Hugging Face's logo

tomaarsen
/
csr-mxbai-embed-large-v1-nq-updated-reconstruction-4

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
xet
Community
csr-mxbai-embed-large-v1-nq-updated-reconstruction-4 / README.md
tomaarsen's picture
tomaarsen HF Staff
Add new SparseEncoder model
7f028c2 verified
preview code
|
raw
history blame contribute delete
104 kB
 ---
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: 47.46504952064221
energy_consumed: 0.12211166786032028
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: 0.373
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 8
type: NanoMSMARCO_8
metrics:
- type: dot_accuracy@1
value: 0.16
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.2
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.28
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.4
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.16
name: Dot Precision@1
- type: dot_precision@3
value: 0.06666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.056000000000000015
name: Dot Precision@5
- type: dot_precision@10
value: 0.04
name: Dot Precision@10
- type: dot_recall@1
value: 0.16
name: Dot Recall@1
- type: dot_recall@3
value: 0.2
name: Dot Recall@3
- type: dot_recall@5
value: 0.28
name: Dot Recall@5
- type: dot_recall@10
value: 0.4
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2553207334684595
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2125238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.2276491742120407
name: Dot Map@100
- type: query_active_dims
value: 8.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.998046875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 8.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.998046875
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 8
type: NanoBEIR_mean_8
metrics:
- type: dot_accuracy@1
value: 0.16
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.2
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.28
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.4
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.16
name: Dot Precision@1
- type: dot_precision@3
value: 0.06666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.056000000000000015
name: Dot Precision@5
- type: dot_precision@10
value: 0.04
name: Dot Precision@10
- type: dot_recall@1
value: 0.16
name: Dot Recall@1
- type: dot_recall@3
value: 0.2
name: Dot Recall@3
- type: dot_recall@5
value: 0.28
name: Dot Recall@5
- type: dot_recall@10
value: 0.4
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2553207334684595
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2125238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.2276491742120407
name: Dot Map@100
- type: query_active_dims
value: 8.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.998046875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 8.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.998046875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 16
type: NanoMSMARCO_16
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.38
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.12666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.1
name: Dot Precision@5
- type: dot_precision@10
value: 0.05800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.24
name: Dot Recall@1
- type: dot_recall@3
value: 0.38
name: Dot Recall@3
- type: dot_recall@5
value: 0.5
name: Dot Recall@5
- type: dot_recall@10
value: 0.58
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3970913773706993
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.34011111111111114
name: Dot Mrr@10
- type: dot_map@100
value: 0.3530097721306681
name: Dot Map@100
- type: query_active_dims
value: 16.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.99609375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 16.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.99609375
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 16
type: NanoBEIR_mean_16
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.38
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.12666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.1
name: Dot Precision@5
- type: dot_precision@10
value: 0.05800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.24
name: Dot Recall@1
- type: dot_recall@3
value: 0.38
name: Dot Recall@3
- type: dot_recall@5
value: 0.5
name: Dot Recall@5
- type: dot_recall@10
value: 0.58
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3970913773706993
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.34011111111111114
name: Dot Mrr@10
- type: dot_map@100
value: 0.3530097721306681
name: Dot Map@100
- type: query_active_dims
