wehub-resource-sync
229 lines
9.3 KiB
Python
229 lines
9.3 KiB
Python
import os
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import json
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import logging
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import datasets
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from tqdm import tqdm
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from typing import List, Optional
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from FlagEmbedding.abc.evaluation import AbsEvalDataLoader
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from .utils.normalize_text import normalize_text
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logger = logging.getLogger(__name__)
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class MKQAEvalDataLoader(AbsEvalDataLoader):
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"""
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Data loader class for MKQA.
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"""
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def available_dataset_names(self) -> List[str]:
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"""
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Get the available dataset names.
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Returns:
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List[str]: All the available dataset names.
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"""
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return ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']
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def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:
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"""
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Get the avaialble splits.
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Args:
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dataset_name (str): Dataset name.
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Returns:
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List[str]: All the available splits for the dataset.
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"""
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return ["test"]
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def load_corpus(self, dataset_name: Optional[str] = None) -> datasets.DatasetDict:
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"""Load the corpus.
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Args:
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dataset_name (Optional[str], optional): Name of the dataset. Defaults to None.
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Returns:
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datasets.DatasetDict: Loaded datasets instance of corpus.
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"""
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if self.dataset_dir is not None:
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# same corpus for all languages
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save_dir = self.dataset_dir
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return self._load_local_corpus(save_dir, dataset_name=dataset_name)
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else:
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return self._load_remote_corpus(dataset_name=dataset_name)
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def _load_local_qrels(self, save_dir: str, dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:
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"""Try to load qrels from local datasets.
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Args:
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save_dir (str): Directory that save the data files.
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dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.
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split (str, optional): Split of the dataset. Defaults to ``'test'``.
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Raises:
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ValueError: No local qrels found, will try to download from remote.
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Returns:
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datasets.DatasetDict: Loaded datasets instance of qrels.
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"""
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checked_split = self.check_splits(split)
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if len(checked_split) == 0:
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raise ValueError(f"Split {split} not found in the dataset.")
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split = checked_split[0]
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qrels_path = os.path.join(save_dir, f"{split}_qrels.jsonl")
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if self.force_redownload or not os.path.exists(qrels_path):
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logger.warning(f"Qrels not found in {qrels_path}. Trying to download the qrels from the remote and save it to {save_dir}.")
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return self._load_remote_qrels(dataset_name=dataset_name, split=split, save_dir=save_dir)
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else:
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qrels_data = datasets.load_dataset('json', data_files=qrels_path, cache_dir=self.cache_dir)['train']
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qrels = {}
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for data in qrels_data:
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qid = data['qid']
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qrels[qid] = data['answers']
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return datasets.DatasetDict(qrels)
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def _load_remote_corpus(
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self,
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dataset_name: Optional[str] = None,
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save_dir: Optional[str] = None
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) -> datasets.DatasetDict:
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"""
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Refer to: https://arxiv.org/pdf/2402.03216. We use the corpus from the BeIR dataset.
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"""
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corpus = datasets.load_dataset(
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"BeIR/nq", "corpus",
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cache_dir=self.cache_dir,
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trust_remote_code=True,
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download_mode=self.hf_download_mode
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)["corpus"]
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if save_dir is not None:
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os.makedirs(save_dir, exist_ok=True)
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save_path = os.path.join(save_dir, "corpus.jsonl")
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corpus_dict = {}
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with open(save_path, "w", encoding="utf-8") as f:
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for data in tqdm(corpus, desc="Loading and Saving corpus"):
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docid, title, text = str(data["_id"]), normalize_text(data["title"]).lower(), normalize_text(data["text"]).lower()
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_data = {
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"id": docid,
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"title": title,
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"text": text
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}
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corpus_dict[docid] = {
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"title": title,
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"text": text
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}
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f.write(json.dumps(_data, ensure_ascii=False) + "\n")
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logging.info(f"{self.eval_name} corpus saved to {save_path}")
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else:
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corpus_dict = {}
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for data in tqdm(corpus, desc="Loading corpus"):
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docid, title, text = str(data["_id"]), normalize_text(data["title"]), normalize_text(data["text"])
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corpus_dict[docid] = {
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"title": title,
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"text": text
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}
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return datasets.DatasetDict(corpus_dict)
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def _load_remote_qrels(
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self,
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dataset_name: str,
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split: str = 'test',
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save_dir: Optional[str] = None
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) -> datasets.DatasetDict:
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"""Load remote qrels from HF.
