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:1047690
- loss:CoSENTLoss
base_model: Alibaba-NLP/gte-modernbert-base
widget:
- source_sentence: That is evident from their failure , three times in a row , to
    get a big enough turnout to elect a president .
  sentences:
  - 'given a text, decide to which of a predefined set of classes it belongs.  examples:
    language identification, genre classification, sentiment analysis, and spam detection'
  - Three times in a row , they failed to get a big _ enough turnout to elect a president
    .
  - He said the Government still did not know the real reason the original Saudi buyer
    pulled out on August 21 .
- source_sentence: these use built-in and learned knowledge to make decisions and
    accomplish tasks that fulfill the intentions of the user.
  sentences:
  - It also features a 4.5 in back-lit LCD screen and memory expansion facilities
    .
  - '- set of interrelated components - collect, process, store and distribute info.
    - support decision-making, coordination, and control'
  - software programs that work without direct human intervention to carry out specific
    tasks for an individual user, business process, or software application -siri
    adapts to your preferences over time
- source_sentence: any location in storage can be accessed at any moment in approximately
    the same amount of time.
  sentences:
  - your study can adopt the original model used by the cited theorist but you can
    modify different variables depending on your study of the whole theory
  - an access method that can access any storage location directly and in any order;
    primary storage devices and disk storage devices use random access...
  - Branson said that his preference would be to operate a fully commercial service
    on routes to New York , Barbados and Dubai .
- source_sentence: United issued a statement saying it will " work professionally
    and cooperatively with all its unions . "
  sentences:
  - network that acts like the human brain; type of ai
  - a database system consists of one or more databases and a database management
    system (dbms).
  - Senior vice president Sara Fields said the airline " will work professionally
    and cooperatively with all our unions . "
- source_sentence: A European Union spokesman said the Commission was consulting EU
    member states " with a view to taking appropriate action if necessary " on the
    matter .
  sentences:
  - Justice Minister Martin Cauchon and Prime Minister Jean Chretien both have said
    the government will introduce legislation to decriminalize possession of small
    amounts of pot .
  - Laos 's second most important export destination - said it was consulting EU member
    states ' ' with a view to taking appropriate action if necessary ' ' on the matter
    .
  - the form data assumes and the possible range of values that the attribute defined
    as that type of data may express  1. text 2. numerical
datasets:
- redis/langcache-sentencepairs-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: val
      type: val
    metrics:
    - type: cosine_accuracy
      value: 0.7638310529446758
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8640533685684204
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6912742186395134
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.825770378112793
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.6289243437982501
      name: Cosine Precision
    - type: cosine_recall
      value: 0.7673469387755102
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7353968345121902
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.4778469995044085
      name: Cosine Mcc
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: test
      type: test
    metrics:
    - type: cosine_accuracy
      value: 0.7037777526966672
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8524033427238464
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7122170715871171
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8118724822998047
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5989283084033827
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8783612662942272
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6476665223951498
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.44182914870985407
      name: Cosine Mcc
---

# 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-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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
- **Maximum Sequence Length:** 8192 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': 8192, '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 = [
    'A European Union spokesman said the Commission was consulting EU member states " with a view to taking appropriate action if necessary " on the matter .',
    "Laos 's second most important export destination - said it was consulting EU member states ' ' with a view to taking appropriate action if necessary ' ' on the matter .",
    'the form data assumes and the possible range of values that the attribute defined as that type of data may express  1. text 2. numerical',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0078, 0.8789, 0.4961],
#         [0.8789, 1.0000, 0.4648],
#         [0.4961, 0.4648, 1.0078]], dtype=torch.bfloat16)
```

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## Evaluation

### Metrics

#### Binary Classification

* Datasets: `val` and `test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | val        | test       |
|:--------------------------|:-----------|:-----------|
| cosine_accuracy           | 0.7638     | 0.7038     |
| cosine_accuracy_threshold | 0.8641     | 0.8524     |
| cosine_f1                 | 0.6913     | 0.7122     |
| cosine_f1_threshold       | 0.8258     | 0.8119     |
| cosine_precision          | 0.6289     | 0.5989     |
| cosine_recall             | 0.7673     | 0.8784     |
| **cosine_ap**             | **0.7354** | **0.6477** |
| cosine_mcc                | 0.4778     | 0.4418     |

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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: 8,405 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                           | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 24.89 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.3 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> |
* Samples:
  | sentence1                                                                                                                             | sentence2                                                                                                                                          | label          |
  |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code>                             | <code>" The foodservice pie business does not fit our long-term growth strategy .</code>                                                           | <code>1</code> |
  | <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code>       | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code>                | <code>0</code> |
  | <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### LangCache Sentence Pairs (all)

* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1)
* Size: 8,405 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                           | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 24.89 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.3 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> |
* Samples:
  | sentence1                                                                                                                             | sentence2                                                                                                                                          | label          |
  |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code>                             | <code>" The foodservice pie business does not fit our long-term growth strategy .</code>                                                           | <code>1</code> |
  | <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code>       | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code>                | <code>0</code> |
  | <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Training Logs
| Epoch | Step | val_cosine_ap | test_cosine_ap |
|:-----:|:----:|:-------------:|:--------------:|
| -1    | -1   | 0.7354        | 0.6477         |


### 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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