mirror of
https://github.com/explosion/spaCy.git
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268 lines
8.4 KiB
Python
268 lines
8.4 KiB
Python
"""
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This example shows how to use an LSTM sentiment classification model trained
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using Keras in spaCy. spaCy splits the document into sentences, and each
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sentence is classified using the LSTM. The scores for the sentences are then
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aggregated to give the document score. This kind of hierarchical model is quite
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difficult in "pure" Keras or Tensorflow, but it's very effective. The Keras
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example on this dataset performs quite poorly, because it cuts off the documents
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so that they're a fixed size. This hurts review accuracy a lot, because people
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often summarise their rating in the final sentence
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Prerequisites:
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spacy download en_vectors_web_lg
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pip install keras==2.0.9
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Compatible with: spaCy v2.0.0+
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"""
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import plac
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import random
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import pathlib
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import cytoolz
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import numpy
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from keras.models import Sequential, model_from_json
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from keras.layers import LSTM, Dense, Embedding, Bidirectional
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from keras.layers import TimeDistributed
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from keras.optimizers import Adam
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import thinc.extra.datasets
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from spacy.compat import pickle
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import spacy
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class SentimentAnalyser(object):
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@classmethod
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def load(cls, path, nlp, max_length=100):
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with (path / "config.json").open() as file_:
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model = model_from_json(file_.read())
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with (path / "model").open("rb") as file_:
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lstm_weights = pickle.load(file_)
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embeddings = get_embeddings(nlp.vocab)
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model.set_weights([embeddings] + lstm_weights)
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return cls(model, max_length=max_length)
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def __init__(self, model, max_length=100):
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self._model = model
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self.max_length = max_length
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def __call__(self, doc):
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X = get_features([doc], self.max_length)
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y = self._model.predict(X)
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self.set_sentiment(doc, y)
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def pipe(self, docs, batch_size=1000):
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for minibatch in cytoolz.partition_all(batch_size, docs):
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minibatch = list(minibatch)
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sentences = []
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for doc in minibatch:
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sentences.extend(doc.sents)
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Xs = get_features(sentences, self.max_length)
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ys = self._model.predict(Xs)
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for sent, label in zip(sentences, ys):
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sent.doc.sentiment += label - 0.5
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for doc in minibatch:
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yield doc
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def set_sentiment(self, doc, y):
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doc.sentiment = float(y[0])
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# Sentiment has a native slot for a single float.
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# For arbitrary data storage, there's:
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# doc.user_data['my_data'] = y
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def get_labelled_sentences(docs, doc_labels):
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labels = []
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sentences = []
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for doc, y in zip(docs, doc_labels):
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for sent in doc.sents:
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sentences.append(sent)
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labels.append(y)
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return sentences, numpy.asarray(labels, dtype="int32")
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def get_features(docs, max_length):
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docs = list(docs)
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Xs = numpy.zeros((len(docs), max_length), dtype="int32")
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for i, doc in enumerate(docs):
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j = 0
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for token in doc:
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vector_id = token.vocab.vectors.find(key=token.orth)
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if vector_id >= 0:
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Xs[i, j] = vector_id
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else:
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Xs[i, j] = 0
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j += 1
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if j >= max_length:
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break
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return Xs
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def train(
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train_texts,
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train_labels,
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dev_texts,
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dev_labels,
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lstm_shape,
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lstm_settings,
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lstm_optimizer,
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batch_size=100,
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nb_epoch=5,
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by_sentence=True,
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):
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print("Loading spaCy")
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nlp = spacy.load("en_vectors_web_lg")
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nlp.add_pipe(nlp.create_pipe("sentencizer"))
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embeddings = get_embeddings(nlp.vocab)
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model = compile_lstm(embeddings, lstm_shape, lstm_settings)
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print("Parsing texts...")
