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csr-mxbai-embed-large-v1-nq-cos-sim-scale-50-gamma-0.1-detach-2

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:
- 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: 41.30839791316536
energy_consumed: 0.10627266624088727
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: 0.262
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: 0.195
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.323
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.394
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.47
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.195
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.10766666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0788
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04699999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.195
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.323
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.394
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.47
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.32377386157136745
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.278015476190476
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2884464006986836
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: 0.404
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.611
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.681
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.404
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12219999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0681
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.404
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.56
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.611
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.681
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.539833012308952
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.49499206349206337
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5040685370722027
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: 0.607
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.781
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.831
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.876
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.607
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26033333333333336
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16620000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0876
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.607
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.781
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.831
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.876
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7454352025587541
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7031380952380955
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7079722555257966
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: 0.797
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.918
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.94
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.971
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.797
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.306
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18800000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09710000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.797
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.918
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.94
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.971
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8883813392071823
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8614698412698414
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8625825721970143
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: 0.882
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.971
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.984
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.987
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.882
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3236666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19680000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09870000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.882
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.971
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.984
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.987
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9420700985601923
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9267666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9274088353313353
name: Cosine 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: nq eval 128
type: nq_eval_128
metrics:
- type: cosine_accuracy@1
value: 0.924
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.983
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.987
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.99
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.924
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3276666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19740000000000005
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.099
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.924
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.983
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.987
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9632306047329049
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.954
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9544732574612811
name: Cosine 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: nq eval 256
type: nq_eval_256
metrics:
- type: cosine_accuracy@1
value: 0.949
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.985
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.991
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.993
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.949
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32833333333333325
