mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-30 20:06:30 +03:00
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
from __future__ import unicode_literals
|
|
import json
|
|
import random
|
|
import contextlib
|
|
import shutil
|
|
import pytest
|
|
import tempfile
|
|
from pathlib import Path
|
|
|
|
|
|
from ...gold import GoldParse
|
|
from ...pipeline import EntityRecognizer
|
|
from ...lang.en import English
|
|
|
|
try:
|
|
unicode
|
|
except NameError:
|
|
unicode = str
|
|
|
|
|
|
@pytest.fixture
|
|
def train_data():
|
|
return [
|
|
["hey",[]],
|
|
["howdy",[]],
|
|
["hey there",[]],
|
|
["hello",[]],
|
|
["hi",[]],
|
|
["i'm looking for a place to eat",[]],
|
|
["i'm looking for a place in the north of town",[[31,36,"location"]]],
|
|
["show me chinese restaurants",[[8,15,"cuisine"]]],
|
|
["show me chines restaurants",[[8,14,"cuisine"]]],
|
|
["yes",[]],
|
|
["yep",[]],
|
|
["yeah",[]],
|
|
["show me a mexican place in the centre",[[31,37,"location"], [10,17,"cuisine"]]],
|
|
["bye",[]],["goodbye",[]],
|
|
["good bye",[]],
|
|
["stop",[]],
|
|
["end",[]],
|
|
["i am looking for an indian spot",[[20,26,"cuisine"]]],
|
|
["search for restaurants",[]],
|
|
["anywhere in the west",[[16,20,"location"]]],
|
|
["central indian restaurant",[[0,7,"location"],[8,14,"cuisine"]]],
|
|
["indeed",[]],
|
|
["that's right",[]],
|
|
["ok",[]],
|
|
["great",[]]
|
|
]
|
|
|
|
@pytest.fixture
|
|
def additional_entity_types():
|
|
return ['cuisine', 'location']
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def temp_save_model(model):
|
|
model_dir = tempfile.mkdtemp()
|
|
model.to_disk(model_dir)
|
|
yield model_dir
|
|
shutil.rmtree(model_dir.as_posix())
|
|
|
|
|
|
@pytest.mark.xfail
|
|
@pytest.mark.models('en')
|
|
def test_issue910(EN, train_data, additional_entity_types):
|
|
'''Test that adding entities and resuming training works passably OK.
|
|
There are two issues here:
|
|
|
|
1) We have to readd labels. This isn't very nice.
|
|
2) There's no way to set the learning rate for the weight update, so we
|
|
end up out-of-scale, causing it to learn too fast.
|
|
'''
|
|
nlp = EN
|
|
doc = nlp(u"I am looking for a restaurant in Berlin")
|
|
ents_before_train = [(ent.label_, ent.text) for ent in doc.ents]
|
|
# Fine tune the ner model
|
|
for entity_type in additional_entity_types:
|
|
nlp.entity.add_label(entity_type)
|
|
|
|
sgd = Adam(nlp.entity.model[0].ops, 0.001)
|
|
for itn in range(10):
|
|
random.shuffle(train_data)
|
|
for raw_text, entity_offsets in train_data:
|
|
doc = nlp.make_doc(raw_text)
|
|
nlp.tagger(doc)
|
|
nlp.tensorizer(doc)
|
|
gold = GoldParse(doc, entities=entity_offsets)
|
|
loss = nlp.entity.update(doc, gold, sgd=sgd, drop=0.5)
|
|
|
|
with temp_save_model(nlp.entity) as model_dir:
|
|
# Load the fine tuned model
|
|
loaded_ner = EntityRecognizer(nlp.vocab)
|
|
loaded_ner.from_disk(model_dir)
|
|
|
|
for raw_text, entity_offsets in train_data:
|
|
doc = nlp.make_doc(raw_text)
|
|
nlp.tagger(doc)
|
|
loaded_ner(doc)
|
|
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
|
|
for start, end, label in entity_offsets:
|
|
assert ents[(start, end)] == label
|