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[DOC] Clarify relationshi load_best_model_at_end and save_total_limit (#24614)

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* Update training_args.py

Clarify the relationship between `load_best_model_at_end` and `save_total_limit`.

* fix: faulty quotes

* make quality

* Update src/transformers/training_args.py

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

* DOCS: add explicit `True`

* DOCS: make style/quality

---------

Co-authored-by: Bram Vanroy <Bram.Vanroy@UGent.be>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Bram Vanroy
2023-07-13 13:36:16 +02:00
committed by GitHub
parent 21946a8cf4
commit 6ba4d5de3a
1 changed files with 22 additions and 6 deletions
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src/transformers/training_args.py
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@@ -283,7 +283,11 @@ class TrainingArguments:
float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
save_total_limit (`int`, *optional*):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
`output_dir`.
`output_dir`. When `load_best_model_at_end` is enabled, the "best" checkpoint according to
`metric_for_best_model` will always be retained in addition to the most recent ones. For example, for
`save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained
alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two
checkpoints are saved: the last one and the best one (if they are different).
save_safetensors (`bool`, *optional*, defaults to `False`):
Use [safetensors](https://huggingface.co/docs/safetensors) saving and loading for state dicts instead of
default `torch.load` and `torch.save`.
@@ -371,7 +375,10 @@ class TrainingArguments:
except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the
`["start_positions", "end_positions"]` keys.
load_best_model_at_end (`bool`, *optional*, defaults to `False`):
Whether or not to load the best model found during training at the end of training.
Whether or not to load the best model found during training at the end of training. When this option is
enabled, the best checkpoint will always be saved. See
[`save_total_limit`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.save_total_limit)
for more.
<Tip>
@@ -761,8 +768,13 @@ class TrainingArguments:
default=None,
metadata={
"help": (
"Limit the total amount of checkpoints. "
"Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints"
"If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in"
" `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to"
" `metric_for_best_model` will always be retained in addition to the most recent ones. For example,"
" for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be"
" retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`,"
" it is possible that two checkpoints are saved: the last one and the best one (if they are different)."
" Default is unlimited checkpoints"
)
},
)
@@ -924,10 +936,14 @@ class TrainingArguments:
label_names: Optional[List[str]] = field(
default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."}
)
load_best_model_at_end: Optional[bool] = field(
default=False,
metadata={"help": "Whether or not to load the best model found during training at the end of training."},
metadata={
"help": (
"Whether or not to load the best model found during training at the end of training. When this option"
" is enabled, the best checkpoint will always be saved. See `save_total_limit` for more."
)
},
)
metric_for_best_model: Optional[str] = field(
default=None, metadata={"help": "The metric to use to compare two different models."}