Create README.md

README.md

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+---
+language: vi
+tags:
+- hate-speech-detection
+- vietnamese
+- phobert
+license: apache-2.0
+datasets:
+- VN-HSD
+metrics:
+- accuracy
+- f1
+model-index:
+- name: phobert-hsd
+  results:
+  - task:
+      type: text-classification
+      name: Hate Speech Detection
+    dataset:
+      name: VN-HSD
+      type: custom
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: <INSERT_ACCURACY>
+    - name: F1 Score
+      type: f1
+      value: <INSERT_F1_SCORE>
+base_model:
+- vinai/phobert-base
+pipeline_tag: text-classification
+---
+
+# PhoBERT‑HSD: Hate Speech Detection for Vietnamese Text
+
+Fine‑tuned from [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the **VN‑HSD** dataset.
+
+## Model Details
+
+* **Base Model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
+* **Dataset**: VN‑HSD (ViSoLex‑HSD unified hate speech corpus)
+* **Fine‑tuning**: HuggingFace Transformers
+
+### Hyperparameters
+
+* Batch size: `32`
+* Learning rate: `5e-5`
+* Epochs: `100`
+* Max sequence length: `256`
+
+## Results
+
+* **Accuracy**: `<INSERT_ACCURACY>`
+* **F1 Score**: `<INSERT_F1_SCORE>`
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-hsd")
+model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-hsd")
+
+text = "Đừng nói những lời thô tục như vậy!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+pred = model(**inputs).logits.argmax(dim=-1).item()
+print(f"Label: {['CLEAN','OFFENSIVE','HATE'][pred]}")
+```