I am training a model using HuggingFace Trainer class.(GPT2 text Classification) The following code does a decent job:

def preprocess_function(examples):
    return tokenizer(examples["text"], truncation=True ,max_length=MAXLEN,
                     padding=True
   )
    
dataset_train = Dataset.from_pandas(train_sp , preserve_index=False)
dataset_val = Dataset.from_pandas(val_sp ,preserve_index=False)

dataset_train = dataset_train.map(preprocess_function, batched=True,load_from_cache_file=False)
dataset_val = dataset_val.map(preprocess_function, batched=True,load_from_cache_file=False)


columns_to_return = ['input_ids', 'label', 'attention_mask']
dataset_train.set_format(type='torch', columns=columns_to_return)
dataset_val.set_format(type='torch', columns=columns_to_return)


data_collator = DataCollatorWithPadding(tokenizer=tokenizer  )

training_args = TrainingArguments(
    output_dir="/content/Model1", #The output directory
    overwrite_output_dir=True, #overwrite the content of the output directory
    num_train_epochs=3, # number of training epochs
    per_device_train_batch_size=16, # batch size for training
    per_device_eval_batch_size=8,  # batch size for evaluation
    eval_steps = 400, # Number of update steps between two evaluations.
    save_steps=800, # after # steps model is saved 
    warmup_steps=500,# number of warmup steps for learning rate scheduler
    prediction_loss_only=True,
    #remove_unused_columns=True
    )

#---------------------------------------------------#
trainer = Trainer(
    model=model1,
    args=training_args,    
    #data_collator=gpt2_classificaiton_collator,
    
    train_dataset=dataset_train,
    eval_dataset=dataset_val,
   
    tokenizer=tokenizer,
    data_collator=data_collator
)

trainer.train()

I got error _forward_unimplemented() got an unexpected keyword argument 'input_ids'

what should I do?

Input_ids and label

error mg

My Model argiteture