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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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2
tests/pipelines/test_pipelines_common.py
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@@ -143,7 +143,7 @@ class CommonPipelineTest(unittest.TestCase):
self.assertIsInstance(text_classifier, MyPipeline)
def test_check_task(self):
task = get_task("gpt2")
task = get_task("openai-community/gpt2")
self.assertEqual(task, "text-generation")
with self.assertRaises(RuntimeError):

4
tests/pipelines/test_pipelines_fill_mask.py
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@@ -169,13 +169,13 @@ class FillMaskPipelineTests(unittest.TestCase):
@slow
@require_torch
def test_large_model_pt(self):
unmasker = pipeline(task="fill-mask", model="distilroberta-base", top_k=2, framework="pt")
unmasker = pipeline(task="fill-mask", model="distilbert/distilroberta-base", top_k=2, framework="pt")
self.run_large_test(unmasker)
@slow
@require_tf
def test_large_model_tf(self):
unmasker = pipeline(task="fill-mask", model="distilroberta-base", top_k=2, framework="tf")
unmasker = pipeline(task="fill-mask", model="distilbert/distilroberta-base", top_k=2, framework="tf")
self.run_large_test(unmasker)
def run_large_test(self, unmasker):

2
tests/pipelines/test_pipelines_token_classification.py
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@@ -468,7 +468,7 @@ class TokenClassificationPipelineTests(unittest.TestCase):
@slow
def test_aggregation_strategy_byte_level_tokenizer(self):
sentence = "Groenlinks praat over Schiphol."
ner = pipeline("ner", model="xlm-roberta-large-finetuned-conll02-dutch", aggregation_strategy="max")
ner = pipeline("ner", model="FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", aggregation_strategy="max")
self.assertEqual(
nested_simplify(ner(sentence)),
[

8
tests/pipelines/test_pipelines_zero_shot.py
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@@ -199,7 +199,9 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase):
@slow
@require_torch
def test_large_model_pt(self):
zero_shot_classifier = pipeline("zero-shot-classification", model="roberta-large-mnli", framework="pt")
zero_shot_classifier = pipeline(
"zero-shot-classification", model="FacebookAI/roberta-large-mnli", framework="pt"
)
outputs = zero_shot_classifier(
"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
)
@@ -254,7 +256,9 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase):
@slow
@require_tf
def test_large_model_tf(self):
zero_shot_classifier = pipeline("zero-shot-classification", model="roberta-large-mnli", framework="tf")
zero_shot_classifier = pipeline(
"zero-shot-classification", model="FacebookAI/roberta-large-mnli", framework="tf"
)
outputs = zero_shot_classifier(
"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
)