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63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from dataclasses import dataclass, field
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from typing import Optional
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from transformers import TrainingArguments
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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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sentence_pooling_method: str = field(default='cls')
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normlized: bool = field(default=True)
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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 train data"}
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)
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max_example_num_per_dataset: int = field(
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default=1000000, metadata={"help": "sample negatives from top-k"}
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)
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train_group_size: int = field(default=8)
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query_max_len: int = field(
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default=32,
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metadata={
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"help": "The maximum total input sequence length after tokenization for passage. 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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passage_max_len: int = field(
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default=128,
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metadata={
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"help": "The maximum total input sequence length after tokenization for passage. 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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@dataclass
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class RetrieverTrainingArguments(TrainingArguments):
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negatives_x_device: bool = field(default=False, metadata={"help": "share negatives across devices"})
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temperature: Optional[float] = field(default=1.0)
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fix_position_embedding: bool = field(default=False,
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metadata={"help": "Freeze the parameters of position embeddings"})
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