def test_text_classifier_tutorial(tmp_path):
notebook_path = Path(TUTORIALS_PATH) / "Training_a_text_classifier.ipynb"
# adapt notebook to CI (make its execution quicker + comment lines)
notebook = load_notebook(str(notebook_path))
for cell in notebook["cells"]:
if cell["source"].startswith("!pip install"):
cell["source"] = re.sub(r"!pip install", r"#!pip install",
cell["source"])
if cell["source"].startswith("trainer_config ="):
cell["source"] = re.sub(r"num_epochs=[0-9][0-9]?", r"num_epochs=1",
cell["source"])
if cell["source"].startswith("pl.train("):
cell["source"] = re.sub(r"training=train_ds", r"training=valid_ds",
cell["source"])
if cell["source"].startswith("pl_trained.explore"):
cell["source"] = re.sub(r"pl_trained.explore",
r"#pl_trained.explore", cell["source"])
# dump adapted notebook
mod_notebook_path = tmp_path / notebook_path.name
with mod_notebook_path.open("w") as file:
file.write(str(dump_notebook(notebook)))
# test adapted notebook
fixture = NBRegressionFixture(exec_timeout=100)
fixture.check(str(mod_notebook_path))
def test_slot_filling_tutorial(tmp_path):
notebook_path = (Path(TUTORIALS_PATH) /
"Training_a_sequence_tagger_for_Slot_Filling.ipynb")
# adapt notebook to CI (make its execution quicker + comment lines)
notebook = load_notebook(str(notebook_path))
for cell in notebook["cells"]:
if cell["source"].startswith("!pip install"):
cell["source"] = re.sub(r"!pip install", r"#!pip install",
cell["source"])
if cell["source"].startswith(
"from biome.text.configuration import FeaturesConfiguration"):
cell["source"] = re.sub(
r"https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip",
r"https://biome-tutorials-data.s3-eu-west-1.amazonaws.com/token_classifier/wiki-news-300d-1M.head.vec",
cell["source"],
)
if cell["source"].startswith("trainer_config ="):
cell["source"] = re.sub(
r"TrainerConfiguration\(\)",
r"TrainerConfiguration(num_epochs=1)",
cell["source"],
)
if cell["source"].startswith("pl.train("):
cell["source"] = re.sub(
r"pl.train",
r"from biome.text.configuration import TrainerConfiguration\npl.train",
cell["source"],
)
cell["source"] = re.sub(
r"training=train_ds",
r"training=valid_ds",
cell["source"],
)
cell["source"] = re.sub(
r"test=test_ds,",
r"test=test_ds, trainer=TrainerConfiguration(num_epochs=1)",
cell["source"],
)
# dump adapted notebook
mod_notebook_path = tmp_path / notebook_path.name
with mod_notebook_path.open("w") as file:
file.write(str(dump_notebook(notebook)))
# test adapted notebook
fixture = NBRegressionFixture(exec_timeout=200)
fixture.check(str(mod_notebook_path))