Skip to content

Trainer / Core : Do not change init signature order #30126

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
younesbelkada merged 2 commits into from
Apr 8, 2024
Merged

Trainer / Core : Do not change init signature order #30126

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6075cab
Update trainer.py
younesbelkada Apr 8, 2024
7928d25
fix copies
younesbelkada Apr 8, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions src/transformers/trainer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -304,9 +304,6 @@ class Trainer:
The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the
maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
interrupted training or reuse the fine-tuned model.
image_processor ([`BaseImageProcessor`], *optional*):
The image processor used to preprocess the data. If provided, it will be saved along the model to make it easier
to rerun an interrupted training or reuse the fine-tuned model.
model_init (`Callable[[], PreTrainedModel]`, *optional*):
A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start
from a new instance of the model as given by this function.
Expand All @@ -331,6 +328,9 @@ class Trainer:
by this function will be reflected in the predictions received by `compute_metrics`.

Note that the labels (second parameter) will be `None` if the dataset does not have them.
image_processor ([`BaseImageProcessor`], *optional*):
The image processor used to preprocess the data. If provided, it will be saved along the model to make it easier
to rerun an interrupted training or reuse the fine-tuned model.

Important attributes:

Expand Down Expand Up @@ -361,12 +361,12 @@ def __init__(
train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
image_processor: Optional["BaseImageProcessor"] = None,
model_init: Optional[Callable[[], PreTrainedModel]] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
image_processor: Optional["BaseImageProcessor"] = None,
):
if args is None:
output_dir = "tmp_trainer"
Expand Down