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README.md

@@ -1,36 +1,29 @@
 ---
-language:
+language: 
 - vi
 tags:
 - hate-speech-detection
 - vietnamese-nlp
 - text-classification
-- offensive-language-detection
+- offensive-speech
 license: mit
 datasets:
 - vihsd
 base_model: vinai/bartpho-syllable-base
 ---
 
-# BARTpho
+# BARTPHO
 
-BARTpho fine-tuned cho bài toán phân loại Hate Speech tiếng Việt
+BARTpho fine-tuned cho bài toán phân loại Hate Speech tiếng Việt.
 
 ## Model Details
 
-### Model Type
-BARTpho (Bidirectional and Auto-Regressive Transformer cho tiếng Việt)
-
-### Base Model
-This model is fine-tuned from [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base)
-
-### Training Info
+- **Model type**: Fine-tuned transformer model
+- **Architecture**: BARTpho (Bidirectional and Auto-Regressive Transformer cho tiếng Việt)
+- **Base model**: [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base)
 - **Task**: Hate Speech Classification
 - **Language**: Vietnamese
-- **Labels**: 
-  - `0`: CLEAN (Normal content)
-  - `1`: OFFENSIVE (Mildly offensive content)
-  - `2`: HATE (Hate speech)
+- **Labels**: CLEAN (0), OFFENSIVE (1), HATE (2)
 
 ## 📊 Model Performance
 
@@ -40,36 +33,14 @@ This model is fine-tuned from [vinai/bartpho-syllable-base](https://huggingface.
 | F1 Macro | 0.6791 |
 | F1 Weighted | 0.8886 |
 
-## 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)
+## Model Description
 
-The model combines the powerful pretrained representations with task-specific fine-tuning for effective hate speech detection in Vietnamese social media content.
+BARTpho fine-tuned cho bài toán phân loại Hate Speech tiếng Việt. Model này được fine-tune từ `vinai/bartpho-syllable-base` trên dataset ViHSD (Vietnamese Hate Speech Dataset).
 
 ## 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
+### Basic Usage
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -80,132 +51,82 @@ 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)
+# Classify text
+text = "Văn bản tiếng Việt cần phân loại"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
 
-# 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()
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_label = torch.argmax(predictions, dim=-1).item()
 
 # Label mapping
-label_mapping = {
+label_names = {
     0: "CLEAN",
     1: "OFFENSIVE",
     2: "HATE"
 }
 
-print(f"Predicted: {label_mapping[predicted_label]} (Confidence: {confidence:.2%})")
+print(f"Predicted label: {label_names[predicted_label]}")
+print(f"Confidence scores: {predictions[0].tolist()}")
 ```
 
-### 3. Batch Processing
+### Using the Pipeline
 
 ```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)
+from transformers import pipeline
 
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.argmax(outputs.logits, dim=-1)
+classifier = pipeline(
+    "text-classification",
+    model="visolex/hate-speech-bartpho",
+    tokenizer="visolex/hate-speech-bartpho"
+)
 
-for text, pred in zip(texts, predictions):
-    label = ["CLEAN", "OFFENSIVE", "HATE"][pred.item()]
-    print(f"{text[:50]} -> {label}")
+result = classifier("Văn bản tiếng Việt cần phân loại")
+print(result)
 ```
 
 ## 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.
+- Dataset: ViHSD (Vietnamese Hate Speech Dataset)
+- Training samples: ~8,000 samples
+- Validation samples: ~1,000 samples
+- Test samples: ~1,000 samples
+
+### Training Procedure
+- Framework: PyTorch + Transformers
+- Optimizer: AdamW
+- Learning Rate: 2e-5
+- Batch Size: 32
+- Epochs: Varies by model
+- Max Sequence Length: 256
 
 ### Label Distribution
-- **CLEAN (0)**: Normal content without offensive language
-- **OFFENSIVE (1)**: Mildly offensive or inappropriate content
-- **HATE (2)**: Hate speech, extremist language, severe threats
+- CLEAN (0): Normal content without offensive language
+- OFFENSIVE (1): Mildly offensive content
+- HATE (2): Hate speech and extremist language
 
-## Use Cases
+## Evaluation
 
-- **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
+Model được đánh giá trên test set của ViHSD với các metrics:
+- Accuracy: Overall classification accuracy
+- F1 Macro: Macro-averaged F1 score across all labels
+- F1 Weighted: Weighted F1 score based on label frequency
 
-## Limitations and Considerations
+## Limitations and Bias
 
-⚠️ **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
+- Model chỉ được train trên dữ liệu tiếng Việt từ mạng xã hội
+- Performance có thể giảm trên domain khác (email, document, etc.)
+- Model có thể có bias từ dữ liệu training
+- Cần đánh giá thêm trên dữ liệu real-world
 
 ## 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
+## Contact
 
-- **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.
-
-## Acknowledgments
-
-- Base model trained by vinai
-- Dataset: ViHSD (Vietnamese Hate Speech Detection Dataset)
-- Framework: [Hugging Face Transformers](https://huggingface.co/transformers)