transformer_discord_notifier

class transformer_discord_notifier.DiscordProgressCallback(token: Optional[str] = None, channel: Optional[Union[str, int]] = None)[source]
on_epoch_begin(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the beginning of an epoch.

on_epoch_end(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the end of an epoch.

on_evaluate(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called after an evaluation phase.

on_init_end(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the end of the initialization of the Trainer.

on_log(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called after logging the last logs.

on_prediction_step(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called after a prediction step.

on_save(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called after a checkpoint save.

on_step_begin(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the beginning of a training step. If using gradient accumulation, one training step might take several inputs.

on_step_end(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the end of a training step. If using gradient accumulation, one training step might take several inputs.

on_train_begin(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the beginning of training.

on_train_end(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs)[source]

Event called at the end of training.

class transformer_discord_notifier.MyClient(*, loop=None, **options)[source]