explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-08-06 13:20:20 +03:00
Code Issues Packages Projects Releases Wiki Activity

Replace config string with spacy.blank

Browse Source
This commit is contained in:
shademe 2022-11-18 14:50:39 +01:00
parent d6d5c52135
commit 3fd19b8bd8
No known key found for this signature in database
GPG Key ID: 6FCA9FC635B2A402
1 changed files with 4 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
spacy/tests/training/test_training.py
View File

@ -3,6 +3,7 @@ from confection import Config
import numpy
import pytest
import spacy
import srsly
from spacy.lang.en import English
from spacy.tokens import Doc, DocBin
@ -14,7 +15,7 @@ from spacy.training.align import get_alignments
from spacy.training.converters import json_to_docs
from spacy.training.loop import train_while_improving
from spacy.util import get_words_and_spaces, load_model_from_path, minibatch
from spacy.util import load_config_from_str, load_model_from_config
from spacy.util import load_config_from_str
from thinc.api import compounding, Adam
from ..util import make_tempdir
@ -1116,38 +1117,6 @@ def test_retokenized_docs(doc):
assert example.get_aligned("ORTH", as_string=True) == expected2
training_config_string = """
[nlp]
lang = "en"
pipeline = ["tok2vec", "tagger"]
[components]
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 342
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v2"
[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.width}
"""
def test_training_before_update(doc):
def before_update(nlp, args):
assert args["step"] == 0
@ -1161,8 +1130,8 @@ def test_training_before_update(doc):
def generate_batch():
yield 1, [Example(doc, doc)]
config = Config().from_str(training_config_string, interpolate=False)
nlp = load_model_from_config(config, auto_fill=True, validate=True)
nlp = spacy.blank("en", config={"training": {}})
nlp.add_pipe("tagger")
optimizer = Adam()
generator = train_while_improving(
nlp,