Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- AyoubChLin/CNN_News_Articles_2011-2022
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- news classification
 ---
+# Fine-Tuned BART Model for Text Classification on CNN News Articles
+
+[![Hugging Face Model](https://img.shields.io/huggingface/model/IT-community/Bart_News_text_classification?color=blue&logo=huggingface)](https://huggingface.co/IT-community/Bart_News_text_classification)
+[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+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_News_text_classification")
+model = AutoModelForSequenceClassification.from_pretrained("IT-community/Bart_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
+
+## Contact
+This work was done by the IT-community club.