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19820000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09930000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.949
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.985
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.991
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.993
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9742124713902499
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9678444444444445
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9680795428781169
name: Cosine 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:** 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-50-gamma-0.1-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([[0.6239, 0.1049, 0.1287]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Dataset: `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 | 0.195 |
| cosine_accuracy@3 | 0.323 |
| cosine_accuracy@5 | 0.394 |
| cosine_accuracy@10 | 0.47 |
| cosine_precision@1 | 0.195 |
| cosine_precision@3 | 0.1077 |
| cosine_precision@5 | 0.0788 |
| cosine_precision@10 | 0.047 |
| cosine_recall@1 | 0.195 |
| cosine_recall@3 | 0.323 |
| cosine_recall@5 | 0.394 |
| cosine_recall@10 | 0.47 |
| **cosine_ndcg@10** | **0.3238** |
| cosine_mrr@10 | 0.278 |
| cosine_map@100 | 0.2884 |
| 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 | 0.404 |
| cosine_accuracy@3 | 0.56 |
| cosine_accuracy@5 | 0.611 |
| cosine_accuracy@10 | 0.681 |
| cosine_precision@1 | 0.404 |
| cosine_precision@3 | 0.1867 |
| cosine_precision@5 | 0.1222 |
| cosine_precision@10 | 0.0681 |
| cosine_recall@1 | 0.404 |
| cosine_recall@3 | 0.56 |
| cosine_recall@5 | 0.611 |
| cosine_recall@10 | 0.681 |
| **cosine_ndcg@10** | **0.5398** |
| cosine_mrr@10 | 0.495 |
| cosine_map@100 | 0.5041 |
| 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 | 0.607 |
| cosine_accuracy@3 | 0.781 |
| cosine_accuracy@5 | 0.831 |
| cosine_accuracy@10 | 0.876 |
| cosine_precision@1 | 0.607 |
| cosine_precision@3 | 0.2603 |
| cosine_precision@5 | 0.1662 |
| cosine_precision@10 | 0.0876 |
| cosine_recall@1 | 0.607 |
| cosine_recall@3 | 0.781 |
| cosine_recall@5 | 0.831 |
| cosine_recall@10 | 0.876 |
| **cosine_ndcg@10** | **0.7454** |
| cosine_mrr@10 | 0.7031 |
| cosine_map@100 | 0.708 |
| 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 | 0.797 |
| cosine_accuracy@3 | 0.918 |
| cosine_accuracy@5 | 0.94 |
| cosine_accuracy@10 | 0.971 |
| cosine_precision@1 | 0.797 |
| cosine_precision@3 | 0.306 |
| cosine_precision@5 | 0.188 |
| cosine_precision@10 | 0.0971 |
| cosine_recall@1 | 0.797 |
| cosine_recall@3 | 0.918 |
| cosine_recall@5 | 0.94 |
| cosine_recall@10 | 0.971 |
| **cosine_ndcg@10** | **0.8884** |
| cosine_mrr@10 | 0.8615 |
| cosine_map@100 | 0.8626 |
| 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 | 0.882 |
| cosine_accuracy@3 | 0.971 |
| cosine_accuracy@5 | 0.984 |
| cosine_accuracy@10 | 0.987 |
| cosine_precision@1 | 0.882 |
| cosine_precision@3 | 0.3237 |
| cosine_precision@5 | 0.1968 |
| cosine_precision@10 | 0.0987 |
| cosine_recall@1 | 0.882 |
| cosine_recall@3 | 0.971 |
| cosine_recall@5 | 0.984 |
| cosine_recall@10 | 0.987 |
| **cosine_ndcg@10** | **0.9421** |
| cosine_mrr@10 | 0.9268 |
| cosine_map@100 | 0.9274 |
| query_active_dims | 64.0 |
| 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 | 0.924 |
| cosine_accuracy@3 | 0.983 |
| cosine_accuracy@5 | 0.987 |
| cosine_accuracy@10 | 0.99 |
| cosine_precision@1 | 0.924 |
| cosine_precision@3 | 0.3277 |
| cosine_precision@5 | 0.1974 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.924 |
| cosine_recall@3 | 0.983 |
| cosine_recall@5 | 0.987 |
| cosine_recall@10 | 0.99 |
| **cosine_ndcg@10** | **0.9632** |
| cosine_mrr@10 | 0.954 |
| cosine_map@100 | 0.9545 |
| query_active_dims | 128.0 |
| query_sparsity_ratio | 0.9688 |
| corpus_active_dims | 128.0 |
| corpus_sparsity_ratio | 0.9688 |
#### 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 | 0.949 |
| cosine_accuracy@3 | 0.985 |
| cosine_accuracy@5 | 0.991 |
| cosine_accuracy@10 | 0.993 |
| cosine_precision@1 | 0.949 |
| cosine_precision@3 | 0.3283 |
| cosine_precision@5 | 0.1982 |
| cosine_precision@10 | 0.0993 |
| cosine_recall@1 | 0.949 |
| cosine_recall@3 | 0.985 |
| cosine_recall@5 | 0.991 |
| cosine_recall@10 | 0.993 |
| **cosine_ndcg@10** | **0.9742** |
| cosine_mrr@10 | 0.9678 |
| cosine_map@100 | 0.9681 |
| 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": 0.1,
"loss": "SparseMultipleNegativesRankingLoss(scale=50.0, 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": 0.1,
"loss": "SparseMultipleNegativesRankingLoss(scale=50.0, 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 | - | - | 0.2582 | 0.4445 | 0.6785 | 0.8729 | 0.9382 | 0.9661 | 0.9715 |
| 0.0646 | 100 | 0.2786 | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.2487 | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.24 | 0.2349 | 0.3247 | 0.5166 | 0.7410 | 0.8795 | 0.9475 | 0.9624 | 0.9695 |
| 0.2586 | 400 | 0.2346 | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.2315 | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.2296 | 0.2252 | 0.3333 | 0.5439 | 0.7608 | 0.8848 | 0.9432 | 0.9647 | 0.9731 |
| 0.4525 | 700 | 0.2278 | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.2262 | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.225 | 0.2204 | 0.3232 | 0.5521 | 0.7555 | 0.8924 | 0.9448 | 0.9609 | 0.9732 |
| 0.6464 | 1000 | 0.2238 | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.2226 | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.2224 | 0.2180 | 0.3311 | 0.5476 | 0.7420 | 0.8863 | 0.9456 | 0.9615 | 0.9746 |
| 0.8403 | 1300 | 0.2217 | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.2212 | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 0.2212 | 0.2171 | 0.3226 | 0.5407 | 0.7449 | 0.8858 | 0.9449 | 0.9652 | 0.9722 |
| -1 | -1 | - | - | 0.3238 | 0.5398 | 0.7454 | 0.8884 | 0.9421 | 0.9632 | 0.9742 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.106 kWh
- **Carbon Emitted**: 0.041 kg of CO2
- **Hours Used**: 0.261 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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