Doc styling (#8067)

* Important files

* Styling them all

* Revert "Styling them all"

This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

* More fixes

* Fix modeling auto and script

* Remove new line

* Fixes

* More fixes

* Fix more files

* Style

* Add FSMT

* More fixes

* More fixes

* More fixes

* More fixes

* Fixes

* More fixes

* More fixes

* Last fixes

* Make sphinx happy
This commit is contained in:
Sylvain Gugger
2020-10-26 18:26:02 -04:00
committed by GitHub
parent 04a17f8550
commit 08f534d2da
271 changed files with 9726 additions and 8991 deletions

View File

@@ -29,11 +29,12 @@ methods for using all the tokenizers:
:class:`~transformers.BatchEncoding` holds the output of the tokenizer's encoding methods (``__call__``,
``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by these
methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by HuggingFace
`tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition several advanced
alignment methods which can be used to map between the original string (character and words) and the token space (e.g.,
getting the index of the token comprising a given character or the span of characters corresponding to a given token).
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
these methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by
HuggingFace `tokenizers library <https://github.com/huggingface/tokenizers>`__), this class provides in addition
several advanced alignment methods which can be used to map between the original string (character and words) and the
token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
to a given token).
PreTrainedTokenizer