explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-24 00:46:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

Update training docs

Browse Source
This commit is contained in:
ines 2017-04-16 20:35:56 +02:00
parent 5cb17b9f33
commit b15bdb5279
1 changed files with 19 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
website/docs/usage/training.jade
View File

@ -1,13 +1,10 @@
include ../../_includes/_mixins
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.
p
| 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.
| Once the model is trained, you can then
| #[+a("/docs/usage/saving-loading") save and load] it.
+h(2, "train-pos-tagger") Training the part-of-speech tagger
@ -48,7 +45,21 @@ p
p
+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.
from spacy.vocab import Vocab
@ -67,7 +78,7 @@ p
p
+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
| spaCy currently uses linear models for the tagger, parser and entity