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csr-mxbai-embed-large-v1-nq-no-reconstruction

Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
csr
Generated from Trainer
dataset_size:99000
loss:CSRLoss
loss:SparseMultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Model card Files
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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: 53.740159900184786
energy_consumed: 0.13825542420719417
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: 0.409
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: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.20666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.38
name: Dot Recall@1
- type: dot_recall@3
value: 0.62
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.603846580732656
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.529079365079365
name: Dot Mrr@10
- type: dot_map@100
value: 0.535577429489216
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: 0.4
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: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.34
name: Dot Precision@3
- type: dot_precision@5
value: 0.336
name: Dot Precision@5
- type: dot_precision@10
value: 0.28600000000000003
name: Dot Precision@10
- type: dot_recall@1
value: 0.02662938222230507
name: Dot Recall@1
- type: dot_recall@3
value: 0.08583886950771044
name: Dot Recall@3
- type: dot_recall@5
value: 0.10539572959638349
name: Dot Recall@5
- type: dot_recall@10
value: 0.1390606096616216
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.33155673498755867
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4815555555555555
name: Dot Mrr@10
- type: dot_map@100
value: 0.14591039936040862
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: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
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: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.43
name: Dot Recall@1
- type: dot_recall@3
value: 0.6
name: Dot Recall@3
- type: dot_recall@5
value: 0.73
name: Dot Recall@5
- type: dot_recall@10
value: 0.76
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6020077639360719
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5624999999999999
name: Dot Mrr@10
- type: dot_map@100
value: 0.5519887965031844
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: 0.4066666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5933333333333334
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7066666666666667
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7733333333333334
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4066666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.25333333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.21333333333333335
name: Dot Precision@5
- type: dot_precision@10
value: 0.15133333333333332
name: Dot Precision@10
- type: dot_recall@1
value: 0.27887646074076833
name: Dot Recall@1
- type: dot_recall@3
value: 0.4352796231692368
name: Dot Recall@3
- type: dot_recall@5
value: 0.5184652431987945
name: Dot Recall@5
- type: dot_recall@10
value: 0.5796868698872072
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5124703598854289
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5243783068783068
name: Dot Mrr@10
- type: dot_map@100
value: 0.411158875117603
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: 0.44
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: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.44
name: Dot Recall@1
- type: dot_recall@3
value: 0.66
name: Dot Recall@3
- type: dot_recall@5
value: 0.78
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6402220356297674
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.576079365079365
name: Dot Mrr@10
- type: dot_map@100
value: 0.5819739218018417
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: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.35999999999999993
name: Dot Precision@3
- type: dot_precision@5
value: 0.344
name: Dot Precision@5
- type: dot_precision@10
value: 0.29200000000000004
name: Dot Precision@10
- type: dot_recall@1
value: 0.018848269093365854
name: Dot Recall@1
- type: dot_recall@3
value: 0.07354907247001424
name: Dot Recall@3
- type: dot_recall@5
value: 0.09781289475269293
name: Dot Recall@5
- type: dot_recall@10
value: 0.1418672876485781
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.33652365839683074
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4957698412698413
name: Dot Mrr@10
- type: dot_map@100
value: 0.14165509490208594
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: 0.56
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.86
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.56
name: Dot Precision@1
- type: dot_precision@3
value: 0.23333333333333336
name: Dot Precision@3
