spaCy/examples/training/rehearsal.py
adrianeboyd 70da1fd2d6
Add warning for misaligned character offset spans (#5007)
* Add warning for misaligned character offset spans

* Resolve conflict

* Filter warnings in example scripts

Filter warnings in example scripts to show warnings once, in particular
warnings about misaligned entities.

Co-authored-by: Ines Montani <ines@ines.io>
2020-05-19 16:01:18 +02:00

98 lines
3.3 KiB
Python

"""Prevent catastrophic forgetting with rehearsal updates."""
import plac
import random
import warnings
import srsly
import spacy
from spacy.gold import GoldParse
from spacy.util import minibatch, compounding
LABEL = "ANIMAL"
TRAIN_DATA = [
(
"Horses are too tall and they pretend to care about your feelings",
{"entities": [(0, 6, "ANIMAL")]},
),
("Do they bite?", {"entities": []}),
(
"horses are too tall and they pretend to care about your feelings",
{"entities": [(0, 6, "ANIMAL")]},
),
("horses pretend to care about your feelings", {"entities": [(0, 6, "ANIMAL")]}),
(
"they pretend to care about your feelings, those horses",
{"entities": [(48, 54, "ANIMAL")]},
),
("horses?", {"entities": [(0, 6, "ANIMAL")]}),
]
def read_raw_data(nlp, jsonl_loc):
for json_obj in srsly.read_jsonl(jsonl_loc):
if json_obj["text"].strip():
doc = nlp.make_doc(json_obj["text"])
yield doc
def read_gold_data(nlp, gold_loc):
docs = []
golds = []
for json_obj in srsly.read_jsonl(gold_loc):
doc = nlp.make_doc(json_obj["text"])
ents = [(ent["start"], ent["end"], ent["label"]) for ent in json_obj["spans"]]
gold = GoldParse(doc, entities=ents)
docs.append(doc)
golds.append(gold)
return list(zip(docs, golds))
def main(model_name, unlabelled_loc):
n_iter = 10
dropout = 0.2
batch_size = 4
nlp = spacy.load(model_name)
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
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
sizes = compounding(1.0, 4.0, 1.001)
with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
# show warnings for misaligned entity spans once
warnings.filterwarnings("once", category=UserWarning, module='spacy')
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
random.shuffle(raw_docs)
losses = {}
r_losses = {}
# batch up the examples using spaCy's minibatch
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__":
plac.call(main)