README.md

---
license: apache-2.0
datasets:
- AyoubChLin/CNN_News_Articles_2011-2022
language:
- en
metrics:
- f1
- accuracy
pipeline_tag: zero-shot-classification
tags:
- zero shot
- text classification
- news classification
---

# Huggingface Model: BART-MNLI-ZeroShot-Text-Classification
This is a Huggingface model fine-tuned on the CNN news dataset for zero-shot text classification task using BART-MNLI. The model achieved an f1 score of 94% and an accuracy of 94% on the CNN test dataset with a maximum length of 128 tokens.

## Authors
This work was done by [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/) & [BOUBEKRI Faycal](https://www.linkedin.com/in/faycal-boubekri-832848199/)

## Model Architecture
The model architecture is based on the BART-MNLI transformer model. BART (Bidirectional and Auto-Regressive Transformers) is a denoising autoencoder that is pre-trained on a large corpus of text and fine-tuned on downstream natural language processing tasks.

## Dataset
The CNN news dataset was used for fine-tuning the model. This dataset contains news articles from the CNN website and is labeled into 6 categories, including politics, health, entertainment, tech, travel, world, and sports.

## Fine-tuning Parameters
The model was fine-tuned for 1 epoch on a maximum length of 256 tokens. The training took approximately 6 hours to complete.

## Evaluation Metrics
The model achieved an f1 score of 94% and an accuracy of 94% on the CNN test dataset with a maximum length of 128 tokens.

# Usage
The model can be used for zero-shot text classification tasks on news articles. It can be accessed via the Huggingface Transformers library using the following code:

```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/Bart-MNLI-CNN_news")

model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/Bart-MNLI-CNN_news")
classifier = pipeline(
    "zero-shot-classification",
    model=model,
    tokenizer=tokenizer,
    device=0
)
```
## Acknowledgments
We would like to acknowledge the Huggingface team for their open-source implementation of transformer models and the CNN news dataset for providing the labeled dataset for fine-tuning.