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import os
import random
import json
import shutil
from tqdm import tqdm
# from FlagEmbedding import FlagLLMModel
# model = FlagLLMModel('BAAI/bge-multilingual-gemma2',
# query_instruction_for_retrieval="Given a question, retrieve passages that answer the question.",
# query_instruction_format="<instruct>{}\n<query>{}",
# use_fp16=True,
# devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation
avaliable_languages = ['ar', 'bn', 'en', 'es', 'fa', 'fi', 'fr', 'hi', 'id', 'ja', 'ko', 'ru', 'sw', 'te', 'th', 'zh', 'de', 'yo']
base_dir = '/share_2/chaofan/dataset/miracl/data'
new_dir = '/share/chaofan/code/bge_demo/data'
new_emb_dir = '/share/chaofan/code/bge_demo/emb'
for lang in tqdm(avaliable_languages, desc='language'):
qrels_path = os.path.join(base_dir, lang, 'dev_qrels.jsonl')
queries_path = os.path.join(base_dir, lang, 'dev_queries.jsonl')
corpus_path = os.path.join(base_dir, lang, 'corpus.jsonl')
os.makedirs(os.path.join(new_dir, lang), exist_ok=True)
new_qrels_path = os.path.join(new_dir, lang, 'dev_qrels.jsonl')
new_queries_path = os.path.join(new_dir, lang, 'dev_queries.jsonl')
new_corpus_path = os.path.join(new_dir, lang, 'corpus.jsonl')
useful_corpus = set()
with open(qrels_path) as f:
for line in f:
data = json.loads(line)
useful_corpus.add(data['docid'])
corpus_ids = []
corpus = {}
with open(corpus_path) as f:
for line in f:
data = json.loads(line)
corpus_ids.append(data['id'])
corpus[data['id']] = data
new_corpus = []
random.shuffle(corpus_ids)
for i in range(min(1000000, len(corpus_ids))):
if corpus_ids[i] not in useful_corpus:
useful_corpus.add(corpus_ids[i])
print(f'language {lang}, all corpus {len(corpus_ids)}, use corpus {len(useful_corpus)}')
with open(new_corpus_path, 'w') as f:
for idx in useful_corpus:
f.write(json.dumps(corpus[idx]) + '\n')
shutil.copy(qrels_path, new_qrels_path)
shutil.copy(queries_path, new_queries_path) |