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csr-mxbai-embed-large-v1-nq-updated-3-gamma

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: 47.434702684263996
energy_consumed: 0.12203359561891627
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: 0.375
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: 0.26
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.3
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.38
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.16
name: Dot Precision@1
- type: dot_precision@3
value: 0.08666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.06000000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.038000000000000006
name: Dot Precision@10
- type: dot_recall@1
value: 0.16
name: Dot Recall@1
- type: dot_recall@3
value: 0.26
name: Dot Recall@3
- type: dot_recall@5
value: 0.3
name: Dot Recall@5
- type: dot_recall@10
value: 0.38
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2643920551837278
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2287222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.2421742990834593
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: 0.26
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.3
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.38
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.16
name: Dot Precision@1
- type: dot_precision@3
value: 0.08666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.06000000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.038000000000000006
name: Dot Precision@10
- type: dot_recall@1
value: 0.16
name: Dot Recall@1
- type: dot_recall@3
value: 0.26
name: Dot Recall@3
- type: dot_recall@5
value: 0.3
name: Dot Recall@5
- type: dot_recall@10
value: 0.38
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2643920551837278
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2287222222222222
name: Dot Mrr@10
- type: dot_map@100
value: 0.2421742990834593
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: 0.2
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.36
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.56
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.2
name: Dot Precision@1
- type: dot_precision@3
value: 0.11999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.10400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.056000000000000015
name: Dot Precision@10
- type: dot_recall@1
value: 0.2
name: Dot Recall@1
- type: dot_recall@3
value: 0.36
name: Dot Recall@3
- type: dot_recall@5
value: 0.52
name: Dot Recall@5
- type: dot_recall@10
value: 0.56
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.37793342795121726
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3195238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.3313906364396061
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: 0.2
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.36
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.56
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.2
name: Dot Precision@1
- type: dot_precision@3
value: 0.11999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.10400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.056000000000000015
name: Dot Precision@10
- type: dot_recall@1
value: 0.2
name: Dot Recall@1
- type: dot_recall@3
value: 0.36
name: Dot Recall@3
- type: dot_recall@5
value: 0.52
name: Dot Recall@5
- type: dot_recall@10
value: 0.56
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.37793342795121726
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3195238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.3313906364396061
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: 0.28
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: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.1533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.124
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.28
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: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.49220107783094286
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.414984126984127
name: Dot Mrr@10
- type: dot_map@100
value: 0.4254258308486964
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: 0.28
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: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.1533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.124
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.28
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: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.49220107783094286
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.414984126984127