- type: dot_precision@5
value: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.54
name: Dot Recall@1
- type: dot_recall@3
value: 0.65
name: Dot Recall@3
- type: dot_recall@5
value: 0.73
name: Dot Recall@5
- type: dot_recall@10
value: 0.83
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6813657040884066
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.647301587301587
name: Dot Mrr@10
- type: dot_map@100
value: 0.6310147772294485
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: 0.47333333333333333
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6333333333333334
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7133333333333333
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7999999999999999
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.47333333333333333
name: Dot Precision@1
- type: dot_precision@3
value: 0.27111111111111114
name: Dot Precision@3
- type: dot_precision@5
value: 0.22
name: Dot Precision@5
- type: dot_precision@10
value: 0.15666666666666665
name: Dot Precision@10
- type: dot_recall@1
value: 0.33294942303112196
name: Dot Recall@1
- type: dot_recall@3
value: 0.46118302415667145
name: Dot Recall@3
- type: dot_recall@5
value: 0.5359376315842309
name: Dot Recall@5
- type: dot_recall@10
value: 0.6039557625495261
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5527037993716682
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5730502645502644
name: Dot Mrr@10
- type: dot_map@100
value: 0.4515479313111254
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: 0.2
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.56
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.2
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.07833333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.24499999999999997
name: Dot Recall@3
- type: dot_recall@5
value: 0.28333333333333327
name: Dot Recall@5
- type: dot_recall@10
value: 0.3473333333333333
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.27333419680435084
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3666031746031747
name: Dot Mrr@10
- type: dot_map@100
value: 0.21266834216817831
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: 0.74
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.86
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.92
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.74
name: Dot Precision@1
- type: dot_precision@3
value: 0.5866666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.556
name: Dot Precision@5
- type: dot_precision@10
value: 0.484
name: Dot Precision@10
- type: dot_recall@1
value: 0.08366724054361292
name: Dot Recall@1
- type: dot_recall@3
value: 0.16227352802558825
name: Dot Recall@3
- type: dot_recall@5
value: 0.2213882427797012
name: Dot Recall@5
- type: dot_recall@10
value: 0.3353731792736538
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5972307350486245
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8152222222222223
name: Dot Mrr@10
- type: dot_map@100
value: 0.45303559906331897
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: 0.98
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.86
name: Dot Precision@1
- type: dot_precision@3
value: 0.34666666666666657
name: Dot Precision@3
- type: dot_precision@5
value: 0.20799999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.10399999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8066666666666668
name: Dot Recall@1
- type: dot_recall@3
value: 0.9433333333333332
name: Dot Recall@3
- type: dot_recall@5
value: 0.9433333333333332
name: Dot Recall@5
- type: dot_recall@10
value: 0.9433333333333332
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9054259418093692
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9133333333333333
name: Dot Mrr@10
- type: dot_map@100
value: 0.8844551282051283
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: 0.5
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.22399999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.13799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.2725793650793651
name: Dot Recall@1
- type: dot_recall@3
value: 0.4129047619047619
name: Dot Recall@3
- type: dot_recall@5
value: 0.4605714285714286
name: Dot Recall@5
- type: dot_recall@10
value: 0.5500873015873016
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.49585690755175454
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5641666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.4425504355719097
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: 0.84
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.92
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: 0.84
name: Dot Precision@1
- type: dot_precision@3
value: 0.4733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.17399999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.71
