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Doc styling (#8067)

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* Important files

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* 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

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* Add FSMT

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* 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
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29
src/transformers/generation_utils.py
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@@ -150,8 +150,8 @@ class GenerationMixin:
Parameters:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
The sequence used as a prompt for the generation. If :obj:`None` the method initializes
it as an empty :obj:`torch.LongTensor` of shape :obj:`(1,)`.
The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
:obj:`torch.LongTensor` of shape :obj:`(1,)`.
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
initial input_ids for the decoder of encoder-decoder type models. If :obj:`None` then only
decoder_start_token_id is passed as the first token to the decoder.
@@ -210,9 +210,9 @@ class GenerationMixin:
Return:
:obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`:
The generated sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or
shorter if all batches finished early due to the :obj:`eos_token_id`.
:obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all
batches finished early due to the :obj:`eos_token_id`.
Examples::
@@ -531,8 +531,9 @@ class GenerationMixin:
use_cache,
model_kwargs,
):
"""Generate sequences for each example without beam search (num_beams == 1).
All returned sequence are generated independantly.
"""
Generate sequences for each example without beam search (num_beams == 1). All returned sequence are generated
independantly.
"""
# length of generated sentences / unfinished sentences
unfinished_sents = input_ids.new(batch_size).fill_(1)
@@ -935,8 +936,10 @@ def calc_banned_bad_words_ids(prev_input_ids: Iterable[int], bad_words_ids: Iter
def set_scores_to_inf_for_banned_tokens(scores: torch.Tensor, banned_tokens: List[List[int]]) -> None:
"""Modifies the scores in place by setting the banned token positions to `-inf`. Banned token is expected to be
a list of list of banned tokens to ban in the format [[batch index, vocabulary position],...]
"""
Modifies the scores in place by setting the banned token positions to `-inf`. Banned token is expected to be a list
of list of banned tokens to ban in the format [[batch index, vocabulary position],...
Args:
scores: logits distribution of shape (batch size, vocabulary size)
banned_tokens: list of list of tokens to ban of length (batch_size)
@@ -965,7 +968,9 @@ def top_k_top_p_filtering(
filter_value: float = -float("Inf"),
min_tokens_to_keep: int = 1,
) -> Tensor:
"""Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
"""
Filter a distribution of logits using top-k and/or nucleus (top-p) filterin
Args:
logits: logits distribution shape (batch size, vocabulary size)
if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
@@ -1033,8 +1038,8 @@ class BeamHypotheses(object):
def is_done(self, best_sum_logprobs, cur_len):
"""
If there are enough hypotheses and that none of the hypotheses being generated
can become better than the worst one in the heap, then we are done with this sentence.
If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
one in the heap, then we are done with this sentence.
"""
if len(self) < self.num_beams: