---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80000
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-base
pipeline_tag: sentence-similarity
library_name: sentence-transformers
datasets:
- mshojaei77/Persian_sft
language:
- fa
---

# maux-gte-persian-v3 (fp16)
**A high-performance Persian sentence embedding model based on Alibaba-NLP/gte-multilingual-base, released in fp16 for efficient inference.**

---

## Model Overview

This is the **fp16 (half-precision)** version of [maux-gte-persian-v3](https://huggingface.co/xmanii/maux-gte-persian-v3), a Sentence Transformers model fine-tuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) for robust Persian sentence and paragraph embeddings.  
The fp16 format enables faster and more memory-efficient inference, especially on modern GPUs.

**Key Features:**
- **Base Model:** Alibaba-NLP/gte-multilingual-base
- **Fine-tuned on:** [mshojaei77/Persian_sft](https://huggingface.co/datasets/mshojaei77/Persian_sft) (80,000 Persian sentence pairs)
- **Output Dimension:** 768
- **Max Sequence Length:** 8192 tokens
- **Similarity Function:** Cosine Similarity
- **Loss Function:** MultipleNegativesRankingLoss
- **Format:** fp16 (`model.safetensors`)

---

## Performance

- **Excellent performance** on Persian semantic similarity, search, and clustering tasks.
- **Outperforms** or matches [jinaai-v3](https://huggingface.co/jinaai/jina-embeddings-v3-base-fa) in most Persian benchmarks (see [comparison charts](./jinaai_v3_vs_maux_v3_comparison.png)).
- Efficient for large-scale inference due to fp16 weights.

---

## Model Architecture

```python
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, ...})
  (2): Normalize()
)
```

---

## Usage

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("xmanii/maux-gte-persian-v3-fp16", trust_remote_code=True)
sentences = [
    'برج میلاد در تهران هست',
    'یکی از برج های مسکونی تهران برج تهران است',
    'تهران برج های زیادی دارد'
]
embeddings = model.encode(sentences)
print(embeddings.shape)  # [3, 768]

# Compute cosine similarity
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)  # [3, 3]
```

---

## Training Details

- **Dataset:** [mshojaei77/Persian_sft](https://huggingface.co/datasets/mshojaei77/Persian_sft)
- **Loss:** MultipleNegativesRankingLoss (scale=20.0, similarity_fct="cos_sim")
- **Batch size:** 64
- **Precision:** bf16 during training, fp16 for this release
- **Frameworks:** Python 3.10, Sentence Transformers 4.1.0, Transformers 4.51.3, PyTorch 2.7.0+cu126

---

## Files

- `model.safetensors` (fp16 weights)
- All necessary config and tokenizer files
- Custom code: `modeling.py`, `configuration.py` (required for loading)

---

## Citation

If you use this model, please cite:

```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",
}
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

---

## Acknowledgements

- Special thanks to [mshojaei77](https://huggingface.co/mshojaei77) for the Persian_sft dataset.
- Built on top of [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base).

---

## License

This model is distributed under the same license as the base model and dataset.

---

**For questions or feedback, please open an issue or discussion on the Hugging Face model page.**