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from typing import Optional
from pydantic import Field
from autotrain.trainers.common import AutoTrainParams
class ExtractiveQuestionAnsweringParams(AutoTrainParams):
"""
ExtractiveQuestionAnsweringParams
Parameters:
data_path (str): Path to the dataset.
model (str): Pre-trained model name. 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 inputs. Default is 128.
max_doc_stride (int): Maximum document stride for splitting context. Default is 128.
batch_size (int): Batch size for training. Default is 8.
warmup_ratio (float): Warmup proportion for learning rate scheduler. Default is 0.1.
gradient_accumulation (int): Number of gradient accumulation steps. Default is 1.
optimizer (str): Optimizer type. Default is "adamw_torch".
scheduler (str): Learning rate scheduler type. 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.
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): Column name for context/text. Default is "context".
question_column (str): Column name for questions. Default is "question".
answer_column (str): Column name for answers. Default is "answers".
logging_steps (int): Number of steps between logging. Default is -1.
project_name (str): Name of the project for output directory. Default is "project-name".
auto_find_batch_size (bool): Automatically find optimal 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]): Authentication token for 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 during training. Default is "epoch".
username (Optional[str]): Hugging Face username for authentication. Default is None.
log (str): Logging method for experiment tracking. Default is "none".
early_stopping_patience (int): Number of epochs with no improvement for early stopping. Default is 5.
early_stopping_threshold (float): Threshold for early stopping improvement. Default is 0.01.
"""
data_path: str = Field(None, title="Path to the dataset")
model: str = Field("bert-base-uncased", title="Pre-trained model name")
lr: float = Field(5e-5, title="Learning rate for the optimizer")
epochs: int = Field(3, title="Number of training epochs")
max_seq_length: int = Field(128, title="Maximum sequence length for inputs")
max_doc_stride: int = Field(128, title="Maximum document stride for splitting context")
batch_size: int = Field(8, title="Batch size for training")
warmup_ratio: float = Field(0.1, title="Warmup proportion for learning rate scheduler")
gradient_accumulation: int = Field(1, title="Number of gradient accumulation steps")
optimizer: str = Field("adamw_torch", title="Optimizer type")
scheduler: str = Field("linear", title="Learning rate scheduler type")
weight_decay: float = Field(0.0, title="Weight decay for the optimizer")
max_grad_norm: float = Field(1.0, title="Maximum gradient norm for clipping")
seed: int = Field(42, title="Random seed for reproducibility")
train_split: str = Field("train", title="Name of the training data split")
valid_split: Optional[str] = Field(None, title="Name of the validation data split")
text_column: str = Field("context", title="Column name for context/text")
question_column: str = Field("question", title="Column name for questions")
answer_column: str = Field("answers", title="Column name for answers")
logging_steps: int = Field(-1, title="Number of steps between logging")
project_name: str = Field("project-name", title="Name of the project for output directory")
auto_find_batch_size: bool = Field(False, title="Automatically find optimal batch size")
mixed_precision: Optional[str] = Field(None, title="Mixed precision training mode (fp16, bf16, or None)")
save_total_limit: int = Field(1, title="Maximum number of checkpoints to save")
token: Optional[str] = Field(None, title="Authentication token for Hugging Face Hub")
push_to_hub: bool = Field(False, title="Whether to push the model to Hugging Face Hub")
eval_strategy: str = Field("epoch", title="Evaluation strategy during training")
username: Optional[str] = Field(None, title="Hugging Face username for authentication")
log: str = Field("none", title="Logging method for experiment tracking")
early_stopping_patience: int = Field(5, title="Number of epochs with no improvement for early stopping")
early_stopping_threshold: float = Field(0.01, title="Threshold for early stopping improvement")
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