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| from typing import Optional | |
| from pydantic import Field | |
| from autotrain.trainers.common import AutoTrainParams | |
| class Seq2SeqParams(AutoTrainParams): | |
| """ | |
| Seq2SeqParams is a configuration class for sequence-to-sequence training parameters. | |
| Attributes: | |
| data_path (str): Path to the dataset. | |
| model (str): Name of the model to be used. Default is "google/flan-t5-base". | |
| username (Optional[str]): Hugging Face Username. | |
| seed (int): Random seed for reproducibility. Default is 42. | |
| train_split (str): Name of the training data split. Default is "train". | |
| valid_split (Optional[str]): Name of the validation data split. | |
| project_name (str): Name of the project or output directory. Default is "project-name". | |
| token (Optional[str]): Hub Token for authentication. | |
| push_to_hub (bool): Whether to push the model to the Hugging Face Hub. Default is False. | |
| text_column (str): Name of the text column in the dataset. Default is "text". | |
| target_column (str): Name of the target text column in the dataset. Default is "target". | |
| lr (float): Learning rate for training. Default is 5e-5. | |
| epochs (int): Number of training epochs. Default is 3. | |
| max_seq_length (int): Maximum sequence length for input text. Default is 128. | |
| max_target_length (int): Maximum sequence length for target text. Default is 128. | |
| batch_size (int): Training batch size. Default is 2. | |
| warmup_ratio (float): Proportion of warmup steps. Default is 0.1. | |
| gradient_accumulation (int): Number of gradient accumulation steps. Default is 1. | |
| optimizer (str): Optimizer to be used. Default is "adamw_torch". | |
| scheduler (str): Learning rate scheduler to be used. Default is "linear". | |
| weight_decay (float): Weight decay for the optimizer. Default is 0.0. | |
| max_grad_norm (float): Maximum gradient norm for clipping. Default is 1.0. | |
| logging_steps (int): Number of steps between logging. Default is -1 (disabled). | |
| eval_strategy (str): Evaluation strategy. Default is "epoch". | |
| auto_find_batch_size (bool): Whether to automatically find the batch size. Default is False. | |
| mixed_precision (Optional[str]): Mixed precision training mode (fp16, bf16, or None). | |
| save_total_limit (int): Maximum number of checkpoints to save. Default is 1. | |
| peft (bool): Whether to use Parameter-Efficient Fine-Tuning (PEFT). Default is False. | |
| quantization (Optional[str]): Quantization mode (int4, int8, or None). Default is "int8". | |
| lora_r (int): LoRA-R parameter for PEFT. Default is 16. | |
| lora_alpha (int): LoRA-Alpha parameter for PEFT. Default is 32. | |
| lora_dropout (float): LoRA-Dropout parameter for PEFT. Default is 0.05. | |
| target_modules (str): Target modules for PEFT. Default is "all-linear". | |
| log (str): Logging method for experiment tracking. Default is "none". | |
| early_stopping_patience (int): Patience for early stopping. Default is 5. | |
| early_stopping_threshold (float): Threshold for early stopping. Default is 0.01. | |
| """ | |
| data_path: str = Field(None, title="Data path") | |
| model: str = Field("google/flan-t5-base", title="Model name") | |
| username: Optional[str] = Field(None, title="Hugging Face Username") | |
| seed: int = Field(42, title="Seed") | |
| train_split: str = Field("train", title="Train split") | |
| valid_split: Optional[str] = Field(None, title="Validation split") | |
| project_name: str = Field("project-name", title="Output directory") | |
| token: Optional[str] = Field(None, title="Hub Token") | |
| push_to_hub: bool = Field(False, title="Push to hub") | |
| text_column: str = Field("text", title="Text column") | |
| target_column: str = Field("target", title="Target text column") | |
| lr: float = Field(5e-5, title="Learning rate") | |
| epochs: int = Field(3, title="Number of training epochs") | |
| max_seq_length: int = Field(128, title="Max sequence length") | |
| max_target_length: int = Field(128, title="Max target sequence length") | |
| batch_size: int = Field(2, title="Training batch size") | |
| warmup_ratio: float = Field(0.1, title="Warmup proportion") | |
| gradient_accumulation: int = Field(1, title="Gradient accumulation steps") | |
| optimizer: str = Field("adamw_torch", title="Optimizer") | |
| scheduler: str = Field("linear", title="Scheduler") | |
| weight_decay: float = Field(0.0, title="Weight decay") | |
| max_grad_norm: float = Field(1.0, title="Max gradient norm") | |
| logging_steps: int = Field(-1, title="Logging steps") | |
| eval_strategy: str = Field("epoch", title="Evaluation strategy") | |
| auto_find_batch_size: bool = Field(False, title="Auto find batch size") | |
| mixed_precision: Optional[str] = Field(None, title="fp16, bf16, or None") | |
| save_total_limit: int = Field(1, title="Save total limit") | |
| token: Optional[str] = Field(None, title="Hub Token") | |
| push_to_hub: bool = Field(False, title="Push to hub") | |
| peft: bool = Field(False, title="Use PEFT") | |
| quantization: Optional[str] = Field("int8", title="int4, int8, or None") | |
| lora_r: int = Field(16, title="LoRA-R") | |
| lora_alpha: int = Field(32, title="LoRA-Alpha") | |
| lora_dropout: float = Field(0.05, title="LoRA-Dropout") | |
| target_modules: str = Field("all-linear", title="Target modules for PEFT") | |
| log: str = Field("none", title="Logging using experiment tracking") | |
| early_stopping_patience: int = Field(5, title="Early stopping patience") | |
| early_stopping_threshold: float = Field(0.01, title="Early stopping threshold") | |