name: Dot Recall@3
- type: dot_recall@5
value: 0.79
name: Dot Recall@5
- type: dot_recall@10
value: 0.87
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.802663278529999
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8856666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.7334779802028212
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: 0.42
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: 0.42
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: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.66
name: Dot Recall@3
- type: dot_recall@5
value: 0.78
name: Dot Recall@5
- type: dot_recall@10
value: 0.84
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6354592257726257
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5694126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.5752130160409359
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: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.35999999999999993
name: Dot Precision@3
- type: dot_precision@5
value: 0.34
name: Dot Precision@5
- type: dot_precision@10
value: 0.29
name: Dot Precision@10
- type: dot_recall@1
value: 0.018848269093365854
name: Dot Recall@1
- type: dot_recall@3
value: 0.07354907247001424
name: Dot Recall@3
- type: dot_recall@5
value: 0.0962744332142314
name: Dot Recall@5
- type: dot_recall@10
value: 0.14178823626517886
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3352519406973144
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.49602380952380964
name: Dot Mrr@10
- type: dot_map@100
value: 0.14142955254174144
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: 0.56
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.86
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.56
name: Dot Precision@1
- type: dot_precision@3
value: 0.23333333333333336
name: Dot Precision@3
- type: dot_precision@5
value: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.54
name: Dot Recall@1
- type: dot_recall@3
value: 0.65
name: Dot Recall@3
- type: dot_recall@5
value: 0.73
name: Dot Recall@5
- type: dot_recall@10
value: 0.83
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6813657040884066
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.647301587301587
name: Dot Mrr@10
- type: dot_map@100
value: 0.6311451301239768
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: 0.86
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.98
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.86
name: Dot Precision@1
- type: dot_precision@3
value: 0.4
name: Dot Precision@3
- type: dot_precision@5
value: 0.26799999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.13799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7373333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.9353333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.9733333333333334
name: Dot Recall@5
- type: dot_recall@10
value: 0.9966666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9283913808760963
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9166666666666665
name: Dot Mrr@10
- type: dot_map@100
value: 0.8996944444444444
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: 0.54
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.82
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.86
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.54
name: Dot Precision@1
- type: dot_precision@3
value: 0.37999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.30400000000000005
name: Dot Precision@5
- type: dot_precision@10
value: 0.204
name: Dot Precision@10
- type: dot_recall@1
value: 0.11466666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.23766666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.31466666666666665
name: Dot Recall@5
- type: dot_recall@10
value: 0.4196666666666665
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.42030245497944485
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6498333333333332
name: Dot Mrr@10
- type: dot_map@100
value: 0.3374015286377059
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: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
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: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.25333333333333335
name: Dot Precision@3
- type: dot_precision@5
value: 0.17999999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.09599999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.28
name: Dot Recall@1
- type: dot_recall@3
value: 0.76
name: Dot Recall@3
- type: dot_recall@5
value: 0.9
name: Dot Recall@5
- type: dot_recall@10
value: 0.96
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.651941051318052
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5498571428571428
name: Dot Mrr@10
- type: dot_map@100
value: 0.5515326278659611
