chore: import upstream snapshot with attribution
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wehub-resource-sync
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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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logger = logging.getLogger(__name__)
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class MLDREvalDataLoader(AbsEvalDataLoader):
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"""
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Data loader class for MLDR.
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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 ["ar", "de", "en", "es", "fr", "hi", "it", "ja", "ko", "pt", "ru", "th", "zh"]
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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 (Optional[str], optional): Dataset name. Defaults to ``None``.
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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 ["train", "dev", "test"]
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def _load_remote_corpus(
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self,
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dataset_name: str,
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save_dir: Optional[str] = None
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) -> datasets.DatasetDict:
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"""Load the corpus dataset from HF.
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Args:
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dataset_name (str): Name of the dataset.
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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 corpus.
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"""
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corpus = datasets.load_dataset(
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"Shitao/MLDR", f"corpus-{dataset_name}",
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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, text = str(data["docid"]), data["text"]
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_data = {
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"id": docid,
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"text": text
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}
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corpus_dict[docid] = {"text": text}
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f.write(json.dumps(_data, ensure_ascii=False) + "\n")
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logging.info(f"{self.eval_name} {dataset_name} corpus saved to {save_path}")
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else:
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corpus_dict = {str(data["docid"]): {"text": data["text"]} for data in tqdm(corpus, desc="Loading corpus")}
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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 the 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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qrels_data = datasets.load_dataset(
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"Shitao/MLDR", dataset_name,
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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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)[split]
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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 f:
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for data in tqdm(qrels_data, desc="Loading and Saving qrels"):
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qid = str(data["query_id"])
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if qid not in qrels_dict:
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qrels_dict[qid] = {}
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for doc in data["positive_passages"]:
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docid = str(doc["docid"])
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_data = {
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"qid": qid,
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"docid": docid,
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"relevance": 1
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}
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qrels_dict[qid][docid] = 1
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f.write(json.dumps(_data, ensure_ascii=False) + "\n")
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for doc in data["negative_passages"]:
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docid = str(doc["docid"])
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_data = {
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"qid": qid,
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"docid": docid,
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"relevance": 0
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}
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qrels_dict[qid][docid] = 0
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f.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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for data in tqdm(qrels_data, desc="Loading qrels"):
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qid = str(data["query_id"])
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if qid not in qrels_dict:
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qrels_dict[qid] = {}
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for doc in data["positive_passages"]:
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docid = str(doc["docid"])
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qrels_dict[qid][docid] = 1
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for doc in data["negative_passages"]:
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docid = str(doc["docid"])
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qrels_dict[qid][docid] = 0
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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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queries_data = datasets.load_dataset(
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"Shitao/MLDR", dataset_name,
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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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)[split]
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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 f:
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for data in tqdm(queries_data, desc="Loading and Saving queries"):
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qid, query = str(data["query_id"]), data["query"]
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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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f.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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for data in tqdm(queries_data, desc="Loading queries"):
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qid, query = str(data["query_id"]), data["query"]
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queries_dict[qid] = query
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return datasets.DatasetDict(queries_dict)
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