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="{}\n{}", # 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)