mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 16:07:41 +03:00
fcbf899b08
* remove _convert_examples * fix test_gold, raise TypeError if tuples are used instead of Example's * throwing proper errors when the wrong type of objects are passed * fix deprectated format in tests * fix deprectated format in parser tests * fix tests for NEL, morph, senter, tagger, textcat * update regression tests with new Example format * use make_doc * more fixes to nlp.update calls * few more small fixes for rehearse and evaluate * only import ml_datasets if really necessary
97 lines
3.3 KiB
Python
97 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 Example
|
|
from spacy.util import minibatch, compounding
|
|
|
|
# TODO: further fix & test this script for v.3 ? (read_gold_data is never called)
|
|
|
|
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 Example.from_dict(doc, {})
|
|
|
|
|
|
def read_gold_data(nlp, gold_loc):
|
|
examples = []
|
|
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"]]
|
|
example = Example.from_dict(doc, {"entities": ents})
|
|
examples.append(example)
|
|
return examples
|
|
|
|
|
|
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_examples = 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.learn_rate = 0.1
|
|
optimizer.b1 = 0.0
|
|
optimizer.b2 = 0.0
|
|
sizes = compounding(1.0, 4.0, 1.001)
|
|
|
|
train_examples = []
|
|
for text, annotations in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
|
|
with nlp.select_pipes(enable="ner") 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_examples)
|
|
random.shuffle(raw_examples)
|
|
losses = {}
|
|
r_losses = {}
|
|
# batch up the examples using spaCy's minibatch
|
|
raw_batches = minibatch(raw_examples, size=4)
|
|
for batch in minibatch(train_examples, size=sizes):
|
|
nlp.update(batch, 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)
|