mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 05:37:03 +03:00
114 lines
2.6 KiB
Python
114 lines
2.6 KiB
Python
from typing import Callable, Iterable, Iterator
|
|
|
|
import pytest
|
|
from thinc.api import Config
|
|
|
|
from spacy import Language
|
|
from spacy.training import Example
|
|
from spacy.training.initialize import init_nlp_student
|
|
from spacy.training.loop import distill, train
|
|
from spacy.util import load_model_from_config, registry
|
|
|
|
|
|
@pytest.fixture
|
|
def config_str():
|
|
return """
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["senter"]
|
|
disabled = []
|
|
before_creation = null
|
|
after_creation = null
|
|
after_pipeline_creation = null
|
|
batch_size = 1000
|
|
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
|
|
|
|
[components]
|
|
|
|
[components.senter]
|
|
factory = "senter"
|
|
|
|
[training]
|
|
dev_corpus = "corpora.dev"
|
|
train_corpus = "corpora.train"
|
|
max_steps = 50
|
|
seed = 1
|
|
gpu_allocator = null
|
|
|
|
[distillation]
|
|
corpus = "corpora.train"
|
|
dropout = 0.1
|
|
max_epochs = 0
|
|
max_steps = 50
|
|
student_to_teacher = {}
|
|
|
|
[distillation.batcher]
|
|
@batchers = "spacy.batch_by_words.v1"
|
|
size = 3000
|
|
discard_oversize = false
|
|
tolerance = 0.2
|
|
|
|
[distillation.optimizer]
|
|
@optimizers = "Adam.v1"
|
|
beta1 = 0.9
|
|
beta2 = 0.999
|
|
L2_is_weight_decay = true
|
|
L2 = 0.01
|
|
grad_clip = 1.0
|
|
use_averages = true
|
|
eps = 1e-8
|
|
learn_rate = 1e-4
|
|
|
|
[corpora]
|
|
|
|
[corpora.dev]
|
|
@readers = "sentence_corpus"
|
|
|
|
[corpora.train]
|
|
@readers = "sentence_corpus"
|
|
"""
|
|
|
|
|
|
SENT_STARTS = [0] * 14
|
|
SENT_STARTS[0] = 1
|
|
SENT_STARTS[5] = 1
|
|
SENT_STARTS[9] = 1
|
|
|
|
TRAIN_DATA = [
|
|
(
|
|
"I like green eggs. Eat blue ham. I like purple eggs.",
|
|
{"sent_starts": SENT_STARTS},
|
|
),
|
|
(
|
|
"She likes purple eggs. They hate ham. You like yellow eggs.",
|
|
{"sent_starts": SENT_STARTS},
|
|
),
|
|
]
|
|
|
|
|
|
@pytest.mark.slow
|
|
def test_distill_loop(config_str):
|
|
@registry.readers("sentence_corpus")
|
|
def create_sentence_corpus() -> Callable[[Language], Iterable[Example]]:
|
|
return SentenceCorpus()
|
|
|
|
class SentenceCorpus:
|
|
def __call__(self, nlp: Language) -> Iterator[Example]:
|
|
for t in TRAIN_DATA:
|
|
yield Example.from_dict(nlp.make_doc(t[0]), t[1])
|
|
|
|
orig_config = Config().from_str(config_str)
|
|
teacher = load_model_from_config(orig_config, auto_fill=True, validate=True)
|
|
teacher.initialize()
|
|
train(teacher)
|
|
|
|
orig_config = Config().from_str(config_str)
|
|
student = init_nlp_student(orig_config, teacher)
|
|
student.initialize()
|
|
distill(teacher, student)
|
|
|
|
doc = student(TRAIN_DATA[0][0])
|
|
assert doc.sents[0].text == "I like green eggs."
|
|
assert doc.sents[1].text == "Eat blue ham."
|
|
assert doc.sents[2].text == "I like purple eggs."
|