name: Dot Mrr@10
- type: dot_map@100
value: 0.4254258308486964
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: 0.26
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.26
name: Dot Precision@1
- type: dot_precision@3
value: 0.18666666666666668
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.07800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.26
name: Dot Recall@1
- type: dot_recall@3
value: 0.56
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5211165234079713
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43816666666666676
name: Dot Mrr@10
- type: dot_map@100
value: 0.44682904023702474
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: 0.26
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.26
name: Dot Precision@1
- type: dot_precision@3
value: 0.18666666666666668
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.07800000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.26
name: Dot Recall@1
- type: dot_recall@3
value: 0.56
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5211165234079713
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43816666666666676
name: Dot Mrr@10
- type: dot_map@100
value: 0.44682904023702474
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: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.078
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5452270995944036
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4693571428571428
name: Dot Mrr@10
- type: dot_map@100
value: 0.4800750120044718
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: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.078
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.78
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5452270995944036
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4693571428571428
name: Dot Mrr@10
- type: dot_map@100
value: 0.4800750120044718
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: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.88
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6012297417081948
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5137063492063492
name: Dot Mrr@10
- type: dot_map@100
value: 0.5174560618904659
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: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.88
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6012297417081948
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5137063492063492
name: Dot Mrr@10
- type: dot_map@100
value: 0.5174560618904659
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: 0.26
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.26
name: Dot Precision@1
- type: dot_precision@3
value: 0.2
name: Dot Precision@3
- type: dot_precision@5
value: 0.16
name: Dot Precision@5
- type: dot_precision@10
value: 0.11599999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.125
name: Dot Recall@1
- type: dot_recall@3
value: 0.264
name: Dot Recall@3
- type: dot_recall@5
value: 0.3413333333333334
name: Dot Recall@5
- type: dot_recall@10
value: 0.45966666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.35170577305757716
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43010317460317443
name: Dot Mrr@10
- type: dot_map@100
value: 0.2687894311785997
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: 0.84
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.86
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.94
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.84
name: Dot Precision@1
- type: dot_precision@3
value: 0.64
name: Dot Precision@3
- type: dot_precision@5
value: 0.5840000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.496
name: Dot Precision@10
- type: dot_recall@1
value: 0.10173542236179474
name: Dot Recall@1
- type: dot_recall@3
value: 0.17058256895318635
name: Dot Recall@3
- type: dot_recall@5
value: 0.2583095364918772
name: Dot Recall@5
- type: dot_recall@10
value: 0.3630014394355859
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6336927949275843
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8761904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.4758024592559406
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: 0.92
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: 0.86
name: Dot Precision@1
- type: dot_precision@3
value: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.19599999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.09799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8066666666666665
name: Dot Recall@1
- type: dot_recall@3
value: 0.8766666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.9066666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.9066666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8718114197539545
