Create README.md

README.md

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+---
+language:
+- vi
+task_categories:
+- text-classification
+- token-classification
+---
+
+## Dataset Card for ViHOS
+
+### 1. Dataset Summary
+
+**ViHOS** (Vietnamese Hate and Offensive Spans) is the first Vietnamese corpus with **span-level** annotations of hateful and offensive content. It contains **26,476** annotated spans over **11,056** comments:
+
+* **5,360** comments containing ≥1 hate/offensive spans
+* **5,696** clean comments (no spans)
+
+Annotations cover a rich variety of phenomena (teencodes, metaphors, puns, etc.), with both span‑extraction and sequence‑labeling formats.
+
+### 2. Supported Tasks and Leaderboard
+
+* **Primary Task**: Sequence labeling / span extraction for hate/offensive detection
+* **Metrics**: Precision, recall, F1‑score on span detection
+
+*No public leaderboard; share your results in the ViSoLex repo!*
+
+
+### 3. Languages
+
+* Vietnamese
+
+
+### 4. Data Fields
+
+| Column        | Type     | Description                                                                 |
+| ------------- | -------- | --------------------------------------------------------------------------- |
+| `sentence`     | `string` | The raw Vietnamese comment.                                                 |
+| `word`     | `string` | Words in the sentence after the processing of tokenization using VnCoreNLP tokenizer                                              |
+| `index_spans` | `string` | JSON‑encoded list of indices (for span extraction) *or* list of BIO labels. |
+| `tag`        | `string` | The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word)                               |
+| `type`        | `string` | Split: `train` / `validation` / `test`.                                     |
+
+
+### 5. Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("visolex/ViHOS")
+
+train_ds = ds.filter(lambda x: x["type"] == "train")
+dev_ds   = ds.filter(lambda x: x["type"] == "dev")
+test_ds  = ds.filter(lambda x: x["type"] == "test")
+
+# Example
+print(train_ds[0])
+```
+
+### 6. Dataset Creation & Sources
+
+* **GitHub Repository** (source code & raw data):
+  [https://github.com/phusroyal/ViHOS](https://github.com/phusroyal/ViHOS)
+* **Hugging Face Dataset**:
+  [https://huggingface.co/datasets/phusroyal/ViHOS](https://huggingface.co/datasets/phusroyal/ViHOS)
+* **Original Paper**:
+  Hoang et al. (2023), “ViHOS: Hate Speech Spans Detection for Vietnamese” see citation below
+
+We downloaded the original train/validation/test JSONs, converted both span‑extraction and sequence‑labeling views into a single CSV, and added a `type` column.
+
+### 7. Licenses and Citation
+
+#### License
+
+Please refer to the `LICENSE` in the GitHub repo. If unspecified, assume **CC BY 4.0**.
+
+#### How to Cite
+
+**ViHOS Paper**
+
+```bibtex
+@inproceedings{hoang-etal-2023-vihos,
+  title     = {ViHOS: Hate Speech Spans Detection for Vietnamese},
+  author    = {Hoang, Phu Gia and Luu, Canh Duc and Tran, Khanh Quoc and Nguyen, Kiet Van and Nguyen, Ngan Luu‑Thuy},
+  booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
+  month     = may,
+  year      = {2023},
+  address   = {Dubrovnik, Croatia},
+  publisher = {Association for Computational Linguistics},
+  url       = {https://aclanthology.org/2023.eacl-main.47},
+  doi       = {10.18653/v1/2023.eacl-main.47},
+  pages     = {652--669}
+}
+```
+
+**Hugging Face Dataset**
+
+```bibtex
+@misc{phusroyal_vihos,
+  title        = {ViHOS: Vietnamese Hate and Offensive Spans},
+  author       = {{phusroyal}},
+  howpublished = {\url{https://huggingface.co/datasets/phusroyal/ViHOS}},
+  year         = {2023}
+}
+```