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config.yaml

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+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