# 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("IT-community/BART_cnn_news_text_classification")
model = AutoModelForSequenceClassification.from_pretrained("IT-community/BART_cnn_news_text_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


## About Us

We are a scientific club from Saad Dahleb Blida University named IT Community, created in 2016 by students. We are interested in all IT fields,
 This work was done by IT Community Club.

### Contributions

[Cherguelaine Ayoub](https://huggingface.co/AyoubChLin):

  - Added preprocessing code for CNN news articles 

  - Improved model performance with additional fine-tuning on a larger dataset 
{"language": ["en"], "license": "apache-2.0", "tags": ["news classification"], "datasets": ["AyoubChLin/CNN_News_Articles_2011-2022"], "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "money in the pocket"}, {"text": "no one can win this cup in quatar.."}]}
transformers pytorch safetensors bart text-classification news classification en dataset:AyoubChLin/CNN_News_Articles_2011-2022 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #text-classification #news classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# 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



### Example Usage


## 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


## About Us

We are a scientific club from Saad Dahleb Blida University named IT Community, created in 2016 by students. We are interested in all IT fields,
 This work was done by IT Community Club.

### Contributions

Cherguelaine Ayoub:

  - Added preprocessing code for CNN news articles 

  - Improved model performance with additional fine-tuning on a larger dataset 
# 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 ### Example Usage ## 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 ## About Us

We are a scientific club from Saad Dahleb Blida University named IT Community, created in 2016 by students. We are interested in all IT fields,
 This work was done by IT Community Club. ### Contributions

Cherguelaine Ayoub:

  - Added preprocessing code for CNN news articles 

  - Improved model performance with additional fine-tuning on a larger dataset
TAGS
#transformers #pytorch #safetensors #bart #text-classification #news classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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 ### Example Usage ## 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 ## About Us

We are a scientific club from Saad Dahleb Blida University named IT Community, created in 2016 by students. We are interested in all IT fields,
 This work was done by IT Community Club. ### Contributions

Cherguelaine Ayoub:

  - Added preprocessing code for CNN news articles 

  - Improved model performance with additional fine-tuning on a larger dataset