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8873333333333332
name: Dot Mrr@10
- type: dot_map@100
value: 0.8545556012614837
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: 0.52
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.74
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.52
name: Dot Precision@1
- type: dot_precision@3
value: 0.32666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.244
name: Dot Precision@5
- type: dot_precision@10
value: 0.136
name: Dot Precision@10
- type: dot_recall@1
value: 0.27924603174603174
name: Dot Recall@1
- type: dot_recall@3
value: 0.46423809523809517
name: Dot Recall@3
- type: dot_recall@5
value: 0.5373730158730158
name: Dot Recall@5
- type: dot_recall@10
value: 0.5967063492063491
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.524168679753325
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6079999999999999
name: Dot Mrr@10
- type: dot_map@100
value: 0.4629550043583209
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.78
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.96
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: 0.78
name: Dot Precision@1
- type: dot_precision@3
value: 0.5266666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.33599999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.17199999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.39
name: Dot Recall@1
- type: dot_recall@3
value: 0.79
name: Dot Recall@3
- type: dot_recall@5
value: 0.84
name: Dot Recall@5
- type: dot_recall@10
value: 0.86
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8057192735678995
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.86
name: Dot Mrr@10
- type: dot_map@100
value: 0.7534450002577441
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: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.088
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.88
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6004025758045302
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5123253968253968
name: Dot Mrr@10
- type: dot_map@100
value: 0.516161874779488
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: 0.36
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: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.3399999999999999
name: Dot Precision@3
- type: dot_precision@5
value: 0.324
name: Dot Precision@5
- type: dot_precision@10
value: 0.29600000000000004
name: Dot Precision@10
- type: dot_recall@1
value: 0.02204584498659392
name: Dot Recall@1
- type: dot_recall@3
value: 0.07879591204712627
name: Dot Recall@3
- type: dot_recall@5
value: 0.10547939299642282
name: Dot Recall@5
- type: dot_recall@10
value: 0.14785915311216402
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.33938410167518285
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4792698412698413
name: Dot Mrr@10
- type: dot_map@100
value: 0.15285384570175153
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: 0.48
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: 0.84
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.48
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: 0.092
name: Dot Precision@10
- type: dot_recall@1
value: 0.45
name: Dot Recall@1
- type: dot_recall@3
value: 0.65
name: Dot Recall@3
- type: dot_recall@5
value: 0.72
name: Dot Recall@5
- type: dot_recall@10
value: 0.81
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6405630856499873
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6049444444444443
name: Dot Mrr@10
- type: dot_map@100
value: 0.5830795845663244
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: 0.9
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.98
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: 0.9
name: Dot Precision@1
- type: dot_precision@3
value: 0.40666666666666657
name: Dot Precision@3
- type: dot_precision@5
value: 0.264
name: Dot Precision@5
- type: dot_precision@10
value: 0.13599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.7773333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.9420000000000001
name: Dot Recall@3
- type: dot_recall@5
value: 0.986
name: Dot Recall@5
- type: dot_recall@10
value: 0.9933333333333334
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9416151444086611
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9406666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.9166897546897548
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: 0.72
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.96
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.54
