101 lines
4.3 KiB
Python
101 lines
4.3 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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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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@dataclass
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class DataArguments:
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knowledge_distillation: bool = field(
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default=False, metadata={"help": "Use knowledge distillation when `pos_scores` and `neg_scores` are in features of training data"}
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)
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train_data: str = field(
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default=None, metadata={"help": "One or more paths to training data", "nargs": "+"}
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)
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cache_path: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the cached data"}
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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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max_example_num_per_dataset: int = field(
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default=None, metadata={"help": "the max number of examples for each dataset"}
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)
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query_instruction_for_retrieval: str= field(
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default=None, metadata={"help": "instruction for query"}
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)
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passage_instruction_for_retrieval: str = field(
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default=None, metadata={"help": "instruction for passage"}
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)
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same_task_within_batch: bool = field(
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default=False, metadata={"help": "All samples in the same batch comes from the same task."}
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)
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shuffle_ratio: float = field(
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default=0.0, metadata={"help": "The ratio of shuffling the text"}
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)
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small_threshold: int = field(
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default=0, metadata={"help": "The threshold of small dataset. All small dataset in the same directory will be merged into one dataset."}
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)
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drop_threshold: int = field(
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default=0, metadata={"help": "The threshold for dropping merged small dataset. If the number of examples in the merged small dataset is less than this threshold, it will be dropped."}
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)
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def __post_init__(self):
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for train_dir in self.train_data:
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if not os.path.exists(train_dir):
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raise FileNotFoundError(f"cannot find file: {train_dir}, please set a true path")
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@dataclass
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class RetrieverTrainingArguments(TrainingArguments):
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negatives_cross_device: bool = field(default=False, metadata={"help": "share negatives across devices"})
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temperature: Optional[float] = field(default=0.02)
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fix_position_embedding: bool = field(default=False, metadata={"help": "Freeze the parameters of position embeddings"})
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sentence_pooling_method: str = field(default='cls', metadata={"help": "the pooling method, should be cls or mean"})
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normlized: bool = field(default=True)
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enable_sub_batch: bool = field(default=True, metadata={"help": "Freeze the parameters of position embeddings"})
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unified_finetuning: bool = field(default=False, metadata={"help": "use unify fine-tuning"})
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use_self_distill: bool = field(default=False, metadata={"help": "use self-distill when using unify fine-tuning"})
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fix_encoder: bool = field(default=False, metadata={"help": "Freeze the parameters of encoder"})
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colbert_dim: int = field(default=-1, metadata={"help": "Dim of colbert linear"})
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self_distill_start_step: int = field(default=-1, metadata={"help": "Num of step when using self-distill"})
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