mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-10 19:57:17 +03:00
Add parser example to docs
This commit is contained in:
parent
2d6ec99884
commit
0575e9cf20
|
@ -1,6 +1,56 @@
|
|||
//- 💫 DOCS > USAGE > TRAINING > TAGGER & PARSER
|
||||
|
||||
+under-construction
|
||||
+h(3, "example-train-parser") Updating the parser
|
||||
|
||||
p
|
||||
| This example shows how to train spaCy's dependency parser, starting off
|
||||
| with an existing model or a blank model. You'll need a set of
|
||||
| #[strong training examples] and the respective #[strong heads] and
|
||||
| #[strong dependency label] for each token of the example texts.
|
||||
|
||||
+github("spacy", "examples/training/train_parser.py")
|
||||
|
||||
+h(4) Step by step guide
|
||||
|
||||
+list("numbers")
|
||||
+item
|
||||
| #[strong Load the model] you want to start with, or create an
|
||||
| #[strong empty model] using
|
||||
| #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
|
||||
| language. If you're using a blank model, don't forget to add the
|
||||
| parser to the pipeline. If you're using an existing model,
|
||||
| make sure to disable all other pipeline components during training
|
||||
| using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
|
||||
| This way, you'll only be training the parser.
|
||||
|
||||
+item
|
||||
| #[strong Add the dependency labels] to the parser using the
|
||||
| #[+api("dependencyparser#add_label") #[code add_label]] method. If
|
||||
| you're starting off with a pre-trained spaCy model, this is usually
|
||||
| not necessary – but it doesn't hurt either, just to be safe.
|
||||
|
||||
+item
|
||||
| #[strong Shuffle and loop over] the examples and create a
|
||||
| #[code Doc] and #[code GoldParse] object for each example. Make sure
|
||||
| to pass in the #[code heads] and #[code deps] when you create the
|
||||
| #[code GoldParse].
|
||||
|
||||
+item
|
||||
| For each example, #[strong update the model]
|
||||
| by calling #[+api("language#update") #[code nlp.update]], which steps
|
||||
| through the words of the input. At each word, it makes a
|
||||
| #[strong prediction]. It then consults the annotations provided on the
|
||||
| #[code GoldParse] instance, to see whether it was
|
||||
| right. If it was wrong, it adjusts its weights so that the correct
|
||||
| action will score higher next time.
|
||||
|
||||
+item
|
||||
| #[strong Save] the trained model using
|
||||
| #[+api("language#to_disk") #[code nlp.to_disk]].
|
||||
|
||||
+item
|
||||
| #[strong Test] the model to make sure the parser works as expected.
|
||||
|
||||
|
||||
+h(3, "training-json") JSON format for training
|
||||
|
||||
|
|
|
@ -80,6 +80,15 @@ include ../_includes/_mixins
|
|||
|
||||
+github("spacy", "examples/training/train_new_entity_type.py")
|
||||
|
||||
+h(3, "parser") Training spaCy's parser
|
||||
|
||||
p
|
||||
| This example shows how to update spaCy's dependency parser,
|
||||
| starting off with an existing, pre-trained model, or from scratch
|
||||
| using a blank #[code Language] class.
|
||||
|
||||
+github("spacy", "examples/training/train_parser.py")
|
||||
|
||||
+h(3, "textcat") Training spaCy's text classifier
|
||||
+tag-new(2)
|
||||
|
||||
|
|
Loading…
Reference in New Issue
Block a user