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Doc styling (#8067)

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This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

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* Fix modeling auto and script

* Remove new line

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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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31
src/transformers/training_args.py
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@@ -35,11 +35,11 @@ def default_logdir() -> str:
@dataclass
class TrainingArguments:
"""
TrainingArguments is the subset of the arguments we use in our example scripts
**which relate to the training loop itself**.
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using :class:`~transformers.HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on the command line.
Using :class:`~transformers.HfArgumentParser` we can turn this class into argparse arguments to be able to specify
them on the command line.
Parameters:
output_dir (:obj:`str`):
@@ -128,7 +128,8 @@ class TrainingArguments:
Number of update steps between two evaluations if :obj:`evaluation_strategy="steps"`. Will default to the
same value as :obj:`logging_steps` if not set.
dataloader_num_workers (:obj:`int`, `optional`, defaults to 0):
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.
past_index (:obj:`int`, `optional`, defaults to -1):
Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
make use of the past hidden states for their predictions. If this argument is set to a positive int, the
@@ -143,15 +144,14 @@ class TrainingArguments:
If using `nlp.Dataset` datasets, whether or not to automatically remove the columns unused by the model
forward method.
(Note: this behavior is not implemented for :class:`~transformers.TFTrainer` yet.)
label_names (:obj:`List[str]`, `optional`):
The list of keys in your dictionary of inputs that correspond to the labels.
(Note that this behavior is not implemented for :class:`~transformers.TFTrainer` yet.) label_names
(:obj:`List[str]`, `optional`): The list of keys in your dictionary of inputs that correspond to the
labels.
Will eventually default to :obj:`["labels"]` except if the model used is one of the
:obj:`XxxForQuestionAnswering` in which case it will default to
:obj:`["start_positions", "end_positions"]`.
load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to load the best model found during training at the end of training.
:obj:`XxxForQuestionAnswering` in which case it will default to :obj:`["start_positions",
"end_positions"]`. load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or
not to load the best model found during training at the end of training.
.. note::
@@ -164,10 +164,9 @@ class TrainingArguments:
loss).
If you set this value, :obj:`greater_is_better` will default to :obj:`True`. Don't forget to set it to
:obj:`False` if your metric is better when lower.
greater_is_better (:obj:`bool`, `optional`)
Use in conjunction with :obj:`load_best_model_at_end` and :obj:`metric_for_best_model` to specify if better
models should have a greater metric or not. Will default to:
:obj:`False` if your metric is better when lower. greater_is_better (:obj:`bool`, `optional`) Use in
conjunction with :obj:`load_best_model_at_end` and :obj:`metric_for_best_model` to specify if better models
should have a greater metric or not. Will default to:
- :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or
:obj:`"eval_loss"`.