---
language: en
license: apache-2.0
library_name: transformers

tags:
- text-classification
- bert
- ag-news
- news-classification
- pytorch
- transformers
- nlp

datasets:
- ag_news

metrics:
- accuracy
- f1

pipeline_tag: text-classification

model-index:
- name: ag-news-bert-classifier
  results:
  - task:
      type: text-classification
      name: News Topic Classification

    dataset:
      name: AG News
      type: ag_news
      split: test

    metrics:
    - type: accuracy
      value: 0.947
      name: Test Accuracy

    - type: f1
      value: 0.94
      name: Macro F1
---

# 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

---

# Model Details

| Property | Value |
|---|---|
| Base Model | `bert-base-uncased` |
| Task | Text Classification |
| Framework | PyTorch + Transformers |
| Dataset | AG News |
| Classes | 4 |
| Test Accuracy | 94.7% |
| Macro F1 | 0.94 |

---

# Label Mapping

| ID | Label |
|---|---|
| 0 | World |
| 1 | Sports |
| 2 | Business |
| 3 | Sci/Tech |

---

# Training Configuration

| Parameter | Value |
|---|---|
| Epochs | 3 |
| Batch Size | 16 |
| Learning Rate | 2e-5 |
| Max Sequence Length | 128 |
| Warmup Steps | 500 |
| Optimizer | AdamW |
| Weight Decay | 0.01 |
| LR Scheduler | Linear Warmup + Linear Decay |
| Gradient Clipping | Max Norm = 1.0 |

---

# Dataset Information

| Split | Samples |
|---|---|
| Train | 120,000 |
| Test | 7,600 |

Dataset source:

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

---

# Performance

## Classification Report

| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| World | 0.97 | 0.95 | 0.96 |
| Sports | 0.98 | 0.99 | 0.99 |
| Business | 0.92 | 0.91 | 0.92 |
| Sci/Tech | 0.91 | 0.93 | 0.92 |

---

# Usage

## Install

```bash
pip install transformers torch
```

## Inference

```python
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:

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

---

# Example Predictions

| Text | Prediction |
|---|---|
| Apple announces new MacBook Pro with M3 chip | Sci/Tech |
| Federal Reserve raises interest rates | Business |
| UN calls for ceasefire in ongoing conflict | World |
| Real Madrid wins Champions League final | Sports |

---

# 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

---

# 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

---

# Citation

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