File size: 15,636 Bytes

---
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:483820
- loss:MultipleNegativesSymmetricRankingLoss
base_model: Alibaba-NLP/gte-modernbert-base
widget:
- source_sentence: 'See Precambrian time scale # Proposed Geologic timeline for another
    set of periods 4600 -- 541 MYA .'
  sentences:
  - In 2014 election , Biju Janata Dal candidate Tathagat Satapathy Bharatiya Janata
    party candidate Rudra Narayan Pany defeated with a margin of 1.37,340 votes .
  - In Scotland , the Strathclyde Partnership for Transport , formerly known as Strathclyde
    Passenger Transport Executive , comprises the former Strathclyde region , which
    includes the urban area around Glasgow .
  - 'See Precambrian Time Scale # Proposed Geological Timeline for another set of
    periods of 4600 -- 541 MYA .'
- source_sentence: It is also 5 kilometers northeast of Tamaqua , 27 miles south of
    Allentown and 9 miles northwest of Hazleton .
  sentences:
  - In 1948 he moved to Massachusetts , and eventually settled in Vermont .
  - Suddenly I remembered that I was a New Zealander , I caught the first plane home
    and came back .
  - It is also 5 miles northeast of Tamaqua , 27 miles south of Allentown , and 9
    miles northwest of Hazleton .
- source_sentence: The party has a Member of Parliament , a member of the House of
    Lords , three members of the London Assembly and two Members of the European Parliament
    .
  sentences:
  - The party has one Member of Parliament , one member of the House of Lords , three
    Members of the London Assembly and two Members of the European Parliament .
  - Grapsid crabs dominate in Australia , Malaysia and Panama , while gastropods Cerithidea
    scalariformis and Melampus coeffeus are important seed predators in Florida mangroves
    .
  - Music Story is a music service website and international music data provider that
    curates , aggregates and analyses metadata for digital music services .
- source_sentence: 'The play received two 1969 Tony Award nominations : Best Actress
    in a Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .'
  sentences:
  - Ravishanker is a fellow of the International Statistical Institute and an elected
    member of the American Statistical Association .
  - 'In 1969 , the play received two Tony - Award nominations : Best Actress in a
    Theatre Play ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .'
  - AMD and Nvidia both have proprietary methods of scaling , CrossFireX for AMD ,
    and SLI for Nvidia .
- source_sentence: He was a close friend of Ángel Cabrera and is a cousin of golfer
    Tony Croatto .
  sentences:
  - He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto
    .
  - Eugenijus Bartulis ( born December 7 , 1949 in Kaunas ) is a Lithuanian Roman
    Catholic priest , and Bishop of Šiauliai .
  - UWIRE also distributes its members content to professional media outlets , including
    Yahoo , CNN and CBS News .
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: test
      type: test
    metrics:
    - type: cosine_accuracy
      value: 0.7035681462730365
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8473721742630005
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.712274188436637
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.8116312026977539
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5987668417446905
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8788826815642458
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6473811496690576
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.4419218320172892
      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 = [
    'He was a close friend of Ángel Cabrera and is a cousin of golfer Tony Croatto .',
    'He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto .',
    'UWIRE also distributes its members content to professional media outlets , including Yahoo , CNN and CBS News .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[0.9922, 0.9922, 0.5352],
#         [0.9922, 0.9961, 0.5391],
#         [0.5352, 0.5391, 1.0000]], dtype=torch.bfloat16)
```

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## Evaluation

### Metrics

#### Binary Classification

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

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.7036     |
| cosine_accuracy_threshold | 0.8474     |
| cosine_f1                 | 0.7123     |
| cosine_f1_threshold       | 0.8116     |
| cosine_precision          | 0.5988     |
| cosine_recall             | 0.8789     |
| **cosine_ap**             | **0.6474** |
| cosine_mcc                | 0.4419     |

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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: 26,850 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: 8 tokens</li><li>mean: 27.35 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                                             | sentence2                                                                                                                      | label          |
  |:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <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>1</code> |
  | <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
  | <code>The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .</code>        | <code>The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .</code>                      | <code>1</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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: 26,850 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: 8 tokens</li><li>mean: 27.35 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                                             | sentence2                                                                                                                      | label          |
  |:----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <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>1</code> |
  | <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
  | <code>The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .</code>        | <code>The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .</code>                      | <code>1</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Logs
| Epoch | Step | test_cosine_ap |
|:-----:|:----:|:--------------:|
| -1    | -1   | 0.6474         |


### 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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