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from dataclasses import dataclass, field
from .. import models
@dataclass
class RetroDataModelArguments:
pass
@dataclass
class DataArguments(RetroDataModelArguments):
max_seq_length: int = field(
default=512,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_answer_length: int = field(
default=30,
metadata={
"help": "Maximum length of an answer (in tokens) to be generated. This is not "
"a hard limit but the model's internal length limit."
},
)
doc_stride: int = field(
default=128,
metadata={
"help": "When splitting up a long document into chunks, how much stride to take between chunks."
},
)
return_token_type_ids: bool = field(
default=True,
metadata={
"help": "Whether to return token type ids."
},
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch (which can "
"be faster on GPU but will be slower on TPU)."
},
)
preprocessing_num_workers: int = field(
default=5,
metadata={
"help": "The number of processes to use for the preprocessing."
},
)
overwrite_cache: bool = field(
default=False,
metadata={
"help": "Overwrite the cached training and evaluation sets"
},
)
version_2_with_negative: bool = field(
default=True,
metadata={
"help": ""
},
)
null_score_diff_threshold: float = field(
default=0.0,
metadata={
"help": "If null_score - best_non_null is greater than the threshold predict null."
},
)
rear_threshold: float = field(
default=0.0,
metadata={
"help": "Rear threshold."
},
)
n_best_size: int = field(
default=20,
metadata={
"help": "The total number of n-best predictions to generate when looking for an answer."
},
)
use_choice_logits: bool = field(
default=False,
metadata={
"help": "Whether to use choice logits."
},
)
start_n_top: int = field(
default=-1,
metadata={
"help": ""
},
)
end_n_top: int = field(
default=-1,
metadata={
"help": ""
},
)
beta1: int = field(
default=1,
metadata={
"help": ""
},
)
beta2: int = field(
default=1,
metadata={
"help": ""
},
)
best_cof: int = field(
default=1,
metadata={
"help": ""
},
)
@dataclass
class ModelArguments(RetroDataModelArguments):
use_auth_token: bool = field(
default=False,
metadata={
# "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
# "with private models)."
"help": ""
},
)
@dataclass
class SketchModelArguments(ModelArguments):
sketch_revision: str = field(
default="main",
metadata={
"help": "The revision of the pretrained sketch model."
},
)
sketch_model_name: str = field(
default="monologg/koelectra-small-v3-discriminator",
metadata={
"help": "The name of the pretrained sketch model."
},
)
sketch_model_mode: str = field(
default="finetune",
metadata={
"help": "Choices = ['finetune', 'transfer']"
},
)
sketch_tokenizer_name: str = field(
default=None,
metadata={
"help": "The name of the pretrained sketch tokenizer."
},
)
sketch_architectures: str = field(
default="ElectraForSequenceClassification",
metadata={
"help": ""
},
)
@dataclass
class IntensiveModelArguments(ModelArguments):
intensive_revision: str = field(
default="main",
metadata={
"help": "The revision of the pretrained intensive model."
},
)
intensive_model_name: str = field(
default="monologg/koelectra-base-v3-discriminator",
metadata={
"help": "The name of the pretrained intensive model."
},
)
intensive_model_mode: str = field(
default="finetune",
metadata={
"help": "Choices = ['finetune', 'transfer']"
},
)
intensive_tokenizer_name: str = field(
default=None,
metadata={
"help": "The name of the pretrained intensive tokenizer."
},
)
intensive_architectures: str = field(
default="ElectraForQuestionAnsweringAVPool",
metadata={
"help": ""
},
)
@dataclass
class RetroArguments(DataArguments, SketchModelArguments, IntensiveModelArguments):
def __post_init__(self):
# Sketch
model_cls = getattr(models, self.sketch_architectures, None)
if model_cls is None:
raise ValueError(f"The sketch architecture '{self.sketch_architectures}' is not supported.")
# raise AttributeError
self.sketch_model_cls = model_cls
self.sketch_model_type = model_cls.model_type
if self.sketch_tokenizer_name is None:
self.sketch_tokenizer_name = self.sketch_model_name
# Intensive
model_cls = getattr(models, self.intensive_architectures, None)
if model_cls is None:
raise AttributeError
self.intensive_model_cls = model_cls
self.intensive_model_type = model_cls.model_type
# Tokenizer
if self.intensive_tokenizer_name is None:
self.intensive_tokenizer_name = self.intensive_model_name
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