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: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6
name: Dot Precision@1
- type: dot_precision@3
value: 0.2733333333333334
name: Dot Precision@3
- type: dot_precision@5
value: 0.17599999999999993
name: Dot Precision@5
- type: dot_precision@10
value: 0.1
name: Dot Precision@10
- type: dot_recall@1
value: 0.565
name: Dot Recall@1
- type: dot_recall@3
value: 0.74
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.88
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7313116540920006
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6887698412698413
name: Dot Mrr@10
- type: dot_map@100
value: 0.6840924219150025
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: 0.6326530612244898
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8571428571428571
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8775510204081632
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9795918367346939
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6326530612244898
name: Dot Precision@1
- type: dot_precision@3
value: 0.5986394557823129
name: Dot Precision@3
- type: dot_precision@5
value: 0.5265306122448979
name: Dot Precision@5
- type: dot_precision@10
value: 0.4326530612244897
name: Dot Precision@10
- type: dot_recall@1
value: 0.0443108966783425
name: Dot Recall@1
- type: dot_recall@3
value: 0.12651297913694023
name: Dot Recall@3
- type: dot_recall@5
value: 0.1807810185085916
name: Dot Recall@5
- type: dot_recall@10
value: 0.2908183366162545
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4946170299181126
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7585276967930031
name: Dot Mrr@10
- type: dot_map@100
value: 0.3733282842478698
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: 0.5732810047095762
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7628571428571429
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8105808477237049
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8707378335949765
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5732810047095762
name: Dot Precision@1
- type: dot_precision@3
value: 0.356305599162742
name: Dot Precision@3
- type: dot_precision@5
value: 0.27281004709576134
name: Dot Precision@5
- type: dot_precision@10
value: 0.1866656200941915
name: Dot Precision@10
- type: dot_recall@1
value: 0.3370312131842067
name: Dot Recall@1
- type: dot_recall@3
value: 0.512044128836203
name: Dot Recall@3
- type: dot_recall@5
value: 0.5718216761338938
name: Dot Recall@5
- type: dot_recall@10
value: 0.6465436195186451
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6117808847297039
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6785680645884727
name: Dot Mrr@10
- type: dot_map@100
value: 0.5323095762329995
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-reconstruction")
# 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([[55.6462, 14.4637, 16.8866]])
```
<!--
### 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_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 | 0.38 | 0.4 | 0.44 |
| dot_accuracy@3 | 0.62 | 0.52 | 0.64 |
| dot_accuracy@5 | 0.72 | 0.62 | 0.78 |
| dot_accuracy@10 | 0.84 | 0.68 | 0.8 |
| dot_precision@1 | 0.38 | 0.4 | 0.44 |
| dot_precision@3 | 0.2067 | 0.34 | 0.2133 |
| dot_precision@5 | 0.144 | 0.336 | 0.16 |
| dot_precision@10 | 0.084 | 0.286 | 0.084 |
| dot_recall@1 | 0.38 | 0.0266 | 0.43 |
| dot_recall@3 | 0.62 | 0.0858 | 0.6 |
| dot_recall@5 | 0.72 | 0.1054 | 0.73 |
| dot_recall@10 | 0.84 | 0.1391 | 0.76 |
| **dot_ndcg@10** | **0.6038** | **0.3316** | **0.602** |
| dot_mrr@10 | 0.5291 | 0.4816 | 0.5625 |
| dot_map@100 | 0.5356 | 0.1459 | 0.552 |
| 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 | 0.4067 |
| dot_accuracy@3 | 0.5933 |
| dot_accuracy@5 | 0.7067 |
| dot_accuracy@10 | 0.7733 |
| dot_precision@1 | 0.4067 |
| dot_precision@3 | 0.2533 |
| dot_precision@5 | 0.2133 |
| dot_precision@10 | 0.1513 |
| dot_recall@1 | 0.2789 |
| dot_recall@3 | 0.4353 |
| dot_recall@5 | 0.5185 |
| dot_recall@10 | 0.5797 |
| **dot_ndcg@10** | **0.5125** |
| dot_mrr@10 | 0.5244 |
| dot_map@100 | 0.4112 |
| 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 | 0.44 | 0.42 | 0.56 |
| dot_accuracy@3 | 0.66 | 0.54 | 0.7 |
| dot_accuracy@5 | 0.78 | 0.58 | 0.78 |
| dot_accuracy@10 | 0.84 | 0.7 | 0.86 |
| dot_precision@1 | 0.44 | 0.42 | 0.56 |
| dot_precision@3 | 0.22 | 0.36 | 0.2333 |
| dot_precision@5 | 0.156 | 0.344 | 0.16 |
| dot_precision@10 | 0.084 | 0.292 | 0.094 |
| dot_recall@1 | 0.44 | 0.0188 | 0.54 |
| dot_recall@3 | 0.66 | 0.0735 | 0.65 |
| dot_recall@5 | 0.78 | 0.0978 | 0.73 |
| dot_recall@10 | 0.84 | 0.1419 | 0.83 |
| **dot_ndcg@10** | **0.6402** | **0.3365** | **0.6814** |
| dot_mrr@10 | 0.5761 | 0.4958 | 0.6473 |
| dot_map@100 | 0.582 | 0.1417 | 0.631 |
| 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 | 0.4733 |
