---
language: 
- en
tags:
- text-classification
- news
- bert
- ag-news
license: apache-2.0
datasets:
- ag_news
model-index:
- name: bert-base-uncased-agnews4-v01
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: AG News
      type: ag_news
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.855
      - name: F1 Score
        type: F1 Score
        value: 0.856
---

# bert-base-uncased-agnews4-v01

## Model description

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)  
on the [AG News dataset](https://huggingface.co/datasets/ag_news).  
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.

---

## 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.

---

## How to use

```python
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])
````

---

## 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).

---

## Evaluation results

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

> ⚠️ Replace with your actual evaluation results if available.

---

## Citation

If you use this model, please cite:

```bibtex
@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}},
}
```

---

## License

* Base model: [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0)
* Fine-tuned weights: Apache 2.0

---

## Author

**Author:** [Vantish]

```