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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:99000
- loss:CSRLoss
- loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
widget:
- text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
continue to take somewhat differing stances on regional conflicts such the Yemeni
Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
which has fought against Saudi-backed forces, and the Syrian Civil War, where
the UAE has disagreed with Saudi support for Islamist movements.[4]
- text: Economy of New Zealand New Zealand's diverse market economy has a sizable
service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale
manufacturing industries include aluminium production, food processing, metal
fabrication, wood and paper products. Mining, manufacturing, electricity, gas,
water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary
sector continues to dominate New Zealand's exports, despite accounting for 6.5%
of GDP in 2013.[17]
- text: who was the first president of indian science congress meeting held in kolkata
in 1914
- text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as
a single after a fourteen-year breakup. It was also the first song written by
bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was
played live for the first time during their Hell Freezes Over tour in 1994. It
returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at
No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream
Rock Tracks chart. The song was not played live by the Eagles after the "Hell
Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S.
- text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion
who is considered by Christians to be one of the first Gentiles to convert to
the faith, as related in Acts of the Apostles.'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 53.486914244267936
energy_consumed: 0.1376039079919011
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: 0.406
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Sparse CSR model trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 128
type: NanoMSMARCO_128
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.2
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5700574882386609
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.49757936507936507
name: Dot Mrr@10
- type: dot_map@100
value: 0.5099077397336835
name: Dot Map@100
- type: query_active_dims
value: 128.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.96875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.96875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 128
type: NanoNFCorpus_128
metrics:
- type: dot_accuracy@1
value: 0.3
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: 0.62
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.26799999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.234
name: Dot Precision@10
- type: dot_recall@1
value: 0.038852553787646696
name: Dot Recall@1
- type: dot_recall@3
value: 0.060787676252818314
name: Dot Recall@3
- type: dot_recall@5
value: 0.08871070532106025
name: Dot Recall@5
- type: dot_recall@10
value: 0.1164679743390103
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.27742011622390783
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.41685714285714276
name: Dot Mrr@10
- type: dot_map@100
value: 0.1342268199818926
name: Dot Map@100
- type: query_active_dims
value: 128.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.96875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.96875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 128
type: NanoNQ_128
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
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: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.41
name: Dot Recall@1
- type: dot_recall@3
value: 0.56
name: Dot Recall@3
- type: dot_recall@5
value: 0.59
name: Dot Recall@5
- type: dot_recall@10
value: 0.71
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5655257382100716
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5366666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.5200476570220556
name: Dot Map@100
- type: query_active_dims
value: 128.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.96875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.96875
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 128
type: NanoBEIR_mean_128
metrics:
- type: dot_accuracy@1
value: 0.36666666666666664
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5733333333333334
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6266666666666666
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7200000000000001
