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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.
#
import argparse
from tqdm import tqdm
import sqlite3
import re
from pygaggle.rerank.base import Text
from pygaggle.data.segmentation import SegmentProcessor
import spacy
'''After WikiExtractor and https://github.com/facebookresearch/DrQA/tree/main/scripts/retriever pre-processing is done on the XML dump,
the final pre-processing is done in this script to generate the WIKI_6_3, WIKI_8_4, and WIKI_100w corpuses (without tables and lists) '''
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='generate .tsv files for wiki corpuses of passages without tables and lists')
parser.add_argument('-db_path', type=str, required=True, help='path to .db file containing preprocessed wiki pages from DrQA')
parser.add_argument('-output_path_6_3', type=str, default="../collections/wiki_6_3.tsv", help='path to write .tsv with segment 6, stride 3')
parser.add_argument('-output_path_8_4', type=str, default="../collections/wiki_8_4.tsv", help='path to write .tsv with segment 8, stride 4')
parser.add_argument('-output_path_100w', type=str, default="../collections/wiki_100w.tsv", help='path to write .tsv with 100 word splits')
args = parser.parse_args()
sqliteConnection = sqlite3.connect(args.db_path)
cursor = sqliteConnection.cursor()
sqlite_select_query = "SELECT id, text FROM documents"
cursor.execute(sqlite_select_query)
pages = cursor.fetchall()
cursor.close()
print("PAGES: ", len(pages))
documents = {}
nlp = spacy.load("en_core_web_lg")
# To avoid duplicate pages
for row in tqdm(pages):
text = row[1]
title = row[0]
if title in documents:
documents[title] += " " + text
else:
documents[title] = text
f1 = open(args.output_path_6_3, "w")
f2 = open(args.output_path_8_4, "w")
f3 = open(args.output_path_100w, "w")
f1.write("id\ttext\ttitle\n")
f2.write("id\ttext\ttitle\n")
f3.write("id\ttext\ttitle\n")
SegmentProcessor = SegmentProcessor()
id1 = 1
id2 = 1
id3 = 1
for document in tqdm(documents):
texts = []
text = documents[document]
text = text.strip()
if text.startswith("REDIRECT") or text.startswith("redirect"):
continue
if text.endswith(". References."):
text = text[:-len(" References.")].strip()
text = re.sub('\{\{cite .*?\}\}', ' ', text, flags=re.DOTALL)
text = text.replace(r"TABLETOREPLACE", " ")
text = text.replace(r"'''", " ")
text = text.replace(r"[[", " ")
text = text.replace(r"]]", " ")
text = text.replace(r"{{", " ")
text = text.replace(r"}}", " ")
text = text.replace("<br>", " ")
text = text.replace(""", "\"")
text = text.replace("&", "&")
text = text.replace("& amp;", "&")
text = text.replace("nbsp;", " ")
text = text.replace("formatnum:", "")
#text = re.sub('<poem.*?</poem>', ' ', text, flags=re.DOTALL) # might have useful information?
