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Text Classification & Regression Parameters

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Text Classification & Regression Parameters

class autotrain.trainers.text_classification.params.TextClassificationParams

< >

( data_path: str = None model: str = 'bert-base-uncased' lr: float = 5e-05 epochs: int = 3 max_seq_length: int = 128 batch_size: int = 8 warmup_ratio: float = 0.1 gradient_accumulation: int = 1 optimizer: str = 'adamw_torch' scheduler: str = 'linear' weight_decay: float = 0.0 max_grad_norm: float = 1.0 seed: int = 42 train_split: str = 'train' valid_split: Optional = None text_column: str = 'text' target_column: str = 'target' logging_steps: int = -1 project_name: str = 'project-name' auto_find_batch_size: bool = False mixed_precision: Optional = None save_total_limit: int = 1 token: Optional = None push_to_hub: bool = False eval_strategy: str = 'epoch' username: Optional = None log: str = 'none' early_stopping_patience: int = 5 early_stopping_threshold: float = 0.01 )

Parameters

  • data_path (str) — Path to the dataset.
  • model (str) — Name of the model to use. Default is “bert-base-uncased”.
  • lr (float) — Learning rate. Default is 5e-5.
  • epochs (int) — Number of training epochs. Default is 3.
  • max_seq_length (int) — Maximum sequence length. Default is 128.
  • batch_size (int) — Training batch size. Default is 8.
  • warmup_ratio (float) — Warmup proportion. Default is 0.1.
  • gradient_accumulation (int) — Number of gradient accumulation steps. Default is 1.
  • optimizer (str) — Optimizer to use. Default is “adamw_torch”.
  • scheduler (str) — Scheduler to use. Default is “linear”.
  • weight_decay (float) — Weight decay. Default is 0.0.
  • max_grad_norm (float) — Maximum gradient norm. Default is 1.0.
  • seed (int) — Random seed. Default is 42.
  • train_split (str) — Name of the training split. Default is “train”.
  • valid_split (Optional[str]) — Name of the validation split. Default is None.
  • text_column (str) — Name of the text column in the dataset. Default is “text”.
  • target_column (str) — Name of the target column in the dataset. Default is “target”.
  • logging_steps (int) — Number of steps between logging. Default is -1.
  • project_name (str) — Name of the project. 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 setting (fp16, bf16, or None). Default is None.
  • save_total_limit (int) — Total number of checkpoints to save. Default is 1.
  • token (Optional[str]) — Hub token for authentication. Default is None.
  • push_to_hub (bool) — Whether to push the model to the hub. Default is False.
  • eval_strategy (str) — Evaluation strategy. 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 continue training. Default is 0.01.

TextClassificationParams is a configuration class for text classification training parameters.

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