mirror of https://github.com/explosion/spaCy.git
Update multiprocessing example
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@ -34,21 +34,24 @@ def main(output_dir, model='en_core_web_sm', n_jobs=4, batch_size=1000,
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data, _ = thinc.extra.datasets.imdb()
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data, _ = thinc.extra.datasets.imdb()
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texts, _ = zip(*data[-limit:])
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texts, _ = zip(*data[-limit:])
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partitions = partition_all(batch_size, texts)
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partitions = partition_all(batch_size, texts)
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items = ((i, [nlp(text) for text in texts], output_dir) for i, texts
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executor = Parallel(n_jobs=n_jobs)
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in enumerate(partitions))
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do = delayed(transform_texts)
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Parallel(n_jobs=n_jobs)(delayed(transform_texts)(*item) for item in items)
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tasks = (do(nlp, i, batch, output_dir)
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for i, batch in enumerate(partitions))
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executor(tasks)
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def transform_texts(batch_id, docs, output_dir):
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def transform_texts(nlp, batch_id, texts, output_dir):
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print(nlp.pipe_names)
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out_path = Path(output_dir) / ('%d.txt' % batch_id)
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out_path = Path(output_dir) / ('%d.txt' % batch_id)
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if out_path.exists(): # return None in case same batch is called again
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if out_path.exists(): # return None in case same batch is called again
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return None
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return None
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print('Processing batch', batch_id)
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print('Processing batch', batch_id)
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with out_path.open('w', encoding='utf8') as f:
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with out_path.open('w', encoding='utf8') as f:
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for doc in docs:
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for doc in nlp.pipe(texts):
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f.write(' '.join(represent_word(w) for w in doc if not w.is_space))
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f.write(' '.join(represent_word(w) for w in doc if not w.is_space))
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f.write('\n')
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f.write('\n')
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print('Saved {} texts to {}.txt'.format(len(docs), batch_id))
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print('Saved {} texts to {}.txt'.format(len(texts), batch_id))
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def represent_word(word):
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def represent_word(word):
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