02 | 41.0612 |
| query_sparsity_ratio | 0.9964 | 0.9954 | 0.9962 | 0.9929 | 0.9952 | 0.9934 | 0.9971 | 0.9957 | 0.9981 | 0.9928 | 0.9871 | 0.9922 | 0.9987 |
| corpus_active_dims | 265.6181 | 371.9038 | 336.9138 | 334.8184 | 295.1452 | 374.9946 | 275.468 | 330.989 | 63.4294 | 370.2647 | 371.9895 | 362.6149 | 307.7058 |
| corpus_sparsity_ratio | 0.9913 | 0.9878 | 0.989 | 0.989 | 0.9903 | 0.9877 | 0.991 | 0.9892 | 0.9979 | 0.9879 | 0.9878 | 0.9881 | 0.9899 |
#### 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 | 0.3 |
| dot_accuracy@3 | 0.5 |
| dot_accuracy@5 | 0.58 |
| dot_accuracy@10 | 0.6733 |
| dot_precision@1 | 0.3 |
| dot_precision@3 | 0.2067 |
| dot_precision@5 | 0.1733 |
| dot_precision@10 | 0.118 |
| dot_recall@1 | 0.1801 |
| dot_recall@3 | 0.3399 |
| dot_recall@5 | 0.4152 |
| dot_recall@10 | 0.5011 |
| **dot_ndcg@10** | **0.4016** |
| dot_mrr@10 | 0.4195 |
| dot_map@100 | 0.3082 |
| query_active_dims | 138.12 |
| query_sparsity_ratio | 0.9955 |
| corpus_active_dims | 346.3697 |
| corpus_sparsity_ratio | 0.9887 |
#### 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 | 0.4302 |
| dot_accuracy@3 | 0.6182 |
| dot_accuracy@5 | 0.6783 |
| dot_accuracy@10 | 0.7722 |
| dot_precision@1 | 0.4302 |
| dot_precision@3 | 0.2742 |
| dot_precision@5 | 0.2152 |
| dot_precision@10 | 0.1564 |
| dot_recall@1 | 0.2433 |
| dot_recall@3 | 0.3891 |
| dot_recall@5 | 0.4466 |
| dot_recall@10 | 0.5378 |
| **dot_ndcg@10** | **0.4809** |
| dot_mrr@10 | 0.5383 |
| dot_map@100 | 0.4055 |
| query_active_dims | 161.5901 |
| query_sparsity_ratio | 0.9947 |
| corpus_active_dims | 302.8481 |
| corpus_sparsity_ratio | 0.9901 |
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## Training Details
### Training Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 99,000 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.79 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.02 tokens</li><li>max: 153 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what are the 5 characteristics of a star?</code> | <code>Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness.</code> |
| <code>are copic markers alcohol ink?</code> | <code>Copic Ink is alcohol-based and flammable. Keep away from direct sunlight and extreme temperatures.</code> |
| <code>what is the difference between appellate term and appellate division?</code> | <code>Appellate terms An appellate term is an intermediate appellate court that hears appeals from the inferior courts within their designated counties or judicial districts, and are intended to ease the workload on the Appellate Division and provide a less expensive forum closer to the people.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Evaluation Dataset
#### gooaq
* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 1,000 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.93 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.84 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
| question | answer |
|:-----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>should you take ibuprofen with high blood pressure?</code> | <code>In general, people with high blood pressure should use acetaminophen or possibly aspirin for over-the-counter pain relief. Unless your health care provider has said it's OK, you should not use ibuprofen, ketoprofen, or naproxen sodium. If aspirin or acetaminophen doesn't help with your pain, call your doctor.</code> |
| <code>how old do you have to be to work in sc?</code> | <code>The general minimum age of employment for South Carolina youth is 14, although the state allows younger children who are performers to work in show business. If their families are agricultural workers, children younger than age 14 may also participate in farm labor.</code> |
| <code>how to write a topic proposal for a research paper?</code> | <code>['Write down the main topic of your paper. ... ', 'Write two or three short sentences under the main topic that explain why you chose that topic. ... ', 'Write a thesis sentence that states the angle and purpose of your research paper. ... ', 'List the items you will cover in the body of the paper that support your thesis statement.']</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `bf16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
| 0.0323 | 100 | 15.2006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 200 | 0.2384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0970 | 300 | 0.1932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 400 | 0.1428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1616 | 500 | 0.144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 600 | 0.1345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1972 | 610 | - | 0.1199 | 0.4364 | 0.2195 | 0.4998 | 0.3853 | - | - | - | - | - | - | - | - | - | - |
| 0.2262 | 700 | 0.1406 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 800 | 0.1012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2909 | 900 | 0.112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 1000 | 0.0736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3555 | 1100 | 0.0943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 1200 | 0.0901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3943 | 1220 | - | 0.1126 | 0.4706 | 0.2490 | 0.5154 | 0.4117 | - | - | - | - | - | - | - | - | - | - |
| 0.4202 | 1300 | 0.0988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4525 | 1400 | 0.0953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4848 | 1500 | 0.1145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 1600 | 0.0928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5495 | 1700 | 0.0963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 1800 | 0.0724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5915 | 1830 | - | 0.0736 | 0.4576 | 0.2457 | 0.5015 | 0.4016 | - | - | - | - | - | - | - | - | - | - |
| 0.6141 | 1900 | 0.0753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 2000 | 0.0657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6787 | 2100 | 0.0741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 2200 | 0.0671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7434 | 2300 | 0.1013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 2400 | 0.0795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.7886** | **2440** | **-** | **0.0719** | **0.4785** | **0.2816** | **0.519** | **0.4264** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.8080 | 2500 | 0.0666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 2600 | 0.0589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8727 | 2700 | 0.0569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 2800 | 0.0754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9373 | 2900 | 0.0724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9696 | 3000 | 0.0658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9858 | 3050 | - | 0.0661 | 0.4447 | 0.2587 | 0.5014 | 0.4016 | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.4785 | 0.2816 | 0.5190 | 0.4809 | 0.2528 | 0.4305 | 0.7203 | 0.3784 | 0.6986 | 0.7379 | 0.3073 | 0.3141 | 0.6331 | 0.4998 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.019 kWh
- **Carbon Emitted**: 0.001 kg of CO2
- **Hours Used**: 0.174 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
- **CPU Model**: AMD Ryzen 9 6900HX with Radeon Graphics
- **RAM Size**: 30.61 GB
### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
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