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arguments.py
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arguments.py
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import os
from dataclasses import dataclass, field
from typing import Optional
from transformers import TrainingArguments
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
@dataclass
class DataArguments:
train_data: str = field(
default=None, metadata={"help": "Path to train data"}
)
train_group_size: int = field(default=8)
query_max_len: int = field(
default=32,
metadata={
"help": "The maximum total input sequence length after tokenization for passage. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
passage_max_len: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization for passage. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_example_num_per_dataset: int = field(
default=100000000, metadata={"help": "the max number of examples for each dataset"}
)
query_instruction_for_retrieval: str= field(
default=None, metadata={"help": "instruction for query"}
)
passage_instruction_for_retrieval: str = field(
default=None, metadata={"help": "instruction for passage"}
)
def __post_init__(self):
if not os.path.exists(self.train_data):
raise FileNotFoundError(f"cannot find file: {self.train_data}, please set a true path")
@dataclass
class RetrieverTrainingArguments(TrainingArguments):
negatives_cross_device: bool = field(default=False, metadata={"help": "share negatives across devices"})
temperature: Optional[float] = field(default=0.02)
fix_position_embedding: bool = field(default=False, metadata={"help": "Freeze the parameters of position embeddings"})
sentence_pooling_method: str = field(default='cls', metadata={"help": "the pooling method, should be cls or mean"})
normlized: bool = field(default=True)
use_inbatch_neg: bool = field(default=True, metadata={"help": "use passages in the same batch as negatives"})