| | from dataclasses import dataclass, field |
| | from typing import Optional, List |
| |
|
| |
|
| | @dataclass |
| | class QueryEncoderArgs: |
| | model_name_or_path: str = field( |
| | default="facebook/dpr-question_encoder-single-nq-base", |
| | metadata={ |
| | "help": "Path to pretrained model or model identifier from huggingface.co/models" |
| | }, |
| | ) |
| | cache_dir: str = field( |
| | default="/share/LMs", |
| | metadata={"help": "The path to save pretrain model."}, |
| | ) |
| | device: str = field( |
| | default="cuda:0", |
| | metadata={"help": "The device to run query encoder."}, |
| | ) |
| |
|
| |
|
| | @dataclass |
| | class SearcherArgs: |
| | index_dir: str = field( |
| | default="/share/ninglu_shao/code/Citation/faiss.wikipedia-dpr-100w.dpr_single-nq.20200115.cd5034", |
| | metadata={"help": "The path to index."}, |
| | ) |
| | doc_path: str = field( |
| | default="/share/ninglu_shao/data/Citation/psgs_w100.jsonl", |
| | metadata={"help": "The path to document."}, |
| | ) |
| |
|
| |
|
| | @dataclass |
| | class DataArgs: |
| | dataset_list: List[str] = field( |
| | default_factory=lambda: ["asqa"], |
| | metadata={"help": "The name of dataset."}, |
| | ) |
| | dataset_dir: str = field( |
| | default="/share/ninglu_shao/data/Citation", |
| | metadata={"help": "The path to dataset."}, |
| | ) |
| | save_dir: str = field( |
| | default="data/output", |
| | metadata={"help": "The path to save retrieved dataset."}, |
| | ) |
| |
|
| |
|
| | @dataclass |
| | class RetrieveArgs: |
| | k: int = field( |
| | default=5, |
| | metadata={"help": "The number of retrieved results."}, |
| | ) |
| |
|
| |
|
| | @dataclass |
| | class Args: |
| | trial_num: int = field( |
| | default=5, |
| | metadata={"help": "The number of trial."}, |
| | ) |