📅 2024-07-12 — Session: BERT Model Training and Evaluation
🕒 00:00–02:50
🏷️ Labels: BERT, Text Classification, Model Training, NLP, Data Balancing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to execute a comprehensive workflow for training and evaluating a BERT model for text classification.
Key Activities
- BERT Model Training: Followed a detailed workflow for installing necessary libraries, preparing data, training the model, and evaluating its performance.
- Handling Warnings: Addressed common warnings during BERT fine-tuning, specifically related to newly initialized weights and the AdamW optimizer deprecation.
- Training Loss Interpretation: Analyzed training loss values to understand the model’s learning progress and guide further training steps.
- Debugging with Smaller Data Subsets: Implemented classification on smaller data samples for debugging and testing, including tokenization and DataLoader creation.
- Prediction Mapping: Created a reverse mapping from numeric to textual labels for model predictions.
- Data Balancing: Resampled minority classes to handle unbalanced data before training.
- Training Strategies: Explored strategies to expedite BERT training, such as reducing epochs and using mixed precision training.
Achievements
- Successfully trained and evaluated a BERT model with improved handling of warnings and optimized training strategies.
- Implemented effective data balancing techniques and prediction mapping.
Pending Tasks
- Further tuning of hyperparameters to enhance model accuracy.
- Continued evaluation and testing with larger datasets.