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