Fine-Tuning BERT for Text Classification

In the realm of medical research and diagnostics, the classification and analysis of cancer-related data hold immense importance. Leveraging state-of-the-art natural language processing (NLP) techniques, this project endeavors to classify textual data associated with cancer-related information using BERT (Bidirectional Encoder Representations from Transformers) - a powerful language model developed by Google.

Table of Contents

1. Overview
2. Dataset
3. Methodology
4. Running the Code in Google Colab
5. Screenshots
6. Acknowledgements
7. License

Overview

This project aims to utilize the capabilities of BERT for text classification tasks in the context of cancer-related data. Specifically, we employ the Hugging Face BERT model for text classification, focusing on predicting the type of cancer based on textual information provided in the dataset.

Dataset

The dataset used in this project consists of textual data and corresponding cancer types. Each entry in the dataset contains a text column representing information related to cancer, along with the respective cancer type. This data serves as the basis for training and evaluating the BERT model for accurate classification.

Methodology

1. Preprocessing: Before feeding the data into the BERT model, preprocessing steps are performed to prepare the text data for tokenization. This may include tasks such as cleaning, normalization, and encoding.

2. Tokenization: The text data is tokenized using the respective BERT tokenizer, which breaks down the input text into individual tokens suitable for input to the model.

3. Fine-Tuning BERT: The pre-trained BERT model is fine-tuned on the cancer-related dataset using techniques such as transfer learning. This process involves adjusting the parameters of the BERT model to better fit the specific classification task at hand.

4. Model Evaluation: The performance of the fine-tuned BERT model is evaluated using appropriate metrics for text classification tasks. This includes measures such as accuracy, precision, recall, and F1-score.

Running the Code in Google Colab

To run the code provided in this project using Google Colab:

1. Download the Repository: Download the ZIP file of this GitHub repository containing the Jupyter Notebook file.

2. Upload to Google Colab: Extract the Jupyter Notebook file from the downloaded ZIP file and upload it to Google Colab.

3. Run Each Cell: Open the uploaded Jupyter Notebook in Google Colab and run each cell sequentially. Ensure that all necessary dependencies are installed, including TensorFlow and the Hugging Face Transformers library.

4. Execute the Code: Execute the code cells to perform preprocessing, fine-tuning BERT, and evaluating the model's performance. Additionally, you can use the provided predictive_system function to make predictions on new textual data.

Screenshots

Acknowledgements

• This project utilizes the Hugging Face BERT model for text classification.
• The dataset used in this project is sourced from: https://www.kaggle.com/datasets/falgunipatel19/biomedical-text-publication-classification.

License

This project is licensed under the terms of the MIT license. See the LICENSE file for details.