Finish text classifier example
@@ -80,7 +80,7 @@ def load_data():
return (train_texts, train_cats), (dev_texts, dev_cats)
-def main():
+def main(model_loc=None):
nlp = spacy.lang.en.English()
tokenizer = nlp.tokenizer
textcat = TextCategorizer(tokenizer.vocab, labels=['POSITIVE'])
@@ -95,6 +95,15 @@ def main():
train_texts, train_cats,
dev_texts, dev_cats, n_iter=20)):
print(progress.format(i=i, loss=loss, **scores))
+ # How to save, load and use
+ nlp.pipeline.append(textcat)
+ if model_loc is not None:
+ nlp.to_disk(model_loc)
+
+ nlp = spacy.load(model_loc)
+ doc = nlp(u'This movie sucked!')
+ print(doc.cats)
+
if __name__ == '__main__':
