transformer_discord_notifier¶
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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.
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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.
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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.
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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
.
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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.
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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.
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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.
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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.
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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.
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