Moving fill-mask pipeline to new testing scheme (#12943)
* Fill mask pipelines test updates. * Model eval !! * Adding slow test with actual values. * Making all tests pass (skipping quite a bit.) * Doc styling. * Better doc cleanup. * Making an explicit test with no pad token tokenizer. * Typo.
This commit is contained in:
Nicolas Patry
@@ -189,6 +189,7 @@ class ReformerModelTester:
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def get_pipeline_config(self):
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config = self.get_config()
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config.vocab_size = 100
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config.is_decoder = False
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return config
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def create_and_check_reformer_model(self, config, input_ids, input_mask, choice_labels):
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@@ -74,10 +74,10 @@ def get_tiny_config_from_class(configuration_class):
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@lru_cache(maxsize=100)
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def get_tiny_tokenizer_from_checkpoint(checkpoint):
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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logger.warning("Training new from iterator ...")
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logger.info("Training new from iterator ...")
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vocabulary = string.ascii_letters + string.digits + " "
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tokenizer = tokenizer.train_new_from_iterator(vocabulary, vocab_size=len(vocabulary), show_progress=False)
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logger.warning("Trained.")
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logger.info("Trained.")
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return tokenizer
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@@ -109,9 +109,7 @@ class PipelineTestCaseMeta(type):
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# Some test tokenizer contain broken vocabs or custom PreTokenizer, so we
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# provide some default tokenizer and hope for the best.
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except: # noqa: E722
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logger.warning(f"Tokenizer cannot be created from checkpoint {checkpoint}")
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tokenizer = get_tiny_tokenizer_from_checkpoint("gpt2")
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tokenizer.model_max_length = model.config.max_position_embeddings
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self.skipTest(f"Ignoring {ModelClass}, cannot create a simple tokenizer")
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self.run_pipeline_test(model, tokenizer)
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return test
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@@ -14,304 +14,307 @@
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import unittest
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from transformers import pipeline
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from transformers.testing_utils import nested_simplify, require_tf, require_torch, slow
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from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
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from transformers.pipelines import PipelineException
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from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
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from .test_pipelines_common import MonoInputPipelineCommonMixin
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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EXPECTED_FILL_MASK_RESULT = [
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[
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{"sequence": "My name is John", "score": 0.00782308354973793, "token": 610, "token_str": " John"},
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{"sequence": "My name is Chris", "score": 0.007475061342120171, "token": 1573, "token_str": " Chris"},
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],
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[
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{
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"sequence": "The largest city in France is Paris",
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"score": 0.2510891854763031,
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"token": 2201,
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"token_str": " Paris",
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},
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{
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"sequence": "The largest city in France is Lyon",
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"score": 0.21418564021587372,
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"token": 12790,
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"token_str": " Lyon",
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},
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],
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]
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@is_pipeline_test
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class FillMaskPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_MASKED_LM_MAPPING
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tf_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING
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EXPECTED_FILL_MASK_TARGET_RESULT = [EXPECTED_FILL_MASK_RESULT[0]]
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class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "fill-mask"
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pipeline_loading_kwargs = {"top_k": 2}
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small_models = ["sshleifer/tiny-distilroberta-base"] # Models tested without the @slow decorator
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large_models = ["distilroberta-base"] # Models tested with the @slow decorator
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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invalid_inputs = [
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"This is <mask> <mask>" # More than 1 mask_token in the input is not supported
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"This is" # No mask_token is not supported
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]
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expected_check_keys = ["sequence"]
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@require_torch
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def test_torch_fill_mask(self):
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valid_inputs = "My name is <mask>"
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unmasker = pipeline(task="fill-mask", model=self.small_models[0])
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outputs = unmasker(valid_inputs)
