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Update training docs

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ines 2017-04-16 20:35:56 +02:00
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website/docs/usage/training.jade
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@ -1,13 +1,10 @@
include ../../_includes/_mixins include ../../_includes/_mixins
p p
| This tutorial describes how to train new statistical models for spaCy's | This workflow describes how to train new statistical models for spaCy's
| part-of-speech tagger, named entity recognizer and dependency parser. | part-of-speech tagger, named entity recognizer and dependency parser.
| Once the model is trained, you can then
p | #[+a("/docs/usage/saving-loading") save and load] it.
| I'll start with some quick code examples, that describe how to train
| each model. I'll then provide a bit of background about the algorithms,
| and explain how the data and feature templates work.
+h(2, "train-pos-tagger") Training the part-of-speech tagger +h(2, "train-pos-tagger") Training the part-of-speech tagger
@ -48,7 +45,21 @@ p
p p
+button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example +button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example
+h(2, "train-entity") Training the dependency parser +h(2, "extend-entity") Extending the named entity recognizer
p
| All #[+a("/docs/usage/models") spaCy models] support online learning, so
| you can update a pre-trained model with new examples. You can even add
| new classes to an existing model, to recognise a new entity type,
| part-of-speech, or syntactic relation. Updating an existing model is
| particularly useful as a "quick and dirty solution", if you have only a
| few corrections or annotations.
p.o-inline-list
+button(gh("spaCy", "examples/training/train_new_entity_type.py"), true, "secondary") Full example
+button("/docs/usage/training-ner", false, "secondary") Usage Workflow
+h(2, "train-dependency") Training the dependency parser
+code. +code.
from spacy.vocab import Vocab from spacy.vocab import Vocab
@ -67,7 +78,7 @@ p
p p
+button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example +button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example
+h(2, 'feature-templates') Customizing the feature extraction +h(2, "feature-templates") Customizing the feature extraction
p p
| spaCy currently uses linear models for the tagger, parser and entity | spaCy currently uses linear models for the tagger, parser and entity