value: 16.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.99609375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 16.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.99609375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 32
type: NanoMSMARCO_32
metrics:
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.46
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.15333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.12400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.46
name: Dot Recall@3
- type: dot_recall@5
value: 0.62
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4872873611978302
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4205555555555555
name: Dot Mrr@10
- type: dot_map@100
value: 0.43261790702081204
name: Dot Map@100
- type: query_active_dims
value: 32.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9921875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 32.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9921875
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 32
type: NanoBEIR_mean_32
metrics:
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.46
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.15333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.12400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.46
name: Dot Recall@3
- type: dot_recall@5
value: 0.62
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4872873611978302
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4205555555555555
name: Dot Mrr@10
- type: dot_map@100
value: 0.43261790702081204
name: Dot Map@100
- type: query_active_dims
value: 32.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9921875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 32.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9921875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO 64
type: NanoMSMARCO_64
metrics:
- type: dot_accuracy@1
value: 0.42
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: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
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: 0.07800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
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: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.591060924123
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5316666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.5405635822735777
name: Dot Map@100
- type: query_active_dims
value: 64.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.984375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 64.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.984375
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean 64
type: NanoBEIR_mean_64
metrics:
- type: dot_accuracy@1
value: 0.42
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: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
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: 0.07800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
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: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.591060924123
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5316666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.5405635822735777
name: Dot Map@100
- type: query_active_dims
value: 64.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.984375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 64.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.984375
name: Corpus Sparsity Ratio
- 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: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5877041624403332
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5139126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.5216553078498245
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: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5877041624403332
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5139126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.5216553078498245
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: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14800000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
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: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6246741093433497
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5611904761904761
name: Dot Mrr@10
- type: dot_map@100
value: 0.5700740174857822
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: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14800000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
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: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6246741093433497
