Todo for the project

• The code starts by installing and importing the necessary packages, including TensorFlow, pandas, scikit-learn, numpy, regular expressions, NLTK, Matplotlib, and the Hugging Face Transformers library.

• Several functions are defined for preprocessing and cleaning text data, including removing stopwords, short words, special characters, and converting text to lowercase.

• Reads a dataset from a CSV file, drops unnecessary columns, removes NaN values, and shuffles the dataset.

• Loads the DistilBERT tokenizer and model from the Hugging Face Transformers library.

• Sets the maximum length for input sentences.
• Tokenizes and encodes sentences using the DistilBERT tokenizer.
• Prepares input sentences, attention masks, and labels for model training.

• Defines a neural network model that uses DistilBERT embeddings.
• The model includes a Dense layer, Dropout layer, and output layer.

• Saves the model input (input_ids, attention_masks, labels) into pickle files for later use.

• Splits the data into training and validation sets.
• Defines the loss function, metrics, and optimizer for the model.
• Compiles the model.

• Trains the model on the training data, validating on the validation set.
• Saves the best model based on validation loss.

• Uses TensorBoard to visualize training and validation curves.

• Loads the saved model weights.
• Uses the model to make predictions on the validation set.
• Calculates and prints the F1 score and classification report.

• Creates and compiles a new model for future use.
• Prints the F1 score and classification report on the validation set.

The code essentially demonstrates the process of fine-tuning a DistilBERT model for text classification using TensorFlow and Keras.