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https://github.com/hiyouga/LLaMA-Factory.git
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101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
from dataclasses import dataclass, field
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from typing import Literal, Optional
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@dataclass
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class DataArguments:
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r"""
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Arguments pertaining to what data we are going to input our model for training and evaluation.
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"""
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template: Optional[str] = field(
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default=None,
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metadata={"help": "Which template to use for constructing prompts in training and inference."},
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)
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dataset: Optional[str] = field(
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default=None,
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metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."},
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)
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dataset_dir: str = field(
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default="data",
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metadata={"help": "Path to the folder containing the datasets."},
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)
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split: str = field(
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default="train",
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metadata={"help": "Which dataset split to use for training and evaluation."},
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)
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cutoff_len: int = field(
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default=1024,
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metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
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)
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reserved_label_len: int = field(
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default=1,
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metadata={"help": "The minimum cutoff length reserved for the tokenized labels in the dataset."},
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)
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train_on_prompt: bool = field(
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default=False,
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metadata={"help": "Whether to disable the mask on the prompt or not."},
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)
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streaming: bool = field(
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default=False,
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metadata={"help": "Enable dataset streaming."},
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)
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buffer_size: int = field(
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default=16384,
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metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."},
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)
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mix_strategy: Literal["concat", "interleave_under", "interleave_over"] = field(
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default="concat",
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metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."},
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)
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interleave_probs: Optional[str] = field(
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default=None,
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metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
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)
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overwrite_cache: bool = field(
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default=False,
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metadata={"help": "Overwrite the cached training and evaluation sets."},
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the pre-processing."},
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)
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max_samples: Optional[int] = field(
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default=None,
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metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."},
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)
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eval_num_beams: Optional[int] = field(
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default=None,
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metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"},
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)
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ignore_pad_token_for_loss: bool = field(
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default=True,
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metadata={
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"help": "Whether or not to ignore the tokens corresponding to padded labels in the loss computation."
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},
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)
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val_size: float = field(
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default=0.0,
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metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."},
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)
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packing: Optional[bool] = field(
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default=None,
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metadata={
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"help": "Whether or not to pack the sequences in training. Will automatically enable in pre-training."
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},
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)
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tokenized_path: Optional[str] = field(
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default=None,
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metadata={"help": "Path to save or load the tokenized datasets."},
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)
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def __post_init__(self):
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if self.reserved_label_len >= self.cutoff_len:
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raise ValueError("`reserved_label_len` must be smaller than `cutoff_len`.")
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if self.streaming and self.val_size > 1e-6 and self.val_size < 1:
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raise ValueError("Streaming mode should have an integer val size.")
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if self.streaming and self.max_samples is not None:
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raise ValueError("`max_samples` is incompatible with `streaming`.")
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