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--- |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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base_model: robzchhangte/MizBERT |
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pipeline_tag: sentence-similarity |
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license: apache-2.0 |
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--- |
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# MizoEmbed |
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MizoEmbed is the first 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. |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [robzchhangte/MizBERT](https://huggingface.co/robzchhangte/MizBERT) <!-- at revision 48fbb5f83050aa1b3d4565e784228c0b621815a7 --> |
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- **Embedding Dimension:** 768 tokens |
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- **Context Length:** 512 |
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- **Language:** Mizo |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Lms18/mizo_embed") |
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# Run inference |
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sentences = [ |
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'Alassio hian he tuipui kama resort lian pathumte chu a ti zo a, thlasik khaw vawt tak avanga a huante enkawl chu a chhuang hle.', |
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'Alassio-a thlasik lum chuan a huan mawi tak takte chu a siam a ni.', |
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'Snowboarder pakhat chu snowboarding a ni.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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## License |
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This model is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details. |
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## Citation |
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### BibTeX |
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#### MizBERT |
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```bibtex |
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@article{lalramhluna2024mizbert, |
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title={MizBERT: A Mizo BERT Model}, |
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author={Lalramhluna, Robert and Dash, Sandeep and Pakray, Dr Partha}, |
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journal={ACM Transactions on Asian and Low-Resource Language Information Processing}, |
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year={2024}, |
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publisher={ACM New York, NY} |
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} |
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``` |
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#### SentenceTransformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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