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Fixed: Better names for nlp variables in pipelines' tests and docs. (#11752)

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* Fixed: Better names for nlp variables in pipelines' tests and docs.

* Fixed: Better variable names
This commit is contained in:
Vyom Pathak
2021-05-18 19:17:28 +05:30
committed by GitHub
parent cebb96f53a
commit fd3b12e8c3
12 changed files with 163 additions and 159 deletions
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48
tests/test_pipelines_fill_mask.py
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@@ -63,16 +63,16 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
@require_torch
def test_torch_fill_mask(self):
valid_inputs = "My name is <mask>"
nlp = pipeline(task="fill-mask", model=self.small_models[0])
outputs = nlp(valid_inputs)
unmasker = pipeline(task="fill-mask", model=self.small_models[0])
outputs = unmasker(valid_inputs)
self.assertIsInstance(outputs, list)
# This passes
outputs = nlp(valid_inputs, targets=[" Patrick", " Clara"])
outputs = unmasker(valid_inputs, targets=[" Patrick", " Clara"])
self.assertIsInstance(outputs, list)
# This used to fail with `cannot mix args and kwargs`
outputs = nlp(valid_inputs, something=False)
outputs = unmasker(valid_inputs, something=False)
self.assertIsInstance(outputs, list)
@require_torch
@@ -81,13 +81,13 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
valid_targets = [[" Teven", " Patrick", " Clara"], [" Sam"]]
invalid_targets = [[], [""], ""]
for model_name in self.small_models:
nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
for targets in valid_targets:
outputs = nlp(valid_inputs, targets=targets)
outputs = unmasker(valid_inputs, targets=targets)
self.assertIsInstance(outputs, list)
self.assertEqual(len(outputs), len(targets))
for targets in invalid_targets:
self.assertRaises(ValueError, nlp, valid_inputs, targets=targets)
self.assertRaises(ValueError, unmasker, valid_inputs, targets=targets)
@require_tf
def test_tf_fill_mask_with_targets(self):
@@ -95,13 +95,13 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
valid_targets = [[" Teven", " Patrick", " Clara"], [" Sam"]]
invalid_targets = [[], [""], ""]
for model_name in self.small_models:
nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf")
unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf")
for targets in valid_targets:
outputs = nlp(valid_inputs, targets=targets)
outputs = unmasker(valid_inputs, targets=targets)
self.assertIsInstance(outputs, list)
self.assertEqual(len(outputs), len(targets))
for targets in invalid_targets:
self.assertRaises(ValueError, nlp, valid_inputs, targets=targets)
self.assertRaises(ValueError, unmasker, valid_inputs, targets=targets)
@require_torch
@slow
@@ -113,7 +113,7 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
]
valid_targets = [" Patrick", " Clara"]
for model_name in self.large_models:
nlp = pipeline(
unmasker = pipeline(
task="fill-mask",
model=model_name,
tokenizer=model_name,
@@ -121,14 +121,14 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
top_k=2,
)
mono_result = nlp(valid_inputs[0], targets=valid_targets)
mono_result = unmasker(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
@@ -146,17 +146,17 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
self.assertRaises(Exception, unmasker, [None])
valid_inputs = valid_inputs[:1]
mono_result = nlp(valid_inputs[0], targets=valid_targets)
mono_result = unmasker(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
@@ -174,7 +174,7 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
self.assertRaises(Exception, unmasker, [None])
@require_tf
@slow
@@ -186,16 +186,16 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
]
valid_targets = [" Patrick", " Clara"]
for model_name in self.large_models:
nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", top_k=2)
unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", top_k=2)
mono_result = nlp(valid_inputs[0], targets=valid_targets)
mono_result = unmasker(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
@@ -213,17 +213,17 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
self.assertRaises(Exception, unmasker, [None])
valid_inputs = valid_inputs[:1]
mono_result = nlp(valid_inputs[0], targets=valid_targets)
mono_result = unmasker(valid_inputs[0], targets=valid_targets)
self.assertIsInstance(mono_result, list)
self.assertIsInstance(mono_result[0], dict)
for mandatory_key in mandatory_keys:
self.assertIn(mandatory_key, mono_result[0])
multi_result = [nlp(valid_input) for valid_input in valid_inputs]
multi_result = [unmasker(valid_input) for valid_input in valid_inputs]
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], (dict, list))
@@ -241,4 +241,4 @@ class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
for key in mandatory_keys:
self.assertIn(key, result)
self.assertRaises(Exception, nlp, [None])
self.assertRaises(Exception, unmasker, [None])