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visolex
/
textcnn-hsd

Text Classification
PyTorch
Vietnamese
hate-speech-detection
vietnamese-nlp
offensive-speech
Model card Files
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Community
textcnn-hsd
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---
language: 
- vi
tags:
- hate-speech-detection
- vietnamese-nlp
- text-classification
- offensive-speech
license: mit
datasets:
- vihsd
base_model: Unknown
---

# TEXTCNN

textcnn fine-tuned cho bài toán phân loại Hate Speech.

## Model Details

- **Model type**: Fine-tuned transformer model
- **Architecture**: Unknown
- **Base model**: [Unknown](https://huggingface.co/Unknown)
- **Task**: Hate Speech Classification
- **Language**: Vietnamese
- **Labels**: CLEAN (0), OFFENSIVE (1), HATE (2)

## 📊 Model Performance

| Metric | Score |
|--------|-------|
| Accuracy | 0.8388 |
| F1 Macro | 0.3041 |
| F1 Weighted | 0.7652 |


## Model Description

textcnn fine-tuned cho bài toán phân loại Hate Speech. Model này được fine-tune từ `Unknown` trên dataset ViHSD (Vietnamese Hate Speech Dataset).

## How to Use

### Basic Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "visolex/hate-speech-textcnn"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Classify text
text = "Văn bản tiếng Việt cần phân loại"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_label = torch.argmax(predictions, dim=-1).item()

# Label mapping
label_names = {
    0: "CLEAN",
    1: "OFFENSIVE",
    2: "HATE"
}

print(f"Predicted label: {label_names[predicted_label]}")
print(f"Confidence scores: {predictions[0].tolist()}")
```

### Using the Pipeline

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="visolex/hate-speech-textcnn",
    tokenizer="visolex/hate-speech-textcnn"
)

result = classifier("Văn bản tiếng Việt cần phân loại")
print(result)
```

## Training Details

### Training Data
- 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 content
- HATE (2): Hate speech and extremist language

## Evaluation

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 Bias

- 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


## Contact


## License

This model is distributed under the MIT License.