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:8000000
- loss:ArcFaceInBatchLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: '"How much would I need to narrate a ""Let''s Play"" video in order
    to make money from it on YouTube?"'
  sentences:
  - How much money do people make from YouTube videos with 1 million views?
  - '"How much would I need to narrate a ""Let''s Play"" video in order to make money
    from it on YouTube?"'
  - '"Does the sentence, ""I expect to be disappointed,"" make sense?"'
- source_sentence: '"I appreciate that.'
  sentences:
  - '"How is the Mariner rewarded in ""The Rime of the Ancient Mariner"" by Samuel
    Taylor Coleridge?"'
  - '"I appreciate that.'
  - I can appreciate that.
- source_sentence: '"""It is very easy to defeat someone, but too hard to win some
    one"". What does the previous sentence mean?"'
  sentences:
  - '"How can you use the word ""visceral"" in a sentence?"'
  - '"""It is very easy to defeat someone, but too hard to win some one"". What does
    the previous sentence mean?"'
  - '"What does ""The loudest one in the room is the weakest one in the room."" Mean?"'
- source_sentence: '" We condemn this raid which is in our view illegal and morally
    and politically unjustifiable , " London-based NCRI official Ali Safavi told Reuters
    by telephone .'
  sentences:
  - 'London-based NCRI official Ali Safavi told Reuters : " We condemn this raid ,
    which is in our view illegal and morally and politically unjustifiable . "'
  - The social awkwardness is complicated by the fact that Marianne is a white girl
    living with a black family .
  - art's cause, this in my opinion
- source_sentence: '"If you click ""like"" on an old post that someone made on your
    wall yet you''re no longer Facebook friends, will they still receive a notification?"'
  sentences:
  - '"Is there is any two wheeler having a gear box which has the feature ""automatic
    neutral"" when the engine is off while it is in gear?"'
  - '"If you click ""like"" on an old post that someone made on your wall yet you''re
    no longer Facebook friends, will they still receive a notification?"'
  - '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would
    you be concerned?"'
datasets:
- redis/langcache-sentencepairs-v2
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: custom-information-retrieval
      name: Custom Information Retrieval
    dataset:
      name: test
      type: test
    metrics:
    - type: cosine_accuracy@1
      value: 0.6162
      name: Cosine Accuracy@1
    - type: cosine_precision@1
      value: 0.6162
      name: Cosine Precision@1
    - type: cosine_recall@1
      value: 0.5987
      name: Cosine Recall@1
    - type: cosine_ndcg@10
      value: 0.7883
      name: Cosine Ndcg@10
    - type: cosine_mrr@1
      value: 0.6162
      name: Cosine Mrr@1
    - type: cosine_map@100
      value: 0.7416
      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 [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for sentence pair similarity.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
- **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': 512, 'do_lower_case': True, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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-small")
# Run inference
sentences = [
    '"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"',
    '"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"',
    '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would you be concerned?"',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
```

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## Evaluation

### Metrics

#### Custom Information Retrieval

* Dataset: `test`
* Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code>

| Metric | Value |
|:-------------------------------------|:-----------|
| cosine_accuracy@1 | 0.6162 |
| cosine_precision@1 | 0.6162 |
| cosine_recall@1 | 0.5987 |
| **cosine_ndcg@10** | **0.7883** |
| cosine_mrr@1 | 0.6162 |
| cosine_map@100 | 0.7416 |

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## Training Details

### Training Dataset

#### LangCache Sentence Pairs (all)

* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2)
* Size: ~8,000,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Loss: <code>losses.ArcFaceInBatchLoss</code> 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-v2)
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Logs

| Epoch | Step | test_cosine_ndcg@10 |
|:-----:|:----:|:-------------------:|
| 4.0   | 40000| 0.7880              |


### 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",
}
```

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