sentence-transformers safetensors bert sentence-similarity feature-extraction arxiv:1908.10084 arxiv:1705.00652 base_model:robzchhangte/MizBERT base_model:finetune:robzchhangte/MizBERT license:apache-2.0 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: robzchhangte/MizBERT
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
---

# MizoEmbed-1


<img src="https://huggingface.co/sarkii/MizoEmbed-1/resolve/main/glitch.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>


MizoEmbed-1 is the first dense embedding model developed specifically for the Mizo language. This pioneering model provides vector representations of Mizo text, enabling various natural language processing tasks and applications for the underrepresented language.

The model maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [robzchhangte/MizBERT](https://huggingface.co/robzchhangte/MizBERT) <!-- at revision 48fbb5f83050aa1b3d4565e784228c0b621815a7 -->
- **Embedding Dimension:** 768 tokens
- **Input Context Length:** 512
- **Language:** Mizo



## 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("sarkii/MizoEmbed-1")
# Run inference
sentences = [
    'Mizoram a ka zin chu ka hlawkpui hle mai.',
    'Ka zinna ram Mizoram ah Reiek Tlang a awm.',
    'Inkhelh hi ka thiam vaklo mahse ka inkhel lui tho thin.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Downstream Usage (Sentence Transformers)

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## License
This model is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.

## Citation

#### MizBERT
```bibtex
@article{lalramhluna2024mizbert,
  title={MizBERT: A Mizo BERT Model},
  author={Lalramhluna, Robert and Dash, Sandeep and Pakray, Dr Partha},
  journal={ACM Transactions on Asian and Low-Resource Language Information Processing},
  year={2024},
  publisher={ACM New York, NY}
}
```

#### SentenceTransformers
```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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@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}
}
```

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