metadata
language: en
license: mit
datasets:
- toxic_comment_classification
tags:
- text-classification
- toxicity-detection
- sentiment-analysis
- multi-task-learning
pipeline_tag: text-classification
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 has been trained only on English data and is not suitable for other languages.
- The toxicity detection function may produce false positives for some edge cases.
- The model may lose some information when processing long texts due to the maximum input length limit of 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}
}