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93 lines
3.9 KiB
Plaintext
93 lines
3.9 KiB
Plaintext
//- 💫 DOCS > USAGE > DEEP LEARNING
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include ../../_includes/_mixins
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p
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| In this example, we'll be using #[+a("https://keras.io/") Keras], as
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| it's the most popular deep learning library for Python. Using Keras,
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| we will write a custom sentiment analysis model that predicts whether a
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| document is positive or negative. Then, we will use it to find which entities
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| are commonly associated with positive or negative documents. Here's a
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| quick example of how that can look at runtime.
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+aside("What's Keras?")
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| #[+a("https://keras.io/") Keras] gives you a high-level, declarative
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| interface to define neural networks. Models are trained using Google's
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| #[+a("https://www.tensorflow.org") TensorFlow] by default.
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| #[+a("http://deeplearning.net/software/theano/") Theano] is also
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| supported.
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+under-construction
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p
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| For most applications, I it's recommended to use pre-trained word embeddings
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| without "fine-tuning". This means that you'll use the same embeddings
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| across different models, and avoid learning adjustments to them on your
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| training data. The embeddings table is large, and the values provided by
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| the pre-trained vectors are already pretty good. Fine-tuning the
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| embeddings table is therefore a waste of your "parameter budget". It's
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| usually better to make your network larger some other way, e.g. by
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| adding another LSTM layer, using attention mechanism, using character
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| features, etc.
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+h(2, "attribute-hooks") Attribute hooks
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+under-construction
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p
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| Earlier, we saw how to store data in the new generic #[code user_data]
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| dict. This generalises well, but it's not terribly satisfying. Ideally,
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| we want to let the custom data drive more "native" behaviours. For
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| instance, consider the #[code .similarity()] methods provided by spaCy's
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| #[+api("doc") #[code Doc]], #[+api("token") #[code Token]] and
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| #[+api("span") #[code Span]] objects:
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+code("Polymorphic similarity example").
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span.similarity(doc)
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token.similarity(span)
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doc1.similarity(doc2)
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p
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| By default, this just averages the vectors for each document, and
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| computes their cosine. Obviously, spaCy should make it easy for you to
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| install your own similarity model. This introduces a tricky design
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| challenge. The current solution is to add three more dicts to the
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| #[code Doc] object:
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+aside("Implementation note")
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| The hooks live on the #[code Doc] object because the #[code Span] and
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| #[code Token] objects are created lazily, and don't own any data. They
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| just proxy to their parent #[code Doc]. This turns out to be convenient
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| here — we only have to worry about installing hooks in one place.
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+table(["Name", "Description"])
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+row
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+cell #[code user_hooks]
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+cell Customise behaviour of #[code doc.vector], #[code doc.has_vector], #[code doc.vector_norm] or #[code doc.sents]
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+row
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+cell #[code user_token_hooks]
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+cell Customise behaviour of #[code token.similarity], #[code token.vector], #[code token.has_vector], #[code token.vector_norm] or #[code token.conjuncts]
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+row
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+cell #[code user_span_hooks]
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+cell Customise behaviour of #[code span.similarity], #[code span.vector], #[code span.has_vector], #[code span.vector_norm] or #[code span.root]
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p
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| To sum up, here's an example of hooking in custom #[code .similarity()]
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| methods:
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+code("Add custom similarity hooks").
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class SimilarityModel(object):
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def __init__(self, model):
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self._model = model
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def __call__(self, doc):
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doc.user_hooks['similarity'] = self.similarity
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doc.user_span_hooks['similarity'] = self.similarity
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doc.user_token_hooks['similarity'] = self.similarity
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def similarity(self, obj1, obj2):
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y = self._model([obj1.vector, obj2.vector])
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return float(y[0])
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