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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: 16.638146863146233
energy_consumed: 0.04280437678001716
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.193
hardware_used: 1 x NVIDIA GeForce RTX 3090
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: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
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: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.54
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5061981336542133
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43174603174603166
name: Dot Mrr@10
- type: dot_map@100
value: 0.44263003895418085
name: Dot Map@100
- type: query_active_dims
value: 118.5999984741211
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.996114278275535
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 397.6775817871094
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9869707888805744
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666663
name: Dot Precision@3
- type: dot_precision@5
value: 0.128
name: Dot Precision@5
- type: dot_precision@10
value: 0.07200000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.32
name: Dot Recall@1
- type: dot_recall@3
value: 0.5
name: Dot Recall@3
- type: dot_recall@5
value: 0.64
name: Dot Recall@5
- type: dot_recall@10
value: 0.72
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5061402921245981
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43823809523809515
name: Dot Mrr@10
- type: dot_map@100
value: 0.4500866595693115
name: Dot Map@100
- type: query_active_dims
value: 105.08000183105469
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.996557237342538
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 381.3874816894531
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9875045055471643
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.48
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.26
name: Dot Precision@3
- type: dot_precision@5
value: 0.23199999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.19599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.01204138289831077
name: Dot Recall@1
- type: dot_recall@3
value: 0.028423242145972874
name: Dot Recall@3
- type: dot_recall@5
value: 0.04013720529494631
name: Dot Recall@5
- type: dot_recall@10
value: 0.06944452178864681
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2238211925399539
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4057777777777777
name: Dot Mrr@10
- type: dot_map@100
value: 0.07440414426513103
name: Dot Map@100
- type: query_active_dims
value: 183.05999755859375
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9940023590341854
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 823.3663940429688
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9730238387378621
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.46
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.48
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.2733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.23199999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.21400000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.010708049564977435
name: Dot Recall@1
- type: dot_recall@3
value: 0.04042324214597287
name: Dot Recall@3
- type: dot_recall@5
value: 0.05817733939406678
name: Dot Recall@5
- type: dot_recall@10
value: 0.0849823575856454
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.24157503472859507
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3932222222222223
name: Dot Mrr@10
- type: dot_map@100
value: 0.08415340735361837
name: Dot Map@100
- type: query_active_dims
value: 150.77999877929688
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9950599567925006
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 807.0741577148438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9735576253943109
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
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: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.27
name: Dot Recall@1
- type: dot_recall@3
value: 0.48
name: Dot Recall@3
- type: dot_recall@5
value: 0.54
name: Dot Recall@5
- type: dot_recall@10
value: 0.64
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.45385561138570657
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.40454761904761893
name: Dot Mrr@10
- type: dot_map@100
value: 0.40238133013339067
name: Dot Map@100
- type: query_active_dims
value: 108.23999786376953
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9964537055938743
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 581.3165893554688
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9809541776634733
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.24
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: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
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: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.23
name: Dot Recall@1
- type: dot_recall@3
value: 0.44
name: Dot Recall@3
- type: dot_recall@5
value: 0.51
name: Dot Recall@5
- type: dot_recall@10
value: 0.67
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4431148339670733
