Hey! I am trying to continue training by loading a checkpoint. But for some reason, it always starts from scratch. Probably I am just missing something.
training_arguments = Seq2SeqTrainingArguments(
            predict_with_generate=True,
            evaluation_strategy='steps',
            per_device_train_batch_size=training_config['per_device_train_batch_size'],
            per_device_eval_batch_size=training_config['per_device_eval_batch_size'],
            fp16=True,
            output_dir=training_output_path,
            overwrite_output_dir=True,
            logging_steps=training_config['logging_steps'],
            save_steps=training_config['save_steps'],
            eval_steps=training_config['eval_steps'],
            warmup_steps=training_config['warmup_steps'],
            metric_for_best_model='eval_loss',
            greater_is_better=False)

trainer = Seq2SeqTrainer(
            model=model,
            tokenizer=tokenizer,
            args=training_arguments,
            compute_metrics=compute_metrics,
            train_dataset=train_ds,
            eval_dataset=eval_ds,
        )

Here are the logs:
loading weights file .../models/checkpoint-2000/pytorch_model.bin
All model checkpoint weights were used when initializing EncoderDecoderModel.
***** Running training *****
  Num examples = 222862
  Num Epochs = 3
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 8
  Gradient Accumulation steps = 1
  Total optimization steps = 83574

I am missing some like:
Continuing training from checkpoint, will skip to saved global_step
Continuing training from epoch 0
Continuing training from global step 48000
Continuing training from 0 non-embedding floating-point operations
Will skip the first 48000 steps in the first epoch

Which I found here: Load from checkpoint not skipping steps - Transformers - Hugging Face Forums 22
Maybe somebody can help me? Thank you in advance!