metadata
license: mit
library_name: transformers
pipeline_tag: text-classification
tags:
- transformers
- pytorch
- text-classification
- news-classification
- ag-news
- nlp
News Classification using RoBERTa
Model Description
This model is a fine-tuned RoBERTa model for multiclass news classification. It predicts the category of a news headline or article among four classes:
- World
- Sports
- Business
- Sci/Tech
The model was fine-tuned on the AG News dataset using Hugging Face Transformers and PyTorch.
Model Details
- Developed by: MhoOmm
- Model Type: RoBERTa for Sequence Classification
- Language: English
- License: MIT
- Finetuned From: roberta-base
Labels
| Label ID | Category |
|---|---|
| 0 | World |
| 1 | Sports |
| 2 | Business |
| 3 | Sci/Tech |
Intended Use
Direct Use
This model can be used for:
- News article categorization
- Content organization systems
- News recommendation pipelines
- NLP learning projects
- Text classification demonstrations
Out-of-Scope Use
This model is not intended for:
- Medical diagnosis
- Financial advice
- Legal decision making
- Safety-critical systems
- Classification of non-English news
Usage
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
import torch
model_name = "MhoOmm/news-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
text = "Apple launches a new AI-powered processor"
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True
)
with torch.no_grad():
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
print(model.config.id2label[prediction])
Training Data
The model was fine-tuned on the AG News dataset.
Dataset Statistics
- Training Samples: 120,000
- Test Samples: 7,600
- Classes: 4
Categories
- World
- Sports
- Business
- Sci/Tech
Training Procedure
Preprocessing
- RoBERTa tokenizer
- Maximum sequence length: 128
- Truncation enabled
- Dynamic padding
Hyperparameters
- Learning Rate: 2e-5
- Batch Size: 16
- Epochs: 3
- Optimizer: AdamW
- Framework: PyTorch
- Trainer: Hugging Face Trainer
Evaluation
Metrics
The model was evaluated using classification accuracy.
Example Predictions
| Input | Prediction |
|---|---|
| India defeats Australia in the cricket final | Sports |
| Stock market reaches a new all-time high | Business |
| Apple unveils a new AI-powered device | Sci/Tech |
| Global leaders meet to discuss climate policy | World |
Results
- Accuracy: 94.76%
Limitations
- Trained only on English-language news articles.
- May perform poorly on informal text such as tweets and social media posts.
- Restricted to the four AG News categories.
- Performance may decrease on domains significantly different from the training data.
Architecture
- Base Model: RoBERTa Base
- Hidden Size: 768
- Transformer Layers: 12
- Attention Heads: 12
- Classification Head: Linear Layer (768 → 4)
Software
- PyTorch
- Transformers
- Hugging Face Hub
- Gradio
Author
Created by MhoOmm as an end-to-end NLP project demonstrating:
- Transformer fine-tuning
- Text classification
- Model deployment with Hugging Face
- Interactive inference using Gradio
Citation
If you use this model, please cite the original RoBERTa paper:
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, et al.