topic-classification-for-news-title

RoBERTa-Base Quantized Model for Topic Classification

This repository hosts a quantized version of the RoBERTa model, fine-tuned for topic classification using the AG News dataset. The model has been optimized using FP16 quantization for efficient deployment without significant accuracy loss.

Model Details

• Model Architecture: RoBERTa Base
• Task: Multi-class Topic Classification (4 classes)
• Dataset: AG News (Hugging Face Datasets)
• Quantization: Float16
• Fine-tuning Framework: Hugging Face Transformers

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Installation

pip install transformers torch datasets 

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Loading the Model

from transformers import RobertaTokenizer
from transformers import RobertaForSequenceClassification

import torch

# Load tokenizer and model

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained("roberta-base", num_labels=4).to(device)
# Define test sentences
samples = [
    "Tensions rise in the Middle East as diplomats gather for emergency talks to prevent further escalation.",
"Tesla reports a 25% increase in quarterly revenue, driven by strong demand for its Model Y vehicles in Asia.",

"Researchers develop a new quantum computing chip that significantly reduces energy consumption.",
    "Argentina defeats Brazil 2-1 in the Copa América final, securing their 16th continental title.",
    "Meta unveils its latest AI model capable of generating 3D virtual environments from text prompts."
]



from transformers import pipeline

# Load pipeline for inference
classifier = pipeline("text-classification", model=trainer.model, tokenizer=tokenizer, device=0)  # device=-1 if using CPU

predictions = classifier(samples)

# Print results
for text, pred in zip(samples, predictions):
    print(f"\nText: {text}\nPredicted Topic: {pred['label']} (Score: {pred['score']:.4f})")

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Performance Metrics

• Accuracy: 0.9471
• Precision: 0.9471
• Recall: 0.9471
• F1 Score: 0.9471

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Fine-Tuning Details

Dataset

The dataset is sourced from Hugging Face’s ag_news dataset. It contains 120,000 training samples and 7,600 test samples, with each news article labeled into one of four categories: World, Sports, Business, or Sci/Tech. The original dataset was used as provided, and input texts were tokenized using the RoBERTa tokenizer and truncated/padded to a maximum length of 128 tokens.

Training

• Epochs: 3
• Batch size: 8
• Learning rate: 2e-5
• Evaluation strategy: epoch

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Quantization

Post-training quantization was applied using PyTorch’s half() precision (FP16) to reduce model size and inference time.

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Repository Structure

.
├── config.json                   # Model configuration
├── merges.txt                   # Byte Pair Encoding (BPE) merge rules for tokenizer
├── model.safetensors            # Quantized model weights
├── README.md                    # Model documentation
├── special_tokens_map.json      # Tokenizer special tokens
├── tokenizer_config.json        # Tokenizer configuration
├── vocab.json                   # Tokenizer vocabulary

├── README.md                      # Model documentation

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Limitations

• The model is trained specifically for binary topic classification on ag news dataset.
• FP16 quantization may result in slight numerical instability in edge cases.

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Contributing

Feel free to open issues or submit pull requests to improve the model or documentation.