---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: Qwen/Qwen2.5-0.5B
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on Qwen/Qwen2.5-0.5B

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B). It maps sentences & paragraphs to a 896-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) <!-- at revision 060db6499f32faf8b98477b0a26969ef7d8b9987 -->
- **Maximum Sequence Length:** 32768 tokens
- **Output Dimensionality:** 896 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modalities:** Text, Message
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'flat'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen2Model'})
  (1): Pooling({'embedding_dimension': 896, 'pooling_mode': 'lasttoken', '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0078, 0.9844, 0.9688],
#         [0.9844, 0.9961, 0.9375],
#         [0.9688, 0.9375, 1.0000]], dtype=torch.bfloat16)
```
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## Training Details

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.6.0
- Transformers: 5.5.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.14.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2

## Citation

### BibTeX

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