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3818015873015873
name: Dot Mrr@10
- type: dot_map@100
value: 0.3762054598208147
name: Dot Map@100
- type: query_active_dims
value: 97.18000030517578
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.996816067089143
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 564.0422973632812
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9815201396578442
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: 0.3066666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.49333333333333335
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5800000000000001
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6733333333333333
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3066666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.20222222222222222
name: Dot Precision@3
- type: dot_precision@5
value: 0.16133333333333333
name: Dot Precision@5
- type: dot_precision@10
value: 0.11333333333333334
name: Dot Precision@10
- type: dot_recall@1
value: 0.1940137942994369
name: Dot Recall@1
- type: dot_recall@3
value: 0.34947441404865764
name: Dot Recall@3
- type: dot_recall@5
value: 0.4200457350983155
name: Dot Recall@5
- type: dot_recall@10
value: 0.4831481739295489
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.39462497919329126
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4140238095238094
name: Dot Mrr@10
- type: dot_map@100
value: 0.3064718377842342
name: Dot Map@100
- type: query_active_dims
value: 136.63333129882812
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9955234476345315
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 565.0999949325504
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9814854860450642
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.35158555729984303
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5366091051805337
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.609105180533752
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7153218210361068
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.35158555729984303
name: Dot Precision@1
- type: dot_precision@3
value: 0.24084772370486657
name: Dot Precision@3
- type: dot_precision@5
value: 0.195861852433281
name: Dot Precision@5
- type: dot_precision@10
value: 0.14448037676609105
name: Dot Precision@10
- type: dot_recall@1
value: 0.18710365017134828
name: Dot Recall@1
- type: dot_recall@3
value: 0.3166600122342838
name: Dot Recall@3
- type: dot_recall@5
value: 0.38032257819651705
name: Dot Recall@5
- type: dot_recall@10
value: 0.4791896835492342
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.41388777461576925
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4636842715108021
name: Dot Mrr@10
- type: dot_map@100
value: 0.33650048535941457
name: Dot Map@100
- type: query_active_dims
value: 195.48228298827937
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9935953645570972
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 525.5023385946348
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9827828340674059
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.2
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.38
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.42
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.52
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.2
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: 0.06000000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.08833333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.18166666666666664
name: Dot Recall@3
- type: dot_recall@5
value: 0.19233333333333336
name: Dot Recall@5
- type: dot_recall@10
value: 0.2523333333333333
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2097369113981719
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2989603174603175
name: Dot Mrr@10
- type: dot_map@100
value: 0.16798141398273245
name: Dot Map@100
- type: query_active_dims
value: 250.86000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9917810103987172
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 643.326904296875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9789225180428257
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.62
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.78
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: 0.62
name: Dot Precision@1
- type: dot_precision@3
value: 0.4733333333333334
name: Dot Precision@3
- type: dot_precision@5
value: 0.452
name: Dot Precision@5
- type: dot_precision@10
value: 0.39599999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.06769969786296744
name: Dot Recall@1
- type: dot_recall@3
value: 0.14199136819511296
name: Dot Recall@3
- type: dot_recall@5
value: 0.192778624550143
name: Dot Recall@5
- type: dot_recall@10
value: 0.2816492423802407
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4998791588316728
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7168571428571429
name: Dot Mrr@10
- type: dot_map@100
value: 0.3705445544087827
name: Dot Map@100
- type: query_active_dims
value: 146.02000427246094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9952159096955487
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 481.7581481933594
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9842160360332429
