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Deprecate prepare_seq2seq_batch (#10287)

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* Deprecate prepare_seq2seq_batch

* Fix last tests

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* More review comments

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
This commit is contained in:
Sylvain Gugger
2021-02-22 12:36:16 -05:00
committed by GitHub
parent e73a3e1891
commit 9e147d31f6
31 changed files with 325 additions and 320 deletions
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28
docs/source/model_doc/pegasus.rst
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@@ -78,20 +78,20 @@ Usage Example
.. code-block:: python
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
import torch
src_text = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
]
>>> from transformers import PegasusForConditionalGeneration, PegasusTokenizer
>>> import torch
>>> src_text = [
... """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
>>> ]
model_name = 'google/pegasus-xsum'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors="pt").to(torch_device)
translated = model.generate(**batch)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
>>> model_name = 'google/pegasus-xsum'
>>> device = 'cuda' if torch.cuda.is_available() else 'cpu'
>>> tokenizer = PegasusTokenizer.from_pretrained(model_name)
>>> model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)
>>> batch = tokenizer(src_text, truncation=True, padding='longest', return_tensors="pt").to(torch_device)
>>> translated = model.generate(**batch)
>>> tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
>>> assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."
@@ -107,7 +107,7 @@ PegasusTokenizer
warning: ``add_tokens`` does not work at the moment.
.. autoclass:: transformers.PegasusTokenizer
:members: __call__, prepare_seq2seq_batch
:members:
PegasusTokenizerFast