Update rehearsal example
@@ -4,7 +4,7 @@ import random
import srsly
import spacy
from spacy.gold import GoldParse
-from spacy.util import minibatch
+from spacy.util import minibatch, compounding
LABEL = "ANIMAL"
@@ -54,9 +54,17 @@ def main(model_name, unlabelled_loc):
nlp.get_pipe("ner").add_label(LABEL)
raw_docs = list(read_raw_data(nlp, unlabelled_loc))
optimizer = nlp.resume_training()
+ # Avoid use of Adam when resuming training. I don't understand this well
+ # yet, but I'm getting weird results from Adam. Try commenting out the
+ # nlp.update(), and using Adam -- you'll find the models drift apart.
+ # I guess Adam is losing precision, introducing gradient noise?
+ optimizer.alpha = 0.1
+ optimizer.b1 = 0.0
+ optimizer.b2 = 0.0
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
+ sizes = compounding(1.0, 4.0, 1.001)
with nlp.disable_pipes(*other_pipes):
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
@@ -64,13 +72,22 @@ def main(model_name, unlabelled_loc):
losses = {}
r_losses = {}
# batch up the examples using spaCy's minibatch
- raw_batches = minibatch(raw_docs, size=batch_size)
- for doc, gold in TRAIN_DATA:
- nlp.update([doc], [gold], sgd=optimizer, drop=dropout, losses=losses)
+ raw_batches = minibatch(raw_docs, size=4)
+ for batch in minibatch(TRAIN_DATA, size=sizes):
+ docs, golds = zip(*batch)
+ nlp.update(docs, golds, sgd=optimizer, drop=dropout, losses=losses)
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses)
print("Losses", losses)
print("R. Losses", r_losses)
+ print(nlp.get_pipe('ner').model.unseen_classes)
+ test_text = "Do you like horses?"
+ doc = nlp(test_text)
+ print("Entities in '%s'" % test_text)
+ for ent in doc.ents:
+ print(ent.label_, ent.text)
+
+
if __name__ == "__main__":