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.66
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: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.22
name: Dot Precision@3
- type: dot_precision@5
value: 0.156
name: Dot Precision@5
- type: dot_precision@10
value: 0.08599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.7366666666666666
name: Dot Recall@5
- type: dot_recall@10
value: 0.7966666666666665
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6190748153469672
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5678888888888888
name: Dot Mrr@10
- type: dot_map@100
value: 0.5644736817593311
name: Dot Map@100
- type: query_active_dims
value: 253.3800048828125
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9916984468618435
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 749.9185180664062
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9754302300613852
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.22
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.46
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.54
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666663
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.09
name: Dot Precision@10
- type: dot_recall@1
value: 0.13933333333333334
name: Dot Recall@1
- type: dot_recall@3
value: 0.26035714285714284
name: Dot Recall@3
- type: dot_recall@5
value: 0.31182539682539684
name: Dot Recall@5
- type: dot_recall@10
value: 0.3924047619047619
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3071601294876744
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3309126984126985
name: Dot Mrr@10
- type: dot_map@100
value: 0.2510011498241125
name: Dot Map@100
- type: query_active_dims
value: 85.69999694824219
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971921893405333
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 416.93829345703125
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9863397453162627
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.68
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.78
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.68
name: Dot Precision@1
- type: dot_precision@3
value: 0.35333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.24
name: Dot Precision@5
- type: dot_precision@10
value: 0.13799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.53
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.69
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6197567693807055
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7347142857142859
name: Dot Mrr@10
- type: dot_map@100
value: 0.540453368331375
name: Dot Map@100
- type: query_active_dims
value: 152.5399932861328
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9950022936476596
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 553.4066772460938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9818685971677447
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.36
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: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.1733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.124
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.34666666666666673
name: Dot Recall@1
- type: dot_recall@3
value: 0.4706666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.5506666666666666
name: Dot Recall@5
- type: dot_recall@10
value: 0.7506666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5326024015174656
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4782936507936508
name: Dot Mrr@10
- type: dot_map@100
value: 0.4734890338060357
name: Dot Map@100
- type: query_active_dims
value: 52.900001525878906
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982668238802871
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 61.35552978515625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9979897932709142
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.184
name: Dot Precision@5
- type: dot_precision@10
value: 0.13799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.059666666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.12866666666666668
name: Dot Recall@3
- type: dot_recall@5
value: 0.18966666666666662
name: Dot Recall@5
- type: dot_recall@10
value: 0.2836666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2574919427490159
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.42540476190476184
name: Dot Mrr@10
- type: dot_map@100
value: 0.17688082476501285
name: Dot Map@100
- type: query_active_dims
value: 197.1999969482422
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9935390866604993
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 676.0037231445312
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9778519191683201
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.02
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.14
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.22
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.38
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.02
name: Dot Precision@1
- type: dot_precision@3
value: 0.04666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.044000000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.038000000000000006
name: Dot Precision@10