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5611904761904761
name: Dot Mrr@10
- type: dot_map@100
value: 0.5700740174857822
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: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
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: 0.15600000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.11399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.1573333333333333
name: Dot Recall@1
- type: dot_recall@3
value: 0.24733333333333335
name: Dot Recall@3
- type: dot_recall@5
value: 0.313
name: Dot Recall@5
- type: dot_recall@10
value: 0.43799999999999994
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.35656565827441056
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.479611111111111
name: Dot Mrr@10
- type: dot_map@100
value: 0.27824724841197973
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: 0.8
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.88
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: 0.8
name: Dot Precision@1
- type: dot_precision@3
value: 0.6
name: Dot Precision@3
- type: dot_precision@5
value: 0.5800000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.484
name: Dot Precision@10
- type: dot_recall@1
value: 0.09363124545761783
name: Dot Recall@1
- type: dot_recall@3
value: 0.1617934849974966
name: Dot Recall@3
- type: dot_recall@5
value: 0.2269008951554618
name: Dot Recall@5
- type: dot_recall@10
value: 0.33039847394029737
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.607206208169174
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.852
name: Dot Mrr@10
- type: dot_map@100
value: 0.4541106866963296
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: 0.9
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: 0.9
name: Dot Precision@1
- type: dot_precision@3
value: 0.32666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.204
name: Dot Precision@5
- type: dot_precision@10
value: 0.102
name: Dot Precision@10
- type: dot_recall@1
value: 0.8466666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.8933333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.9333333333333332
name: Dot Recall@5
- type: dot_recall@10
value: 0.9333333333333332
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9080731736277194
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.92
name: Dot Mrr@10
- type: dot_map@100
value: 0.8921016869970377
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: 0.56
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.56
name: Dot Precision@1
- type: dot_precision@3
value: 0.31999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.236
name: Dot Precision@5
- type: dot_precision@10
value: 0.13
name: Dot Precision@10
- type: dot_recall@1
value: 0.29924603174603176
name: Dot Recall@1
- type: dot_recall@3
value: 0.46729365079365076
name: Dot Recall@3
- type: dot_recall@5
value: 0.5337301587301587
name: Dot Recall@5
- type: dot_recall@10
value: 0.5473412698412699
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5253203704684166
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6316666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.48003870359394873
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: 0.76
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.94
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.76
name: Dot Precision@1
- type: dot_precision@3
value: 0.5
name: Dot Precision@3
- type: dot_precision@5
value: 0.316
name: Dot Precision@5
- type: dot_precision@10
value: 0.17199999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.38
name: Dot Recall@1
- type: dot_recall@3
value: 0.75
name: Dot Recall@3
- type: dot_recall@5
value: 0.79
name: Dot Recall@5
- type: dot_recall@10
value: 0.86
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7910580229553633
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8333333333333333
name: Dot Mrr@10
- type: dot_map@100
value: 0.7410767962182596
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: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.82
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.15200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08199999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.42
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.76
name: Dot Recall@5
- type: dot_recall@10
value: 0.82
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6248295446703863
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5613809523809523
name: Dot Mrr@10
- type: dot_map@100
value: 0.5703445525063172
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: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.3533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.32