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36666666666666664
name: Dot Precision@1
- type: dot_precision@3
value: 0.23111111111111113
name: Dot Precision@3
- type: dot_precision@5
value: 0.17866666666666667
name: Dot Precision@5
- type: dot_precision@10
value: 0.13133333333333333
name: Dot Precision@10
- type: dot_recall@1
value: 0.26961751792921557
name: Dot Recall@1
- type: dot_recall@3
value: 0.40692922541760607
name: Dot Recall@3
- type: dot_recall@5
value: 0.45290356844035345
name: Dot Recall@5
- type: dot_recall@10
value: 0.5421559914463367
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4710011142242134
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.48370105820105813
name: Dot Mrr@10
- type: dot_map@100
value: 0.3880607389125439
name: Dot Map@100
- type: query_active_dims
value: 128.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.96875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.96875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 256
type: NanoMSMARCO_256
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14800000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08
name: Dot Precision@10
- type: dot_recall@1
value: 0.32
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.74
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.556581518059458
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.47826984126984123
name: Dot Mrr@10
- type: dot_map@100
value: 0.49049453698389867
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus 256
type: NanoNFCorpus_256
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.28800000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.258
name: Dot Precision@10
- type: dot_recall@1
value: 0.04394699993743869
name: Dot Recall@1
- type: dot_recall@3
value: 0.07346911892860693
name: Dot Recall@3
- type: dot_recall@5
value: 0.0955352050901188
name: Dot Recall@5
- type: dot_recall@10
value: 0.13423937941849148
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3138240971606582
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4724126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.1554159267082162
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ 256
type: NanoNQ_256
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.59
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.79
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6092334692116076
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5592142857142858
name: Dot Mrr@10
- type: dot_map@100
value: 0.5537561375100075
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 256
type: NanoBEIR_mean_256
metrics:
- type: dot_accuracy@1
value: 0.38666666666666666
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5666666666666668
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7599999999999999
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38666666666666666
name: Dot Precision@1
- type: dot_precision@3
value: 0.24
name: Dot Precision@3
- type: dot_precision@5
value: 0.19600000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.14266666666666666
name: Dot Precision@10
- type: dot_recall@1
value: 0.2613156666458129
name: Dot Recall@1
- type: dot_recall@3
value: 0.41448970630953563
name: Dot Recall@3
- type: dot_recall@5
value: 0.5051784016967064
name: Dot Recall@5
- type: dot_recall@10
value: 0.5747464598061639
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.49321302814390794
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5032989417989419
name: Dot Mrr@10
- type: dot_map@100
value: 0.39988886706737414
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.176
name: Dot Precision@5
- type: dot_precision@10
value: 0.11799999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.14733333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.2723333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.359
name: Dot Recall@5
- type: dot_recall@10
value: 0.469
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3709538178023985
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4734126984126983
name: Dot Mrr@10
- type: dot_map@100
value: 0.2810456840827194
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.78
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.88
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.78
name: Dot Precision@1
- type: dot_precision@3
value: 0.6666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.5880000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.484
name: Dot Precision@10
- type: dot_recall@1
value: 0.08494800977438213
name: Dot Recall@1
- type: dot_recall@3
value: 0.17317448416542106
name: Dot Recall@3
- type: dot_recall@5
value: 0.23034114850972465
name: Dot Recall@5
