Fine-tuning the BERT model for sentimental analysis

This project involves fine-tuning the BERT (Bidirectional Encoder Representations from Transformers) model, specifically the bert-base-uncased variant, to perform sentiment analysis on the Finetune_BERT dataset from Kaggle. BERT, a transformer-based model pre-trained on a large corpus of text, is particularly effective for natural language understanding tasks. By fine-tuning, the model adapts to the specific domain and task of sentiment classification, enabling it to categorize text as positive, negative, or neutral based on its emotional tone.

The dataset from Kaggle contains labeled examples for sentiment analysis, providing training data for BERT to learn from. The project utilizes the bert-base-uncased model, a version of BERT that does not differentiate between uppercase and lowercase letters, making it suitable for a wide range of text inputs. Key steps in this project include data preprocessing, model fine-tuning using transfer learning, and performance evaluation on unseen test data. The goal is to build an accurate and robust sentiment analysis model using state-of-the-art NLP techniques.