[chore] exclude paddleocr directory from pre-commit hooks

texteller/models/ocr_model/train/training_args.py

@@ -0,0 +1,31 @@
+CONFIG = {
+    "seed": 42,  # Random seed for reproducibility
+    "use_cpu": False,  # Whether to use CPU (it's easier to debug with CPU when starting to test the code)
+    "learning_rate": 5e-5,  # Learning rate
+    "num_train_epochs": 10,  # Total number of training epochs
+    "per_device_train_batch_size": 4,  # Batch size per GPU for training
+    "per_device_eval_batch_size": 8,  # Batch size per GPU for evaluation
+    "output_dir": "train_result",  # Output directory
+    "overwrite_output_dir": False,  # If the output directory exists, do not delete its content
+    "report_to": ["tensorboard"],  # Report logs to TensorBoard
+    "save_strategy": "steps",  # Strategy to save checkpoints
+    "save_steps": 500,  # Interval of steps to save checkpoints, can be int or a float (0~1), when float it represents the ratio of total training steps (e.g., can set to 1.0 / 2000)
+    "save_total_limit": 5,  # Maximum number of models to save. The oldest models will be deleted if this number is exceeded
+    "logging_strategy": "steps",  # Log every certain number of steps
+    "logging_steps": 500,  # Number of steps between each log
+    "logging_nan_inf_filter": False,  # Record logs for loss=nan or inf
+    "optim": "adamw_torch",  # Optimizer
+    "lr_scheduler_type": "cosine",  # Learning rate scheduler
+    "warmup_ratio": 0.1,  # Ratio of warmup steps in total training steps (e.g., for 1000 steps, the first 100 steps gradually increase lr from 0 to the set lr)
+    "max_grad_norm": 1.0,  # For gradient clipping, ensure the norm of the gradients does not exceed 1.0 (default 1.0)
+    "fp16": False,  # Whether to use 16-bit floating point for training (generally not recommended, as loss can easily explode)
+    "bf16": False,  # Whether to use Brain Floating Point (bfloat16) for training (recommended if architecture supports it)
+    "gradient_accumulation_steps": 1,  # Gradient accumulation steps, consider this parameter to achieve large batch size effects when batch size cannot be large
+    "jit_mode_eval": False,  # Whether to use PyTorch jit trace during eval (can speed up the model, but the model must be static, otherwise will throw errors)
+    "torch_compile": False,  # Whether to use torch.compile to compile the model (for better training and inference performance)
+    "dataloader_pin_memory": True,  # Can speed up data transfer between CPU and GPU
+    "dataloader_num_workers": 1,  # Default is not to use multiprocessing for data loading, usually set to 4*number of GPUs used
+    "evaluation_strategy": "steps",  # Evaluation strategy, can be "steps" or "epoch"
+    "eval_steps": 500,  # If evaluation_strategy="step"
+    "remove_unused_columns": False,  # Don't change this unless you really know what you are doing.
+}