- type: dot_recall@10
value: 0.3258962243107224
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6091876327956771
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8355238095238097
name: Dot Mrr@10
- type: dot_map@100
value: 0.45081375839318744
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.86
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.96
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.86
name: Dot Precision@1
- type: dot_precision@3
value: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.19999999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8066666666666668
name: Dot Recall@1
- type: dot_recall@3
value: 0.8666666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.9266666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.9266666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8841127708415583
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.894
name: Dot Mrr@10
- type: dot_map@100
value: 0.8619688731284475
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
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: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.32666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.23199999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.14
name: Dot Precision@10
- type: dot_recall@1
value: 0.25257936507936507
name: Dot Recall@1
- type: dot_recall@3
value: 0.4653809523809523
name: Dot Recall@3
- type: dot_recall@5
value: 0.5155952380952381
name: Dot Recall@5
- type: dot_recall@10
value: 0.575563492063492
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5042980843824951
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5653333333333334
name: Dot Mrr@10
- type: dot_map@100
value: 0.4452452302579616
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.78
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.86
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.78
name: Dot Precision@1
- type: dot_precision@3
value: 0.5
name: Dot Precision@3
- type: dot_precision@5
value: 0.32
name: Dot Precision@5
- type: dot_precision@10
value: 0.17199999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.39
name: Dot Recall@1
- type: dot_recall@3
value: 0.75
name: Dot Recall@3
- type: dot_recall@5
value: 0.8
name: Dot Recall@5
- type: dot_recall@10
value: 0.86
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7821924588182537
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8334920634920635
name: Dot Mrr@10
- type: dot_map@100
value: 0.7213993449971364
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.2
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.4
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5949657949660191
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5246825396825396
name: Dot Mrr@10
- type: dot_map@100
value: 0.5350828017012228
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
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: 0.35999999999999993
name: Dot Precision@3
- type: dot_precision@5
value: 0.292
name: Dot Precision@5
- type: dot_precision@10
value: 0.262
name: Dot Precision@10
- type: dot_recall@1
value: 0.03443480481548747
name: Dot Recall@1
- type: dot_recall@3
value: 0.08039614346191623
name: Dot Recall@3
- type: dot_recall@5
value: 0.09609895574877417
name: Dot Recall@5
- type: dot_recall@10
value: 0.1425768627754566
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3161920036806807
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4961031746031745
name: Dot Mrr@10
- type: dot_map@100
value: 0.1515139700880487
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.5
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: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.23333333333333336
name: Dot Precision@3
- type: dot_precision@5
value: 0.16399999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.47
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.74
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6331595818344276
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5986666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.5865551394231594
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.92
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 1.0
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1.0
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.92
name: Dot Precision@1
- type: dot_precision@3
value: 0.40666666666666657
name: Dot Precision@3
- type: dot_precision@5
value: 0.264
name: Dot Precision@5
- type: dot_precision@10
value: 0.13799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7973333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.958
name: Dot Recall@3
- type: dot_recall@5
