MhoOmm/News_Classifier_Model

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.

https://arxiv.org/abs/1907.11692