name: Dot Precision@5
- type: dot_precision@10
value: 0.272
name: Dot Precision@10
- type: dot_recall@1
value: 0.03517605061787946
name: Dot Recall@1
- type: dot_recall@3
value: 0.07646787868408336
name: Dot Recall@3
- type: dot_recall@5
value: 0.11598401724221898
name: Dot Recall@5
- type: dot_recall@10
value: 0.15931797747485815
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.33447068554509884
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5147698412698413
name: Dot Mrr@10
- type: dot_map@100
value: 0.15438429278142912
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: 0.72
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.76
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.48
name: Dot Recall@1
- type: dot_recall@3
value: 0.67
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.79
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6479593376479322
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6163333333333333
name: Dot Mrr@10
- type: dot_map@100
value: 0.6035174820443362
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: 0.96
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: 0.3999999999999999
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: 0.7973333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.922
name: Dot Recall@3
- type: dot_recall@5
value: 0.9893333333333334
name: Dot Recall@5
- type: dot_recall@10
value: 0.996
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9493554410777213
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9456666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.9286237373737373
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: 0.56
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.78
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.56
name: Dot Precision@1
- type: dot_precision@3
value: 0.4
name: Dot Precision@3
- type: dot_precision@5
value: 0.29200000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.20999999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.11866666666666668
name: Dot Recall@1
- type: dot_recall@3
value: 0.24966666666666665
name: Dot Recall@3
- type: dot_recall@5
value: 0.30266666666666675
name: Dot Recall@5
- type: dot_recall@10
value: 0.4316666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4265505670611979
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6682142857142856
name: Dot Mrr@10
- type: dot_map@100
value: 0.3385559757581844
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: 0.84
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: 0.16799999999999998
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: 0.84
name: Dot Recall@5
- type: dot_recall@10
value: 0.94
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6674878961390456
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5782460317460317
name: Dot Mrr@10
- type: dot_map@100
value: 0.5802628384687207
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: 0.7
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: 0.7
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.184
name: Dot Precision@5
- type: dot_precision@10
value: 0.1
name: Dot Precision@10
- type: dot_recall@1
value: 0.665
name: Dot Recall@1
- type: dot_recall@3
value: 0.79
name: Dot Recall@3
- type: dot_recall@5
value: 0.81
name: Dot Recall@5
- type: dot_recall@10
value: 0.88
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7776207541845983
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7519444444444445
name: Dot Mrr@10
- type: dot_map@100
value: 0.742050969601677
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: 0.5306122448979592
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8367346938775511
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8979591836734694
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9795918367346939
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5306122448979592
name: Dot Precision@1
- type: dot_precision@3
value: 0.5306122448979591
name: Dot Precision@3
- type: dot_precision@5
value: 0.5142857142857142
name: Dot Precision@5
- type: dot_precision@10
value: 0.43469387755102035
name: Dot Precision@10
- type: dot_recall@1
value: 0.03672756127909814
name: Dot Recall@1
- type: dot_recall@3
value: 0.11122615754561782
name: Dot Recall@3
- type: dot_recall@5
value: 0.17495428374251296
name: Dot Recall@5
- type: dot_recall@10
value: 0.28731694149491666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.47801832046439025
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7052073210236476
name: Dot Mrr@10
- type: dot_map@100
value: 0.3658602219028105
