diff --git a/examples/training/train_tagger.py b/examples/training/train_tagger.py index 6b1fbcae8..b887b4592 100644 --- a/examples/training/train_tagger.py +++ b/examples/training/train_tagger.py @@ -50,7 +50,7 @@ def main(lang='en', output_dir=None, n_iter=25): lang_cls.Defaults.tag_map.update(TAG_MAP) # add tag map to defaults nlp = lang_cls() # initialise Language class - # add the parser to the pipeline + # add the tagger to the pipeline # nlp.create_pipe works for built-ins that are registered with spaCy tagger = nlp.create_pipe('tagger') nlp.add_pipe(tagger) diff --git a/website/usage/_training/_tagger-parser.jade b/website/usage/_training/_tagger-parser.jade index 437ded9c9..c32577a73 100644 --- a/website/usage/_training/_tagger-parser.jade +++ b/website/usage/_training/_tagger-parser.jade @@ -1,6 +1,6 @@ //- 💫 DOCS > USAGE > TRAINING > TAGGER & PARSER -+h(3, "example-train-parser") Updating the parser ++h(3, "example-train-parser") Updating the Dependency Parser p | This example shows how to train spaCy's dependency parser, starting off @@ -51,6 +51,49 @@ p +item | #[strong Test] the model to make sure the parser works as expected. ++h(3, "example-train-tagger") Updating the Part-of-speech Tagger + +p + | In this example, we're training spaCy's part-of-speech tagger with a + | custom tag map. We start off with a blank #[code Language] class, update + | its defaults with our custom tags and then train the tagger. You'll need + | a set of #[strong training examples] and the respective + | #[strong custom tags], as well as a dictionary mapping those tags to the + | #[+a("http://universaldependencies.github.io/docs/u/pos/index.html") Universal Dependencies scheme]. + ++github("spacy", "examples/training/train_tagger.py") + ++h(4) Step by step guide + ++list("numbers") + +item + | #[strong Create] a new #[code Language] class and before initialising + | it, update the #[code tag_map] in its #[code Defaults] with your + | custom tags. + + +item + | #[strong Create a new tagger] component and add it to the pipeline. + + +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 tags] 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 diff --git a/website/usage/examples.jade b/website/usage/examples.jade index d6ad8bc23..6641a83c6 100644 --- a/website/usage/examples.jade +++ b/website/usage/examples.jade @@ -80,7 +80,7 @@ include ../_includes/_mixins +github("spacy", "examples/training/train_new_entity_type.py") - +h(3, "parser") Training spaCy's parser + +h(3, "parser") Training spaCy's Dependency Parser p | This example shows how to update spaCy's dependency parser, @@ -89,6 +89,15 @@ include ../_includes/_mixins +github("spacy", "examples/training/train_parser.py") + +h(3, "tagger") Training spaCy's Part-of-speech Tagger + + p + | In this example, we're training spaCy's part-of-speech tagger with a + | custom tag map, mapping our own tags to the mapping those tags to the + | #[+a("http://universaldependencies.github.io/docs/u/pos/index.html") Universal Dependencies scheme]. + + +github("spacy", "examples/training/train_tagger.py") + +h(3, "textcat") Training spaCy's text classifier +tag-new(2)