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train_docs = list(nlp.pipe(train_texts))
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dev_docs = list(nlp.pipe(dev_texts))
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if by_sentence:
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train_docs, train_labels = get_labelled_sentences(train_docs, train_labels)
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dev_docs, dev_labels = get_labelled_sentences(dev_docs, dev_labels)
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train_X = get_features(train_docs, lstm_shape["max_length"])
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dev_X = get_features(dev_docs, lstm_shape["max_length"])
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model.fit(
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train_X,
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train_labels,
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validation_data=(dev_X, dev_labels),
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epochs=nb_epoch,
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batch_size=batch_size,
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)
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return model
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def compile_lstm(embeddings, shape, settings):
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model = Sequential()
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model.add(
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Embedding(
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embeddings.shape[0],
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embeddings.shape[1],
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input_length=shape["max_length"],
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trainable=False,
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weights=[embeddings],
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mask_zero=True,
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)
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)
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model.add(TimeDistributed(Dense(shape["nr_hidden"], use_bias=False)))
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model.add(
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Bidirectional(
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LSTM(
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shape["nr_hidden"],
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recurrent_dropout=settings["dropout"],
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dropout=settings["dropout"],
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)
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)
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)
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model.add(Dense(shape["nr_class"], activation="sigmoid"))
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model.compile(
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optimizer=Adam(lr=settings["lr"]),
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loss="binary_crossentropy",
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metrics=["accuracy"],
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)
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return model
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def get_embeddings(vocab):
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return vocab.vectors.data
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def evaluate(model_dir, texts, labels, max_length=100):
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nlp = spacy.load("en_vectors_web_lg")
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nlp.add_pipe(nlp.create_pipe("sentencizer"))
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nlp.add_pipe(SentimentAnalyser.load(model_dir, nlp, max_length=max_length))
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correct = 0
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i = 0
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for doc in nlp.pipe(texts, batch_size=1000):
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correct += bool(doc.sentiment >= 0.5) == bool(labels[i])
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i += 1
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return float(correct) / i
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def read_data(data_dir, limit=0):
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examples = []
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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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examples.append((text, label))
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random.shuffle(examples)
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if limit >= 1:
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examples = examples[:limit]
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return zip(*examples) # Unzips into two lists
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@plac.annotations(
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train_dir=("Location of training file or directory"),
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dev_dir=("Location of development file or directory"),
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model_dir=("Location of output model directory",),
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is_runtime=("Demonstrate run-time usage", "flag", "r", bool),
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nr_hidden=("Number of hidden units", "option", "H", int),
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max_length=("Maximum sentence length", "option", "L", int),
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dropout=("Dropout", "option", "d", float),
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learn_rate=("Learn rate", "option", "e", float),
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nb_epoch=("Number of training epochs", "option", "i", int),
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batch_size=("Size of minibatches for training LSTM", "option", "b", int),
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nr_examples=("Limit to N examples", "option", "n", int),
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)
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def main(
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model_dir=None,
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train_dir=None,
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dev_dir=None,
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is_runtime=False,
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nr_hidden=64,
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max_length=100, # Shape
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dropout=0.5,
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learn_rate=0.001, # General NN config
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nb_epoch=5,
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batch_size=256,
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nr_examples=-1,
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): # Training params
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if model_dir is not None:
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model_dir = pathlib.Path(model_dir)
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if train_dir is None or dev_dir is None:
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imdb_data = thinc.extra.datasets.imdb()
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if is_runtime:
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if dev_dir is None:
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dev_texts, dev_labels = zip(*imdb_data[1])
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else:
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dev_texts, dev_labels = read_data(dev_dir)
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acc = evaluate(model_dir, dev_texts, dev_labels, max_length=max_length)
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print(acc)
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else:
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if train_dir is None:
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train_texts, train_labels = zip(*imdb_data[0])
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else:
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print("Read data")
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train_texts, train_labels = read_data(train_dir, limit=nr_examples)
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if dev_dir is None:
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dev_texts, dev_labels = zip(*imdb_data[1])
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else:
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dev_texts, dev_labels = read_data(dev_dir, imdb_data, limit=nr_examples)
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train_labels = numpy.asarray(train_labels, dtype="int32")
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dev_labels = numpy.asarray(dev_labels, dtype="int32")
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lstm = train(
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train_texts,
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train_labels,
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dev_texts,
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dev_labels,
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{"nr_hidden": nr_hidden, "max_length": max_length, "nr_class": 1},
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{"dropout": dropout, "lr": learn_rate},
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{},
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nb_epoch=nb_epoch,
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batch_size=batch_size,
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)
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weights = lstm.get_weights()
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if model_dir is not None:
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with (model_dir / "model").open("wb") as file_:
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pickle.dump(weights[1:], file_)
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with (model_dir / "config.json").open("w") as file_:
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file_.write(lstm.to_json())
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if __name__ == "__main__":
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plac.call(main)
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