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Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
text-embeddings-inference
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main-v2
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: prestoai/qwen3-embedding-0.6b-arabic-ecom
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on prestoai/qwen3-embedding-0.6b-arabic-ecom

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) <!-- at revision 80f273fd53c6644d65e14a2ac1fbf74b8c924097 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 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': 'Qwen3Model'})
  (1): Pooling({'embedding_dimension': 1024, '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, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8491, 0.6747],
#         [0.8491, 1.0000, 0.7339],
#         [0.6747, 0.7339, 1.0000]])
```
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### Downstream Usage (Sentence Transformers)

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## Training Details

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0
- Datasets: 5.0.0
- Tokenizers: 0.22.2

## Citation

### BibTeX

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