SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Black 20 release

Browse Source
This commit is contained in:
Lysandre
2020-08-26 17:20:22 +02:00
parent e78c110338
commit a75c64d80c
191 changed files with 4807 additions and 3503 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
tests/test_pipelines.py
View File

@@ -164,7 +164,8 @@ class MonoColumnInputTestCase(unittest.TestCase):
for result, expect in zip(multi_result, expected_multi_result):
for key in expected_check_keys or []:
self.assertEqual(
set([o[key] for o in result]), set([o[key] for o in expect]),
set([o[key] for o in result]),
set([o[key] for o in expect]),
)
if isinstance(multi_result[0], list):
@@ -214,7 +215,13 @@ class MonoColumnInputTestCase(unittest.TestCase):
"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, framework="pt", topk=2,)
nlp = pipeline(
task="fill-mask",
model=model_name,
tokenizer=model_name,
framework="pt",
topk=2,
)
self._test_mono_column_pipeline(
nlp, valid_inputs, mandatory_keys, invalid_inputs, expected_check_keys=["sequence"]
)
@@ -231,7 +238,13 @@ class MonoColumnInputTestCase(unittest.TestCase):
"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, framework="tf", topk=2,)
nlp = pipeline(
task="fill-mask",
model=model_name,
tokenizer=model_name,
framework="tf",
topk=2,
)
self._test_mono_column_pipeline(
nlp, valid_inputs, mandatory_keys, invalid_inputs, expected_check_keys=["sequence"]
)
@@ -274,7 +287,13 @@ class MonoColumnInputTestCase(unittest.TestCase):
]
valid_targets = [" Patrick", " Clara"]
for model_name in LARGE_FILL_MASK_FINETUNED_MODELS:
nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt", topk=2,)
nlp = pipeline(
task="fill-mask",
model=model_name,
tokenizer=model_name,
framework="pt",
topk=2,
)
self._test_mono_column_pipeline(
nlp,
valid_inputs,
@@ -343,7 +362,12 @@ class MonoColumnInputTestCase(unittest.TestCase):
invalid_inputs = [4, "<mask>"]
mandatory_keys = ["summary_text"]
for model_name in TF_SUMMARIZATION_FINETUNED_MODELS:
nlp = pipeline(task="summarization", model=model_name, tokenizer=model_name, framework="tf",)
nlp = pipeline(
task="summarization",
model=model_name,
tokenizer=model_name,
framework="tf",
)
self._test_mono_column_pipeline(
nlp, VALID_INPUTS, mandatory_keys, invalid_inputs=invalid_inputs, **SUMMARIZATION_KWARGS
)
@@ -355,7 +379,10 @@ class MonoColumnInputTestCase(unittest.TestCase):
for model_name, task in TRANSLATION_FINETUNED_MODELS:
nlp = pipeline(task=task, model=model_name, tokenizer=model_name)
self._test_mono_column_pipeline(
nlp, VALID_INPUTS, mandatory_keys, invalid_inputs,
nlp,
VALID_INPUTS,
mandatory_keys,
invalid_inputs,
)
@require_tf
@@ -655,7 +682,9 @@ class QAPipelineTests(unittest.TestCase):
class NerPipelineTests(unittest.TestCase):
def _test_ner_pipeline(
self, nlp: Pipeline, output_keys: Iterable[str],
self,
nlp: Pipeline,
output_keys: Iterable[str],
):
ungrouped_ner_inputs = [