---
license: apache-2.0
datasets:
- AyoubChLin/CNN_News_Articles_2011-2022
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- news classification
widget:
- text: money in the pocket
- text: no one can win this cup in quatar..
---
# Fine-Tuned BART Model for Text Classification on CNN News Articles


This is a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model for text classification on CNN news articles. The model was fine-tuned on a dataset of CNN news articles with labels indicating the article topic, using a batch size of 32, learning rate of 6e-5, and trained for one epoch.

## How to Use

### Install

```bash
pip install transformers
```

### Example Usage

```python 
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Softechlb/articles_classification")
model = AutoModelForSequenceClassification.from_pretrained("Softechlb/articles_classification")

# Tokenize input text
text = "This is an example CNN news article about politics."
inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors="pt")

# Make prediction
outputs = model(inputs["input_ids"], attention_mask=inputs["attention_mask"])
predicted_label = torch.argmax(outputs.logits)

print(predicted_label)
```
## Evaluation

The model achieved the following performance metrics on the test set:

Accuracy: 0.9591836734693877

F1-score: 0.958301875401112

Recall: 0.9591836734693877

Precision: 0.9579673040369542