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43 lines
1.4 KiB
Python
43 lines
1.4 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 ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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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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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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cache_dir: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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@dataclass
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class DataArguments:
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train_data: str = field(
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default=None, metadata={"help": "Path to corpus"}
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)
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train_group_size: int = field(default=8)
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max_len: 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 for input text. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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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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