| exp_name: 'cosine_lr' | |
| # Training dataset (from Huggingface) | |
| data_source: "MedCat/MedCAT-PT-v1" | |
| # The base model (from HuggingFace model hub) | |
| model_name: "microsoft/BioGPT-Large" | |
| # Tokenizer | |
| tokenizer_device: 'cpu' # 'cpu', 'cuda:0', 'cuda:1' | |
| tokenizer_batch_size: 1_000 | |
| max_length: 512 | |
| # Checkpoints configuration | |
| output_folder: "./checkpoints/MedCAT-PT" # Where to save checkpoints during the training | |
| save_total_limit: 5 # Limit on number of checkpoints to keep | |
| save_model_to: "./checkpoints/MedCAT-PT/" # Where to save the last checkpoint + base_model + data_version | |
| save_strategy: "steps" # Saving strategy (either 'steps' or 'epoch') | |
| save_steps: 10_000 # Save model every ... steps | |
| # Logging configuration | |
| logging_dir: "./logs" # Directory for logs + base_model + data_version | |
| logging_steps: 1_000 # Frequency of logging | |
| # Training configuration | |
| learning_rate: 5e-5 # Default 5e-5 | |
| lr_scheduler_type: "cosine" # default linear | |
| # warmup_steps: 2000 # default 0 | |
| per_device_train_batch_size: 16 # Training batch size | |
| per_device_eval_batch_size: 16 # Evaluation batch size | |
| num_train_epochs: 1 # Number of epochs | |
| # max_steps: 500 # Total training steps (or use num_train_epochs instead) | |
| eval_steps: 10_000 # Frequency of evaluation. Should equal to logging_steps (can be different, but should be equal) | |
| evaluation_samples: 20_000 # evaluation samples used to evaluate the model during training process | |
| evaluation_strategy: "steps" # Evaluation strategy (either 'steps' or 'epoch') | |
| seed: 3407 # Random seed for reproducibility |