This model is a fine-tuned version of xlm-roberta-large on the ViHSD (Vietnamese Hate Speech Detection Dataset) for classifying Vietnamese text into three categories: CLEAN, OFFENSIVE, and HATE.

Model Details

Base Model: xlm-roberta-large
Description: XLM-RoBERTa Large fine-tuned cho bài toán phân loại Hate Speech tiếng Việt
Architecture: XLM-RoBERTa Large (Cross-lingual Language Model)
Dataset: ViHSD (Vietnamese Hate Speech Detection Dataset)
Fine-tuning Framework: HuggingFace Transformers + PyTorch
Task: Hate Speech Classification (3 classes)

Hyperparameters

Batch size: 32
Learning rate: 2e-5
Epochs: 100
Max sequence length: 256
Weight decay: 0.01
Warmup steps: 500
Early stopping patience: 5
Optimizer: AdamW
Learning rate scheduler: Cosine with warmup

Dataset

Model was trained on ViHSD (Vietnamese Hate Speech Detection Dataset) containing ~10,000 Vietnamese comments from social media.

Label Descriptions:

CLEAN (0): Normal content without offensive language
OFFENSIVE (1): Mildly offensive or inappropriate content
HATE (2): Hate speech, extremist language, severe threats

Evaluation Results

The model was evaluated on test set with the following metrics:

Accuracy: 0.9203
Macro-F1: 0.7625
Weighted-F1: 0.9177
Macro-Precision: 0.7877
Macro-Recall: 0.7409

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "visolex/xlm-r-hsd"
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()}")

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: 100 (with early stopping patience: 5)
Weight Decay: 0.01
Warmup Steps: 500

Contact & Support

GitHub: ViSoLex Hate Speech Detection
Issues: Report Issues
Questions: Open a discussion on the model's Hugging Face page

License

This model is distributed under the MIT License.

Acknowledgments

Base model: xlm-roberta-large
Dataset: ViHSD (Vietnamese Hate Speech Detection Dataset)
Framework: Hugging Face Transformers
ViSoLex Toolkit