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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. | |
# | |
"""Convert MSMARCO queries""" | |
import json | |
import argparse | |
from transformers import AutoTokenizer, AutoModel | |
import spacy | |
from convert_common import read_stopwords, SpacyTextParser, get_retokenized | |
from pyserini.analysis import Analyzer, get_lucene_analyzer | |
from tqdm import tqdm | |
import os | |
""" | |
add fields to query json with text(lemmatized), text_unlemm, contents(analyzer), raw, entity(NER), text_bert_tok(BERT token) | |
""" | |
parser = argparse.ArgumentParser(description='Convert MSMARCO-adhoc queries.') | |
parser.add_argument('--input', metavar='input file', help='input file', | |
type=str, required=True) | |
parser.add_argument('--output', metavar='output file', help='output file', | |
type=str, required=True) | |
parser.add_argument('--min_query_token_qty', type=int, default=0, | |
metavar='min # of query tokens', help='ignore queries that have smaller # of tokens') | |
args = parser.parse_args() | |
print(args) | |
arg_vars = vars(args) | |
inpFile = open(args.input) | |
outFile = open(args.output, 'w') | |
minQueryTokQty = args.min_query_token_qty | |
if os.getcwd().endswith('ltr_msmarco'): | |
stopwords = read_stopwords('stopwords.txt', lower_case=True) | |
else: | |
stopwords = read_stopwords('./scripts/ltr_msmarco/stopwords.txt', lower_case=True) | |
print(stopwords) | |
nlp = SpacyTextParser('en_core_web_sm', stopwords, keep_only_alpha_num=True, lower_case=True) | |
analyzer = Analyzer(get_lucene_analyzer()) | |
nlp_ent = spacy.load("en_core_web_sm") | |
bert_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") | |
# Input file is a TSV file | |
ln = 0 | |
for line in tqdm(inpFile): | |
ln += 1 | |
line = line.strip() | |
if not line: | |
continue | |
fields = line.split('\t') | |
if len(fields) != 2: | |
print('Misformated line %d ignoring:' % ln) | |
print(line.replace('\t', '<field delimiter>')) | |
continue | |
did, query = fields | |
query_lemmas, query_unlemm = nlp.proc_text(query) | |
analyzed = analyzer.analyze(query) | |
for token in analyzed: | |
if ' ' in token: | |
print(analyzed) | |
query_toks = query_lemmas.split() | |
doc = nlp_ent(query) | |
entity = {} | |
for i in range(len(doc.ents)): | |
entity[doc.ents[i].text] = doc.ents[i].label_ | |
entity = json.dumps(entity) | |
if len(query_toks) >= minQueryTokQty: | |
doc = {"id": did, | |
"text": query_lemmas, | |
"text_unlemm": query_unlemm, | |
"analyzed": ' '.join(analyzed), | |
"entity": entity, | |
"raw": query} | |
doc["text_bert_tok"] = get_retokenized(bert_tokenizer, query.lower()) | |
docStr = json.dumps(doc) + '\n' | |
outFile.write(docStr) | |
if ln % 10000 == 0: | |
print('Processed %d queries' % ln) | |
print('Processed %d queries' % ln) | |
inpFile.close() | |
outFile.close() |