Implementing Triplet loss in Vit

[John6666]

I think overriding the function is the most reliable way.

Specify Loss for Trainer / TrainingArguments

I’d like to fine-tune for a regression task rather than a classification task. How do I change the default loss in either TrainingArguments or Trainer()?

https://stackoverflow.com/questions/66302371/how-to-specify-the-loss-function-when-finetuning-a-model-using-the-huggingface-t

Trainer.evaluate() doesn't return evaluation loss

I am using Seq2SeqTrainer to lora finetune madlad 3b model using my own dataset. When I run trainer.evaluate(), the evaluation loss is not printing. Code: output_dir = "madlad_run_5" training_args = Seq2SeqTrainingArguments( output_dir, per_device_train_batch_size=32, # per_device_eval_batch_size=2, num_train_epochs=1, # max_steps=1000, # gradient_accumulation_steps=8, bf16=True, torch_compile=True, # gradient_checkpointing=True, # torch_empty_cache_st…