Update textcat training example and docs

This commit is contained in:
ines 2017-10-27 00:48:45 +02:00
parent b61866a2e4
commit a7b9074b4c
4 changed files with 65 additions and 16 deletions

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@ -2,7 +2,7 @@
# coding: utf8
"""Train a multi-label convolutional neural network text classifier on the
IMDB dataset, using the TextCategorizer component. The dataset will be loaded
automatically via Thinc's built-in dataset loader. The model is then added to
automatically via Thinc's built-in dataset loader. The model is added to
spacy.pipeline, and predictions are available via `doc.cats`.
For more details, see the documentation:
@ -41,7 +41,7 @@ def main(model=None, output_dir=None, n_iter=20):
if 'textcat' not in nlp.pipe_names:
# textcat = nlp.create_pipe('textcat')
textcat = TextCategorizer(nlp.vocab, labels=['POSITIVE'])
nlp.add_pipe(textcat, first=True)
nlp.add_pipe(textcat, last=True)
# otherwise, get it, so we can add labels to it
else:
textcat = nlp.get_pipe('textcat')

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@ -1,13 +1,63 @@
//- 💫 DOCS > USAGE > TRAINING > TEXT CLASSIFICATION
+under-construction
+h(3, "example-textcat") Example: Training spaCy's text classifier
+h(3, "example-textcat") Adding a text classifier to a spaCy model
+tag-new(2)
p
| This example shows how to use and train spaCy's new
| #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
| on IMDB movie reviews.
| This example shows how to train a multi-label convolutional neural
| network text classifier on IMDB movie reviews, using spaCy's new
| #[+api("textcategorizer") #[code TextCategorizer]] component. The
| dataset will be loaded automatically via Thinc's built-in dataset
| loader. Predictions are available via
| #[+api("doc#attributes") #[code Doc.cats]].
+github("spacy", "examples/training/train_textcat.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 text classifier] to the pipeline, and add the labels
| you want to train for example, #[code POSITIVE].
+item
| #[strong Load and pre-process the dataset], shuffle the data and
| split off a part of it to hold back for evaluation. This way, you'll
| be able to see results on each training iteration.
+item
| #[strong Loop over] the training examples, partition them into
| batches and create #[code Doc] and #[code GoldParse] objects for each
| example in the batch.
+item
| #[strong Update the model] by calling
| #[+api("language#update") #[code nlp.update]], which steps
| through the examples and 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 prediction will score higher next time.
+item
| Optionally, you can also #[strong evaluate the text classifier] on
| each iteration, by checking how it performs on the development data
| held back from the dataset. This lets you print the
| #[strong precision], #[strong recall] and #[strong F-score].
+item
| #[strong Save] the trained model using
| #[+api("language#to_disk") #[code nlp.to_disk]].
+item
| #[strong Test] the model to make sure the text classifier works as
| expected.

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@ -113,9 +113,12 @@ include ../_includes/_mixins
+tag-new(2)
p
| This example shows how to use and train spaCy's new
| #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
| on IMDB movie reviews.
| This example shows how to train a multi-label convolutional neural
| network text classifier on IMDB movie reviews, using spaCy's new
| #[+api("textcategorizer") #[code TextCategorizer]] component. The
| dataset will be loaded automatically via Thinc's built-in dataset
| loader. Predictions are available via
| #[+api("doc#attributes") #[code Doc.cats]].
+github("spacy", "examples/training/train_textcat.py")

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@ -2,8 +2,4 @@
include ../_includes/_mixins
+under-construction
+h(2, "example") Example
+github("spacy", "examples/training/train_textcat.py")
include _training/_textcat