Update README.md

README.md

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   - split: train
     path: data/train-*
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+
+
+# Bangla Sentiment Dataset (SentiGOLD v1)
+
+**SentiGOLD 70k v1** is a high-quality Bangla (Bengali) text dataset developed for multiclass sentiment classification. It contains over 70,000 labeled text samples annotated by native speakers. The dataset was curated and released by **Gigatech Limited** in collaboration with the **Bangladesh Computer Council (BCC)**.
+
+Each text entry in the dataset is categorized into one of the following sentiment classes:
+
+- **SP**: Strongly Positive 
+- **WP**: Weakly Positive
+- **WN**: Weakly Positive Negative  
+- **SN**: Strongly Negative  
+- **NU**: Neutral  
+
+This dataset provides a valuable resource for building and evaluating sentiment analysis models in the Bangla language.
+
+
+## Use the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('SayedShaun/bangla-sentigold')
+print(dataset)
+
+>>> DatasetDict({
+>>>     train: Dataset({
+>>>         features: ['ID', 'Text', 'Polarity', 'Domain'],
+>>>         num_rows: 70000
+>>>    })
+>>> })
+```
+
+
+## Source and Citation
+
+**[SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and Its Evaluation](https://arxiv.org/abs/2306.06147)**  
+
+```bibtex
+@inproceedings{islam2023sentigold,
+  title={Sentigold: A large bangla gold standard multi-domain sentiment analysis dataset and its evaluation},
+  author={Islam, Md Ekramul and Chowdhury, Labib and Khan, Faisal Ahamed and Hossain, Shazzad and Hossain, Md Sourave and Rashid, Mohammad Mamun Or and Mohammed, Nabeel and Amin, Mohammad Ruhul},
+  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
+  pages={4207--4218},
+  year={2023}
+}