mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
93 lines
3.9 KiB
Plaintext
93 lines
3.9 KiB
Plaintext
//- 💫 DOCS > USAGE > DEEP LEARNING
|
|
|
|
include ../../_includes/_mixins
|
|
|
|
p
|
|
| In this example, we'll be using #[+a("https://keras.io/") Keras], as
|
|
| it's the most popular deep learning library for Python. Using Keras,
|
|
| we will write a custom sentiment analysis model that predicts whether a
|
|
| document is positive or negative. Then, we will use it to find which entities
|
|
| are commonly associated with positive or negative documents. Here's a
|
|
| quick example of how that can look at runtime.
|
|
|
|
+aside("What's Keras?")
|
|
| #[+a("https://keras.io/") Keras] gives you a high-level, declarative
|
|
| interface to define neural networks. Models are trained using Google's
|
|
| #[+a("https://www.tensorflow.org") TensorFlow] by default.
|
|
| #[+a("http://deeplearning.net/software/theano/") Theano] is also
|
|
| supported.
|
|
|
|
+under-construction
|
|
|
|
p
|
|
| For most applications, I it's recommended to use 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.
|
|
|
|
+h(2, "attribute-hooks") Attribute hooks
|
|
|
|
+under-construction
|
|
|
|
p
|
|
| Earlier, we saw how to store data in the new generic #[code 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 #[code .similarity()] methods provided by spaCy's
|
|
| #[+api("doc") #[code Doc]], #[+api("token") #[code Token]] and
|
|
| #[+api("span") #[code Span]] objects:
|
|
|
|
+code("Polymorphic similarity example").
|
|
span.similarity(doc)
|
|
token.similarity(span)
|
|
doc1.similarity(doc2)
|
|
|
|
p
|
|
| 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
|
|
| #[code Doc] object:
|
|
|
|
+aside("Implementation note")
|
|
| The hooks live on the #[code Doc] object because the #[code Span] and
|
|
| #[code Token] objects are created lazily, and don't own any data. They
|
|
| just proxy to their parent #[code Doc]. This turns out to be convenient
|
|
| here — we only have to worry about installing hooks in one place.
|
|
|
|
+table(["Name", "Description"])
|
|
+row
|
|
+cell #[code user_hooks]
|
|
+cell Customise behaviour of #[code doc.vector], #[code doc.has_vector], #[code doc.vector_norm] or #[code doc.sents]
|
|
|
|
+row
|
|
+cell #[code user_token_hooks]
|
|
+cell Customise behaviour of #[code token.similarity], #[code token.vector], #[code token.has_vector], #[code token.vector_norm] or #[code token.conjuncts]
|
|
|
|
+row
|
|
+cell #[code user_span_hooks]
|
|
+cell Customise behaviour of #[code span.similarity], #[code span.vector], #[code span.has_vector], #[code span.vector_norm] or #[code span.root]
|
|
|
|
p
|
|
| To sum up, here's an example of hooking in custom #[code .similarity()]
|
|
| methods:
|
|
|
|
+code("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])
|