Vantish/bert-based-uncased-agnews4-v01

bert-base-uncased-agnews4-v01

Model description

This model is a fine-tuned version of bert-base-uncased
on the AG News dataset.
It classifies English news headlines or articles into four categories:

Label	Category	Description
0	World	International and world news
1	Sports	Sports-related news
2	Business	Financial and economic news
3	Sci/Tech	Science and technology news

The model was trained for text classification using the transformers library from Hugging Face.

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Intended uses & limitations

Intended uses

• News topic classification
• Benchmarking or educational purposes
• Text categorization for NLP pipelines

Limitations

• The model is trained on English text only.
• It may perform poorly on informal or domain-specific text (e.g., social media).
• The dataset contains news articles up to 2015; recent topics or events may be underrepresented.

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How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Vantish/bert-based-uncased-agnews4-v01")
model = AutoModelForSequenceClassification.from_pretrained("Vantish/bert-based-uncased-agnews4-v01")

text = "NASA launches a new satellite to study climate change."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=-1).item()

label_map = {
    0: "World",
    1: "Sports",
    2: "Business",
    3: "Sci/Tech"
}
print("Predicted label:", label_map[pred])

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Training procedure

• Base model: bert-base-uncased
• Dataset: AG News
• Task: Text classification (4 classes)
• Optimizer: AdamW
• Learning rate: 2e-5
• Batch size: 16
• Epochs: 3
• Loss function: CrossEntropyLoss
• Evaluation metric: Accuracy

Hardware

Trained on a single NVIDIA GPU (e.g., T4 or V100).

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Evaluation results

| Metric | Score | | F1 Score | 85.6% | | Accuracy | 85.5% |

⚠️ Replace with your actual evaluation results if available.

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Citation

If you use this model, please cite:

@misc{bert_agnews_finetune,
  title={BERT Base Uncased Fine-Tuned on AG News},
  author={Vantish},
  year={2025},
  howpublished={\url{https://huggingface.co/Vantish/bert-based-uncased-agnews4-v01}},
}

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License

• Base model: Apache 2.0 License
• Fine-tuned weights: Apache 2.0

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Author

Author: [Vantish]