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Args:
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dataset_name (str): Name of the dataset.
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split (str, optional): Split of the dataset. Defaults to ``'test'``.
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save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.
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Returns:
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datasets.DatasetDict: Loaded datasets instance of qrel.
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"""
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endpoint = f"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/Shitao/bge-m3-data"
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queries_download_url = f"{endpoint}/resolve/main/MKQA_test-data.zip"
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qrels_save_dir = self._download_zip_file(queries_download_url, self.cache_dir)
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qrels_save_path = os.path.join(qrels_save_dir, f"{dataset_name}.jsonl")
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if save_dir is not None:
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os.makedirs(save_dir, exist_ok=True)
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save_path = os.path.join(save_dir, f"{split}_qrels.jsonl")
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qrels_dict = {}
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with open(save_path, "w", encoding="utf-8") as f1:
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with open(qrels_save_path, "r", encoding="utf-8") as f2:
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for line in tqdm(f2.readlines(), desc="Loading and Saving qrels"):
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data = json.loads(line)
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qid, answers = str(data["id"]), data["answers"]
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_data = {
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"qid": qid,
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"answers": answers
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}
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if qid not in qrels_dict:
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qrels_dict[qid] = {}
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qrels_dict[qid] = answers
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f1.write(json.dumps(_data, ensure_ascii=False) + "\n")
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logging.info(f"{self.eval_name} {dataset_name} qrels saved to {save_path}")
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else:
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qrels_dict = {}
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with open(qrels_save_path, "r", encoding="utf-8") as f:
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for line in tqdm(f.readlines(), desc="Loading qrels"):
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data = json.loads(line)
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qid, answers = str(data["id"]), data["answers"]
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if qid not in qrels_dict:
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qrels_dict[qid] = {}
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qrels_dict[qid] = answers
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return datasets.DatasetDict(qrels_dict)
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def _load_remote_queries(
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self,
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dataset_name: str,
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split: str = 'test',
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save_dir: Optional[str] = None
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) -> datasets.DatasetDict:
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"""Load the queries from HF.
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Args:
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dataset_name (str): Name of the dataset.
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split (str, optional): Split of the dataset. Defaults to ``'test'``.
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save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.
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Returns:
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datasets.DatasetDict: Loaded datasets instance of queries.
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"""
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endpoint = f"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/Shitao/bge-m3-data"
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queries_download_url = f"{endpoint}/resolve/main/MKQA_test-data.zip"
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queries_save_dir = self._download_zip_file(queries_download_url, self.cache_dir)
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queries_save_path = os.path.join(queries_save_dir, f"{dataset_name}.jsonl")
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if save_dir is not None:
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os.makedirs(save_dir, exist_ok=True)
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save_path = os.path.join(save_dir, f"{split}_queries.jsonl")
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queries_dict = {}
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with open(save_path, "w", encoding="utf-8") as f1:
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with open(queries_save_path, "r", encoding="utf-8") as f2:
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for line in tqdm(f2.readlines(), desc="Loading and Saving queries"):
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data = json.loads(line)
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qid, query = str(data["id"]), data["question"]
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_data = {
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"id": qid,
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"text": query
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}
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queries_dict[qid] = query
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f1.write(json.dumps(_data, ensure_ascii=False) + "\n")
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logging.info(f"{self.eval_name} {dataset_name} queries saved to {save_path}")
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else:
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queries_dict = {}
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with open(queries_save_path, "r", encoding="utf-8") as f:
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for line in tqdm(f.readlines(), desc="Loading queries"):
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data = json.loads(line)
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qid, query = str(data["id"]), data["question"]
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queries_dict[qid] = query
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return datasets.DatasetDict(queries_dict)
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