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[tokenizer.prepare_seq2seq_batch] change deprecation to be easily actionable (#12669)

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* change deprecation to be easily actionable

* Update src/transformers/tokenization_utils_base.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* rework as suggested

* one warning together

* fix format

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Stas Bekman
2021-07-13 09:19:04 -07:00
committed by GitHub
parent 711d901c49
commit 7a22a02a70
1 changed files with 17 additions and 7 deletions
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24
src/transformers/tokenization_utils_base.py
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@@ -3274,13 +3274,23 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
The full set of keys ``[input_ids, attention_mask, labels]``, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.
"""
warnings.warn(
"`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
"regular `__call__` method to prepare your inputs and the tokenizer under the `as_target_tokenizer` "
"context manager to prepare your targets. See the documentation of your specific tokenizer for more "
"details",
FutureWarning,
)
# docstyle-ignore
formatted_warning = """
`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of HuggingFace Transformers. Use the regular
`__call__` method to prepare your inputs and the tokenizer under the `as_target_tokenizer` context manager to prepare
your targets.
Here is a short example:
model_inputs = tokenizer(src_texts, ...)
with tokenizer.as_target_tokenizer():
labels = tokenizer(tgt_texts, ...)
model_inputs["labels"] = labels["input_ids"]
See the documentation of your specific tokenizer for more details on the specific arguments to the tokenizer of choice.
For a more complete example, see the implementation of `prepare_seq2seq_batch`.
"""
warnings.warn(formatted_warning, FutureWarning)
# mBART-specific kwargs that should be ignored by other models.
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)