- type: dot_recall@1
value: 0.02
name: Dot Recall@1
- type: dot_recall@3
value: 0.14
name: Dot Recall@3
- type: dot_recall@5
value: 0.22
name: Dot Recall@5
- type: dot_recall@10
value: 0.38
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.17464966621739791
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.11243650793650796
name: Dot Mrr@10
- type: dot_map@100
value: 0.11564322909400383
name: Dot Map@100
- type: query_active_dims
value: 732.4600219726562
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9760022271812904
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 648.47509765625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9787538464826602
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.078
name: Dot Precision@10
- type: dot_recall@1
value: 0.335
name: Dot Recall@1
- type: dot_recall@3
value: 0.535
name: Dot Recall@3
- type: dot_recall@5
value: 0.575
name: Dot Recall@5
- type: dot_recall@10
value: 0.66
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5064687965907525
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4627222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.46039157929311386
name: Dot Map@100
- type: query_active_dims
value: 276.20001220703125
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9909507891944489
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 729.4652099609375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9761003469641264
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.5306122448979592
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7959183673469388
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9183673469387755
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5306122448979592
name: Dot Precision@1
- type: dot_precision@3
value: 0.5510204081632653
name: Dot Precision@3
- type: dot_precision@5
value: 0.5102040816326532
name: Dot Precision@5
- type: dot_precision@10
value: 0.4122448979591837
name: Dot Precision@10
- type: dot_recall@1
value: 0.03493970479958239
name: Dot Recall@1
- type: dot_recall@3
value: 0.10780840584746129
name: Dot Recall@3
- type: dot_recall@5
value: 0.16707882245178216
name: Dot Recall@5
- type: dot_recall@10
value: 0.26709619093606257
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4628903176649091
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6864431486880467
name: Dot Mrr@10
- type: dot_map@100
value: 0.34320194766414486
name: Dot Map@100
- type: query_active_dims
value: 37.81632614135742
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9987610141490939
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 493.48040771484375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9838319766819067
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, 'architecture': 'DistilBertForMaskedLM'})
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-gooaq-peft-r128")
# Run inference
queries = [
"how many days for doxycycline to work on sinus infection?",
]
documents = [
'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.',
'The average service life of a gas furnace is about 15 years, but the actual life span of an individual unit can vary greatly. There are a number of contributing factors that determine the age a furnace reaches: The quality of the equipment.',
]
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([[85.3246, 22.8328, 29.6908]])
```
<!--
### 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 | 0.32 | 0.3 | 0.24 | 0.2 | 0.62 | 0.44 | 0.22 | 0.68 | 0.36 | 0.28 | 0.02 | 0.36 | 0.5306 |
| dot_accuracy@3 | 0.5 | 0.46 | 0.46 | 0.38 | 0.78 | 0.66 | 0.42 | 0.78 | 0.52 | 0.52 | 0.14 | 0.56 | 0.7959 |
| dot_accuracy@5 | 0.64 | 0.48 | 0.54 | 0.42 | 0.86 | 0.78 | 0.46 | 0.8 | 0.58 | 0.62 | 0.22 | 0.6 | 0.9184 |
| dot_accuracy@10 | 0.72 | 0.6 | 0.72 | 0.52 | 0.92 | 0.84 | 0.54 | 0.88 | 0.78 | 0.78 | 0.38 | 0.66 | 0.9592 |
| dot_precision@1 | 0.32 | 0.3 | 0.24 | 0.2 | 0.62 | 0.44 | 0.22 | 0.68 | 0.36 | 0.28 | 0.02 | 0.36 | 0.5306 |
| dot_precision@3 | 0.1667 | 0.2733 | 0.1533 | 0.14 | 0.4733 | 0.22 | 0.1667 | 0.3533 | 0.1733 | 0.2067 | 0.0467 | 0.2067 | 0.551 |
| dot_precision@5 | 0.128 | 0.232 | 0.108 | 0.092 | 0.452 | 0.156 | 0.144 | 0.24 | 0.124 | 0.184 | 0.044 | 0.132 | 0.5102 |
| dot_precision@10 | 0.072 | 0.214 | 0.074 | 0.06 | 0.396 | 0.086 | 0.09 | 0.138 | 0.082 | 0.138 | 0.038 | 0.078 | 0.4122 |
| dot_recall@1 | 0.32 | 0.0107 | 0.23 | 0.0883 | 0.0677 | 0.44 | 0.1393 | 0.34 | 0.3467 | 0.0597 | 0.02 | 0.335 | 0.0349 |
| dot_recall@3 | 0.5 | 0.0404 | 0.44 | 0.1817 | 0.142 | 0.64 | 0.2604 | 0.53 | 0.4707 | 0.1287 | 0.14 | 0.535 | 0.1078 |
| dot_recall@5 | 0.64 | 0.0582 | 0.51 | 0.1923 | 0.1928 | 0.7367 | 0.3118 | 0.6 | 0.5507 | 0.1897 | 0.22 | 0.575 | 0.1671 |
| dot_recall@10 | 0.72 | 0.085 | 0.67 | 0.2523 | 0.2816 | 0.7967 | 0.3924 | 0.69 | 0.7507 | 0.2837 | 0.38 | 0.66 | 0.2671 |
| **dot_ndcg@10** | **0.5061** | **0.2416** | **0.4431** | **0.2097** | **0.4999** | **0.6191** | **0.3072** | **0.6198** | **0.5326** | **0.2575** | **0.1746** | **0.5065** | **0.4629** |
| dot_mrr@10 | 0.4382 | 0.3932 | 0.3818 | 0.299 | 0.7169 | 0.5679 | 0.3309 | 0.7347 | 0.4783 | 0.4254 | 0.1124 | 0.4627 | 0.6864 |
| dot_map@100 | 0.4501 | 0.0842 | 0.3762 | 0.168 | 0.3705 | 0.5645 | 0.251 | 0.5405 | 0.4735 | 0.1769 | 0.1156 | 0.4604 | 0.3432 |
