def test_tf_sentiment_analysis(self):
mandatory_keys = {"label", "score"}
for model_name in TEXT_CLASSIF_FINETUNED_MODELS:
nlp = pipeline(task="sentiment-analysis",
model=model_name,
tokenizer=model_name)
self._test_mono_column_pipeline(nlp, VALID_INPUTS, mandatory_keys)
def test_tf_fill_mask_results(self):
mandatory_keys = {"sequence", "score", "token"}
valid_inputs = [
"My name is <mask>",
"The largest city in France is <mask>",
]
invalid_inputs = [
"This is <mask> <mask>" # More than 1 mask_token in the input is not supported
"This is" # No mask_token is not supported
]
for model_name in FILL_MASK_FINETUNED_MODELS:
nlp = pipeline(task="fill-mask",
model=model_name,
tokenizer=model_name)
self._test_mono_column_pipeline(
nlp,
valid_inputs,
mandatory_keys,
invalid_inputs,
expected_multi_result=expected_fill_mask_result,
expected_check_keys=["sequence"],
)
def test_tf_defaults(self):
# Test that pipelines can be correctly loaded without any argument
for task in self.pipelines:
with self.subTest(
msg="Testing TF defaults with TF and {}".format(task)):
pipeline(task)
def test_tf_zero_shot_outputs(self):
nlp = pipeline(task="zero-shot-classification",
model="roberta-large-mnli")
self._test_zero_shot_pipeline_outputs(nlp)
def test_tf_zero_shot_classification(self):
for model_name in TEXT_CLASSIF_FINETUNED_MODELS:
nlp = pipeline(task="zero-shot-classification",
model=model_name,
tokenizer=model_name)
self._test_zero_shot_pipeline(nlp)
def test_tf_feature_extraction(self):
for model_name in FEATURE_EXTRACT_FINETUNED_MODELS:
nlp = pipeline(task="feature-extraction",
model=model_name,
tokenizer=model_name)
self._test_mono_column_pipeline(nlp, VALID_INPUTS, {})