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from typing import Optional
from pydantic import Field
from autotrain.trainers.common import AutoTrainParams
class TextRegressionParams(AutoTrainParams):
"""
TextRegressionParams is a configuration class for setting up text regression training parameters.
Attributes:
data_path (str): Path to the dataset.
model (str): Name of the pre-trained model to use. Default is "bert-base-uncased".
lr (float): Learning rate for the optimizer. Default is 5e-5.
epochs (int): Number of training epochs. Default is 3.
max_seq_length (int): Maximum sequence length for the inputs. Default is 128.
batch_size (int): Batch size for training. Default is 8.
warmup_ratio (float): Proportion of training to perform learning rate warmup. Default is 0.1.
gradient_accumulation (int): Number of steps to accumulate gradients before updating. Default is 1.
optimizer (str): Optimizer to use. Default is "adamw_torch".
scheduler (str): Learning rate scheduler to use. Default is "linear".
weight_decay (float): Weight decay to apply. Default is 0.0.
max_grad_norm (float): Maximum norm for the gradients. Default is 1.0.
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. Default is None.
text_column (str): Name of the column containing text data. Default is "text".
target_column (str): Name of the column containing target data. Default is "target".
logging_steps (int): Number of steps between logging. Default is -1 (no logging).
project_name (str): Name of the project for output directory. Default is "project-name".
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). Default is None.
save_total_limit (int): Maximum number of checkpoints to save. Default is 1.
token (Optional[str]): Token for accessing Hugging Face Hub. Default is None.
push_to_hub (bool): Whether to push the model to Hugging Face Hub. Default is False.
eval_strategy (str): Evaluation strategy to use. Default is "epoch".
username (Optional[str]): Hugging Face username. Default is None.
log (str): Logging method for experiment tracking. Default is "none".
early_stopping_patience (int): Number of epochs with no improvement after which training will be stopped. Default is 5.
early_stopping_threshold (float): Threshold for measuring the new optimum, to qualify as an improvement. Default is 0.01.
"""
data_path: str = Field(None, title="Data path")
model: str = Field("bert-base-uncased", title="Model name")
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")
batch_size: int = Field(8, 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")
seed: int = Field(42, title="Seed")
train_split: str = Field("train", title="Train split")
valid_split: Optional[str] = Field(None, title="Validation split")
text_column: str = Field("text", title="Text column")
target_column: str = Field("target", title="Target column(s)")
logging_steps: int = Field(-1, title="Logging steps")
project_name: str = Field("project-name", title="Output directory")
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")
eval_strategy: str = Field("epoch", title="Evaluation strategy")
username: Optional[str] = Field(None, title="Hugging Face Username")
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")
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