spaCy/website/usage/_training/_textcat.jade

//- 💫 DOCS > USAGE > TRAINING > TEXT CLASSIFICATION

+h(3, "example-textcat") Adding a text classifier to a spaCy model
    +tag-new(2)

p
    |  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", 500)

+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 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 text classifier.

    +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.