| dot_accuracy@3 | 0.6333 |
| dot_accuracy@5 | 0.7133 |
| dot_accuracy@10 | 0.8 |
| dot_precision@1 | 0.4733 |
| dot_precision@3 | 0.2711 |
| dot_precision@5 | 0.22 |
| dot_precision@10 | 0.1567 |
| dot_recall@1 | 0.3329 |
| dot_recall@3 | 0.4612 |
| dot_recall@5 | 0.5359 |
| dot_recall@10 | 0.604 |
| **dot_ndcg@10** | **0.5527** |
| dot_mrr@10 | 0.5731 |
| dot_map@100 | 0.4515 |
| 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 | 0.2 | 0.74 | 0.86 | 0.5 | 0.84 | 0.42 | 0.42 | 0.56 | 0.86 | 0.54 | 0.28 | 0.6 | 0.6327 |
| dot_accuracy@3 | 0.52 | 0.86 | 0.98 | 0.62 | 0.92 | 0.66 | 0.54 | 0.7 | 0.98 | 0.76 | 0.76 | 0.76 | 0.8571 |
| dot_accuracy@5 | 0.56 | 0.92 | 0.98 | 0.64 | 0.96 | 0.78 | 0.58 | 0.78 | 0.98 | 0.82 | 0.9 | 0.76 | 0.8776 |
| dot_accuracy@10 | 0.68 | 0.94 | 0.98 | 0.68 | 0.96 | 0.84 | 0.7 | 0.86 | 1.0 | 0.86 | 0.96 | 0.88 | 0.9796 |
| dot_precision@1 | 0.2 | 0.74 | 0.86 | 0.5 | 0.84 | 0.42 | 0.42 | 0.56 | 0.86 | 0.54 | 0.28 | 0.6 | 0.6327 |
| dot_precision@3 | 0.1933 | 0.5867 | 0.3467 | 0.3133 | 0.4733 | 0.22 | 0.36 | 0.2333 | 0.4 | 0.38 | 0.2533 | 0.2733 | 0.5986 |
| dot_precision@5 | 0.132 | 0.556 | 0.208 | 0.224 | 0.316 | 0.156 | 0.34 | 0.16 | 0.268 | 0.304 | 0.18 | 0.176 | 0.5265 |
| dot_precision@10 | 0.088 | 0.484 | 0.104 | 0.138 | 0.174 | 0.084 | 0.29 | 0.094 | 0.138 | 0.204 | 0.096 | 0.1 | 0.4327 |
| dot_recall@1 | 0.0783 | 0.0837 | 0.8067 | 0.2726 | 0.42 | 0.42 | 0.0188 | 0.54 | 0.7373 | 0.1147 | 0.28 | 0.565 | 0.0443 |
| dot_recall@3 | 0.245 | 0.1623 | 0.9433 | 0.4129 | 0.71 | 0.66 | 0.0735 | 0.65 | 0.9353 | 0.2377 | 0.76 | 0.74 | 0.1265 |
| dot_recall@5 | 0.2833 | 0.2214 | 0.9433 | 0.4606 | 0.79 | 0.78 | 0.0963 | 0.73 | 0.9733 | 0.3147 | 0.9 | 0.76 | 0.1808 |
| dot_recall@10 | 0.3473 | 0.3354 | 0.9433 | 0.5501 | 0.87 | 0.84 | 0.1418 | 0.83 | 0.9967 | 0.4197 | 0.96 | 0.88 | 0.2908 |
| **dot_ndcg@10** | **0.2733** | **0.5972** | **0.9054** | **0.4959** | **0.8027** | **0.6355** | **0.3353** | **0.6814** | **0.9284** | **0.4203** | **0.6519** | **0.7313** | **0.4946** |
| dot_mrr@10 | 0.3666 | 0.8152 | 0.9133 | 0.5642 | 0.8857 | 0.5694 | 0.496 | 0.6473 | 0.9167 | 0.6498 | 0.5499 | 0.6888 | 0.7585 |
| dot_map@100 | 0.2127 | 0.453 | 0.8845 | 0.4426 | 0.7335 | 0.5752 | 0.1414 | 0.6311 | 0.8997 | 0.3374 | 0.5515 | 0.6841 | 0.3733 |
| 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 | 0.5733 |
| dot_accuracy@3 | 0.7629 |
| dot_accuracy@5 | 0.8106 |
| dot_accuracy@10 | 0.8707 |
| dot_precision@1 | 0.5733 |
| dot_precision@3 | 0.3563 |
| dot_precision@5 | 0.2728 |
| dot_precision@10 | 0.1867 |
| dot_recall@1 | 0.337 |
| dot_recall@3 | 0.512 |
| dot_recall@5 | 0.5718 |
| dot_recall@10 | 0.6465 |
| **dot_ndcg@10** | **0.6118** |
| dot_mrr@10 | 0.6786 |
| dot_map@100 | 0.5323 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
<!--
## Bias, Risks and Limitations
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### Recommendations
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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 | - | - | 0.6253 | 0.3224 | 0.5893 | 0.5123 | 0.6112 | 0.3278 | 0.6352 | 0.5248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 100 | 0.0542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.0566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.0455 | 0.0390 | 0.5697 | 0.3083 | 0.6074 | 0.4952 | 0.5709 | 0.3402 | 0.6637 | 0.5249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 400 | 0.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.0463 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.056 | 0.0454 | 0.5981 | 0.3334 | 0.6076 | 0.5130 | 0.6217 | 0.3417 | 0.6337 | 0.5324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4525 | 700 | 0.0505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.0549 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.0614 | 0.0350 | 0.6058 | 0.3401 | 0.6084 | 0.5181 | 0.6293 | 0.3178 | 0.6585 | 0.5352 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 1000 | 0.0519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.045 | 0.0384 | 0.6045 | 0.3348 | 0.6124 | 0.5172 | 0.6227 | 0.3333 | 0.6829 | 0.5463 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 1300 | 0.0536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.0389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.9696** | **1500** | **0.0413** | **0.0401** | **0.6038** | **0.3316** | **0.602** | **0.5125** | **0.6402** | **0.3365** | **0.6814** | **0.5527** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.2733 | 0.5972 | 0.9054 | 0.4959 | 0.8027 | 0.6355 | 0.3353 | 0.6814 | 0.9284 | 0.4203 | 0.6519 | 0.7313 | 0.4946 | 0.6118 |
* 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**: 0.054 kg of CO2
- **Hours Used**: 0.409 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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