📅 2024-07-12 — Session: Comprehensive BERT Model Training and Evaluation
🕒 00:00–02:50
🏷️ Labels: BERT, Text Classification, Machine Learning, NLP, Transformers
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to develop a robust workflow for training and evaluating BERT models for text classification tasks.
Key Activities:
- Developed a comprehensive workflow for BERT model training, including library installation, data preparation, model training, evaluation, and full dataset classification.
- Addressed warnings during BERT fine-tuning, specifically handling newly initialized weights and deprecated AdamW optimizer using PyTorch.
- Interpreted training loss values to assess model learning progress and guide further training.
- Enhanced spaCy entity extraction with error handling for long texts.
- Implemented classification on smaller data subsets for debugging and testing, including tokenization and DataLoader creation.
- Created reverse mapping from numeric predictions to textual labels in BERT.
- Balanced data for BERT training using resampling techniques with sklearn.
- Outlined stages of model training and tuning for BERT, focusing on data preparation and hyperparameter tuning.
- Explored strategies for faster BERT training, such as reducing epochs and using mixed precision training.
Achievements:
- Established a clear workflow for BERT model training and evaluation.
- Resolved common warnings and issues in BERT fine-tuning.
- Improved understanding of training loss values and their implications.
- Enhanced data handling and preprocessing techniques for NLP tasks.
Pending Tasks:
- Further optimization of training strategies and hyperparameter tuning for improved model accuracy.
- Continuous evaluation and adjustment of data balancing techniques.
- Exploration of additional techniques for speeding up model training.