| query_active_dims | 105.08 | 150.78 | 97.18 | 250.86 | 146.02 | 253.38 | 85.7 | 152.54 | 52.9 | 197.2 | 732.46 | 276.2 | 37.8163 |
| query_sparsity_ratio | 0.9966 | 0.9951 | 0.9968 | 0.9918 | 0.9952 | 0.9917 | 0.9972 | 0.995 | 0.9983 | 0.9935 | 0.976 | 0.991 | 0.9988 |
| corpus_active_dims | 381.3875 | 807.0742 | 564.0423 | 643.3269 | 481.7581 | 749.9185 | 416.9383 | 553.4067 | 61.3555 | 676.0037 | 648.4751 | 729.4652 | 493.4804 |
| corpus_sparsity_ratio | 0.9875 | 0.9736 | 0.9815 | 0.9789 | 0.9842 | 0.9754 | 0.9863 | 0.9819 | 0.998 | 0.9779 | 0.9788 | 0.9761 | 0.9838 |
#### 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 | 0.3067 |
| dot_accuracy@3 | 0.4933 |
| dot_accuracy@5 | 0.58 |
| dot_accuracy@10 | 0.6733 |
| dot_precision@1 | 0.3067 |
| dot_precision@3 | 0.2022 |
| dot_precision@5 | 0.1613 |
| dot_precision@10 | 0.1133 |
| dot_recall@1 | 0.194 |
| dot_recall@3 | 0.3495 |
| dot_recall@5 | 0.42 |
| dot_recall@10 | 0.4831 |
| **dot_ndcg@10** | **0.3946** |
| dot_mrr@10 | 0.414 |
| dot_map@100 | 0.3065 |
| query_active_dims | 136.6333 |
| query_sparsity_ratio | 0.9955 |
| corpus_active_dims | 565.1 |
| corpus_sparsity_ratio | 0.9815 |
#### 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 | 0.3516 |
| dot_accuracy@3 | 0.5366 |
| dot_accuracy@5 | 0.6091 |
| dot_accuracy@10 | 0.7153 |
| dot_precision@1 | 0.3516 |
| dot_precision@3 | 0.2408 |
| dot_precision@5 | 0.1959 |
| dot_precision@10 | 0.1445 |
| dot_recall@1 | 0.1871 |
| dot_recall@3 | 0.3167 |
| dot_recall@5 | 0.3803 |
| dot_recall@10 | 0.4792 |
| **dot_ndcg@10** | **0.4139** |
| dot_mrr@10 | 0.4637 |
| dot_map@100 | 0.3365 |
| query_active_dims | 195.4823 |
| query_sparsity_ratio | 0.9936 |
| corpus_active_dims | 525.5023 |
| corpus_sparsity_ratio | 0.9828 |
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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')",
"document_regularizer_weight": 3e-05,
"query_regularizer_weight": 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')",
"document_regularizer_weight": 3e-05,
"query_regularizer_weight": 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}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
| 0.0323 | 100 | 81.7292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 200 | 4.3059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0970 | 300 | 0.8078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 400 | 0.4309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1616 | 500 | 0.3837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 600 | 0.282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1972 | 610 | - | 0.1867 | 0.4508 | 0.2059 | 0.3905 | 0.3491 | - | - | - | - | - | - | - | - | - | - |
| 0.2262 | 700 | 0.2593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 800 | 0.2161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2909 | 900 | 0.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 1000 | 0.2259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3555 | 1100 | 0.2161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 1200 | 0.1835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3943 | 1220 | - | 0.1368 | 0.4567 | 0.2373 | 0.4209 | 0.3717 | - | - | - | - | - | - | - | - | - | - |
| 0.4202 | 1300 | 0.1936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4525 | 1400 | 0.1689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4848 | 1500 | 0.1858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 1600 | 0.1639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5495 | 1700 | 0.1376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 1800 | 0.1677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.5915** | **1830** | **-** | **0.1138** | **0.5061** | **0.2416** | **0.4431** | **0.3969** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.6141 | 1900 | 0.1483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 2000 | 0.1513 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6787 | 2100 | 0.1449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 2200 | 0.193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7434 | 2300 | 0.1554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 2400 | 0.1372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7886 | 2440 | - | 0.1148 | 0.5084 | 0.2240 | 0.4428 | 0.3917 | - | - | - | - | - | - | - | - | - | - |
| 0.8080 | 2500 | 0.1308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 2600 | 0.1284 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8727 | 2700 | 0.1309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 2800 | 0.1458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9373 | 2900 | 0.1351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9696 | 3000 | 0.1135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9858 | 3050 | - | 0.1068 | 0.5062 | 0.2238 | 0.4539 | 0.3946 | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.5061 | 0.2416 | 0.4431 | 0.4139 | 0.2097 | 0.4999 | 0.6191 | 0.3072 | 0.6198 | 0.5326 | 0.2575 | 0.1746 | 0.5065 | 0.4629 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.043 kWh
- **Carbon Emitted**: 0.017 kg of CO2
- **Hours Used**: 0.193 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
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