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README.md

@@ -1,68 +1,211 @@
 ---
-language: vi
+language:
+- vi
 tags:
 - hate-speech-detection
-- vietnamese
-- bartpho
-license: apache-2.0
+- vietnamese-nlp
+- text-classification
+- offensive-language-detection
+license: mit
 datasets:
-- visolex/VN-HSD
-metrics:
-- accuracy
-- f1
-model-index:
-- name: bartpho-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/bartpho-base
-pipeline_tag: text-classification
+- vihsd
+base_model: vinai/bartpho-syllable-base
 ---
 
-# BARTPho‑HSD: Hate Speech Detection for Vietnamese Text
+# BARTpho
 
-Fine‑tuned from [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable) on the **VN‑HSD** dataset.
+BARTpho fine-tuned cho bài toán phân loại Hate Speech tiếng Việt
 
 ## Model Details
 
-* **Base Model**: [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable)
-* **Dataset**: VN‑HSD (ViSoLex‑HSD unified hate speech corpus)
-* **Fine‑tuning**: HuggingFace Transformers
+### Model Type
+BARTpho (Bidirectional and Auto-Regressive Transformer cho tiếng Việt)
 
-### Hyperparameters
+### Base Model
+This model is fine-tuned from [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base)
 
-* Batch size: `32`
-* Learning rate: `3e-5`
-* Epochs: `100`
-* Max sequence length: `256`
+### Training Info
+- **Task**: Hate Speech Classification
+- **Language**: Vietnamese
+- **Labels**: 
+  - `0`: CLEAN (Normal content)
+  - `1`: OFFENSIVE (Mildly offensive content)
+  - `2`: HATE (Hate speech)
 
-## Results
+## 📊 Model Performance
 
-* **Accuracy**: `<INSERT_ACCURACY>`
-* **F1 Score**: `<INSERT_F1_SCORE>`
+| Metric | Score |
+|--------|-------|
+| Accuracy | 0.8985 |
+| F1 Macro | 0.6791 |
+| F1 Weighted | 0.8886 |
 
-## Usage
+## Model Description
+
+This model has been fine-tuned on the ViHSD (Vietnamese Hate Speech Dataset) to classify Vietnamese text into three categories: CLEAN, OFFENSIVE, and HATE.
+
+### Architecture
+BARTpho (Bidirectional and Auto-Regressive Transformer cho tiếng Việt)
+
+The model combines the powerful pretrained representations with task-specific fine-tuning for effective hate speech detection in Vietnamese social media content.
+
+## How to Use
+
+### 1. Using Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+# Initialize the hate speech classifier
+classifier = pipeline(
+    "text-classification",
+    model="visolex/hate-speech-bartpho",
+    tokenizer="visolex/hate-speech-bartpho",
+    return_all_scores=True
+)
+
+# Classify text
+results = classifier("Văn bản tiếng Việt cần kiểm tra")
+print(results)
+```
+
+### 2. Using AutoModel
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "visolex/hate-speech-bartpho"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare text
+text = "Văn bản tiếng Việt cần kiểm tra"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)
+
+# Get predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    
+    # Get probabilities
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    
+    # Get predicted label
+    predicted_label = torch.argmax(probabilities, dim=-1).item()
+    confidence = probabilities[0][predicted_label].item()
+
+# Label mapping
+label_mapping = {
+    0: "CLEAN",
+    1: "OFFENSIVE",
+    2: "HATE"
+}
+
+print(f"Predicted: {label_mapping[predicted_label]} (Confidence: {confidence:.2%})")
+```
+
+### 3. Batch Processing
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "visolex/hate-speech-bartpho"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# List of texts to classify
+texts = [
+    "Bài viết rất hay và bổ ích",
+    "Đồ ngu người ta nói đúng mà",
+    "Cút đi đồ chó"
+]
+
+# Tokenize and predict
+inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=256)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+
+for text, pred in zip(texts, predictions):
+    label = ["CLEAN", "OFFENSIVE", "HATE"][pred.item()]
+    print(f"{text[:50]} -> {label}")
+```
+
+## Training Details
+
+### Training Data
+- **Dataset**: ViHSD (Vietnamese Hate Speech Detection Dataset)
+- **Total samples**: ~10,000 Vietnamese comments from social media
+- **Training split**: ~70%
+- **Validation split**: ~15%
+- **Test split**: ~15%
+
+### Training Configuration
+- **Framework**: PyTorch + HuggingFace Transformers
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Batch Size**: 32
+- **Max Length**: 256 tokens
+- **Epochs**: Optimized via early stopping
+
+### Preprocessing
+- Text normalization for Vietnamese
+- Special character handling
+- Emoji and slang processing
+
+## Evaluation Results
+
+Model evaluation metrics on the ViHSD test set: See Model Performance section above for details.
+
+### Label Distribution
+- **CLEAN (0)**: Normal content without offensive language
+- **OFFENSIVE (1)**: Mildly offensive or inappropriate content
+- **HATE (2)**: Hate speech, extremist language, severe threats
+
+## Use Cases
+
+- **Social Media Moderation**: Automatic detection of hate speech in Vietnamese social media platforms
+- **Content Filtering**: Filtering offensive content in Vietnamese text
+- **Research**: Studying hate speech patterns in Vietnamese online communities
+
+## Limitations and Considerations
+
+⚠️ **Important Limitations**:
+- Model trained primarily on social media data, may not generalize to formal text
+- Performance may vary with slang, code-switching, or regional dialects
+- Model reflects biases present in training data
+- Should be used as part of a larger moderation system, not sole decision-maker
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@software{vihsd_bartpho,
+  title = {BARTpho for Vietnamese Hate Speech Detection},
+  author = {ViSoLex Team},
+  year = {2024},
+  url = {https://huggingface.co/visolex/hate-speech-bartpho},
+  base_model = {vinai/bartpho-syllable-base}
+}
+```
+
+## Contact & Support
+
+- **GitHub**: [ViSoLex Hate Speech Detection](https://github.com/visolex/hate-speech-detection)
+- **Issues**: [Report Issues](https://github.com/visolex/hate-speech-detection/issues)
+- **Questions**: Open a discussion on the model's Hugging Face page
+
+## License
+
+This model is distributed under the MIT License.
 
-tokenizer = AutoTokenizer.from_pretrained("visolex/bartpho-hsd")
-model = AutoModelForSequenceClassification.from_pretrained("visolex/bartpho-hsd")
+## Acknowledgments
 
-text = "Anh ta đang lan truyền những lời lẽ căm ghét."
-inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
-pred = model(**inputs).logits.argmax(dim=-1).item()
-print(f"Dự đoán: {['CLEAN','OFFENSIVE','HATE'][pred]}")
-```
+- Base model trained by vinai
+- Dataset: ViHSD (Vietnamese Hate Speech Detection Dataset)
+- Framework: [Hugging Face Transformers](https://huggingface.co/transformers)