tarekziade commited on
Commit
f2cd704
1 Parent(s): 55a16f5

fixed output

Browse files
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wikiextract.py CHANGED
@@ -7,6 +7,7 @@
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  Author: Tarek Ziadé / Mozilla
8
 
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  """
 
10
  from collections import defaultdict
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  from concurrent.futures import ThreadPoolExecutor, as_completed
12
  from threading import Lock
@@ -162,7 +163,6 @@ class WikiExtractor:
162
  total=len(_ROOT_CATS) * _LIMIT_PER_CAT, desc="Processing Categories"
163
  ) as pbar:
164
  self.pbar = pbar
165
- data = []
166
  with ThreadPoolExecutor(max_workers=15) as executor:
167
  future_to_category = {
168
  executor.submit(self.process_category, category): category
@@ -171,23 +171,21 @@ class WikiExtractor:
171
 
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  for future in as_completed(future_to_category):
173
  category_data = future.result()
174
- data.extend(category_data)
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-
176
- return data
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178
 
179
  def main():
180
  nltk.download("punkt")
181
  extractor = WikiExtractor()
182
- data = extractor()
183
- dataset = Dataset.from_pandas(pd.DataFrame(data))
184
 
185
  train_test_split = dataset.train_test_split(test_size=0.1)
186
  dataset_dict = DatasetDict(
187
  {"train": train_test_split["train"], "test": train_test_split["test"]}
188
  )
189
 
190
- dataset_dict.save_to_disk("topics.dataset")
191
 
192
 
193
  if __name__ == "__main__":
 
7
  Author: Tarek Ziadé / Mozilla
8
 
9
  """
10
+ import os
11
  from collections import defaultdict
12
  from concurrent.futures import ThreadPoolExecutor, as_completed
13
  from threading import Lock
 
163
  total=len(_ROOT_CATS) * _LIMIT_PER_CAT, desc="Processing Categories"
164
  ) as pbar:
165
  self.pbar = pbar
 
166
  with ThreadPoolExecutor(max_workers=15) as executor:
167
  future_to_category = {
168
  executor.submit(self.process_category, category): category
 
171
 
172
  for future in as_completed(future_to_category):
173
  category_data = future.result()
174
+ for item in category_data:
175
+ yield item
 
176
 
177
 
178
  def main():
179
  nltk.download("punkt")
180
  extractor = WikiExtractor()
181
+ dataset = Dataset.from_generator(extractor)
 
182
 
183
  train_test_split = dataset.train_test_split(test_size=0.1)
184
  dataset_dict = DatasetDict(
185
  {"train": train_test_split["train"], "test": train_test_split["test"]}
186
  )
187
 
188
+ dataset_dict.save_to_disk(os.path.dirname(__file__))
189
 
190
 
191
  if __name__ == "__main__":