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[breaking|pipelines|tokenizers] Adding slow-fast tokenizers equivalence tests pipelines - Removing sentencepiece as a required dependency (#8073)

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* Fixing roberta for slow-fast tests

* WIP getting equivalence on pipelines

* slow-to-fast equivalence - working on question-answering pipeline

* optional FAISS tests

* Pipeline Q&A

* Move pipeline tests to their own test job again

* update tokenizer to add sequence id methods

* update to tokenizers 0.9.4

* set sentencepiecce as optional

* clean up squad

* clean up pipelines to use sequence_ids

* style/quality

* wording

* Switch to use_fast = True by default

* update tests for use_fast at True by default

* fix rag tokenizer test

* removing protobuf from required dependencies

* fix NER test for use_fast = True by default

* fixing example tests (Q&A examples use slow tokenizers for now)

* protobuf in main deps extras["sentencepiece"] and example deps

* fix protobug install test

* try to fix seq2seq by switching to slow tokenizers for now

* Update src/transformers/tokenization_utils_base.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/tokenization_utils_base.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Thomas Wolf
2020-11-15 22:50:59 +01:00
committed by GitHub
parent 24184e73c4
commit f4e04cd2c6
23 changed files with 689 additions and 262 deletions
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12
tests/test_pipelines_question_answering.py
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@@ -8,10 +8,22 @@ from .test_pipelines_common import CustomInputPipelineCommonMixin
class QAPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
pipeline_task = "question-answering"
pipeline_running_kwargs = {
"padding": "max_length",
"max_seq_len": 25,
"doc_stride": 5,
} # Default is 'longest' but we use 'max_length' to test equivalence between slow/fast tokenizers
small_models = [
"sshleifer/tiny-distilbert-base-cased-distilled-squad"
] # Models tested without the @slow decorator
large_models = [] # Models tested with the @slow decorator
valid_inputs = [
{"question": "Where was HuggingFace founded ?", "context": "HuggingFace was founded in Paris."},
{
"question": "In what field is HuggingFace working ?",
"context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
},
]
def _test_pipeline(self, nlp: Pipeline):
output_keys = {"score", "answer", "start", "end"}