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: 0.6008163265306123
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7674411302982732
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8198430141287284
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8799686028257457
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6008163265306123
name: Dot Precision@1
- type: dot_precision@3
value: 0.3567137624280482
name: Dot Precision@3
- type: dot_precision@5
value: 0.2730989010989011
name: Dot Precision@5
- type: dot_precision@10
value: 0.18620722135007847
name: Dot Precision@10
- type: dot_recall@1
value: 0.3607523760846636
name: Dot Recall@1
- type: dot_recall@3
value: 0.5199318850272447
name: Dot Recall@3
- type: dot_recall@5
value: 0.5776848221695142
name: Dot Recall@5
- type: dot_recall@10
value: 0.6471826663654878
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6226550754065734
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6967979990531011
name: Dot Mrr@10
- type: dot_map@100
value: 0.5483980917195976
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-updated-reconstruction-4")
# 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([[111.0676, 23.1031, 22.6751]])
```
<!--
### 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
* Dataset: `NanoMSMARCO_8`
* 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": 8
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.16 |
| dot_accuracy@3 | 0.2 |
| dot_accuracy@5 | 0.28 |
| dot_accuracy@10 | 0.4 |
| dot_precision@1 | 0.16 |
| dot_precision@3 | 0.0667 |
| dot_precision@5 | 0.056 |
| dot_precision@10 | 0.04 |
| dot_recall@1 | 0.16 |
| dot_recall@3 | 0.2 |
| dot_recall@5 | 0.28 |
| dot_recall@10 | 0.4 |
| **dot_ndcg@10** | **0.2553** |
| dot_mrr@10 | 0.2125 |
| dot_map@100 | 0.2276 |
| query_active_dims | 8.0 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 8.0 |
| corpus_sparsity_ratio | 0.998 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_8`
* 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"
],
"max_active_dims": 8
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.16 |
| dot_accuracy@3 | 0.2 |
| dot_accuracy@5 | 0.28 |
| dot_accuracy@10 | 0.4 |
| dot_precision@1 | 0.16 |
| dot_precision@3 | 0.0667 |
| dot_precision@5 | 0.056 |
| dot_precision@10 | 0.04 |
| dot_recall@1 | 0.16 |
| dot_recall@3 | 0.2 |
| dot_recall@5 | 0.28 |
| dot_recall@10 | 0.4 |
| **dot_ndcg@10** | **0.2553** |
| dot_mrr@10 | 0.2125 |
| dot_map@100 | 0.2276 |
| query_active_dims | 8.0 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 8.0 |
| corpus_sparsity_ratio | 0.998 |
#### Sparse Information Retrieval
* Dataset: `NanoMSMARCO_16`
* 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": 16
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.24 |
| dot_accuracy@3 | 0.38 |
| dot_accuracy@5 | 0.5 |
| dot_accuracy@10 | 0.58 |
| dot_precision@1 | 0.24 |
| dot_precision@3 | 0.1267 |
| dot_precision@5 | 0.1 |
| dot_precision@10 | 0.058 |
| dot_recall@1 | 0.24 |
| dot_recall@3 | 0.38 |
| dot_recall@5 | 0.5 |
| dot_recall@10 | 0.58 |
| **dot_ndcg@10** | **0.3971** |
| dot_mrr@10 | 0.3401 |
| dot_map@100 | 0.353 |
| query_active_dims | 16.0 |
| query_sparsity_ratio | 0.9961 |
| corpus_active_dims | 16.0 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_16`
* 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"
],
"max_active_dims": 16
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.24 |
| dot_accuracy@3 | 0.38 |
| dot_accuracy@5 | 0.5 |
| dot_accuracy@10 | 0.58 |
| dot_precision@1 | 0.24 |
| dot_precision@3 | 0.1267 |
| dot_precision@5 | 0.1 |
| dot_precision@10 | 0.058 |
| dot_recall@1 | 0.24 |
| dot_recall@3 | 0.38 |
| dot_recall@5 | 0.5 |
| dot_recall@10 | 0.58 |
| **dot_ndcg@10** | **0.3971** |
| dot_mrr@10 | 0.3401 |
| dot_map@100 | 0.353 |
| query_active_dims | 16.0 |
| query_sparsity_ratio | 0.9961 |
| corpus_active_dims | 16.0 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Information Retrieval
* Dataset: `NanoMSMARCO_32`
* 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": 32
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.3 |
| dot_accuracy@3 | 0.46 |
| dot_accuracy@5 | 0.62 |
| dot_accuracy@10 | 0.7 |
| dot_precision@1 | 0.3 |
| dot_precision@3 | 0.1533 |
| dot_precision@5 | 0.124 |
| dot_precision@10 | 0.07 |
| dot_recall@1 | 0.3 |
| dot_recall@3 | 0.46 |
| dot_recall@5 | 0.62 |
| dot_recall@10 | 0.7 |
| **dot_ndcg@10** | **0.4873** |
| dot_mrr@10 | 0.4206 |
| dot_map@100 | 0.4326 |
| query_active_dims | 32.0 |
| query_sparsity_ratio | 0.9922 |
| corpus_active_dims | 32.0 |
| corpus_sparsity_ratio | 0.9922 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_32`
* 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"
