Upload README.md

README.md

@@ -0,0 +1,157 @@
+---
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+base_model: robzchhangte/MizBERT
+pipeline_tag: sentence-similarity
+license: apache-2.0
+---
+
+# MizoEmbed
+
+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.
+
+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
+- **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("Lms18/mizo_embed")
+# Run inference
+sentences = [
+    '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.',
+    'Alassio-a thlasik lum chuan a huan mawi tak takte chu a siam a ni.',
+    'Snowboarder pakhat chu snowboarding a ni.',
+]
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## License
+This model is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.
+
+## Citation
+
+### BibTeX
+
+
+#### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->