mirror of
https://github.com/explosion/spaCy.git
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140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
from __future__ import division, unicode_literals, print_function
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import spacy
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import plac
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from pathlib import Path
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import ujson as json
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import numpy
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from keras.utils.np_utils import to_categorical
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from spacy_hook import get_embeddings, get_word_ids
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from spacy_hook import create_similarity_pipeline
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from keras_decomposable_attention import build_model
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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def train(train_loc, dev_loc, shape, settings):
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train_texts1, train_texts2, train_labels = read_snli(train_loc)
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dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
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print("Loading spaCy")
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nlp = spacy.load('en')
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assert nlp.path is not None
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print("Compiling network")
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model = build_model(get_embeddings(nlp.vocab), shape, settings)
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print("Processing texts...")
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Xs = []
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for texts in (train_texts1, train_texts2, dev_texts1, dev_texts2):
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Xs.append(get_word_ids(list(nlp.pipe(texts, n_threads=20, batch_size=20000)),
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max_length=shape[0],
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rnn_encode=settings['gru_encode'],
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tree_truncate=settings['tree_truncate']))
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train_X1, train_X2, dev_X1, dev_X2 = Xs
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print(settings)
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model.fit(
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[train_X1, train_X2],
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train_labels,
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validation_data=([dev_X1, dev_X2], dev_labels),
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nb_epoch=settings['nr_epoch'],
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batch_size=settings['batch_size'])
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if not (nlp.path / 'similarity').exists():
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(nlp.path / 'similarity').mkdir()
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print("Saving to", nlp.path / 'similarity')
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weights = model.get_weights()
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with (nlp.path / 'similarity' / 'model').open('wb') as file_:
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pickle.dump(weights[1:], file_)
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with (nlp.path / 'similarity' / 'config.json').open('wb') as file_:
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file_.write(model.to_json())
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def evaluate(dev_loc):
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dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
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nlp = spacy.load('en',
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create_pipeline=create_similarity_pipeline)
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total = 0.
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correct = 0.
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for text1, text2, label in zip(dev_texts1, dev_texts2, dev_labels):
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doc1 = nlp(text1)
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doc2 = nlp(text2)
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sim = doc1.similarity(doc2)
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if sim.argmax() == label.argmax():
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correct += 1
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total += 1
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return correct, total
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def demo():
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nlp = spacy.load('en',
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create_pipeline=create_similarity_pipeline)
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doc1 = nlp(u'What were the best crime fiction books in 2016?')
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doc2 = nlp(
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u'What should I read that was published last year? I like crime stories.')
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print(doc1)
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print(doc2)
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print("Similarity", doc1.similarity(doc2))
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LABELS = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
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def read_snli(path):
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texts1 = []
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texts2 = []
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labels = []
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with path.open() as file_:
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for line in file_:
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eg = json.loads(line)
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label = eg['gold_label']
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if label == '-':
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continue
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texts1.append(eg['sentence1'])
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texts2.append(eg['sentence2'])
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labels.append(LABELS[label])
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return texts1, texts2, to_categorical(numpy.asarray(labels, dtype='int32'))
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@plac.annotations(
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mode=("Mode to execute", "positional", None, str, ["train", "evaluate", "demo"]),
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train_loc=("Path to training data", "positional", None, Path),
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dev_loc=("Path to development data", "positional", None, Path),
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max_length=("Length to truncate sentences", "option", "L", int),
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nr_hidden=("Number of hidden units", "option", "H", int),
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dropout=("Dropout level", "option", "d", float),
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learn_rate=("Learning rate", "option", "e", float),
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batch_size=("Batch size for neural network training", "option", "b", int),
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nr_epoch=("Number of training epochs", "option", "i", int),
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tree_truncate=("Truncate sentences by tree distance", "flag", "T", bool),
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gru_encode=("Encode sentences with bidirectional GRU", "flag", "E", bool),
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)
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def main(mode, train_loc, dev_loc,
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tree_truncate=False,
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gru_encode=False,
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max_length=100,
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nr_hidden=100,
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dropout=0.2,
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learn_rate=0.001,
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batch_size=100,
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nr_epoch=5):
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shape = (max_length, nr_hidden, 3)
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settings = {
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'lr': learn_rate,
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'dropout': dropout,
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'batch_size': batch_size,
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'nr_epoch': nr_epoch,
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'tree_truncate': tree_truncate,
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'gru_encode': gru_encode
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}
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if mode == 'train':
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train(train_loc, dev_loc, shape, settings)
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elif mode == 'evaluate':
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correct, total = evaluate(dev_loc)
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print(correct, '/', total, correct / total)
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else:
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demo()
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if __name__ == '__main__':
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plac.call(main)
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