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self.assertIsInstance(outputs, list)
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# This passes
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outputs = unmasker(valid_inputs, targets=[" Patrick", " Clara"])
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self.assertIsInstance(outputs, list)
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# This used to fail with `cannot mix args and kwargs`
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outputs = unmasker(valid_inputs, something=False)
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self.assertIsInstance(outputs, list)
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@require_torch
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def test_torch_fill_mask_with_targets(self):
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valid_inputs = ["My name is <mask>"]
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# ' Sam' will yield a warning but work
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valid_targets = [[" Teven", "ĠPatrick", "ĠClara"], ["ĠSam"], [" Sam"]]
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invalid_targets = [[], [""], ""]
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for model_name in self.small_models:
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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for targets in valid_targets:
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outputs = unmasker(valid_inputs, targets=targets)
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self.assertIsInstance(outputs, list)
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self.assertEqual(len(outputs), len(targets))
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for targets in invalid_targets:
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self.assertRaises(ValueError, unmasker, valid_inputs, targets=targets)
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@require_torch
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@slow
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def test_torch_fill_mask_targets_equivalence(self):
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model_name = self.large_models[0]
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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unmasked = unmasker(self.valid_inputs[0])
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tokens = [top_mask["token_str"] for top_mask in unmasked]
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scores = [top_mask["score"] for top_mask in unmasked]
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unmasked_targets = unmasker(self.valid_inputs[0], targets=tokens)
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target_scores = [top_mask["score"] for top_mask in unmasked_targets]
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self.assertEqual(scores, target_scores)
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@require_torch
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def test_torch_fill_mask_with_targets_and_topk(self):
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model_name = self.small_models[0]
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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targets = [" Teven", "ĠPatrick", "ĠClara"]
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top_k = 2
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outputs = unmasker("My name is <mask>", targets=targets, top_k=top_k)
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@require_tf
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def test_small_model_tf(self):
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unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", top_k=2, framework="tf")
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outputs = unmasker("My name is <mask>")
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self.assertEqual(
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nested_simplify(outputs),
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nested_simplify(outputs, decimals=6),
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[
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{"sequence": "My name is Patrick", "score": 0.0, "token": 3499, "token_str": " Patrick"},
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{"sequence": "My name is Te", "score": 0.0, "token": 2941, "token_str": " Te"},
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{"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
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{"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
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],
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)
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outputs = unmasker("The largest city in France is <mask>")
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self.assertEqual(
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nested_simplify(outputs, decimals=6),
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[
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{
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"sequence": "The largest city in France is grouped",
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"score": 2.1e-05,
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"token": 38015,
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"token_str": " grouped",
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},
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{
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"sequence": "The largest city in France is accuser",
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"score": 2.1e-05,
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"token": 25506,
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"token_str": " accuser",
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},
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],
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)
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outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
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self.assertEqual(
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nested_simplify(outputs, decimals=6),
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[
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{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
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{"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
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{"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
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],
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)
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@require_torch
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def test_torch_fill_mask_with_duplicate_targets_and_topk(self):
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model_name = self.small_models[0]
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unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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def test_small_model_pt(self):
