Hooking a deep learning model into spaCy

In this example, we'll be using Keras, as it's the most popular deep learning library for Python. Let's assume you've written a custom sentiment analysis model that predicts whether a document is positive or negative. Now you want to find which entities are commonly associated with positive or negative documents. Here's a quick example of how that can look at runtime.

Runtime usage

def count_entity_sentiment(nlp, texts): '''Compute the net document sentiment for each entity in the texts.''' entity_sentiments = collections.Counter(float) for doc in nlp.pipe(texts, batch_size=1000, n_threads=4): for ent in doc.ents: entity_sentiments[ent.text] += doc.sentiment return entity_sentiments def load_nlp(lstm_path, lang_id='en'): def create_pipeline(nlp): return [nlp.tagger, nlp.entity, SentimentAnalyser.load(lstm_path, nlp)] return spacy.load(lang_id, create_pipeline=create_pipeline)

All you have to do is pass a create_pipeline callback function to spacy.load(). The function should take a spacy.language.Language object as its only argument, and return a sequence of callables. Each callable should accept a Doc object, modify it in place, and return None.

Of course, operating on single documents is inefficient, especially for deep learning models. Usually we want to annotate many texts, and we want to process them in parallel. You should therefore ensure that your model component also supports a .pipe() method. The .pipe() method should be a well-behaved generator function that operates on arbitrarily large sequences. It should consume a small buffer of documents, work on them in parallel, and yield them one-by-one.

Custom Annotator Class

class SentimentAnalyser(object): @classmethod def load(cls, path, nlp): with (path / 'config.json').open() as file_: model = model_from_json(file_.read()) with (path / 'model').open('rb') as file_: lstm_weights = pickle.load(file_) embeddings = get_embeddings(nlp.vocab) model.set_weights([embeddings] + lstm_weights) return cls(model) def __init__(self, model): self._model = model def __call__(self, doc): X = get_features([doc], self.max_length) y = self._model.predict(X) self.set_sentiment(doc, y) def pipe(self, docs, batch_size=1000, n_threads=2): for minibatch in cytoolz.partition_all(batch_size, docs): Xs = get_features(minibatch) ys = self._model.predict(Xs) for i, doc in enumerate(minibatch): doc.sentiment = ys[i] def set_sentiment(self, doc, y): doc.sentiment = float(y[0]) # Sentiment has a native slot for a single float. # For arbitrary data storage, there's: # doc.user_data['my_data'] = y def get_features(docs, max_length): Xs = numpy.zeros((len(docs), max_length), dtype='int32') for i, doc in enumerate(minibatch): for j, token in enumerate(doc[:max_length]): Xs[i, j] = token.rank if token.has_vector else 0 return Xs

By default, spaCy 1.0 downloads and uses the 300-dimensional GloVe common crawl vectors. It's also easy to replace these vectors with ones you've trained yourself, or to disable the word vectors entirely. If you've installed your word vectors into spaCy's Vocab object, here's how to use them in a Keras model:

Training with Keras

def train(train_texts, train_labels, dev_texts, dev_labels, lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5): nlp = spacy.load('en', parser=False, tagger=False, entity=False) embeddings = get_embeddings(nlp.vocab) model = compile_lstm(embeddings, lstm_shape, lstm_settings) train_X = get_features(nlp.pipe(train_texts)) dev_X = get_features(nlp.pipe(dev_texts)) model.fit(train_X, train_labels, validation_data=(dev_X, dev_labels), nb_epoch=nb_epoch, batch_size=batch_size) return model def compile_lstm(embeddings, shape, settings): model = Sequential() model.add( Embedding( embeddings.shape[1], embeddings.shape[0], input_length=shape['max_length'], trainable=False, weights=[embeddings] ) ) model.add(Bidirectional(LSTM(shape['nr_hidden']))) model.add(Dropout(settings['dropout'])) model.add(Dense(shape['nr_class'], activation='sigmoid')) model.compile(optimizer=Adam(lr=settings['lr']), loss='binary_crossentropy', metrics=['accuracy']) return model def get_embeddings(vocab): max_rank = max(lex.rank for lex in vocab if lex.has_vector) vectors = numpy.ndarray((max_rank+1, vocab.vectors_length), dtype='float32') for lex in vocab: if lex.has_vector: vectors[lex.rank] = lex.vector return vectors def get_features(docs, max_length): Xs = numpy.zeros(len(list(docs)), max_length, dtype='int32') for i, doc in enumerate(docs): for j, token in enumerate(doc[:max_length]): Xs[i, j] = token.rank if token.has_vector else 0 return Xs

For most applications, I recommend using pre-trained word embeddings without "fine-tuning". This means that you'll use the same embeddings across different models, and avoid learning adjustments to them on your training data. The embeddings table is large, and the values provided by the pre-trained vectors are already pretty good. Fine-tuning the embeddings table is therefore a waste of your "parameter budget". It's usually better to make your network larger some other way, e.g. by adding another LSTM layer, using attention mechanism, using character features, etc.

Attribute hooks (experimental)

Earlier, we saw how to store data in the new generic user_data dict. This generalises well, but it's not terribly satisfying. Ideally, we want to let the custom data drive more "native" behaviours. For instance, consider the .similarity() methods provided by spaCy's Doc , Token and Span objects:

Polymorphic similarity example

span.similarity(doc) token.similarity(span) doc1.similarity(doc2)

By default, this just averages the vectors for each document, and computes their cosine. Obviously, spaCy should make it easy for you to install your own similarity model. This introduces a tricky design challenge. The current solution is to add three more dicts to the Doc object:

user_hooksCustomise behaviour of doc.vector, doc.has_vector, doc.vector_norm or doc.sents
user_token_hooksCustomise behaviour of token.similarity, token.vector, token.has_vector, token.vector_norm or token.conjuncts
user_span_hooksCustomise behaviour of span.similarity, span.vector, span.has_vector, span.vector_norm or span.root

To sum up, here's an example of hooking in custom .similarity() methods:

Add custom similarity hooks

class SimilarityModel(object): def __init__(self, model): self._model = model def __call__(self, doc): doc.user_hooks['similarity'] = self.similarity doc.user_span_hooks['similarity'] = self.similarity doc.user_token_hooks['similarity'] = self.similarity def similarity(self, obj1, obj2): y = self._model([obj1.vector, obj2.vector]) return float(y[0])