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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:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 42.81821457704325
energy_consumed: 0.11015691860871116
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: 0.274
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: nq eval 4
type: nq_eval_4
metrics:
- type: cosine_accuracy@1
value: 0.341
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.53
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.616
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.71
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.341
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1766666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12319999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.071
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.341
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.53
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.616
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.71
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5177559532868556
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4569571428571428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.46808238304226085
name: Cosine Map@100
- type: query_active_dims
value: 4.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9990234375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 4.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9990234375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 8
type: nq_eval_8
metrics:
- type: cosine_accuracy@1
value: 0.479
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.683
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.743
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.827
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.479
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22766666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14859999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08270000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.479
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.683
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.743
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.827
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6514732993360963
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5954253968253969
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.602459158736598
name: Cosine 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: nq eval 16
type: nq_eval_16
metrics:
- type: cosine_accuracy@1
value: 0.61
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.792
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.843
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.61
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.264
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16860000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.61
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.792
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.843
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7573375805688765
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7114896825396828
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7159603693257915
name: Cosine 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: nq eval 32
type: nq_eval_32
metrics:
- type: cosine_accuracy@1
value: 0.739
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.871
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.899
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.936
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.739
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2903333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17980000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0936
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.739
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.871
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.899
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.936
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8407099394827843
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8098075396825399
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8124255549328265
name: Cosine 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: nq eval 64
type: nq_eval_64
metrics:
- type: cosine_accuracy@1
value: 0.775
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.895
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.925
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.775
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2983333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18500000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0951
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.775
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.895
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.925
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8672657281787072
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8399420634920639
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8417827624389276
name: Cosine Map@100
- type: query_active_dims
value: 63.992000579833984
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.984376952983439
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: nq eval 128
type: nq_eval_128
metrics:
- type: cosine_accuracy@1
value: 0.797
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.901
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.933
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.797
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30033333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18660000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0951
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.797
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.901
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.933
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8780719613731008
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8541857142857148
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8561013158199787
name: Cosine Map@100
- type: query_active_dims
value: 119.21700286865234
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9708942864090204
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 119.6520004272461
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9707880858331919
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 256
type: nq_eval_256
metrics:
- type: cosine_accuracy@1
value: 0.8
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.901
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.933
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30033333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18660000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0951
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.901
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.933
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8788975201919854
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8553369047619053
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8573055135070745
name: Cosine Map@100
- type: query_active_dims
value: 133.42999267578125
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9674243181943893
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 129.16900634765625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9684645980596542