],
"max_active_dims": 32
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.3 |
| dot_accuracy@3 | 0.46 |
| dot_accuracy@5 | 0.62 |
| dot_accuracy@10 | 0.7 |
| dot_precision@1 | 0.3 |
| dot_precision@3 | 0.1533 |
| dot_precision@5 | 0.124 |
| dot_precision@10 | 0.07 |
| dot_recall@1 | 0.3 |
| dot_recall@3 | 0.46 |
| dot_recall@5 | 0.62 |
| dot_recall@10 | 0.7 |
| **dot_ndcg@10** | **0.4873** |
| dot_mrr@10 | 0.4206 |
| dot_map@100 | 0.4326 |
| query_active_dims | 32.0 |
| query_sparsity_ratio | 0.9922 |
| corpus_active_dims | 32.0 |
| corpus_sparsity_ratio | 0.9922 |
#### Sparse Information Retrieval
* Dataset: `NanoMSMARCO_64`
* 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": 64
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.42 |
| dot_accuracy@3 | 0.6 |
| dot_accuracy@5 | 0.68 |
| dot_accuracy@10 | 0.78 |
| dot_precision@1 | 0.42 |
| dot_precision@3 | 0.2 |
| dot_precision@5 | 0.136 |
| dot_precision@10 | 0.078 |
| dot_recall@1 | 0.42 |
| dot_recall@3 | 0.6 |
| dot_recall@5 | 0.68 |
| dot_recall@10 | 0.78 |
| **dot_ndcg@10** | **0.5911** |
| dot_mrr@10 | 0.5317 |
| dot_map@100 | 0.5406 |
| query_active_dims | 64.0 |
| query_sparsity_ratio | 0.9844 |
| corpus_active_dims | 64.0 |
| corpus_sparsity_ratio | 0.9844 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean_64`
* 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"
],
"max_active_dims": 64
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.42 |
| dot_accuracy@3 | 0.6 |
| dot_accuracy@5 | 0.68 |
| dot_accuracy@10 | 0.78 |
| dot_precision@1 | 0.42 |
| dot_precision@3 | 0.2 |
| dot_precision@5 | 0.136 |
| dot_precision@10 | 0.078 |
| dot_recall@1 | 0.42 |
| dot_recall@3 | 0.6 |
| dot_recall@5 | 0.68 |
| dot_recall@10 | 0.78 |
| **dot_ndcg@10** | **0.5911** |
| dot_mrr@10 | 0.5317 |
| dot_map@100 | 0.5406 |
| query_active_dims | 64.0 |
| query_sparsity_ratio | 0.9844 |
| corpus_active_dims | 64.0 |
| corpus_sparsity_ratio | 0.9844 |
#### Sparse Information Retrieval
* Dataset: `NanoMSMARCO_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 | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.36 |
| dot_accuracy@3 | 0.64 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.82 |
| dot_precision@1 | 0.36 |
| dot_precision@3 | 0.2133 |
| dot_precision@5 | 0.144 |
| dot_precision@10 | 0.082 |
| dot_recall@1 | 0.36 |
| dot_recall@3 | 0.64 |
| dot_recall@5 | 0.72 |
| dot_recall@10 | 0.82 |
| **dot_ndcg@10** | **0.5877** |
| dot_mrr@10 | 0.5139 |
| dot_map@100 | 0.5217 |
| query_active_dims | 128.0 |
| query_sparsity_ratio | 0.9688 |
| corpus_active_dims | 128.0 |
| corpus_sparsity_ratio | 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"
],
"max_active_dims": 128
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.36 |
| dot_accuracy@3 | 0.64 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.82 |
| dot_precision@1 | 0.36 |
| dot_precision@3 | 0.2133 |
| dot_precision@5 | 0.144 |
| dot_precision@10 | 0.082 |
| dot_recall@1 | 0.36 |
| dot_recall@3 | 0.64 |
| dot_recall@5 | 0.72 |
| dot_recall@10 | 0.82 |
| **dot_ndcg@10** | **0.5877** |
| dot_mrr@10 | 0.5139 |
| dot_map@100 | 0.5217 |
| query_active_dims | 128.0 |
| query_sparsity_ratio | 0.9688 |
| corpus_active_dims | 128.0 |
| corpus_sparsity_ratio | 0.9688 |
#### Sparse Information Retrieval
* Dataset: `NanoMSMARCO_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 | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.42 |
| dot_accuracy@3 | 0.64 |
| dot_accuracy@5 | 0.74 |
| dot_accuracy@10 | 0.82 |
| dot_precision@1 | 0.42 |
| dot_precision@3 | 0.2133 |
| dot_precision@5 | 0.148 |
| dot_precision@10 | 0.082 |
| dot_recall@1 | 0.42 |
| dot_recall@3 | 0.64 |
| dot_recall@5 | 0.74 |
| dot_recall@10 | 0.82 |
| **dot_ndcg@10** | **0.6247** |
| dot_mrr@10 | 0.5612 |
| dot_map@100 | 0.5701 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 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"
],
"max_active_dims": 256
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.42 |
| dot_accuracy@3 | 0.64 |
| dot_accuracy@5 | 0.74 |
| dot_accuracy@10 | 0.82 |
| dot_precision@1 | 0.42 |
| dot_precision@3 | 0.2133 |
| dot_precision@5 | 0.148 |
| dot_precision@10 | 0.082 |
| dot_recall@1 | 0.42 |
| dot_recall@3 | 0.64 |
| dot_recall@5 | 0.74 |
| dot_recall@10 | 0.82 |
| **dot_ndcg@10** | **0.6247** |
| dot_mrr@10 | 0.5612 |
| dot_map@100 | 0.5701 |
| 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 | 0.36 | 0.8 | 0.9 | 0.56 | 0.76 | 0.42 | 0.44 | 0.5 | 0.92 | 0.56 | 0.36 | 0.7 | 0.5306 |
| dot_accuracy@3 | 0.52 | 0.88 | 0.92 | 0.7 | 0.9 | 0.64 | 0.56 | 0.72 | 0.96 | 0.76 | 0.78 | 0.8 | 0.8367 |
