ag_news_bert_full

AG News BERT Classifier

Fine-tuned :contentReference[oaicite:1]{index=1} model for 4-class news topic classification using the :contentReference[oaicite:2]{index=2} dataset.

The model classifies news articles into:

• World
• Sports
• Business
• Sci/Tech

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Model Details

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Label Mapping

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Training Configuration

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Dataset Information

Dataset source:

:contentReference[oaicite:3]{index=3}

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Performance

Classification Report

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Usage

Install

pip install transformers torch

Inference

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="theshekslaw/ag_news_bert_full"
)

result = classifier(
    "Apple announces new MacBook Pro with M3 chip"
)

print(result)

Example output:

[{'label': 'Sci/Tech', 'score': 0.9895}]

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Example Predictions

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Training Approach

The model was fine-tuned using a custom PyTorch training loop instead of the Hugging Face Trainer API.

Key components:

• Dynamic padding with DataCollatorWithPadding
• Linear learning-rate scheduling
• AdamW optimizer
• Mixed precision training (FP16)
• GPU training on NVIDIA T4

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Limitations

• The model is trained only on AG News categories.
• It may struggle with:
  • ambiguous headlines
  • multi-topic articles
  • very short text
  • domain-specific financial or scientific jargon

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Citation

@misc{ag_news_bert_classifier,
  title={AG News BERT Classifier},
  author={Abhishek Pandey},
  year={2026},
  publisher={Hugging Face}
}