import os
import json
import logging
import datasets
from tqdm import tqdm
from typing import List, Optional
from FlagEmbedding.abc.evaluation import AbsEvalDataLoader
logger = logging.getLogger(__name__)
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class MIRACLEvalDataLoader(AbsEvalDataLoader):
"""
Data loader class for MIRACL.
"""
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def available_dataset_names(self) -> List[str]:
"""
Get the available dataset names.
Returns:
List[str]: All the available dataset names.
"""
return ["ar", "bn", "en", "es", "fa", "fi", "fr", "hi", "id", "ja", "ko", "ru", "sw", "te", "th", "zh", "de", "yo"]
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def available_splits(self, dataset_name: str) -> List[str]:
"""
Get the avaialble splits.
Args:
dataset_name (str): Dataset name.
Returns:
List[str]: All the available splits for the dataset.
"""
if dataset_name in ["de", "yo"]:
return ["dev"]
else:
return ["train", "dev"]
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def _load_remote_corpus(
self,
dataset_name: str,
save_dir: Optional[str] = None
) -> datasets.DatasetDict:
"""Load the corpus dataset from HF.
Args:
dataset_name (str): Name of the dataset.
save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.
Returns:
datasets.DatasetDict: Loaded datasets instance of corpus.
"""
corpus = datasets.load_dataset(
"miracl/miracl-corpus", dataset_name,
cache_dir=self.cache_dir,
trust_remote_code=True,
download_mode=self.hf_download_mode
)["train"]
if save_dir is not None:
os.makedirs(save_dir, exist_ok=True)
save_path = os.path.join(save_dir, "corpus.jsonl")
corpus_dict = {}
with open(save_path, "w", encoding="utf-8") as f:
for data in tqdm(corpus, desc="Loading and Saving corpus"):
docid, title, text = str(data["docid"]), data["title"], data["text"]
_data = {
"id": docid,
"title": title,
"text": text
}
corpus_dict[docid] = {
"title": title,
"text": text
}
f.write(json.dumps(_data, ensure_ascii=False) + "\n")
logging.info(f"{self.eval_name} {dataset_name} corpus saved to {save_path}")
else:
corpus_dict = {str(data["docid"]): {"title": data["title"], "text": data["text"]} for data in tqdm(corpus, desc="Loading corpus")}
return datasets.DatasetDict(corpus_dict)
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def _load_remote_qrels(
self,
dataset_name: str,
split: str = 'dev',
save_dir: Optional[str] = None
) -> datasets.DatasetDict:
"""Load the qrels from HF.
Args:
dataset_name (str): Name of the dataset.
split (str, optional): Split of the dataset. Defaults to ``'dev'``.
save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.
Returns:
datasets.DatasetDict: Loaded datasets instance of qrel.
"""
endpoint = f"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/miracl/miracl"
qrels_download_url = f"{endpoint}/resolve/main/miracl-v1.0-{dataset_name}/qrels/qrels.miracl-v1.0-{dataset_name}-{split}.tsv"
qrels_save_path = self._download_file(qrels_download_url, self.cache_dir)
if save_dir is not None:
os.makedirs(save_dir, exist_ok=True)
save_path = os.path.join(save_dir, f"{split}_qrels.jsonl")
qrels_dict = {}
with open(save_path, "w", encoding="utf-8") as f1:
with open(qrels_save_path, "r", encoding="utf-8") as f2:
for line in tqdm(f2.readlines(), desc="Loading and Saving qrels"):
qid, _, docid, rel = line.strip().split("\t")
qid, docid, rel = str(qid), str(docid), int(rel)
_data = {
"qid": qid,
"docid": docid,
"relevance": rel
}
if qid not in qrels_dict:
qrels_dict[qid] = {}
qrels_dict[qid][docid] = rel
f1.write(json.dumps(_data, ensure_ascii=False) + "\n")
logging.info(f"{self.eval_name} {dataset_name} qrels saved to {save_path}")
else:
qrels_dict = {}
with open(qrels_save_path, "r", encoding="utf-8") as f:
for line in tqdm(f.readlines(), desc="Loading qrels"):
qid, _, docid, rel = line.strip().split("\t")
qid, docid, rel = str(qid), str(docid), int(rel)
if qid not in qrels_dict:
qrels_dict[qid] = {}
qrels_dict[qid][docid] = rel
return datasets.DatasetDict(qrels_dict)
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def _load_remote_queries(
self,
dataset_name: str,
split: str = 'dev',
save_dir: Optional[str] = None
) -> datasets.DatasetDict:
"""Load the queries from HF.
Args:
dataset_name (str): Name of the dataset.
split (str, optional): Split of the dataset. Defaults to ``'dev'``.
save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.
Returns:
datasets.DatasetDict: Loaded datasets instance of queries.
"""
endpoint = f"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/miracl/miracl"
queries_download_url = f"{endpoint}/resolve/main/miracl-v1.0-{dataset_name}/topics/topics.miracl-v1.0-{dataset_name}-{split}.tsv"
queries_save_path = self._download_file(queries_download_url, self.cache_dir)
if save_dir is not None:
os.makedirs(save_dir, exist_ok=True)
save_path = os.path.join(save_dir, f"{split}_queries.jsonl")
queries_dict = {}
with open(save_path, "w", encoding="utf-8") as f1:
with open(queries_save_path, "r", encoding="utf-8") as f2:
for line in tqdm(f2.readlines(), desc="Loading and Saving queries"):
qid, query = line.strip().split("\t")
qid = str(qid)
_data = {
"id": qid,
"text": query
}
queries_dict[qid] = query
f1.write(json.dumps(_data, ensure_ascii=False) + "\n")
logging.info(f"{self.eval_name} {dataset_name} queries saved to {save_path}")
else:
queries_dict = {}
with open(queries_save_path, "r", encoding="utf-8") as f:
for line in tqdm(f.readlines(), desc="Loading queries"):
qid, query = line.strip().split("\t")
qid = str(qid)
queries_dict[qid] = query
return datasets.DatasetDict(queries_dict)
