mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf8
|
|
"""Example of multi-processing with Joblib. Here, we're exporting
|
|
part-of-speech-tagged, true-cased, (very roughly) sentence-separated text, with
|
|
each "sentence" on a newline, and spaces between tokens. Data is loaded from
|
|
the IMDB movie reviews dataset and will be loaded automatically via Thinc's
|
|
built-in dataset loader.
|
|
|
|
Compatible with: spaCy v2.0.0+
|
|
"""
|
|
from __future__ import print_function, unicode_literals
|
|
from toolz import partition_all
|
|
from pathlib import Path
|
|
from joblib import Parallel, delayed
|
|
import thinc.extra.datasets
|
|
import plac
|
|
import spacy
|
|
|
|
|
|
@plac.annotations(
|
|
output_dir=("Output directory", "positional", None, Path),
|
|
model=("Model name (needs tagger)", "positional", None, str),
|
|
n_jobs=("Number of workers", "option", "n", int),
|
|
batch_size=("Batch-size for each process", "option", "b", int),
|
|
limit=("Limit of entries from the dataset", "option", "l", int))
|
|
def main(output_dir, model='en_core_web_sm', n_jobs=4, batch_size=1000,
|
|
limit=10000):
|
|
nlp = spacy.load(model) # load spaCy model
|
|
print("Loaded model '%s'" % model)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
# load and pre-process the IMBD dataset
|
|
print("Loading IMDB data...")
|
|
data, _ = thinc.extra.datasets.imdb()
|
|
texts, _ = zip(*data[-limit:])
|
|
print("Processing texts...")
|
|
partitions = partition_all(batch_size, texts)
|
|
executor = Parallel(n_jobs=n_jobs)
|
|
do = delayed(transform_texts)
|
|
tasks = (do(nlp, i, batch, output_dir)
|
|
for i, batch in enumerate(partitions))
|
|
executor(tasks)
|
|
|
|
|
|
def transform_texts(nlp, batch_id, texts, output_dir):
|
|
print(nlp.pipe_names)
|
|
out_path = Path(output_dir) / ('%d.txt' % batch_id)
|
|
if out_path.exists(): # return None in case same batch is called again
|
|
return None
|
|
print('Processing batch', batch_id)
|
|
with out_path.open('w', encoding='utf8') as f:
|
|
for doc in nlp.pipe(texts):
|
|
f.write(' '.join(represent_word(w) for w in doc if not w.is_space))
|
|
f.write('\n')
|
|
print('Saved {} texts to {}.txt'.format(len(texts), batch_id))
|
|
|
|
|
|
def represent_word(word):
|
|
text = word.text
|
|
# True-case, i.e. try to normalize sentence-initial capitals.
|
|
# Only do this if the lower-cased form is more probable.
|
|
if text.istitle() and is_sent_begin(word) \
|
|
and word.prob < word.doc.vocab[text.lower()].prob:
|
|
text = text.lower()
|
|
return text + '|' + word.tag_
|
|
|
|
|
|
def is_sent_begin(word):
|
|
if word.i == 0:
|
|
return True
|
|
elif word.i >= 2 and word.nbor(-1).text in ('.', '!', '?', '...'):
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
if __name__ == '__main__':
|
|
plac.call(main)
|