README.md

---
tags:
- sentence-transformers
- cross-encoder
- reranker
base_model: mixedbread-ai/mxbai-rerank-base-v2
pipeline_tag: text-ranking
library_name: sentence-transformers
---

# CrossEncoder based on mixedbread-ai/mxbai-rerank-base-v2

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

## Model Details

### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [mixedbread-ai/mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) <!-- at revision 3334116c32105657feed9b0a733f451c012ee61a -->
- **Maximum Sequence Length:** 32768 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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/huggingface/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("cross-encoder-testing/mxbai-rerank-base-v2-v6")
# Get scores for pairs of texts
pairs = [
    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'How many calories in an egg',
    [
        'There are on average between 55 and 80 calories in an egg depending on its size.',
        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
        'Most of the calories in an egg come from the yellow yolk in the center.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Training Details

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 5.3.0.dev0
- Transformers: 4.57.3
- PyTorch: 2.9.1+cu126
- Accelerate: 1.6.0
- Datasets: 4.2.0
- Tokenizers: 0.22.1

## Citation

### BibTeX

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