---
language:
- en
license: apache-2.0
tags:
- cross-encoder
- sentence-transformers
- text-classification
- sentence-pair-classification
- semantic-similarity
- semantic-search
- retrieval
- reranking
- generated_from_trainer
- dataset_size:1452533
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-reranker-modernbert-base
datasets:
- redis/langcache-sentencepairs-v1
pipeline_tag: text-ranking
library_name: sentence-transformers
---

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

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for sentence pair classification.

## Model Details

### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
    - [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)

## 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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("redis/langcache-reranker-v1-softmnrl-triplet")
# Get scores for pairs of texts
pairs = [
    [' What high potential jobs are there other than computer science?', ' What high potential jobs are there other than computer science?'],
    [' Would India ever be able to develop a missile system like S300 or S400 missile?', ' Would India ever be able to develop a missile system like S300 or S400 missile?'],
    [' water from the faucet is being drunk by a yellow dog', 'A yellow dog is drinking water from the faucet'],
    [' water from the faucet is being drunk by a yellow dog', 'The yellow dog is drinking water from a bottle'],
    ['! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``', '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    ' What high potential jobs are there other than computer science?',
    [
        ' What high potential jobs are there other than computer science?',
        ' Would India ever be able to develop a missile system like S300 or S400 missile?',
        'A yellow dog is drinking water from the faucet',
        'The yellow dog is drinking water from a bottle',
        '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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## Training Details

### Training Dataset

#### LangCache Sentence Pairs (subsets=['all'], train+val=True)

* Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
* Size: 1,452,533 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                                           | positive                                                                                        | negative_1                                                                                     |
  |:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
  | type    | string                                                                                           | string                                                                                          | string                                                                                         |
  | details | <ul><li>min: 24 characters</li><li>mean: 114.25 characters</li><li>max: 268 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 114.1 characters</li><li>max: 226 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 93.04 characters</li><li>max: 234 characters</li></ul> |
* Samples:
  | anchor                                                                                                                                   | positive                                                                                                                                 | negative_1                                                                               |
  |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
  | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code>                                                            | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code>                                                            | <code>Are there many Canadians living and working illegally in the United States?</code> |
  | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code>Is there any tricks for straight lines mcqs?</code>                                |
  | <code> Can I pay with a debit card on PayPal?</code>                                                                                     | <code> Can I pay with a debit card on PayPal?</code>                                                                                     | <code>Can you transfer PayPal funds onto a debit card/credit card?</code>                |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "num_negatives": 1,
      "activation_fn": "torch.nn.modules.activation.Sigmoid"
  }
  ```

### Evaluation Dataset

#### LangCache Sentence Pairs (split=test)

* Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1)
* Size: 110,066 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                                         | positive                                                                                       | negative_1                                                                                      |
  |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
  | type    | string                                                                                         | string                                                                                         | string                                                                                          |
  | details | <ul><li>min: 3 characters</li><li>mean: 97.95 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 97.03 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 74.49 characters</li><li>max: 295 characters</li></ul> |
* Samples:
  | anchor                                                                                        | positive                                                                                      | negative_1                                                                                     |
  |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
  | <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>Do you get more homework in 9th grade than 8th?</code>                                   |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "num_negatives": 1,
      "activation_fn": "torch.nn.modules.activation.Sigmoid"
  }
  ```

### 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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