|
from dataclasses import dataclass, field |
|
from typing import Optional |
|
|
|
|
|
@dataclass |
|
class ModelArguments: |
|
""" |
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. |
|
""" |
|
|
|
model_name_or_path: str = field( |
|
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} |
|
) |
|
ptuning_checkpoint: str = field( |
|
default=None, metadata={"help": "Path to p-tuning v2 checkpoints"} |
|
) |
|
config_name: Optional[str] = field( |
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} |
|
) |
|
tokenizer_name: Optional[str] = field( |
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} |
|
) |
|
cache_dir: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, |
|
) |
|
use_fast_tokenizer: bool = field( |
|
default=True, |
|
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, |
|
) |
|
model_revision: str = field( |
|
default="main", |
|
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, |
|
) |
|
use_auth_token: bool = field( |
|
default=False, |
|
metadata={ |
|
"help": ( |
|
"Will use the token generated when running `huggingface-cli login` (necessary to use this script " |
|
"with private models)." |
|
) |
|
}, |
|
) |
|
resize_position_embeddings: Optional[bool] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"Whether to automatically resize the position embeddings if `max_source_length` exceeds " |
|
"the model's position embeddings." |
|
) |
|
}, |
|
) |
|
quantization_bit: Optional[int] = field( |
|
default=None |
|
) |
|
pre_seq_len: Optional[int] = field( |
|
default=None |
|
) |
|
prefix_projection: bool = field( |
|
default=False |
|
) |
|
|
|
|
|
@dataclass |
|
class DataTrainingArguments: |
|
""" |
|
Arguments pertaining to what data we are going to input our model for training and eval. |
|
""" |
|
|
|
lang: Optional[str] = field(default=None, metadata={"help": "Language id for summarization."}) |
|
|
|
dataset_name: Optional[str] = field( |
|
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} |
|
) |
|
dataset_config_name: Optional[str] = field( |
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} |
|
) |
|
prompt_column: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."}, |
|
) |
|
response_column: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."}, |
|
) |
|
history_column: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "The name of the column in the datasets containing the history of chat."}, |
|
) |
|
train_file: Optional[str] = field( |
|
default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."} |
|
) |
|
validation_file: Optional[str] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." |
|
) |
|
}, |
|
) |
|
test_file: Optional[str] = field( |
|
default=None, |
|
metadata={ |
|
"help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." |
|
}, |
|
) |
|
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."}, |
|
) |
|
max_source_length: Optional[int] = field( |
|
default=1024, |
|
metadata={ |
|
"help": ( |
|
"The maximum total input sequence length after tokenization. Sequences longer " |
|
"than this will be truncated, sequences shorter will be padded." |
|
) |
|
}, |
|
) |
|
max_target_length: Optional[int] = field( |
|
default=128, |
|
metadata={ |
|
"help": ( |
|
"The maximum total sequence length for target text after tokenization. Sequences longer " |
|
"than this will be truncated, sequences shorter will be padded." |
|
) |
|
}, |
|
) |
|
val_max_target_length: Optional[int] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"The maximum total sequence length for validation target text after tokenization. Sequences longer " |
|
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`." |
|
"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used " |
|
"during ``evaluate`` and ``predict``." |
|
) |
|
}, |
|
) |
|
pad_to_max_length: bool = field( |
|
default=False, |
|
metadata={ |
|
"help": ( |
|
"Whether to pad all samples to model maximum sentence length. " |
|
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More " |
|
"efficient on GPU but very bad for TPU." |
|
) |
|
}, |
|
) |
|
max_train_samples: Optional[int] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"For debugging purposes or quicker training, truncate the number of training examples to this " |
|
"value if set." |
|
) |
|
}, |
|
) |
|
max_eval_samples: Optional[int] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"For debugging purposes or quicker training, truncate the number of evaluation examples to this " |
|
"value if set." |
|
) |
|
}, |
|
) |
|
max_predict_samples: Optional[int] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"For debugging purposes or quicker training, truncate the number of prediction examples to this " |
|
"value if set." |
|
) |
|
}, |
|
) |
|
num_beams: Optional[int] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " |
|
"which is used during ``evaluate`` and ``predict``." |
|
) |
|
}, |
|
) |
|
ignore_pad_token_for_loss: bool = field( |
|
default=True, |
|
metadata={ |
|
"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not." |
|
}, |
|
) |
|
source_prefix: Optional[str] = field( |
|
default="", metadata={"help": "A prefix to add before every source text (useful for T5 models)."} |
|
) |
|
|
|
forced_bos_token: Optional[str] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"The token to force as the first generated token after the decoder_start_token_id." |
|
"Useful for multilingual models like mBART where the first generated token" |
|
"needs to be the target language token (Usually it is the target language token)" |
|
) |
|
}, |
|
) |
|
|
|
|
|
|
|
def __post_init__(self): |
|
if self.dataset_name is None and self.train_file is None and self.validation_file is None and self.test_file is None: |
|
raise ValueError("Need either a dataset name or a training/validation/test file.") |
|
else: |
|
if self.train_file is not None: |
|
extension = self.train_file.split(".")[-1] |
|
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." |
|
if self.validation_file is not None: |
|
extension = self.validation_file.split(".")[-1] |
|
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." |
|
if self.val_max_target_length is None: |
|
self.val_max_target_length = self.max_target_length |
|
|
