44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import os
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from dataclasses import dataclass, field
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from typing import Optional
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@dataclass
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class DataTrainingArguments:
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train_data: Optional[str] = field(
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default=None, metadata={"help": "Path to pretrain data"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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max_seq_length: Optional[int] = field(
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default=512,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated. Default to the max input length of the model."
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},
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)
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encoder_mlm_probability: float = field(default=0.3, metadata={"help": "mask ratio for encoder"})
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decoder_mlm_probability: float = field(default=0.5, metadata={"help": "mask ratio for decoder"})
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def __post_init__(self):
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if not os.path.exists(self.train_data):
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raise FileNotFoundError(f"cannot find file: {self.train_data}, please set a true path")
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default='bert-base-uncased',
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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