value: 0.986
name: Dot Recall@5
- type: dot_recall@10
value: 0.9966666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9556238046457881
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9533333333333333
name: Dot Mrr@10
- type: dot_map@100
value: 0.9349527472527472
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.58
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.82
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.58
name: Dot Precision@1
- type: dot_precision@3
value: 0.3999999999999999
name: Dot Precision@3
- type: dot_precision@5
value: 0.292
name: Dot Precision@5
- type: dot_precision@10
value: 0.206
name: Dot Precision@10
- type: dot_recall@1
value: 0.12266666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.24966666666666665
name: Dot Recall@3
- type: dot_recall@5
value: 0.30166666666666664
name: Dot Recall@5
- type: dot_recall@10
value: 0.42166666666666663
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4272054291075693
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6870238095238096
name: Dot Mrr@10
- type: dot_map@100
value: 0.3390924092176022
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.78
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.82
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.26
name: Dot Precision@3
- type: dot_precision@5
value: 0.16399999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.78
name: Dot Recall@3
- type: dot_recall@5
value: 0.82
name: Dot Recall@5
- type: dot_recall@10
value: 0.94
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6626337389503802
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5724920634920635
name: Dot Mrr@10
- type: dot_map@100
value: 0.5758487068487068
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.6
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.78
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6
name: Dot Precision@1
- type: dot_precision@3
value: 0.27999999999999997
name: Dot Precision@3
- type: dot_precision@5
value: 0.18
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.575
name: Dot Recall@1
- type: dot_recall@3
value: 0.755
name: Dot Recall@3
- type: dot_recall@5
value: 0.79
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.714313571551759
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6828888888888889
name: Dot Mrr@10
- type: dot_map@100
value: 0.6825983649369914
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.5918367346938775
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8571428571428571
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8979591836734694
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5918367346938775
name: Dot Precision@1
- type: dot_precision@3
value: 0.5374149659863945
name: Dot Precision@3
- type: dot_precision@5
value: 0.4897959183673469
name: Dot Precision@5
- type: dot_precision@10
value: 0.4244897959183674
name: Dot Precision@10
- type: dot_recall@1
value: 0.042649446100483254
name: Dot Recall@1
- type: dot_recall@3
value: 0.1077957848613647
name: Dot Recall@3
- type: dot_recall@5
value: 0.1613396254665287
name: Dot Recall@5
- type: dot_recall@10
value: 0.2701410353829605
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4710841185924516
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7299562682215744
name: Dot Mrr@10
- type: dot_map@100
value: 0.34832336492939087
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
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: 0.5824489795918368
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7643956043956043
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8075353218210363
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8799372056514915
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5824489795918368
name: Dot Precision@1
- type: dot_precision@3
value: 0.3613396127681842
name: Dot Precision@3
- type: dot_precision@5
value: 0.2687535321821036
name: Dot Precision@5
- type: dot_precision@10
value: 0.18480690737833594
name: Dot Precision@10
- type: dot_recall@1
value: 0.34489320198228596
name: Dot Recall@1
- type: dot_recall@3
value: 0.5152626178104862
name: Dot Recall@3
- type: dot_recall@5
value: 0.5682083308579691
name: Dot Recall@5
- type: dot_recall@10
value: 0.6421675088102025
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6096863698438044
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6805314345518426
name: Dot Mrr@10
- type: dot_map@100
value: 0.5318800304044093
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
---
# Sparse CSR model trained on Natural Questions
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions)