text = re.sub('<math.*?</math>', '', text, flags=re.DOTALL)
text = re.sub('<chem.*?</chem>', '', text, flags=re.DOTALL)
text = re.sub('<score.*?</score>', '', text, flags=re.DOTALL)
# clean residual mess from xml dump that shouldn't have made its way here
text = re.sub('\| ?item[0-9]?_?style= ?.*? ', ' ', text)
text = re.sub('\| ?col[0-9]?_?style= ?.*? ', ' ', text)
text = re.sub('\| ?row[0-9]?_?style= ?.*? ', ' ', text)
text = re.sub('\| ?style= ?.*? ', ' ', text)
text = re.sub('\| ?bodystyle= ?.*? ', ' ', text)
text = re.sub('\| ?frame_?style= ?.*? ', ' ', text)
text = re.sub('\| ?data_?style= ?.*? ', ' ', text)
text = re.sub('\| ?label_?style= ?.*? ', ' ', text)
text = re.sub('\| ?headerstyle= ?.*? ', ' ', text)
text = re.sub('\| ?list_?style= ?.*? ', ' ', text)
text = re.sub('\| ?title_?style= ?.*? ', ' ', text)
text = re.sub('\| ?ul_?style= ?.*? ', ' ', text)
text = re.sub('\| ?li_?style= ?.*? ', ' ', text)
text = re.sub('\| ?border-style= ?.*? ', ' ', text)
text = re.sub('\|? ?style=\".*?\"', '', text)
text = re.sub('\|? ?rowspan=\".*?\"', '', text)
text = re.sub('\|? ?colspan=\".*?\"', '', text)
text = re.sub('\|? ?scope=\".*?\"', '', text)
text = re.sub('\|? ?align=\".*?\"', '', text)
text = re.sub('\|? ?valign=\".*?\"', '', text)
text = re.sub('\|? ?lang=\".*?\"', '', text)
text = re.sub('\|? ?bgcolor=\".*?\"', '', text)
text = re.sub('\|? ?bg=\#[a-z]+', '', text)
text = re.sub('\|? ?width=\".*?\"', '', text)
text = re.sub('\|? ?height=[0-9]+', '', text)
text = re.sub('\|? ?width=[0-9]+', '', text)
text = re.sub('\|? ?rowspan=[0-9]+', '', text)
text = re.sub('\|? ?colspan=[0-9]+', '', text)
text = re.sub(r'[\n\t]', ' ', text)
text = re.sub('<.*?/>', '', text)
text = re.sub('\|? ?align=[a-z]+', '', text)
text = re.sub('\|? ?valign=[a-z]+', '', text)
text = re.sub('\|? ?scope=[a-z]+', '', text)
text = re.sub('<ref>.*?</ref>', ' ', text)
text = re.sub('<.*?>', ' ', text)
text = re.sub('File:[A-Za-z0-9 ]+\.[a-z]{3,4}(\|[0-9]+px)?', '', text)
text = re.sub('Source: \[.*?\]', '', text)
text = text.replace("Country flag|", "country:")
text = text.replace("flag|", "country:")
text = text.replace("flagicon|", "country:")
text = text.replace("flagcountry|", "country:")
text = text.replace("Flagu|", "country:")
text = text.replace("display=inline", "")
text = text.replace("display=it", "")
text = text.replace("abbr=on", "")
text = text.replace("disp=table", "")
texts.append(Text(text))
document = document.replace("\n", " ").replace("\t", " ")
segments = SegmentProcessor.segment(texts, seg_size=6, stride=3).segments
for segment in segments:
if segment.text is None:
continue
text = segment.text.replace("\n", " ").replace("\t", " ")
f1.write(str(id1) + '\t' + text + '\t' + document + '\n')
id1+=1
segments = SegmentProcessor.segment(texts, seg_size=8, stride=4).segments
for segment in segments:
if segment.text is None:
continue
text = segment.text.replace("\n", " ").replace("\t", " ")
f2.write(str(id2) + '\t' + text + '\t' + document + '\n')
id2+=1
full_text = ""
for text in texts:
full_text += text.text + " "
doc = nlp.make_doc(full_text)
segments = []
word_count = 0
segment_tokens = []
for token in doc:
segment_tokens.append(token.text_with_ws)
if not token.is_space and not token.is_punct:
word_count+=1
if word_count == 100:
word_count = 0
segments.append(''.join([token for token in segment_tokens]))
segment_tokens = []
if word_count != 0:
for token in doc:
segment_tokens.append(token.text_with_ws)
if not token.is_space and not token.is_punct:
word_count+=1
if word_count == 100:
word_count = 0
segments.append(''.join([token for token in segment_tokens]))
break
if word_count != 0:
segments.append(''.join([token for token in segment_tokens]))
if len(segments) > 0:
segments[0] = document + " " + segments[0]
for segment in segments:
text = segment.replace("\n", " ").replace("\t", " ")
f3.write(str(id3) + '\t' + text + '\t' + document + '\n')
id3+=1
f1.close()
f2.close()
f3.close()
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