| dot_accuracy@5 | 0.66 | 0.92 | 0.96 | 0.72 | 0.94 | 0.76 | 0.6 | 0.76 | 1.0 | 0.78 | 0.84 | 0.82 | 0.898 |
| dot_accuracy@10 | 0.8 | 0.94 | 0.96 | 0.72 | 0.94 | 0.82 | 0.74 | 0.84 | 1.0 | 0.88 | 0.94 | 0.88 | 0.9796 |
| dot_precision@1 | 0.36 | 0.8 | 0.9 | 0.56 | 0.76 | 0.42 | 0.44 | 0.5 | 0.92 | 0.56 | 0.36 | 0.7 | 0.5306 |
| dot_precision@3 | 0.2 | 0.6 | 0.3267 | 0.32 | 0.5 | 0.2133 | 0.3533 | 0.2467 | 0.4 | 0.4 | 0.26 | 0.2867 | 0.5306 |
| dot_precision@5 | 0.156 | 0.58 | 0.204 | 0.236 | 0.316 | 0.152 | 0.32 | 0.16 | 0.268 | 0.292 | 0.168 | 0.184 | 0.5143 |
| dot_precision@10 | 0.114 | 0.484 | 0.102 | 0.13 | 0.172 | 0.082 | 0.272 | 0.088 | 0.138 | 0.21 | 0.094 | 0.1 | 0.4347 |
| dot_recall@1 | 0.1573 | 0.0936 | 0.8467 | 0.2992 | 0.38 | 0.42 | 0.0352 | 0.48 | 0.7973 | 0.1187 | 0.36 | 0.665 | 0.0367 |
| dot_recall@3 | 0.2473 | 0.1618 | 0.8933 | 0.4673 | 0.75 | 0.64 | 0.0765 | 0.67 | 0.922 | 0.2497 | 0.78 | 0.79 | 0.1112 |
| dot_recall@5 | 0.313 | 0.2269 | 0.9333 | 0.5337 | 0.79 | 0.76 | 0.116 | 0.72 | 0.9893 | 0.3027 | 0.84 | 0.81 | 0.175 |
| dot_recall@10 | 0.438 | 0.3304 | 0.9333 | 0.5473 | 0.86 | 0.82 | 0.1593 | 0.79 | 0.996 | 0.4317 | 0.94 | 0.88 | 0.2873 |
| **dot_ndcg@10** | **0.3566** | **0.6072** | **0.9081** | **0.5253** | **0.7911** | **0.6248** | **0.3345** | **0.648** | **0.9494** | **0.4266** | **0.6675** | **0.7776** | **0.478** |
| dot_mrr@10 | 0.4796 | 0.852 | 0.92 | 0.6317 | 0.8333 | 0.5614 | 0.5148 | 0.6163 | 0.9457 | 0.6682 | 0.5782 | 0.7519 | 0.7052 |
| dot_map@100 | 0.2782 | 0.4541 | 0.8921 | 0.48 | 0.7411 | 0.5703 | 0.1544 | 0.6035 | 0.9286 | 0.3386 | 0.5803 | 0.7421 | 0.3659 |
| 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 | 0.6008 |
| dot_accuracy@3 | 0.7674 |
| dot_accuracy@5 | 0.8198 |
| dot_accuracy@10 | 0.88 |
| dot_precision@1 | 0.6008 |
| dot_precision@3 | 0.3567 |
| dot_precision@5 | 0.2731 |
| dot_precision@10 | 0.1862 |
| dot_recall@1 | 0.3608 |
| dot_recall@3 | 0.5199 |
| dot_recall@5 | 0.5777 |
| dot_recall@10 | 0.6472 |
| **dot_ndcg@10** | **0.6227** |
| dot_mrr@10 | 0.6968 |
| dot_map@100 | 0.5484 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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": 3.0,
"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": 3.0,
"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_8_dot_ndcg@10 | NanoBEIR_mean_8_dot_ndcg@10 | NanoMSMARCO_16_dot_ndcg@10 | NanoBEIR_mean_16_dot_ndcg@10 | NanoMSMARCO_32_dot_ndcg@10 | NanoBEIR_mean_32_dot_ndcg@10 | NanoMSMARCO_64_dot_ndcg@10 | NanoBEIR_mean_64_dot_ndcg@10 | NanoMSMARCO_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_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 | - | - | 0.2447 | 0.2447 | 0.3677 | 0.3677 | 0.5086 | 0.5086 | 0.5304 | 0.5304 | 0.6134 | 0.6134 | 0.5961 | 0.5961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 100 | 0.5048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.5017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.531 | 0.6279 | 0.2125 | 0.2125 | 0.4075 | 0.4075 | 0.4686 | 0.4686 | 0.5701 | 0.5701 | 0.6086 | 0.6086 | 0.5877 | 0.5877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 400 | 0.4992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.5574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.5821 | 0.6178 | 0.2312 | 0.2312 | 0.4248 | 0.4248 | 0.4239 | 0.4239 | 0.5142 | 0.5142 | 0.6034 | 0.6034 | 0.6177 | 0.6177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4525 | 700 | 0.5632 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.5786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.5329 | 0.5743 | 0.2662 | 0.2662 | 0.4468 | 0.4468 | 0.4976 | 0.4976 | 0.5630 | 0.5630 | 0.6279 | 0.6279 | 0.6240 | 0.6240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 1000 | 0.5409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.4995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.5269 | 0.5169 | 0.2838 | 0.2838 | 0.3874 | 0.3874 | 0.4738 | 0.4738 | 0.5892 | 0.5892 | 0.5798 | 0.5798 | 0.5962 | 0.5962 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 1300 | 0.5553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.45 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.9696** | **1500** | **0.4551** | **0.5188** | **0.2553** | **0.2553** | **0.3971** | **0.3971** | **0.4873** | **0.4873** | **0.5911** | **0.5911** | **0.5877** | **0.5877** | **0.6247** | **0.6247** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.3566 | 0.6072 | 0.9081 | 0.5253 | 0.7911 | 0.6248 | 0.3345 | 0.6480 | 0.9494 | 0.4266 | 0.6675 | 0.7776 | 0.4780 | 0.6227 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.122 kWh
- **Carbon Emitted**: 0.047 kg of CO2
- **Hours Used**: 0.373 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->