name: Dot Precision@1
- type: dot_precision@3
value: 0.3533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.30000000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.22
name: Dot Precision@10
- type: dot_recall@1
value: 0.11466666666666668
name: Dot Recall@1
- type: dot_recall@3
value: 0.22066666666666662
name: Dot Recall@3
- type: dot_recall@5
value: 0.3106666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.45166666666666655
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.43056509196331577
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6608888888888889
name: Dot Mrr@10
- type: dot_map@100
value: 0.33416401376998806
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: 0.68
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.84
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: 0.22666666666666668
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
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: 0.68
name: Dot Recall@3
- type: dot_recall@5
value: 0.84
name: Dot Recall@5
- type: dot_recall@10
value: 0.96
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6189399449298651
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5095
name: Dot Mrr@10
- type: dot_map@100
value: 0.5115
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: 0.62
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.72
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: 0.62
name: Dot Precision@1
- type: dot_precision@3
value: 0.25999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.17199999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.09799999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.595
name: Dot Recall@1
- type: dot_recall@3
value: 0.71
name: Dot Recall@3
- type: dot_recall@5
value: 0.765
name: Dot Recall@5
- type: dot_recall@10
value: 0.86
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7339772270342122
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6960238095238094
name: Dot Mrr@10
- type: dot_map@100
value: 0.692771989570098
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.5918367346938775
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8367346938775511
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9183673469387755
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5918367346938775
name: Dot Precision@1
- type: dot_precision@3
value: 0.5510204081632653
name: Dot Precision@3
- type: dot_precision@5
value: 0.5224489795918367
name: Dot Precision@5
- type: dot_precision@10
value: 0.436734693877551
name: Dot Precision@10
- type: dot_recall@1
value: 0.04213824203491695
name: Dot Recall@1
- type: dot_recall@3
value: 0.11466092557078722
name: Dot Recall@3
- type: dot_recall@5
value: 0.17849505170580585
name: Dot Recall@5
- type: dot_recall@10
value: 0.28879003826995
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.48796783228419977
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7384839650145772
name: Dot Mrr@10
- type: dot_map@100
value: 0.3666982120682313
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: 0.5670643642072213
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7474411302982732
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8214128728414443
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.8999372056514913
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5670643642072213
name: Dot Precision@1
- type: dot_precision@3
value: 0.3516169544740973
name: Dot Precision@3
- type: dot_precision@5
value: 0.2749576138147567
name: Dot Precision@5
- type: dot_precision@10
value: 0.19082574568288851
name: Dot Precision@10
- type: dot_recall@1
value: 0.3326024775227695
name: Dot Recall@1
- type: dot_recall@3
value: 0.5032008334725021
name: Dot Recall@3
- type: dot_recall@5
value: 0.5776402818256761
name: Dot Recall@5
- type: dot_recall@10
value: 0.6598223317967217
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6138856111392534
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6772099997508161
name: Dot Mrr@10
- type: dot_map@100
value: 0.529958982419825
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-3-gamma")
# 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([[109.6969, 27.9723, 19.3123]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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 | 0.26 |
| dot_accuracy@5 | 0.3 |
| dot_accuracy@10 | 0.38 |
| dot_precision@1 | 0.16 |
| dot_precision@3 | 0.0867 |
| dot_precision@5 | 0.06 |
| dot_precision@10 | 0.038 |
| dot_recall@1 | 0.16 |
| dot_recall@3 | 0.26 |
| dot_recall@5 | 0.3 |
| dot_recall@10 | 0.38 |
| **dot_ndcg@10** | **0.2644** |
| dot_mrr@10 | 0.2287 |
| dot_map@100 | 0.2422 |
