wehub-resource-sync
142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
import os
|
|
from typing import Optional
|
|
from dataclasses import dataclass, field
|
|
|
|
from transformers import TrainingArguments
|
|
|
|
|
|
@dataclass
|
|
class AbsRerankerModelArguments:
|
|
"""
|
|
Abstract class for reranker model arguments.
|
|
"""
|
|
|
|
model_name_or_path: str = field(
|
|
metadata={"help": "The model checkpoint for initialization."}
|
|
)
|
|
config_name: str = field(
|
|
default=None,
|
|
metadata={"help": "Pretrained config name or path if not the same as model_name."}
|
|
)
|
|
tokenizer_name: str = field(
|
|
default=None,
|
|
metadata={"help": "Pretrained tokenizer name or path if not the same as model_name."}
|
|
)
|
|
cache_dir: str = field(
|
|
default=None,
|
|
metadata={"help": "Where do you want to store the pre-trained models downloaded from s3."}
|
|
)
|
|
trust_remote_code: bool = field(
|
|
default=False,
|
|
metadata={"help": "Trust remote code"}
|
|
)
|
|
model_type: str = field(
|
|
default='encoder',
|
|
metadata={"help": "Type of finetune, ['encoder', 'decoder']"}
|
|
)
|
|
use_fast_tokenizer: bool = field(
|
|
default=True,
|
|
metadata={"help": "Whether to use fast tokenizer or not."}
|
|
)
|
|
token: str = field(
|
|
default_factory=lambda: os.getenv('HF_TOKEN', None),
|
|
metadata={"help": "The token to use when accessing the model."}
|
|
)
|
|
# finetune_type: str = field(
|
|
# default='sratch',
|
|
# metadata={"help": "Type of finetune, ['sratch', 'finetune']"}
|
|
# )
|
|
|
|
|
|
@dataclass
|
|
class AbsRerankerDataArguments:
|
|
"""
|
|
Abstract class for reranker data arguments.
|
|
"""
|
|
train_data: str = field(
|
|
default=None, metadata={
|
|
"help": "One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data.",
|
|
"nargs": "+"
|
|
}
|
|
)
|
|
cache_path: Optional[str] = field(
|
|
default=None, metadata={"help": "Where do you want to store the cached 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."
|
|
},
|
|
)
|
|
|
|
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."
|
|
},
|
|
)
|
|
|
|
max_len: int = field(
|
|
default=512,
|
|
metadata={
|
|
"help": "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated."
|
|
},
|
|
)
|
|
|
|
pad_to_multiple_of: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": "If set will pad the sequence to be a multiple of the provided value."
|
|
},
|
|
)
|
|
|
|
max_example_num_per_dataset: int = field(
|
|
default=100000000, metadata={"help": "the max number of examples for each dataset"}
|
|
)
|
|
|
|
query_instruction_for_rerank: str= field(
|
|
default=None, metadata={"help": "instruction for query"}
|
|
)
|
|
query_instruction_format: str = field(
|
|
default="{}{}", metadata={"help": "format for query instruction"}
|
|
)
|
|
|
|
knowledge_distillation: bool = field(
|
|
default=False,
|
|
metadata={"help": "Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data"}
|
|
)
|
|
|
|
passage_instruction_for_rerank: Optional[str] = field(
|
|
default=None, metadata={"help": "instruction for passage"}
|
|
)
|
|
passage_instruction_format: Optional[str] = field(
|
|
default="{}{}", metadata={"help": "format for passage instruction"}
|
|
)
|
|
|
|
shuffle_ratio: float = field(
|
|
default=0.0, metadata={"help": "The ratio of shuffling the text"}
|
|
)
|
|
|
|
sep_token: str = field(
|
|
default='\n', metadata={"help": "The sep token for LLM reranker to discriminate between query and passage"}
|
|
)
|
|
|
|
def __post_init__(self):
|
|
# replace "\\n" with "\n"
|
|
if "\\n" in self.query_instruction_format:
|
|
self.query_instruction_format = self.query_instruction_format.replace("\\n", "\n")
|
|
if "\\n" in self.passage_instruction_format:
|
|
self.passage_instruction_format = self.passage_instruction_format.replace("\\n", "\n")
|
|
|
|
# check the existence of train data
|
|
for train_dir in self.train_data:
|
|
if not os.path.exists(train_dir):
|
|
raise FileNotFoundError(f"cannot find file: {train_dir}, please set a true path")
|
|
|
|
|
|
@dataclass
|
|
class AbsRerankerTrainingArguments(TrainingArguments):
|
|
sub_batch_size: Optional[int] = field(default=None, metadata={"help": "sub batch size for training, not implemented yet"})
|