README.md

---
library_name: transformers
license: mit
language:
- en
tags:
- text-classification
- nlp
- transformers
- bert
pipeline_tag: text-classification
datasets:
- fancyzhx/ag_news
metrics:
- accuracy
- recall
- precision
- f1
---

# AG-News BERT Classification

## Model Details

**Model Name:** AG-News BERT Classification  
**Model Type:** Text Classification  
**Developer:** Mansoor Hamidzadeh  
**Repository:** [mansoorhamidzadeh/ag-news-bert-classification](https://huggingface.co/mansoorhamidzadeh/ag-news-bert-classification)  
**Language(s):** English  
**License:** MIT

## Model Description

### Overview

The AG-News BERT Classification model is a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model designed for text classification tasks, specifically for classifying news articles into four categories: World, Sports, Business, and Sci/Tech. The model leverages the pre-trained BERT architecture, which has been fine-tuned on the AG-News dataset to optimize its performance for this specific task.

### Intended Use

#### Primary Use Case

The primary use case for this model is to automatically classify news articles into one of the four predefined categories:
- World
- Sports
- Business
- Sci/Tech

This can be useful for news aggregation services, content recommendation systems, and any application that requires automated content categorization.

#### Applications

- News aggregators and curators
- Content recommendation engines
- Media monitoring tools
- Sentiment analysis and trend detection in news

## Training Data

### Dataset

- **Name:** AG-News Dataset
- **Source:** [AG News Corpus](https://huggingface.co/datasets/fancyzhx/ag_newss)
- **Description:** The AG-News dataset is a widely used benchmark dataset for text classification. It contains 120,000 training samples and 7,600 test samples of news articles categorized into four classes: World, Sports, Business, and Sci/Tech.

### Data Preprocessing

The text data was preprocessed to tokenize the sentences using the BERT tokenizer, converting the tokens to their corresponding IDs, and creating attention masks.

## Training Procedure

**Training Configuration:**
- **Number of Epochs:** 4
- **Batch Size:** 8
- **Learning Rate:** 1e-5
- **Optimizer:** AdamW

**Training and Validation Losses:**
- **Epoch 1:**
  - Average training loss: 0.1330
  - Average test loss: 0.1762
- **Epoch 2:**
  - Average training loss: 0.0918
  - Average test loss: 0.1733
- **Epoch 3:**
  - Average training loss: 0.0622
  - Average test loss: 0.1922
- **Epoch 4:**
  - Average training loss: 0.0416
  - Average test loss: 0.2305

**Hardware:**
- **Training Environment:** NVIDIA P100 GPU
- **Training Time:** Approximately 3 hours

## Performance

### Evaluation Metrics

The model was evaluated using standard text classification metrics:
- Accuracy
- Precision
- Recall
- F1 Score

### Results

On the AG-News test set, the model achieved the following performance:
- **Accuracy:** 93.8%
- **Precision:** 93.8%
- **Recall:** 93.8%
- **F1 Score:** 93.8%

## Limitations and Biases

### Limitations

- The model may not generalize well to other text types or news sources outside the AG-News dataset.
- Primarily designed for English text and may not perform well on text in other languages.

### Biases

- Potential biases present in the training data, reflecting biases in news reporting.
- Category-specific biases due to the distribution of articles in the dataset.

## Ethical Considerations

- Ensure the model is used in compliance with user privacy and data security standards.
- Be aware of potential biases and take steps to mitigate negative impacts, especially in sensitive applications.

## How to Use

### Inference

To use the model for inference, load it using the Hugging Face Transformers library:

```python
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import TextClassificationPipeline

tokenizer = BertTokenizer.from_pretrained("mansoorhamidzadeh/ag-news-bert-classification")
model = BertForSequenceClassification.from_pretrained("mansoorhamidzadeh/ag-news-bert-classification")

pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)

text = "Sample news article text here."
prediction = pipeline(text)
print(prediction)



```
``` bibtxt
@misc{mansoorhamidzadeh,
  author = {Mansoor Hamidzadeh},
  title = {AG-News BERT Classification},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/mansoorhamidzadeh/ag-news-bert-classification}},
}