| 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 | 0.26 |
| dot_accuracy@5 | 0.3 |
| dot_accuracy@10 | 0.38 |
| dot_precision@1 | 0.16 |
| dot_precision@3 | 0.0867 |
| dot_precision@5 | 0.06 |
| dot_precision@10 | 0.038 |
| dot_recall@1 | 0.16 |
| dot_recall@3 | 0.26 |
| dot_recall@5 | 0.3 |
| dot_recall@10 | 0.38 |
| **dot_ndcg@10** | **0.2644** |
| dot_mrr@10 | 0.2287 |
| dot_map@100 | 0.2422 |
| 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 | 0.2 |
| dot_accuracy@3 | 0.36 |
| dot_accuracy@5 | 0.52 |
| dot_accuracy@10 | 0.56 |
| dot_precision@1 | 0.2 |
| dot_precision@3 | 0.12 |
| dot_precision@5 | 0.104 |
| dot_precision@10 | 0.056 |
| dot_recall@1 | 0.2 |
| dot_recall@3 | 0.36 |
| dot_recall@5 | 0.52 |
| dot_recall@10 | 0.56 |
| **dot_ndcg@10** | **0.3779** |
| dot_mrr@10 | 0.3195 |
| dot_map@100 | 0.3314 |
| 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 | 0.2 |
| dot_accuracy@3 | 0.36 |
| dot_accuracy@5 | 0.52 |
| dot_accuracy@10 | 0.56 |
| dot_precision@1 | 0.2 |
| dot_precision@3 | 0.12 |
| dot_precision@5 | 0.104 |
| dot_precision@10 | 0.056 |
| dot_recall@1 | 0.2 |
| dot_recall@3 | 0.36 |
| dot_recall@5 | 0.52 |
| dot_recall@10 | 0.56 |
| **dot_ndcg@10** | **0.3779** |
| dot_mrr@10 | 0.3195 |
| dot_map@100 | 0.3314 |
| 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 | 0.28 |
| dot_accuracy@3 | 0.46 |
| dot_accuracy@5 | 0.62 |
| dot_accuracy@10 | 0.74 |
| dot_precision@1 | 0.28 |
| dot_precision@3 | 0.1533 |
| dot_precision@5 | 0.124 |
| dot_precision@10 | 0.074 |
| dot_recall@1 | 0.28 |
| dot_recall@3 | 0.46 |
| dot_recall@5 | 0.62 |
| dot_recall@10 | 0.74 |
| **dot_ndcg@10** | **0.4922** |
| dot_mrr@10 | 0.415 |
| dot_map@100 | 0.4254 |
| 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 | 0.28 |
| dot_accuracy@3 | 0.46 |
| dot_accuracy@5 | 0.62 |
| dot_accuracy@10 | 0.74 |
| dot_precision@1 | 0.28 |
| dot_precision@3 | 0.1533 |
| dot_precision@5 | 0.124 |
| dot_precision@10 | 0.074 |
| dot_recall@1 | 0.28 |
| dot_recall@3 | 0.46 |
| dot_recall@5 | 0.62 |
| dot_recall@10 | 0.74 |
| **dot_ndcg@10** | **0.4922** |
| dot_mrr@10 | 0.415 |
| dot_map@100 | 0.4254 |
| 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 | 0.26 |
| dot_accuracy@3 | 0.56 |
| dot_accuracy@5 | 0.66 |
| dot_accuracy@10 | 0.78 |
| dot_precision@1 | 0.26 |
| dot_precision@3 | 0.1867 |
| dot_precision@5 | 0.132 |
| dot_precision@10 | 0.078 |
| dot_recall@1 | 0.26 |
| dot_recall@3 | 0.56 |
| dot_recall@5 | 0.66 |
| dot_recall@10 | 0.78 |
| **dot_ndcg@10** | **0.5211** |
| dot_mrr@10 | 0.4382 |
| dot_map@100 | 0.4468 |
| 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 | 0.26 |
| dot_accuracy@3 | 0.56 |
| dot_accuracy@5 | 0.66 |
| dot_accuracy@10 | 0.78 |
| dot_precision@1 | 0.26 |
| dot_precision@3 | 0.1867 |
| dot_precision@5 | 0.132 |
| dot_precision@10 | 0.078 |
| dot_recall@1 | 0.26 |
| dot_recall@3 | 0.56 |
| dot_recall@5 | 0.66 |
| dot_recall@10 | 0.78 |
| **dot_ndcg@10** | **0.5211** |
| dot_mrr@10 | 0.4382 |
| dot_map@100 | 0.4468 |
| 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 | 0.3 |
| dot_accuracy@3 | 0.58 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.78 |
| dot_precision@1 | 0.3 |
| dot_precision@3 | 0.1933 |
| dot_precision@5 | 0.144 |
| dot_precision@10 | 0.078 |
| dot_recall@1 | 0.3 |
| dot_recall@3 | 0.58 |
| dot_recall@5 | 0.72 |
| dot_recall@10 | 0.78 |
| **dot_ndcg@10** | **0.5452** |
| dot_mrr@10 | 0.4694 |
| dot_map@100 | 0.4801 |
| 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 | 0.3 |
| dot_accuracy@3 | 0.58 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.78 |
| dot_precision@1 | 0.3 |
| dot_precision@3 | 0.1933 |
| dot_precision@5 | 0.144 |
| dot_precision@10 | 0.078 |
| dot_recall@1 | 0.3 |
| dot_recall@3 | 0.58 |
| dot_recall@5 | 0.72 |
| dot_recall@10 | 0.78 |
| **dot_ndcg@10** | **0.5452** |
| dot_mrr@10 | 0.4694 |
| dot_map@100 | 0.4801 |
| 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 | 0.34 |
| dot_accuracy@3 | 0.58 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.88 |
| dot_precision@1 | 0.34 |
| dot_precision@3 | 0.1933 |
| dot_precision@5 | 0.144 |
| dot_precision@10 | 0.088 |
| dot_recall@1 | 0.34 |
| dot_recall@3 | 0.58 |
| dot_recall@5 | 0.72 |
| dot_recall@10 | 0.88 |
| **dot_ndcg@10** | **0.6012** |
| dot_mrr@10 | 0.5137 |
| dot_map@100 | 0.5175 |
| 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 | 0.34 |
| dot_accuracy@3 | 0.58 |
| dot_accuracy@5 | 0.72 |
| dot_accuracy@10 | 0.88 |
| dot_precision@1 | 0.34 |
| dot_precision@3 | 0.1933 |
| dot_precision@5 | 0.144 |
| dot_precision@10 | 0.088 |
| dot_recall@1 | 0.34 |
| dot_recall@3 | 0.58 |
| dot_recall@5 | 0.72 |