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## 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/csr-mxbai-embed-large-v1-nq-no-base-loss")
# Run inference
queries = [
"who is cornelius in the book of acts",
]
documents = [
'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
"Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 4096] [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[133.0582, 24.5010, 26.5905]])
```
<!--
### 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>
-->
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 128
}
```
| Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 |
|:----------------------|:----------------|:-----------------|:-----------|
| dot_accuracy@1 | 0.36 | 0.3 | 0.44 |
| dot_accuracy@3 | 0.6 | 0.52 | 0.6 |
| dot_accuracy@5 | 0.68 | 0.58 | 0.62 |
| dot_accuracy@10 | 0.8 | 0.62 | 0.74 |
| dot_precision@1 | 0.36 | 0.3 | 0.44 |
| dot_precision@3 | 0.2 | 0.2867 | 0.2067 |
| dot_precision@5 | 0.136 | 0.268 | 0.132 |
| dot_precision@10 | 0.08 | 0.234 | 0.08 |
| dot_recall@1 | 0.36 | 0.0389 | 0.41 |
| dot_recall@3 | 0.6 | 0.0608 | 0.56 |
| dot_recall@5 | 0.68 | 0.0887 | 0.59 |
| dot_recall@10 | 0.8 | 0.1165 | 0.71 |
| **dot_ndcg@10** | **0.5701** | **0.2774** | **0.5655** |
| dot_mrr@10 | 0.4976 | 0.4169 | 0.5367 |
| dot_map@100 | 0.5099 | 0.1342 | 0.52 |
| query_active_dims | 128.0 | 128.0 | 128.0 |
| query_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 |
| corpus_active_dims | 128.0 | 128.0 | 128.0 |
| corpus_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_128`
* 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"
],
"max_active_dims": 128
}
```
| Metric | Value |
|:----------------------|:----------|
| dot_accuracy@1 | 0.3667 |
| dot_accuracy@3 | 0.5733 |
| dot_accuracy@5 | 0.6267 |
| dot_accuracy@10 | 0.72 |
| dot_precision@1 | 0.3667 |
| dot_precision@3 | 0.2311 |
| dot_precision@5 | 0.1787 |
| dot_precision@10 | 0.1313 |
| dot_recall@1 | 0.2696 |
| dot_recall@3 | 0.4069 |
| dot_recall@5 | 0.4529 |
| dot_recall@10 | 0.5422 |
| **dot_ndcg@10** | **0.471** |
| dot_mrr@10 | 0.4837 |
| dot_map@100 | 0.3881 |
| query_active_dims | 128.0 |
| query_sparsity_ratio | 0.9688 |
| corpus_active_dims | 128.0 |
| corpus_sparsity_ratio | 0.9688 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 256
}
```
| Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 |
|:----------------------|:----------------|:-----------------|:-----------|
| dot_accuracy@1 | 0.32 | 0.4 | 0.44 |
| dot_accuracy@3 | 0.58 | 0.5 | 0.62 |
| dot_accuracy@5 | 0.74 | 0.54 | 0.7 |
| dot_accuracy@10 | 0.8 | 0.66 | 0.82 |
| dot_precision@1 | 0.32 | 0.4 | 0.44 |
| dot_precision@3 | 0.1933 | 0.3133 | 0.2133 |
| dot_precision@5 | 0.148 | 0.288 | 0.152 |
| dot_precision@10 | 0.08 | 0.258 | 0.09 |
| dot_recall@1 | 0.32 | 0.0439 | 0.42 |
| dot_recall@3 | 0.58 | 0.0735 | 0.59 |
| dot_recall@5 | 0.74 | 0.0955 | 0.68 |
| dot_recall@10 | 0.8 | 0.1342 | 0.79 |
| **dot_ndcg@10** | **0.5566** | **0.3138** | **0.6092** |
| dot_mrr@10 | 0.4783 | 0.4724 | 0.5592 |
| dot_map@100 | 0.4905 | 0.1554 | 0.5538 |
| query_active_dims | 256.0 | 256.0 | 256.0 |
| query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 |
| corpus_active_dims | 256.0 | 256.0 | 256.0 |
| corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_256`
* 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"
],
"max_active_dims": 256
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.3867 |
| dot_accuracy@3 | 0.5667 |
| dot_accuracy@5 | 0.66 |
| dot_accuracy@10 | 0.76 |
| dot_precision@1 | 0.3867 |
| dot_precision@3 | 0.24 |
| dot_precision@5 | 0.196 |
| dot_precision@10 | 0.1427 |
| dot_recall@1 | 0.2613 |
| dot_recall@3 | 0.4145 |
| dot_recall@5 | 0.5052 |
| dot_recall@10 | 0.5747 |
| **dot_ndcg@10** | **0.4932** |
| dot_mrr@10 | 0.5033 |
| dot_map@100 | 0.3999 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
#### Sparse Information Retrieval
* Datasets: `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 | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:----------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| dot_accuracy@1 | 0.34 | 0.78 | 0.86 | 0.46 | 0.78 | 0.4 | 0.4 | 0.5 | 0.92 | 0.58 | 0.36 | 0.6 | 0.5918 |
| dot_accuracy@3 | 0.54 | 0.88 | 0.9 | 0.68 | 0.86 | 0.6 | 0.58 | 0.68 | 1.0 | 0.8 | 0.78 | 0.78 | 0.8571 |
| dot_accuracy@5 | 0.7 | 0.9 | 0.96 | 0.68 | 0.9 | 0.66 | 0.6 | 0.76 | 1.0 | 0.82 | 0.82 | 0.8 | 0.898 |
| dot_accuracy@10 | 0.82 | 0.96 | 0.96 | 0.74 | 1.0 | 0.82 | 0.74 | 0.8 | 1.0 | 0.88 | 0.94 | 0.82 | 0.9592 |
| dot_precision@1 | 0.34 | 0.78 | 0.86 | 0.46 | 0.78 | 0.4 | 0.4 | 0.5 | 0.92 | 0.58 | 0.36 | 0.6 | 0.5918 |
| dot_precision@3 | 0.2133 | 0.6667 | 0.3133 | 0.3267 | 0.5 | 0.2 | 0.36 | 0.2333 | 0.4067 | 0.4 | 0.26 | 0.28 | 0.5374 |
