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