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Former-commit-id: 889f6e910e654d8ec3922c2185042d737ffbf1c3
This commit is contained in:
hiyouga
2024-03-07 20:26:31 +08:00
parent 9a69cadab3
commit 056d2d956a
32 changed files with 752 additions and 316 deletions
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30
src/llmtuner/hparams/data_args.py
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@@ -16,35 +16,35 @@ class DataArguments:
default=None,
metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."},
)
dataset_dir: Optional[str] = field(
dataset_dir: str = field(
default="data",
metadata={"help": "Path to the folder containing the datasets."},
)
split: Optional[str] = field(
split: str = field(
default="train",
metadata={"help": "Which dataset split to use for training and evaluation."},
)
cutoff_len: Optional[int] = field(
cutoff_len: int = field(
default=1024,
metadata={"help": "The cutoff length of the model inputs after tokenization."},
)
reserved_label_len: Optional[int] = field(
reserved_label_len: int = field(
default=1,
metadata={"help": "The minimum cutoff length reserved for label after tokenization."},
)
train_on_prompt: Optional[bool] = field(
train_on_prompt: bool = field(
default=False,
metadata={"help": "Whether to disable the mask on the prompt or not."},
)
streaming: Optional[bool] = field(
streaming: bool = field(
default=False,
metadata={"help": "Enable dataset streaming."},
)
buffer_size: Optional[int] = field(
buffer_size: 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(
mix_strategy: Literal["concat", "interleave_under", "interleave_over"] = field(
default="concat",
metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."},
)
@@ -52,13 +52,13 @@ class DataArguments:
default=None,
metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
)
overwrite_cache: Optional[bool] = field(
overwrite_cache: 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."},
metadata={"help": "The number of processes to use for the pre-processing."},
)
max_samples: Optional[int] = field(
default=None,
@@ -68,23 +68,23 @@ class DataArguments:
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(
ignore_pad_token_for_loss: bool = field(
default=True,
metadata={
"help": "Whether or not to ignore the tokens corresponding to padded labels in the loss computation."
},
)
val_size: Optional[float] = field(
default=0,
val_size: float = field(
default=0.0,
metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."},
)
sft_packing: Optional[bool] = field(
sft_packing: 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."},
metadata={"help": "Path to save or load the pre-processed datasets."},
)
def __post_init__(self):