This project demonstrates the application of a BERT-based model for text classification tasks. The project is implemented using PyTorch and involves various stages of data preprocessing, model training, and evaluation.
The project is focused on building and evaluating a text classification model using a dataset from Kaggle. The key steps involved in this project include:
1) Data Loading and Preprocessing:
Data is loaded from CSV files provided in the Kaggle dataset. Text data is tokenized, cleaned, and prepared for model input using tools like NLTK and custom preprocessing functions.
2) Model Implementation:
A BERT-based model is implemented using the PyTorch framework. The model architecture is designed to handle classification tasks, with appropriate layers and configurations for text data.
3) Training and Evaluation:
The dataset is split into training and validation sets using scikit-learn. The model is trained and optimized using various hyperparameters, and its performance is evaluated using metrics such as accuracy.
4) Results:
The trained model is tested on unseen data, and results are reported in terms of accuracy and other relevant metrics.
Prerequisites:
- Python 3.x
- PyTorch
- scikit-learn
- NLTK
- Matplotlib