Finish text classifier example

examples/training/train_textcat.py

@@ -80,14 +80,14 @@ 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'])
 
     print("Load IMDB data")
     (train_texts, train_cats), (dev_texts, dev_cats) = load_data()
-    
+
     print("Itn.\tLoss\tP\tR\tF")
     progress = '{i:d} {loss:.3f} {textcat_p:.3f} {textcat_r:.3f} {textcat_f:.3f}'
 
@@ -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__':