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
After Change
for itn in range(50):
losses = {}
for batch in minibatch(get_gold_parses(nlp.make_doc, train_data), size=3):
docs, golds = zip(*batch)
nlp.update(docs, golds, losses=losses, sgd=optimizer, update_shared=True,
drop=0.35)
print(losses)