| dot_precision@5 | 0.176 | 0.588 | 0.2 | 0.232 | 0.32 | 0.132 | 0.292 | 0.164 | 0.264 | 0.292 | 0.164 | 0.18 | 0.4898 |
| dot_precision@10 | 0.118 | 0.484 | 0.1 | 0.14 | 0.172 | 0.082 | 0.262 | 0.088 | 0.138 | 0.206 | 0.094 | 0.094 | 0.4245 |
| dot_recall@1 | 0.1473 | 0.0849 | 0.8067 | 0.2526 | 0.39 | 0.4 | 0.0344 | 0.47 | 0.7973 | 0.1227 | 0.36 | 0.575 | 0.0426 |
| dot_recall@3 | 0.2723 | 0.1732 | 0.8667 | 0.4654 | 0.75 | 0.6 | 0.0804 | 0.64 | 0.958 | 0.2497 | 0.78 | 0.755 | 0.1078 |
| dot_recall@5 | 0.359 | 0.2303 | 0.9267 | 0.5156 | 0.8 | 0.66 | 0.0961 | 0.74 | 0.986 | 0.3017 | 0.82 | 0.79 | 0.1613 |
| dot_recall@10 | 0.469 | 0.3259 | 0.9267 | 0.5756 | 0.86 | 0.82 | 0.1426 | 0.78 | 0.9967 | 0.4217 | 0.94 | 0.82 | 0.2701 |
| **dot_ndcg@10** | **0.371** | **0.6092** | **0.8841** | **0.5043** | **0.7822** | **0.595** | **0.3162** | **0.6332** | **0.9556** | **0.4272** | **0.6626** | **0.7143** | **0.4711** |
| dot_mrr@10 | 0.4734 | 0.8355 | 0.894 | 0.5653 | 0.8335 | 0.5247 | 0.4961 | 0.5987 | 0.9533 | 0.687 | 0.5725 | 0.6829 | 0.73 |
| dot_map@100 | 0.281 | 0.4508 | 0.862 | 0.4452 | 0.7214 | 0.5351 | 0.1515 | 0.5866 | 0.935 | 0.3391 | 0.5758 | 0.6826 | 0.3483 |
| query_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 |
| query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
| corpus_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 |
| corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 |
#### 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 | 0.5824 |
| dot_accuracy@3 | 0.7644 |
| dot_accuracy@5 | 0.8075 |
| dot_accuracy@10 | 0.8799 |
| dot_precision@1 | 0.5824 |
| dot_precision@3 | 0.3613 |
| dot_precision@5 | 0.2688 |
| dot_precision@10 | 0.1848 |
| dot_recall@1 | 0.3449 |
| dot_recall@3 | 0.5153 |
| dot_recall@5 | 0.5682 |
| dot_recall@10 | 0.6422 |
| **dot_ndcg@10** | **0.6097** |
| dot_mrr@10 | 0.6805 |
| dot_map@100 | 0.5319 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
<!--
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 4e-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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 4e-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_128_dot_ndcg@10 | NanoNFCorpus_128_dot_ndcg@10 | NanoNQ_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoNFCorpus_256_dot_ndcg@10 | NanoNQ_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:----------:|:-------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:|
| -1 | -1 | - | - | 0.5667 | 0.2784 | 0.6350 | 0.4933 | 0.6324 | 0.2927 | 0.6451 | 0.5234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 100 | 0.2571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.2333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.1939** | **300** | **0.2251** | **0.2188** | **0.6315** | **0.2816** | **0.5812** | **0.4981** | **0.5986** | **0.3188** | **0.6332** | **0.5169** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.2586 | 400 | 0.2203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.2172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.2148 | 0.2090 | 0.6205 | 0.2824 | 0.5906 | 0.4978 | 0.5804 | 0.3145 | 0.6514 | 0.5155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4525 | 700 | 0.2131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.2114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.2103 | 0.2044 | 0.6134 | 0.2956 | 0.5787 | 0.4959 | 0.5765 | 0.3134 | 0.6116 | 0.5005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 1000 | 0.2093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.2086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.2081 | 0.2020 | 0.5954 | 0.2884 | 0.5542 | 0.4794 | 0.5806 | 0.3105 | 0.6062 | 0.4991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 1300 | 0.2075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 0.207 | 0.2011 | 0.5701 | 0.2774 | 0.5655 | 0.4710 | 0.5566 | 0.3138 | 0.6092 | 0.4932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.3710 | 0.6092 | 0.8841 | 0.5043 | 0.7822 | 0.5950 | 0.3162 | 0.6332 | 0.9556 | 0.4272 | 0.6626 | 0.7143 | 0.4711 | 0.6097 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.138 kWh
- **Carbon Emitted**: 0.053 kg of CO2
- **Hours Used**: 0.406 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: 2.6.0+cu124
- 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",
}
```
#### CSRLoss
```bibtex
@misc{wen2025matryoshkarevisitingsparsecoding,
title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
year={2025},
eprint={2503.01776},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.01776},
}
```
#### 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}
}
```
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