| dot_recall@10 | 0.88 |
| **dot_ndcg@10** | **0.6012** |
| dot_mrr@10 | 0.5137 |
| dot_map@100 | 0.5175 |
| 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 | 0.26 | 0.84 | 0.86 | 0.52 | 0.78 | 0.34 | 0.36 | 0.48 | 0.9 | 0.54 | 0.28 | 0.62 | 0.5918 |
| dot_accuracy@3 | 0.54 | 0.86 | 0.92 | 0.68 | 0.96 | 0.58 | 0.54 | 0.7 | 0.98 | 0.72 | 0.68 | 0.72 | 0.8367 |
| dot_accuracy@5 | 0.68 | 0.94 | 0.94 | 0.74 | 0.96 | 0.72 | 0.58 | 0.78 | 1.0 | 0.8 | 0.84 | 0.78 | 0.9184 |
| dot_accuracy@10 | 0.8 | 0.98 | 0.94 | 0.78 | 0.96 | 0.88 | 0.78 | 0.84 | 1.0 | 0.96 | 0.96 | 0.86 | 0.9592 |
| dot_precision@1 | 0.26 | 0.84 | 0.86 | 0.52 | 0.78 | 0.34 | 0.36 | 0.48 | 0.9 | 0.54 | 0.28 | 0.62 | 0.5918 |
| dot_precision@3 | 0.2 | 0.64 | 0.3133 | 0.3267 | 0.5267 | 0.1933 | 0.34 | 0.2333 | 0.4067 | 0.3533 | 0.2267 | 0.26 | 0.551 |
| dot_precision@5 | 0.16 | 0.584 | 0.196 | 0.244 | 0.336 | 0.144 | 0.324 | 0.16 | 0.264 | 0.3 | 0.168 | 0.172 | 0.5224 |
| dot_precision@10 | 0.116 | 0.496 | 0.098 | 0.136 | 0.172 | 0.088 | 0.296 | 0.092 | 0.136 | 0.22 | 0.096 | 0.098 | 0.4367 |
| dot_recall@1 | 0.125 | 0.1017 | 0.8067 | 0.2792 | 0.39 | 0.34 | 0.022 | 0.45 | 0.7773 | 0.1147 | 0.28 | 0.595 | 0.0421 |
| dot_recall@3 | 0.264 | 0.1706 | 0.8767 | 0.4642 | 0.79 | 0.58 | 0.0788 | 0.65 | 0.942 | 0.2207 | 0.68 | 0.71 | 0.1147 |
| dot_recall@5 | 0.3413 | 0.2583 | 0.9067 | 0.5374 | 0.84 | 0.72 | 0.1055 | 0.72 | 0.986 | 0.3107 | 0.84 | 0.765 | 0.1785 |
| dot_recall@10 | 0.4597 | 0.363 | 0.9067 | 0.5967 | 0.86 | 0.88 | 0.1479 | 0.81 | 0.9933 | 0.4517 | 0.96 | 0.86 | 0.2888 |
| **dot_ndcg@10** | **0.3517** | **0.6337** | **0.8718** | **0.5242** | **0.8057** | **0.6004** | **0.3394** | **0.6406** | **0.9416** | **0.4306** | **0.6189** | **0.734** | **0.488** |
| dot_mrr@10 | 0.4301 | 0.8762 | 0.8873 | 0.608 | 0.86 | 0.5123 | 0.4793 | 0.6049 | 0.9407 | 0.6609 | 0.5095 | 0.696 | 0.7385 |
| dot_map@100 | 0.2688 | 0.4758 | 0.8546 | 0.463 | 0.7534 | 0.5162 | 0.1529 | 0.5831 | 0.9167 | 0.3342 | 0.5115 | 0.6928 | 0.3667 |
| 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 | 0.5671 |
| dot_accuracy@3 | 0.7474 |
| dot_accuracy@5 | 0.8214 |
| dot_accuracy@10 | 0.8999 |
| dot_precision@1 | 0.5671 |
| dot_precision@3 | 0.3516 |
| dot_precision@5 | 0.275 |
| dot_precision@10 | 0.1908 |
| dot_recall@1 | 0.3326 |
| dot_recall@3 | 0.5032 |
| dot_recall@5 | 0.5776 |
| dot_recall@10 | 0.6598 |
| **dot_ndcg@10** | **0.6139** |
| dot_mrr@10 | 0.6772 |
| dot_map@100 | 0.53 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 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 | - | - | 0.1761 | 0.1761 | 0.3606 | 0.3606 | 0.4594 | 0.4594 | 0.5242 | 0.5242 | 0.5340 | 0.5340 | 0.6114 | 0.6114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 100 | 0.4772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.5194 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.5562 | 0.5943 | 0.1845 | 0.1845 | 0.3927 | 0.3927 | 0.4948 | 0.4948 | 0.5317 | 0.5317 | 0.5446 | 0.5446 | 0.5852 | 0.5852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2586 | 400 | 0.4754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.5033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.5309 | 0.4936 | 0.2922 | 0.2922 | 0.4045 | 0.4045 | 0.4662 | 0.4662 | 0.5397 | 0.5397 | 0.5570 | 0.5570 | 0.5925 | 0.5925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4525 | 700 | 0.5566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.5634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.549 | 0.4587 | 0.2317 | 0.2317 | 0.3703 | 0.3703 | 0.4874 | 0.4874 | 0.5371 | 0.5371 | 0.5722 | 0.5722 | 0.5795 | 0.5795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6464 | 1000 | 0.5503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.4568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.5555 | 0.4304 | 0.3129 | 0.3129 | 0.3837 | 0.3837 | 0.5105 | 0.5105 | 0.5042 | 0.5042 | 0.5435 | 0.5435 | 0.6011 | 0.6011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8403 | 1300 | 0.518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.4763 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.9696** | **1500** | **0.4828** | **0.4055** | **0.2644** | **0.2644** | **0.3779** | **0.3779** | **0.4922** | **0.4922** | **0.5211** | **0.5211** | **0.5452** | **0.5452** | **0.6012** | **0.6012** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.3517 | 0.6337 | 0.8718 | 0.5242 | 0.8057 | 0.6004 | 0.3394 | 0.6406 | 0.9416 | 0.4306 | 0.6189 | 0.7340 | 0.4880 | 0.6139 |
* 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**: 0.375 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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