README.md

metadata

title: Comment Sentiment and Toxicity Classifier
emoji: 📝
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.27.0
app_file: app.py
pinned: false

Comment Sentiment and Toxicity Classifier

This Space utilises a custom fine-tuned BERT model to classify the sentiment and toxicity of comments. Developed for academic purposes in Australia.

Comment MTL BERT Model

This is a BERT-based multi-task learning model capable of performing sentiment analysis and toxicity detection simultaneously.

Model Architecture

The model is based on the bert-base-uncased pre-trained model with two separate classification heads:

• Sentiment Analysis Head: 3-class classification (Negative, Neutral, Positive)
• Toxicity Detection Head: 6-class multi-label classification (toxic, severe_toxic, obscene, threat, insult, identity_hate)

Technical Parameters

• Hidden size: 768
• Number of attention heads: 12
• Number of hidden layers: 12
• Vocabulary size: 30522
• Maximum position embeddings: 512
• Hidden activation function: gelu
• Dropout probability: 0.1

Usage

Loading the Model

from transformers import AutoTokenizer
from src.model import CommentMTLModel
import torch

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Load model
model = CommentMTLModel(
    model_name="bert-base-uncased",
    num_sentiment_labels=3,
    num_toxicity_labels=6
)

# Load pre-trained weights
state_dict = torch.load("model.bin", map_location=torch.device('cpu'))
model.load_state_dict(state_dict)
model.eval()

Model Inference

# Prepare input
text = "This is a test comment."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Model inference
with torch.no_grad():
    outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])

# Get results
sentiment_logits = outputs["sentiment_logits"]
toxicity_logits = outputs["toxicity_logits"]

# Process sentiment analysis results
sentiment_probs = torch.softmax(sentiment_logits, dim=1)
sentiment_labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
sentiment_prediction = sentiment_labels[sentiment_probs.argmax().item()]

# Process toxicity detection results
toxicity_probs = torch.sigmoid(toxicity_logits)
toxicity_cols = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
toxicity_results = {label: prob.item() for label, prob in zip(toxicity_cols, toxicity_probs[0])}

print(f"Sentiment: {sentiment_prediction}")
print(f"Toxicity probabilities: {toxicity_results}")

Limitations

• This model was trained on English data only and is not suitable for other languages.
• The toxicity detection may produce false positives or negatives in edge cases.
• The model may lose information when processing long texts as the maximum input length is limited to 128 tokens.

Citation

If you use this model, please cite our repository:

@misc{comment-mtl-bert,
  author = {Aseem},
  title = {Comment MTL BERT: Multi-Task Learning for Comment Analysis},
  year = {2023},
  publisher = {GitHub},
  url = {https://huggingface.co/Aseemks07/comment_mtl_bert_best}
}