---
datasets:
- SetFit/ag_news
language:
- en
metrics:
- f1
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
- ag_news_finetuning
- Bert
- Bert_un_cased
---

# BERT News Classification (Fine-tuned on AG News)

This model is a fine-tuned version of **`bert-base-uncased`** for news topic classification on the [AG News dataset](https://huggingface.co/datasets/SetFit/ag_news).  
It predicts one of four categories: **World, Sports, Business, Sci/Tech**.

## Model Details
- **Base Model:** `bert-base-uncased`
- **Fine-tuning Dataset:** `SetFit/ag_news`
- **Language:** English
- **Task:** Text Classification (Single-label)
- **Number of Labels:** 4
- **Training Framework:** Hugging Face Transformers
- **Evaluation Metric:** Weighted F1-score

## Training Procedure
- **Epochs:** 3
- **Batch Size:** 16
- **Learning Rate:** 5e-5
- **Weight Decay:** 0.01
- **Mixed Precision:** FP16 enabled
- **Early Stopping:** Patience of 1 epochs

### Training & Evaluation Results
| Epoch | Training Loss | Validation Loss | F1 Score |
|-------|---------------|-----------------|----------|
| 1     | 0.2005        | 0.1875          | 0.9422   |
| 2     | 0.1171        | 0.1948          | 0.9470   |
| 3     | 0.0853        | 0.2458          | 0.9457   |

**Final Training Metrics:**  
- Global Step: 22500  
- Training Loss: 0.1546  
- Train Runtime: 2593s (~43min)  
- Samples/sec: 231.39  
- Steps/sec: 14.46  
- Total FLOPs: 1.57e+16  

## Intended Use
The model can be used for:
- Classifying short news articles into **World, Sports, Business, Sci/Tech**.
- Benchmarking text classification pipelines with BERT.

## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Maarij-Aqeel/BERT_news_classification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "The stock market saw a significant rise today as..."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_label = outputs.logits.argmax(dim=-1).item()
print(predicted_label)