README.md

---
language:
- en
license: apache-2.0
tags:
- biencoder
- sentence-transformers
- text-classification
- sentence-pair-classification
- semantic-similarity
- semantic-search
- retrieval
- reranking
- generated_from_trainer
- dataset_size:1451941
- loss:MultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
- source_sentence: Gocharya ji authored Krishna Cahrit Manas in the poetic form describing
    about the full life of Lord Krishna ( from birth to Nirvana ) .
  sentences:
  - 'Q: Can I buy coverage for prescription drugs right away?'
  - Krishna Cahrit Manas in poetic form , describing the full life of Lord Krishna
    ( from birth to nirvana ) , wrote Gocharya ji .
  - Baron played actress Violet Carson who portrayed Ena Sharples in the soap .
- source_sentence: The Kilkenny line only reached Maryborough in 1867 .
  sentences:
  - It was also known formerly as ' Crotto ' .
  - The line from Maryborough only reached Kilkenny in 1867 .
  - The line from Kilkenny only reached Maryborough in 1867 .
- source_sentence: Tokelau International Netball Team represents Tokelau in the national
    netball .
  sentences:
  - Ernest Dewey Albinson ( 1898 in Minneapolis , Minnesota - 1971 in Mexico ) was
    an American artist .
  - The Tokelau national netball team represents Tokelau in international netball
    .
  - The Tokelau international netball team represents Tokelau in national netball
    .
- source_sentence: The real number is called the `` imaginary part `` of the real
    number ; the real number is called the `` complex part `` of .
  sentences:
  - The school board consists of Robbie Sanders , Bryan Richards , Linda Fullingim
    , Lori Lambert , & Kelly Teague .
  - Which web design company has the best templates?
  - The real number is called the `` imaginary part `` of the real number , the real
    number of `` complex part `` of .
- source_sentence: All For You was the third and last single of Kate Ryan 's third
    album `` Alive `` .
  sentences:
  - According to John Keay , he was `` country bred `` ( born and educated in India
    ) .
  - All For You was the third single of the third and last album `` Alive `` by Kate
    Ryan .
  - All For You was the third and last single of the third album of Kate Ryan `` Alive
    `` .
datasets:
- redis/langcache-sentencepairs-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_precision@1
- cosine_recall@1
- cosine_ndcg@10
- cosine_mrr@1
- cosine_map@100
model-index:
- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: train
      type: train
    metrics:
    - type: cosine_accuracy@1
      value: 0.37778739987010174
      name: Cosine Accuracy@1
    - type: cosine_precision@1
      value: 0.37778739987010174
      name: Cosine Precision@1
    - type: cosine_recall@1
      value: 0.36103963757730806
      name: Cosine Recall@1
    - type: cosine_ndcg@10
      value: 0.5622280163193171
      name: Cosine Ndcg@10
    - type: cosine_mrr@1
      value: 0.37778739987010174
      name: Cosine Mrr@1
    - type: cosine_map@100
      value: 0.5081953861443469
      name: Cosine Map@100
---

# Redis fine-tuned BiEncoder model for semantic caching on LangCache

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
- **Maximum Sequence Length:** 100 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("redis/langcache-embed-v3")
# Run inference
sentences = [
    "All For You was the third and last single of Kate Ryan 's third album `` Alive `` .",
    'All For You was the third and last single of the third album of Kate Ryan `` Alive `` .',
    'All For You was the third single of the third and last album `` Alive `` by Kate Ryan .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[0.9961, 0.9922, 0.9922],
#         [0.9922, 1.0000, 0.9961],
#         [0.9922, 0.9961, 1.0000]], dtype=torch.bfloat16)
```

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## Evaluation

### Metrics

#### Information Retrieval

* Dataset: `train`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy@1  | 0.3778     |
| cosine_precision@1 | 0.3778     |
| cosine_recall@1    | 0.361      |
| **cosine_ndcg@10** | **0.5622** |
| cosine_mrr@1       | 0.3778     |
| cosine_map@100     | 0.5082     |

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## Training Details

### Training Dataset

#### LangCache Sentence Pairs (all)

* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
* Size: 109,885 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.47 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                      | positive                                                                                                                                      | negative                                                                                                                                      |
  |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code>                        | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code>                           | <code>how can I get financial freedom as soon as possible?</code>                                                                             |
  | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code>                         | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code>                          | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code>                          |
  | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Evaluation Dataset

#### LangCache Sentence Pairs (all)

* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
* Size: 109,885 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.47 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                      | positive                                                                                                                                      | negative                                                                                                                                      |
  |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code>                        | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code>                           | <code>how can I get financial freedom as soon as possible?</code>                                                                             |
  | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code>                         | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code>                          | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code>                          |
  | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Logs
| Epoch | Step | train_cosine_ndcg@10 |
|:-----:|:----:|:--------------------:|
| -1    | -1   | 0.5622               |


### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0

## 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",
}
```

#### MultipleNegativesRankingLoss
```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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