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:76349300
- loss:ArcFaceInBatchLoss
base_model: Alibaba-NLP/gte-modernbert-base
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
- cosine_auc_precision_cache_hit_ratio
- cosine_auc_similarity_distribution
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: 0.5955802603036876
      name: Cosine Accuracy@1
    - type: cosine_precision@1
      value: 0.5955802603036876
      name: Cosine Precision@1
    - type: cosine_recall@1
      value: 0.5780913232288468
      name: Cosine Recall@1
    - type: cosine_ndcg@10
      value: 0.777639866271746
      name: Cosine Ndcg@10
    - type: cosine_mrr@1
      value: 0.5955802603036876
      name: Cosine Mrr@1
    - type: cosine_map@100
      value: 0.7275779687157514
      name: Cosine Map@100
    - type: cosine_auc_precision_cache_hit_ratio
      value: 0.3639683124583609
      name: Cosine Auc Precision Cache Hit Ratio
    - type: cosine_auc_similarity_distribution
      value: 0.15401896350374616
      name: Cosine Auc Similarity Distribution
---

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

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) 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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
- **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-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': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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 = [
    '"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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.6758],
#         [1.0000, 1.0000, 0.6758],
#         [0.6758, 0.6758, 1.0078]], dtype=torch.bfloat16)
```

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## Evaluation

### Metrics

#### Custom Information Retrieval

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

| Metric                               | Value      |
|:-------------------------------------|:-----------|
| cosine_accuracy@1                    | 0.5956     |
| cosine_precision@1                   | 0.5956     |
| cosine_recall@1                      | 0.5781     |
| **cosine_ndcg@10**                   | **0.7776** |
| cosine_mrr@1                         | 0.5956     |
| cosine_map@100                       | 0.7276     |
| cosine_auc_precision_cache_hit_ratio | 0.364      |
| cosine_auc_similarity_distribution   | 0.154      |

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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: 132,354 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: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
  | anchor                                                                                        | positive                                                                                      | negative                                                                                       |
  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
  | <code> What high potential jobs are there other than computer science?</code>                 | <code> What high potential jobs are there other than computer science?</code>                 | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
  | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code>                     |
  | <code> water from the faucet is being drunk by a yellow dog</code>                            | <code>A yellow dog is drinking water from the faucet</code>                                   | <code>Childlessness is low in Eastern European countries.</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)
* Size: 132,354 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: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> |
* Samples:
  | anchor                                                                                        | positive                                                                                      | negative                                                                                       |
  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
  | <code> What high potential jobs are there other than computer science?</code>                 | <code> What high potential jobs are there other than computer science?</code>                 | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> |
  | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code>                     |
  | <code> water from the faucet is being drunk by a yellow dog</code>                            | <code>A yellow dog is drinking water from the faucet</code>                                   | <code>Childlessness is low in Eastern European countries.</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 |
|:-----:|:----:|:-------------------:|
| -1    | -1   | 0.7776              |


### 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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