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:** Cosine Similarity
- **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-cos-sim-scale-5-gamma-1-detach-2")
# 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([[0.8907, 0.0410, 0.0237]])
```
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## Evaluation
### Metrics
#### Sparse Information Retrieval
* Dataset: `nq_eval_4`
* 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": 4
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.341 |
| cosine_accuracy@3 | 0.53 |
| cosine_accuracy@5 | 0.616 |
| cosine_accuracy@10 | 0.71 |
| cosine_precision@1 | 0.341 |
| cosine_precision@3 | 0.1767 |
| cosine_precision@5 | 0.1232 |
| cosine_precision@10 | 0.071 |
| cosine_recall@1 | 0.341 |
| cosine_recall@3 | 0.53 |
| cosine_recall@5 | 0.616 |
| cosine_recall@10 | 0.71 |
| **cosine_ndcg@10** | **0.5178** |
| cosine_mrr@10 | 0.457 |
| cosine_map@100 | 0.4681 |
| query_active_dims | 4.0 |
| query_sparsity_ratio | 0.999 |
| corpus_active_dims | 4.0 |
| corpus_sparsity_ratio | 0.999 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_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 |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.479 |
| cosine_accuracy@3 | 0.683 |
| cosine_accuracy@5 | 0.743 |
| cosine_accuracy@10 | 0.827 |
| cosine_precision@1 | 0.479 |
| cosine_precision@3 | 0.2277 |
| cosine_precision@5 | 0.1486 |
| cosine_precision@10 | 0.0827 |
| cosine_recall@1 | 0.479 |
| cosine_recall@3 | 0.683 |
| cosine_recall@5 | 0.743 |
| cosine_recall@10 | 0.827 |
| **cosine_ndcg@10** | **0.6515** |
| cosine_mrr@10 | 0.5954 |
| cosine_map@100 | 0.6025 |
| query_active_dims | 8.0 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 8.0 |
| corpus_sparsity_ratio | 0.998 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_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 |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.61 |
| cosine_accuracy@3 | 0.792 |
| cosine_accuracy@5 | 0.843 |
| cosine_accuracy@10 | 0.9 |
| cosine_precision@1 | 0.61 |
| cosine_precision@3 | 0.264 |
| cosine_precision@5 | 0.1686 |
| cosine_precision@10 | 0.09 |
| cosine_recall@1 | 0.61 |
| cosine_recall@3 | 0.792 |
| cosine_recall@5 | 0.843 |
| cosine_recall@10 | 0.9 |
| **cosine_ndcg@10** | **0.7573** |
| cosine_mrr@10 | 0.7115 |
| cosine_map@100 | 0.716 |
| query_active_dims | 16.0 |
| query_sparsity_ratio | 0.9961 |
| corpus_active_dims | 16.0 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_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 |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.739 |
| cosine_accuracy@3 | 0.871 |
| cosine_accuracy@5 | 0.899 |
| cosine_accuracy@10 | 0.936 |
| cosine_precision@1 | 0.739 |
| cosine_precision@3 | 0.2903 |
| cosine_precision@5 | 0.1798 |
| cosine_precision@10 | 0.0936 |
| cosine_recall@1 | 0.739 |
| cosine_recall@3 | 0.871 |
| cosine_recall@5 | 0.899 |
| cosine_recall@10 | 0.936 |
| **cosine_ndcg@10** | **0.8407** |
| cosine_mrr@10 | 0.8098 |
| cosine_map@100 | 0.8124 |
| query_active_dims | 32.0 |
| query_sparsity_ratio | 0.9922 |
| corpus_active_dims | 32.0 |
| corpus_sparsity_ratio | 0.9922 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_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 |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.775 |
| cosine_accuracy@3 | 0.895 |
| cosine_accuracy@5 | 0.925 |
| cosine_accuracy@10 | 0.951 |
| cosine_precision@1 | 0.775 |
| cosine_precision@3 | 0.2983 |
| cosine_precision@5 | 0.185 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.775 |
| cosine_recall@3 | 0.895 |
| cosine_recall@5 | 0.925 |
| cosine_recall@10 | 0.951 |
| **cosine_ndcg@10** | **0.8673** |
| cosine_mrr@10 | 0.8399 |
| cosine_map@100 | 0.8418 |
| query_active_dims | 63.992 |
| query_sparsity_ratio | 0.9844 |
| corpus_active_dims | 64.0 |
| corpus_sparsity_ratio | 0.9844 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_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 |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.797 |
| cosine_accuracy@3 | 0.901 |
| cosine_accuracy@5 | 0.933 |
| cosine_accuracy@10 | 0.951 |
| cosine_precision@1 | 0.797 |
| cosine_precision@3 | 0.3003 |
| cosine_precision@5 | 0.1866 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.797 |
| cosine_recall@3 | 0.901 |
| cosine_recall@5 | 0.933 |
| cosine_recall@10 | 0.951 |
| **cosine_ndcg@10** | **0.8781** |
| cosine_mrr@10 | 0.8542 |
| cosine_map@100 | 0.8561 |
| query_active_dims | 119.217 |
| query_sparsity_ratio | 0.9709 |
| corpus_active_dims | 119.652 |
| corpus_sparsity_ratio | 0.9708 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_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 |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.8 |
| cosine_accuracy@3 | 0.901 |
| cosine_accuracy@5 | 0.933 |
| cosine_accuracy@10 | 0.951 |
| cosine_precision@1 | 0.8 |
| cosine_precision@3 | 0.3003 |
| cosine_precision@5 | 0.1866 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.8 |
| cosine_recall@3 | 0.901 |
| cosine_recall@5 | 0.933 |
| cosine_recall@10 | 0.951 |
| **cosine_ndcg@10** | **0.8789** |
| cosine_mrr@10 | 0.8553 |
| cosine_map@100 | 0.8573 |
| query_active_dims | 133.43 |
| query_sparsity_ratio | 0.9674 |
| corpus_active_dims | 129.169 |
| corpus_sparsity_ratio | 0.9685 |
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=5.0, similarity_fct='cos_sim')"
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=5.0, similarity_fct='cos_sim')"
}
```
### 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
- `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`: False
- `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 | nq_eval_4_cosine_ndcg@10 | nq_eval_8_cosine_ndcg@10 | nq_eval_16_cosine_ndcg@10 | nq_eval_32_cosine_ndcg@10 | nq_eval_64_cosine_ndcg@10 | nq_eval_128_cosine_ndcg@10 | nq_eval_256_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:--------------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.2566 | 0.4513 | 0.6853 | 0.8617 | 0.9369 | 0.9685 | 0.9757 |
| 0.0646 | 100 | 2.9836 | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 2.7758 | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 2.6386 | 2.3891 | 0.4003 | 0.5884 | 0.7387 | 0.8220 | 0.8695 | 0.9164 | 0.9372 |
| 0.2586 | 400 | 2.5466 | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 2.4711 | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 2.3918 | 2.1817 | 0.4580 | 0.6189 | 0.7230 | 0.7986 | 0.8554 | 0.8939 | 0.9146 |
| 0.4525 | 700 | 2.2802 | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 2.1309 | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 2.0585 | 1.8844 | 0.4932 | 0.6402 | 0.7482 | 0.8361 | 0.8665 | 0.8857 | 0.8895 |
| 0.6464 | 1000 | 2.0203 | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 1.9934 | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 1.9734 | 1.8208 | 0.5168 | 0.6452 | 0.7592 | 0.8371 | 0.8690 | 0.8775 | 0.8804 |
| 0.8403 | 1300 | 1.9583 | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 1.9496 | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 1.9499 | 1.8020 | 0.5159 | 0.6536 | 0.7568 | 0.8399 | 0.8670 | 0.8785 | 0.8778 |
| -1 | -1 | - | - | 0.5178 | 0.6515 | 0.7573 | 0.8407 | 0.8673 | 0.8781 | 0.8789 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.110 kWh
- **Carbon Emitted**: 0.043 kg of CO2
- **Hours Used**: 0.274 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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