Update example for adding entity type

examples/training/train_new_entity_type.py

@@ -25,7 +25,7 @@ For more details, see the documentation:
 * Saving and loading models: https://spacy.io/docs/usage/saving-loading
 
 Developed for: spaCy 1.7.6
-Last tested for: spaCy 1.7.6
+Last updated for: spaCy 2.0.0a13
 """
 from __future__ import unicode_literals, print_function
 
@@ -34,55 +34,41 @@ from pathlib import Path
 import random
 
 import spacy
-from spacy.gold import GoldParse
+from spacy.gold import GoldParse, minibatch
-from spacy.tagger import Tagger
+from spacy.pipeline import NeuralEntityRecognizer
+from spacy.pipeline import TokenVectorEncoder
+
+
+def get_gold_parses(tokenizer, train_data):
+    '''Shuffle and create GoldParse objects'''
+    random.shuffle(train_data)
+    for raw_text, entity_offsets in train_data:
+        doc = tokenizer(raw_text)
+        gold = GoldParse(doc, entities=entity_offsets)
+        yield doc, gold
 
  
 def train_ner(nlp, train_data, output_dir):
-    # Add new words to vocab
-    for raw_text, _ in train_data:
-        doc = nlp.make_doc(raw_text)
-        for word in doc:
-            _ = nlp.vocab[word.orth]
     random.seed(0)
-    # You may need to change the learning rate. It's generally difficult to
+    optimizer = nlp.begin_training(lambda: [])
-    # guess what rate you should set, especially when you have limited data.
+    nlp.meta['name'] = 'en_ent_animal'
-    nlp.entity.model.learn_rate = 0.001
+    for itn in range(50):
-    for itn in range(1000):
+        losses = {}
-        random.shuffle(train_data)
+        for batch in minibatch(get_gold_parses(nlp.make_doc, train_data), size=3):
-        loss = 0.
+            docs, golds = zip(*batch)
-        for raw_text, entity_offsets in train_data:
+            nlp.update(docs, golds, losses=losses, sgd=optimizer, update_shared=True,
-            gold = GoldParse(doc, entities=entity_offsets)
+                       drop=0.35)
-            # By default, the GoldParse class assumes that the entities
+        print(losses)
-            # described by offset are complete, and all other words should
+    if not output_dir:
-            # have the tag 'O'. You can tell it to make no assumptions
+        return
-            # about the tag of a word by giving it the tag '-'.
+    elif not output_dir.exists():
-            # However, this allows a trivial solution to the current
+        output_dir.mkdir()
-            # learning problem: if words are either 'any tag' or 'ANIMAL',
+    nlp.to_disk(output_dir)
-            # the model can learn that all words can be tagged 'ANIMAL'.
-            #for i in range(len(gold.ner)):
-                #if not gold.ner[i].endswith('ANIMAL'):
-                #    gold.ner[i] = '-'
-            doc = nlp.make_doc(raw_text)
-            nlp.tagger(doc)
-            # As of 1.9, spaCy's parser now lets you supply a dropout probability
-            # This might help the model generalize better from only a few
-            # examples.
-            loss += nlp.entity.update(doc, gold, drop=0.9)
-        if loss == 0:
-            break
-    # This step averages the model's weights. This may or may not be good for
-    # your situation --- it's empirical.
-    nlp.end_training()
-    if output_dir:
-        if not output_dir.exists():
-            output_dir.mkdir()
-        nlp.save_to_directory(output_dir)
 
 
 def main(model_name, output_directory=None):
-    print("Loading initial model", model_name)
+    print("Creating initial model", model_name)
-    nlp = spacy.load(model_name)
+    nlp = spacy.blank(model_name)
     if output_directory is not None:
         output_directory = Path(output_directory)
 
@@ -91,6 +77,11 @@ def main(model_name, output_directory=None):
             "Horses are too tall and they pretend to care about your feelings",
             [(0, 6, 'ANIMAL')],
         ),
+        (
+            "Do they bite?", 
+            [],
+        ),
+ 
         (
             "horses are too tall and they pretend to care about your feelings",
             [(0, 6, 'ANIMAL')]
@@ -109,18 +100,20 @@ def main(model_name, output_directory=None):
         )
 
     ]
-    nlp.entity.add_label('ANIMAL')
+    nlp.pipeline.append(TokenVectorEncoder(nlp.vocab))
+    nlp.pipeline.append(NeuralEntityRecognizer(nlp.vocab))
+    nlp.pipeline[-1].add_label('ANIMAL')
     train_ner(nlp, train_data, output_directory)
 
     # Test that the entity is recognized
-    doc = nlp('Do you like horses?')
+    text = 'Do you like horses?'
     print("Ents in 'Do you like horses?':")
+    doc = nlp(text)
     for ent in doc.ents:
         print(ent.label_, ent.text)
     if output_directory:
         print("Loading from", output_directory)
-        nlp2 = spacy.load('en', path=output_directory)
+        nlp2 = spacy.load(output_directory)
-        nlp2.entity.add_label('ANIMAL')
         doc2 = nlp2('Do you like horses?')
         for ent in doc2.ents:
             print(ent.label_, ent.text)