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unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", top_k=2, framework="pt")
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outputs = unmasker("My name is <mask>")
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self.assertEqual(
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nested_simplify(outputs, decimals=6),
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[
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{"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
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{"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
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],
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)
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outputs = unmasker("The largest city in France is <mask>")
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self.assertEqual(
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nested_simplify(outputs, decimals=6),
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[
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{
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"sequence": "The largest city in France is Maul",
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"score": 2.2e-05,
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"token": 35676,
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"token_str": " Maul",
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},
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{"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
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],
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)
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outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
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self.assertEqual(
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nested_simplify(outputs, decimals=6),
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[
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{"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
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{"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
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{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
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],
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)
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@slow
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@require_torch
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def test_large_model_pt(self):
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unmasker = pipeline(task="fill-mask", model="distilroberta-base", top_k=2, framework="pt")
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self.run_large_test(unmasker)
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@slow
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@require_tf
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def test_large_model_tf(self):
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unmasker = pipeline(task="fill-mask", model="distilroberta-base", top_k=2, framework="tf")
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self.run_large_test(unmasker)
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def run_large_test(self, unmasker):
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outputs = unmasker("My name is <mask>")
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self.assertEqual(
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nested_simplify(outputs),
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[
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{"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
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{"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
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],
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)
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outputs = unmasker("The largest city in France is <mask>")
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self.assertEqual(
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nested_simplify(outputs),
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[
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{
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"sequence": "The largest city in France is Paris",
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"score": 0.251,
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"token": 2201,
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"token_str": " Paris",
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},
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{
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"sequence": "The largest city in France is Lyon",
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"score": 0.214,
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"token": 12790,
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"token_str": " Lyon",
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},
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],
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)
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outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
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{"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
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{"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
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],
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)
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@require_torch
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def test_model_no_pad_pt(self):
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unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", framework="pt")
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unmasker.tokenizer.pad_token_id = None
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unmasker.tokenizer.pad_token = None
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self.run_pipeline_test(unmasker.model, unmasker.tokenizer)
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@require_tf
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def test_model_no_pad_tf(self):
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unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", framework="tf")
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unmasker.tokenizer.pad_token_id = None
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unmasker.tokenizer.pad_token = None
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self.run_pipeline_test(unmasker.model, unmasker.tokenizer)
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def run_pipeline_test(self, model, tokenizer):
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if tokenizer.mask_token_id is None:
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self.skipTest("The provided tokenizer has no mask token, (probably reformer)")
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fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
