""" This script only convert huggingface dataset into topic, qrels and folds files. To prepare corpus falls in the JsonCollection in Anserini/Pyserini format, refer to the scripts in pyserini repository. """ import os import sys import json import gzip from glob import glob from tqdm import tqdm from datasets import load_dataset dirname, abspath, os_join = os.path.dirname, os.path.abspath, os.path.join dataset_dir = dirname(dirname(abspath(__file__))) # output_dir = os_join(dataset_dir, "ir-format-dataset") output_dir = dataset_dir version = "v1.1" languages = [ 'arabic', 'bengali', 'english', 'indonesian', 'finnish', 'korean', 'russian', 'swahili', 'telugu', 'thai', 'japanese', ] def get_output_dir(lang): lang_dir = os_join(output_dir, f"mrtydi-{version}-{lang}", "ir-format-data") os.makedirs(lang_dir, exist_ok=True) return lang_dir def write_topic(qid2query, outp_fn): with open(outp_fn, "w") as f: for qid in qid2query: f.write(f"{qid}\t{qid2query[qid]}\n") def write_qrels(qid2reldocs, outp_fn): with open(outp_fn, "w") as f: for qid in qid2reldocs: for docid in qid2reldocs[qid]: f.write(f"{qid}\tQ0\t{docid}\t1\n") def convert_set(single_set): qid2query = {} qid2reldocs = {} for data_entry in single_set: qid = data_entry["query_id"] query = data_entry["query"] pos_docids = [doc["docid"] for doc in data_entry["positive_passages"]] qid2query[qid] = query qid2reldocs[qid] = pos_docids return qid2query, qid2reldocs def prepare_language(lang): dataset = load_dataset('utils/local-mr-tydi.py', lang) set_names = ["train", "dev", "test"] folds = {} output_dir = get_output_dir(lang) for set_name in set_names: topic_fn = os_join(output_dir, f"topics.{set_name}.txt") qrel_fn = os_join(output_dir, f"qrels.{set_name}.txt") qid2query, qid2reldocs = convert_set(dataset[set_name]) write_topic(qid2query, topic_fn) write_qrels(qid2reldocs, qrel_fn) def main(): for lang in languages: prepare_language(lang) if __name__ == "__main__": main()