Source code for FlagEmbedding.abc.finetune.embedder.AbsArguments

import os
from typing import Optional
from dataclasses import dataclass, field

from transformers import TrainingArguments


[docs] @dataclass class AbsEmbedderModelArguments: """ Abstract class for 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"} ) token: str = field( default_factory=lambda: os.getenv('HF_TOKEN', None), metadata={"help": "The token to use when accessing the model."} )
[docs] @dataclass class AbsEmbedderDataArguments: """ Abstract class for 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." }, ) 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_retrieval: 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_retrieval: 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"} ) # Parameters for SameDatasetDataArguments same_dataset_within_batch: bool = field( default=False, metadata={"help": "All samples in the same batch comes from the same dataset."} ) small_threshold: int = field( default=0, metadata={"help": "The threshold of small dataset. All small dataset in the same directory will be merged into one dataset."} ) drop_threshold: int = field( 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."} ) def __post_init__(self): 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 AbsEmbedderTrainingArguments(TrainingArguments): negatives_cross_device: bool = field(default=False, metadata={"help": "share negatives across devices"}) temperature: Optional[float] = field(default=0.02, metadata={"help": "temperature used for similarity score"}) 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. Available options: cls, mean, last_token. Default: cls", "choices": ['cls', 'mean', 'last_token']}) normalize_embeddings: bool = field(default=True, metadata={"help": "whether to normalize the embeddings"}) sub_batch_size: Optional[int] = field(default=None, metadata={"help": "sub batch size for training"}) kd_loss_type: str = field(default='kl_div', metadata={"help": "the loss type for knowledge distillation. Available options: kl_div, m3_kd_loss. Default: kl_div.", "choices": ['kl_div', 'm3_kd_loss']})