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outputs = fill_masker(f"This is a {tokenizer.mask_token}")
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self.assertEqual(
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outputs,
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[
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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],
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)
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outputs = fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token}"])
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self.assertEqual(
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outputs,
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[
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[
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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],
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[
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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],
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],
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)
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with self.assertRaises(ValueError):
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fill_masker([None])
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# Multiple masks
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with self.assertRaises(PipelineException):
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fill_masker(f"This is {tokenizer.mask_token} {tokenizer.mask_token}")
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# No mask_token is not supported
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with self.assertRaises(PipelineException):
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fill_masker("This is")
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self.run_test_top_k(model, tokenizer)
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self.run_test_targets(model, tokenizer)
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self.run_test_top_k_targets(model, tokenizer)
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self.fill_mask_with_duplicate_targets_and_top_k(model, tokenizer)
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def run_test_targets(self, model, tokenizer):
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vocab = tokenizer.get_vocab()
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targets = list(sorted(vocab.keys()))[:2]
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# Pipeline argument
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fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer, targets=targets)
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outputs = fill_masker(f"This is a {tokenizer.mask_token}")
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self.assertEqual(
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outputs,
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[
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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],
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)
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target_ids = {vocab[el] for el in targets}
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self.assertEqual(set(el["token"] for el in outputs), target_ids)
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self.assertEqual(set(el["token_str"] for el in outputs), set(targets))
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# Call argument
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fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
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outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=targets)
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self.assertEqual(
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outputs,
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[
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
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],
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)
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target_ids = {vocab[el] for el in targets}
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self.assertEqual(set(el["token"] for el in outputs), target_ids)
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self.assertEqual(set(el["token_str"] for el in outputs), set(targets))
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# Score equivalence
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outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=targets)
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tokens = [top_mask["token_str"] for top_mask in outputs]
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scores = [top_mask["score"] for top_mask in outputs]
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unmasked_targets = fill_masker(f"This is a {tokenizer.mask_token}", targets=tokens)
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target_scores = [top_mask["score"] for top_mask in unmasked_targets]
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self.assertEqual(nested_simplify(scores), nested_simplify(target_scores))
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# Raises with invalid
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with self.assertRaises(ValueError):
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outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=[""])
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with self.assertRaises(ValueError):
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outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=[])
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with self.assertRaises(ValueError):
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outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets="")
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def run_test_top_k(self, model, tokenizer):
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fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer, top_k=2)
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outputs = fill_masker(f"This is a {tokenizer.mask_token}")
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self.assertEqual(
|
||||
outputs,
|
||||
[
|
||||
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
|
||||
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
|
||||
],
|
||||
)
|
||||
|
||||
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
|
||||
outputs2 = fill_masker(f"This is a {tokenizer.mask_token}", top_k=2)
|
||||
self.assertEqual(
|
||||
outputs2,
|
||||
[
|
||||
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
|
||||
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
|
||||
],
|
||||
)
|
||||
self.assertEqual(nested_simplify(outputs), nested_simplify(outputs2))
|
||||
|
||||
def run_test_top_k_targets(self, model, tokenizer):
|
||||
vocab = tokenizer.get_vocab()
|
||||
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
|
||||
|
||||
# top_k=2, ntargets=3
|
||||
targets = list(sorted(vocab.keys()))[:3]
|
||||
