From 8f1d3fc3ee2b366a280c9ff61afb31102982bbae Mon Sep 17 00:00:00 2001 From: ines Date: Wed, 1 Nov 2017 17:09:22 +0100 Subject: [PATCH] Update textcat example --- examples/training/train_textcat.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py index 6fa79e75b..d1cf3ab8a 100644 --- a/examples/training/train_textcat.py +++ b/examples/training/train_textcat.py @@ -26,7 +26,7 @@ from spacy.pipeline import TextCategorizer @plac.annotations( model=("Model name. Defaults to blank 'en' model.", "option", "m", str), output_dir=("Optional output directory", "option", "o", Path), - n_examples=("Number of texts to train from", "option", "N", int), + n_texts=("Number of texts to train from", "option", "t", int), n_iter=("Number of training iterations", "option", "n", int)) def main(model=None, output_dir=None, n_iter=20, n_texts=2000): if model is not None: @@ -39,20 +39,19 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000): # add the text classifier to the pipeline if it doesn't exist # nlp.create_pipe works for built-ins that are registered with spaCy if 'textcat' not in nlp.pipe_names: - # textcat = nlp.create_pipe('textcat') - textcat = TextCategorizer(nlp.vocab, labels=['POSITIVE']) + textcat = nlp.create_pipe('textcat') nlp.add_pipe(textcat, last=True) # otherwise, get it, so we can add labels to it else: textcat = nlp.get_pipe('textcat') # add label to text classifier - # textcat.add_label('POSITIVE') + textcat.add_label('POSITIVE') # load the IMBD dataset print("Loading IMDB data...") - print("Using %d training examples" % n_texts) (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts) + print("Using %d training examples" % n_texts) train_docs = [nlp.tokenizer(text) for text in train_texts] train_gold = [GoldParse(doc, cats=cats) for doc, cats in zip(train_docs, train_cats)]