mirror of https://github.com/explosion/spaCy.git
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
|
"""
|
||
|
|
Print part-of-speech tagged, true-cased, (very roughly) sentence-separated
|
||
|
|
text, with each "sentence" on a newline, and spaces between tokens. Supports
|
||
|
|
multi-processing.
|
||
|
|
|
||
|
|
Last updated for: spaCy 2.0.0a18
|
||
|
|
"""
|
||
|
|
from __future__ import print_function, unicode_literals, division
|
||
|
|
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:])
|
||
|
|
partitions = partition_all(batch_size, texts)
|
||
|
|
items = ((i, [nlp(text) for text in texts], output_dir) for i, texts
|
||
|
|
in enumerate(partitions))
|
||
|
|
Parallel(n_jobs=n_jobs)(delayed(transform_texts)(*item) for item in items)
|
||
|
|
|
||
|
|
|
||
|
|
def transform_texts(batch_id, docs, output_dir):
|
||
|
|
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 docs:
|
||
|
|
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(docs), 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)
|