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https://github.com/explosion/spaCy.git
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78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
import numpy as np
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from keras.models import model_from_json
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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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class KerasSimilarityShim(object):
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entailment_types = ["entailment", "contradiction", "neutral"]
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@classmethod
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def load(cls, path, nlp, max_length=100, get_features=None):
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if get_features is None:
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get_features = get_word_ids
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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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weights = pickle.load(file_)
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embeddings = get_embeddings(nlp.vocab)
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weights.insert(1, embeddings)
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model.set_weights(weights)
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return cls(model, get_features=get_features, max_length=max_length)
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def __init__(self, model, get_features=None, max_length=100):
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self.model = model
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self.get_features = get_features
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self.max_length = max_length
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def __call__(self, doc):
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doc.user_hooks["similarity"] = self.predict
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doc.user_span_hooks["similarity"] = self.predict
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return doc
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def predict(self, doc1, doc2):
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x1 = self.get_features([doc1], max_length=self.max_length)
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x2 = self.get_features([doc2], max_length=self.max_length)
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scores = self.model.predict([x1, x2])
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return self.entailment_types[scores.argmax()], scores.max()
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def get_embeddings(vocab, nr_unk=100):
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# the extra +1 is for a zero vector representing sentence-final padding
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num_vectors = max(lex.rank for lex in vocab) + 2
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# create random vectors for OOV tokens
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oov = np.random.normal(size=(nr_unk, vocab.vectors_length))
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oov = oov / oov.sum(axis=1, keepdims=True)
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vectors = np.zeros((num_vectors + nr_unk, vocab.vectors_length), dtype="float32")
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vectors[1 : (nr_unk + 1),] = oov
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for lex in vocab:
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if lex.has_vector and lex.vector_norm > 0:
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vectors[nr_unk + lex.rank + 1] = lex.vector / lex.vector_norm
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return vectors
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def get_word_ids(docs, max_length=100, nr_unk=100):
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Xs = np.zeros((len(docs), max_length), dtype="int32")
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for i, doc in enumerate(docs):
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for j, token in enumerate(doc):
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if j == max_length:
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break
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if token.has_vector:
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Xs[i, j] = token.rank + nr_unk + 1
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else:
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Xs[i, j] = token.rank % nr_unk + 1
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return Xs
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