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visolex/
textcnn-hsd

Name: textcnn-hsd
Rating: 2.5 (5 reviews)

pytorch

bert

vietnamese

hate-speech-detection

text-classification

offensive-language-detection

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TEXTCNN: Hate Speech Detection for Vietnamese Text

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

Model Details

Base Model: unknown
Description: textcnn fine-tuned for Vietnamese Hate Speech Detection
Architecture: Unknown
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: 0.8388
Macro-F1: 0.3041
Weighted-F1: 0.7652
Macro-Precision: 0.2796
Macro-Recall: 0.3333

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "visolex/textcnn-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()}")

⚠️ Note for Vocab-based Models: This model (textcnn) uses custom vocabulary-based tokenization and does not include a Hugging Face tokenizer. You will need to implement custom tokenization or load a tokenizer from a compatible base model. The model expects word-level tokenized input.

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: unknown
Dataset: ViHSD (Vietnamese Hate Speech Detection Dataset)
Framework: Hugging Face Transformers
ViSoLex Toolkit