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47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
from paddle.trainer.PyDataProvider2 import *
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from itertools import izip
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import spacy
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def get_features(doc):
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return numpy.asarray(
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[t.rank+1 for t in doc
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if t.has_vector and not t.is_punct and not t.is_space],
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dtype='int32')
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def read_data(data_dir):
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for subdir, label in (('pos', 1), ('neg', 0)):
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for filename in (data_dir / subdir).iterdir():
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with filename.open() as file_:
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text = file_.read()
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yield text, label
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def on_init(settings, **kwargs):
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print("Loading spaCy")
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nlp = spacy.load('en', entity=False)
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vectors = get_vectors(nlp)
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settings.input_types = [
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# The text is a sequence of integer values, and each value is a word id.
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# The whole sequence is the sentences that we want to predict its
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# sentimental.
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integer_value(vectors.shape[0], seq_type=SequenceType), # text input
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# label positive/negative
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integer_value(2)
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]
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settings.nlp = nlp
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settings.vectors = vectors
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settings['batch_size'] = 32
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@provider(init_hook=on_init)
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def process(settings, data_dir): # settings is not used currently.
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texts, labels = read_data(data_dir)
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for doc, label in izip(nlp.pipe(texts, batch_size=5000, n_threads=3), labels):
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for sent in doc.sents:
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ids = get_features(sent)
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# give data to paddle.
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yield ids, label
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