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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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30
tests/test_pipelines_zero_shot.py
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@@ -45,25 +45,25 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
sum += score
self.assertAlmostEqual(sum, 1.0, places=5)
def _test_entailment_id(self, nlp: Pipeline):
config = nlp.model.config
def _test_entailment_id(self, zero_shot_classifier: Pipeline):
config = zero_shot_classifier.model.config
original_config = deepcopy(config)
config.label2id = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
self.assertEqual(nlp.entailment_id, -1)
self.assertEqual(zero_shot_classifier.entailment_id, -1)
config.label2id = {"entailment": 0, "neutral": 1, "contradiction": 2}
self.assertEqual(nlp.entailment_id, 0)
self.assertEqual(zero_shot_classifier.entailment_id, 0)
config.label2id = {"ENTAIL": 0, "NON-ENTAIL": 1}
self.assertEqual(nlp.entailment_id, 0)
self.assertEqual(zero_shot_classifier.entailment_id, 0)
config.label2id = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
self.assertEqual(nlp.entailment_id, 2)
self.assertEqual(zero_shot_classifier.entailment_id, 2)
nlp.model.config = original_config
zero_shot_classifier.model.config = original_config
def _test_pipeline(self, nlp: Pipeline):
def _test_pipeline(self, zero_shot_classifier: Pipeline):
output_keys = {"sequence", "labels", "scores"}
valid_mono_inputs = [
{"sequences": "Who are you voting for in 2020?", "candidate_labels": "politics"},
@@ -102,12 +102,12 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
"hypothesis_template": "Template without formatting syntax.",
},
]
self.assertIsNotNone(nlp)
self.assertIsNotNone(zero_shot_classifier)
self._test_entailment_id(nlp)
self._test_entailment_id(zero_shot_classifier)
for mono_input in valid_mono_inputs:
mono_result = nlp(**mono_input)
mono_result = zero_shot_classifier(**mono_input)
self.assertIsInstance(mono_result, dict)
if len(mono_result["labels"]) > 1:
self._test_scores_sum_to_one(mono_result)
@@ -115,7 +115,7 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
for key in output_keys:
self.assertIn(key, mono_result)
multi_result = nlp(**valid_multi_input)
multi_result = zero_shot_classifier(**valid_multi_input)
self.assertIsInstance(multi_result, list)
self.assertIsInstance(multi_result[0], dict)
self.assertEqual(len(multi_result), len(valid_multi_input["sequences"]))
@@ -128,9 +128,9 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
self._test_scores_sum_to_one(result)
for bad_input in invalid_inputs:
self.assertRaises(Exception, nlp, **bad_input)
self.assertRaises(Exception, zero_shot_classifier, **bad_input)
if nlp.model.name_or_path in self.large_models:
if zero_shot_classifier.model.name_or_path in self.large_models:
# We also check the outputs for the large models
inputs = [
{
@@ -158,7 +158,7 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
]
for input, expected_output in zip(inputs, expected_outputs):
output = nlp(**input)
output = zero_shot_classifier(**input)
for key in output:
if key == "scores":
for output_score, expected_score in zip(output[key], expected_output[key]):