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
 ---
+# BertForSequenceClassification on CNN News Dataset
+
+This repository contains a fine-tuned Bert base model for sequence classification on the CNN News dataset. The model is able to classify news articles into one of six categories: business, entertainment, health, news, politics, and sport.
+
+The model was fine-tuned for four epochs achieving a validation loss of 0.190814, accuracy of 0.956690, precision of 0.956393, recall of 0.956690, and F1-score of 0.956144.
+
+## Model Description
+
+This model was fine-tuned by AyoubChLin and is based on the Bert base model. The tokenizer and model were created using the Hugging Face Transformers library.
+
+## Usage
+
+You can use this model with the Hugging Face Transformers library for a variety of natural language processing tasks, such as text classification, sentiment analysis, and more.
+
+Here's an example of how to use this model for text classification in Python:
+
+```python
+from transformers import AutoTokenizer, BertForSequenceClassification
+
+model_name = "AyoubChLin/bert_cnn_news"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "This is a news article about politics."
+inputs = tokenizer(text, padding=True, truncation=True, return_tensors="tf")
+
+outputs = model(inputs)
+predicted_class_id = tf.argmax(outputs.logits, axis=-1).numpy()[0]
+
+labels = ["business", "entertainment", "health", "news", "politics", "sport"]
+predicted_label = labels[predicted_class_id]
+```
+
+In this example, we first load the tokenizer and the model using their respective `from_pretrained` methods. We then encode a news article using the tokenizer, pass the inputs through the model, and extract the predicted label using the `argmax` function. Finally, we map the predicted label to its corresponding category using a list of labels.
+
+## Contributors
+
+This model was fine-tuned by AyoubChLin.