Update README.md

README.md

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 license: cc-by-nc-nd-4.0
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+
+### Dataset Generation:
+
+Initially, we select the Amazon Review Dataset as our base data, referenced from Ni et al. (2019)[^1]. We randomly extract 100,000 instances from this dataset. The original labels in this dataset are ratings, scaled from 1 to 5. For our specific task, we categorize them into Positive (rating > 3), Neutral (rating = 3), and Negative (rating < 3), ensuring a balanced number of instances for each label. To generate the synthetic Code-mixed dataset, we apply two distinct methodologies: the Random Code-mixing Algorithm by Krishnan et al. (2021)[^2] and r-CM by Santy et al. (2021)[^3].
+
+### Class Distribution:
+
+#### For train.csv:
+
+| Label    | Count | Percentage |
+|----------|-------|------------|
+| Negative | 20000 | 33.33%     |
+| Neutral  | 20000 | 33.33%     |
+| Positive | 19999 | 33.33%     |
+
+#### For dev.csv:
+
+| Label    | Count | Percentage |
+|----------|-------|------------|
+| Neutral  | 6667  | 33.34%     |
+| Positive | 6667  | 33.34%     |
+| Negative | 6666  | 33.33%     |
+
+#### For test.csv:
+
+| Label    | Count | Percentage |
+|----------|-------|------------|
+| Negative | 6667  | 33.34%     |
+| Positive | 6667  | 33.34%     |
+| Neutral  | 6666  | 33.33%     |
+
+### Cite our Paper:
+
+If you utilize this dataset, kindly cite our paper.
+
+```bibtex
+@article{raihan2023mixed,
+  title={Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi},
+  author={Raihan, Md Nishat and Goswami, Dhiman and Mahmud, Antara},
+  journal={arXiv preprint arXiv:2309.10272},
+  year={2023}
+}
+```
+
+### References
+
+[^1]: Ni, J., Li, J., & McAuley, J. (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 188-197).
+
+[^2]: Krishnan, J., Anastasopoulos, A., Purohit, H., & Rangwala, H. (2021). Multilingual code-switching for zero-shot cross-lingual intent prediction and slot filling. arXiv preprint arXiv:2103.07792.
+
+[^3]: Santy, S., Srinivasan, A., & Choudhury, M. (2021). BERTologiCoMix: How does code-mixing interact with multilingual BERT? In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 111-121).
+
+---