outputs = fill_masker(f"This is a {tokenizer.mask_token}", top_k=2, targets=targets)
|
||||
|
||||
# If we use the most probably targets, and filter differently, we should still
|
||||
# have the same results
|
||||
targets2 = [el["token_str"] for el in sorted(outputs, key=lambda x: x["score"], reverse=True)]
|
||||
outputs2 = fill_masker(f"This is a {tokenizer.mask_token}", top_k=3, targets=targets2)
|
||||
|
||||
# They should yield exactly the same result
|
||||
self.assertEqual(nested_simplify(outputs), nested_simplify(outputs2))
|
||||
|
||||
def fill_mask_with_duplicate_targets_and_top_k(self, model, tokenizer):
|
||||
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
|
||||
vocab = tokenizer.get_vocab()
|
||||
# String duplicates + id duplicates
|
||||
targets = [" Teven", "ĠPatrick", "ĠClara", "ĠClara", " Clara"]
|
||||
top_k = 10
|
||||
outputs = unmasker("My name is <mask>", targets=targets, top_k=top_k)
|
||||
targets = list(sorted(vocab.keys()))[:3]
|
||||
targets = [targets[0], targets[1], targets[0], targets[2], targets[1]]
|
||||
outputs = fill_masker(f"My name is {tokenizer.mask_token}", targets=targets, top_k=10)
|
||||
|
||||
# The target list contains duplicates, so we can't output more
|
||||
# than them
|
||||
self.assertEqual(len(outputs), 3)
|
||||
|
||||
@require_tf
|
||||
def test_tf_fill_mask_with_targets(self):
|
||||
valid_inputs = ["My name is <mask>"]
|
||||
# ' Teven' will yield a warning but work as " Te"
|
||||
invalid_targets = [[], [""], ""]
|
||||
unmasker = pipeline(
|
||||
task="fill-mask", model=self.small_models[0], tokenizer=self.small_models[0], framework="tf"
|
||||
)
|
||||
outputs = unmasker(valid_inputs, targets=[" Teven", "ĠPatrick", "ĠClara"])
|
||||
self.assertEqual(
|
||||
nested_simplify(outputs),
|
||||
[
|
||||
{"sequence": "My name is Clara", "score": 0.0, "token": 13606, "token_str": " Clara"},
|
||||
{"sequence": "My name is Patrick", "score": 0.0, "token": 3499, "token_str": " Patrick"},
|
||||
{"sequence": "My name is Te", "score": 0.0, "token": 2941, "token_str": " Te"},
|
||||
],
|
||||
)
|
||||
# topk
|
||||
outputs = unmasker(valid_inputs, targets=[" Teven", "ĠPatrick", "ĠClara"], top_k=2)
|
||||
self.assertEqual(
|
||||
nested_simplify(outputs),
|
||||
[
|
||||
{"sequence": "My name is Clara", "score": 0.0, "token": 13606, "token_str": " Clara"},
|
||||
{"sequence": "My name is Patrick", "score": 0.0, "token": 3499, "token_str": " Patrick"},
|
||||
],
|
||||
)
|
||||
for targets in invalid_targets:
|
||||
with self.assertRaises(ValueError):
|
||||
unmasker(valid_inputs, targets=targets)
|
||||
|
||||
@require_torch
|
||||
@slow
|
||||
def test_torch_fill_mask_results(self):
|
||||
mandatory_keys = {"sequence", "score", "token"}
|
||||
valid_inputs = [
|
||||
"My name is <mask>",
|
||||
"The largest city in France is <mask>",
|
||||
]
|
||||
valid_targets = ["ĠPatrick", "ĠClara"]
|
||||
for model_name in self.large_models:
|
||||
unmasker = pipeline(
|
||||
task="fill-mask",
|
||||
model=model_name,
|
||||
tokenizer=model_name,
|
||||
framework="pt",
|
||||
top_k=2,
|
||||
)
|
||||
|
||||
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 = [unmasker(valid_input) for valid_input in valid_inputs]
|
||||
self.assertIsInstance(multi_result, list)
|
||||
self.assertIsInstance(multi_result[0], (dict, list))
|
||||
|
||||
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
|
||||
for r, e in zip(result, expected):
|
||||
self.assertEqual(r["sequence"], e["sequence"])
|
||||
self.assertEqual(r["token_str"], e["token_str"])
|
||||
self.assertEqual(r["token"], e["token"])
|
||||
self.assertAlmostEqual(r["score"], e["score"], places=3)
|
||||
|
||||
if isinstance(multi_result[0], list):
|
||||
multi_result = multi_result[0]
|
||||
|
||||
for result in multi_result:
|
||||
for key in mandatory_keys:
|
||||
self.assertIn(key, result)
|
||||
|
||||
self.assertRaises(Exception, unmasker, [None])
|
||||
|
||||
valid_inputs = valid_inputs[:1]
|
||||
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 = [unmasker(valid_input) for valid_input in valid_inputs]
|
||||
self.assertIsInstance(multi_result, list)
|
||||
self.assertIsInstance(multi_result[0], (dict, list))
|
||||
|
||||
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
|
||||
for r, e in zip(result, expected):
|
||||
self.assertEqual(r["sequence"], e["sequence"])
|
||||
self.assertEqual(r["token_str"], e["token_str"])
|
||||
self.assertEqual(r["token"], e["token"])
|
||||
self.assertAlmostEqual(r["score"], e["score"], places=3)
|
||||
|
||||
if isinstance(multi_result[0], list):
|
||||
multi_result = multi_result[0]
|
||||
|
||||
for result in multi_result:
|
||||
for key in mandatory_keys:
|
||||
self.assertIn(key, result)
|
||||
|
||||
self.assertRaises(Exception, unmasker, [None])
|
||||
|
||||
@require_tf
|
||||
@slow
|
||||
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>",
|
||||
]
|
||||
valid_targets = ["ĠPatrick", "ĠClara"]
|
||||
for model_name in self.large_models:
|
||||
unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", top_k=2)
|
||||
|
||||
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 = [unmasker(valid_input) for valid_input in valid_inputs]
|
||||
self.assertIsInstance(multi_result, list)
|
||||
self.assertIsInstance(multi_result[0], (dict, list))
|
||||
|
||||
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
|
||||
for r, e in zip(result, expected):
|
||||
self.assertEqual(r["sequence"], e["sequence"])
|
||||
self.assertEqual(r["token_str"], e["token_str"])
|
||||
self.assertEqual(r["token"], e["token"])
|
||||
self.assertAlmostEqual(r["score"], e["score"], places=3)
|
||||
|
||||
if isinstance(multi_result[0], list):
|
||||
multi_result = multi_result[0]
|
||||
|
||||
for result in multi_result:
|
||||
for key in mandatory_keys:
|
||||
self.assertIn(key, result)
|
||||
|
||||
self.assertRaises(Exception, unmasker, [None])
|
||||
|
||||
valid_inputs = valid_inputs[:1]
|
||||
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 = [unmasker(valid_input) for valid_input in valid_inputs]
|
||||
self.assertIsInstance(multi_result, list)
|
||||
self.assertIsInstance(multi_result[0], (dict, list))
|
||||
|
||||
for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
|
||||
for r, e in zip(result, expected):
|
||||
self.assertEqual(r["sequence"], e["sequence"])
|
||||
self.assertEqual(r["token_str"], e["token_str"])
|
||||
self.assertEqual(r["token"], e["token"])
|
||||
self.assertAlmostEqual(r["score"], e["score"], places=3)
|
||||
|
||||
if isinstance(multi_result[0], list):
|
||||
multi_result = multi_result[0]
|
||||
|
||||
for result in multi_result:
|
||||
for key in mandatory_keys:
|
||||
self.assertIn(key, result)
|
||||
|
||||
self.assertRaises(Exception, unmasker, [None])
|
||||
|
||||
@require_tf
|
||||
@slow
|
||||
def test_tf_fill_mask_targets_equivalence(self):
|
||||
model_name = self.large_models[0]
|
||||
unmasker = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf")
|
||||
unmasked = unmasker(self.valid_inputs[0])
|
||||
tokens = [top_mask["token_str"] for top_mask in unmasked]
|
||||
scores = [top_mask["score"] for top_mask in unmasked]
|
||||
|
||||
unmasked_targets = unmasker(self.valid_inputs[0], targets=tokens)
|
||||
target_scores = [top_mask["score"] for top_mask in unmasked_targets]
|
||||
|
||||
self.assertEqual(scores, target_scores)
|
||||
|
||||
Reference in New Issue
Block a user