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Add documentation for the MS MARCO v2 passage and document corpus (#64)
* init augment collection * update requirements * start repo * update passage v2 * update document v2 * quick cleanup * rephrase long sentence
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| # | ||
| # Pyserini: Reproducible IR research with sparse and dense representations | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
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| import argparse | ||
| from datasets import load_dataset | ||
| import os | ||
| import gzip | ||
| import json | ||
| from tqdm import tqdm | ||
| import glob | ||
| from multiprocessing import Pool, Manager | ||
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| def load_docs(docid_to_doc, f_ins, text_key="passage"): | ||
| print("Loading docs") | ||
| counter = 0 | ||
| if text_key == "passage": | ||
| id_key = "pid" | ||
| else: | ||
| id_key = "docid" | ||
| for f_in in f_ins: | ||
| with gzip.open(f_in, 'rt', encoding='utf8') as in_fh: | ||
| for json_string in tqdm(in_fh): | ||
| input_dict = json.loads(json_string) | ||
| docid_to_doc[input_dict[id_key]] = input_dict | ||
| counter += 1 | ||
| print(f'{counter} docs loaded. Done!') | ||
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| def augment_corpus_with_doc2query_t5(dataset, f_out, start, end, num_queries, text_key="passage"): | ||
| print('Output docs...') | ||
| output = open(f_out, 'w') | ||
| counter = 0 | ||
| for i in tqdm(range(start, end)): | ||
| docid = dataset[i]["id"] | ||
| output_dict = docid_to_doc[docid] | ||
| concatenated_queries = " ".join(dataset[i]["predicted_queries"][:num_queries]) | ||
| output_dict[text_key] = f"{output_dict[text_key]} {concatenated_queries}" | ||
| counter += 1 | ||
| output.write(json.dumps(output_dict) + '\n') | ||
| output.close() | ||
| print(f'{counter} lines output. Done!') | ||
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| if __name__ == '__main__': | ||
| parser = argparse.ArgumentParser( | ||
| description='Concatenate MS MARCO v2 corpus with predicted queries') | ||
| parser.add_argument('--hgf_d2q_dataset', required=True, | ||
| choices=['castorini/msmarco_v2_passage_doc2query-t5_expansions', | ||
| 'castorini/msmarco_v2_doc_segmented_doc2query-t5_expansions']) | ||
| parser.add_argument('--original_psg_path', required=True, help='Input corpus path') | ||
| parser.add_argument('--output_psg_path', required=True, help='Output file for d2q-t5 augmented corpus.') | ||
| parser.add_argument('--num_workers', default=1, type=int, help='Number of workers used.') | ||
| parser.add_argument('--num_queries', default=20, type=int, help='Number of expansions used.') | ||
| parser.add_argument('--task', default="passage", type=str, help='One of passage or document.') | ||
| parser.add_argument('--cache_dir', default=".", type=str, help='Path to cache the hgf dataset') | ||
| args = parser.parse_args() | ||
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| psg_files = glob.glob(os.path.join(args.original_psg_path, '*.gz')) | ||
| os.makedirs(args.output_psg_path, exist_ok=True) | ||
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| manager = Manager() | ||
| docid_to_doc = manager.dict() | ||
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| dataset = load_dataset(args.hgf_d2q_dataset, split="train", cache_dir=args.cache_dir) | ||
| pool = Pool(args.num_workers) | ||
| num_files_per_worker = (len(psg_files) // args.num_workers) | ||
| for i in range(args.num_workers): | ||
| pool.apply_async(load_docs, (docid_to_doc, psg_files[i*num_files_per_worker: min(len(dataset), (i+1)*num_files_per_worker)], args.task)) | ||
| pool.close() | ||
| pool.join() | ||
| assert len(docid_to_doc) == len(dataset) | ||
| print('Total passages loaded: {}'.format(len(docid_to_doc))) | ||
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| pool = Pool(args.num_workers) | ||
| num_examples_per_worker = (len(docid_to_doc)//args.num_workers) + 1 | ||
| for i in range(args.num_workers): | ||
| f_out = os.path.join(args.output_psg_path, 'dt5q_aug_psg' + str(i) + '.json') | ||
| pool.apply_async(augment_corpus_with_doc2query_t5 ,(dataset, f_out, | ||
| i*(num_examples_per_worker), | ||
| min(len(docid_to_doc), (i+1)*num_examples_per_worker), | ||
| args.num_queries, args.task)) | ||
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| pool.close() | ||
| pool.join() | ||
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| print('Done!') |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,5 @@ | ||
| sentencepiece==0.1.95 | ||
| spacy==2.1.6 | ||
| tensorflow==2.4.1 | ||
| transformers==4.4.2 | ||
| transformers>=4.6.0 | ||
| datasets |