Facing an error when trying to test my model
I trained my model on a longformer, but when I want to test my model, I face this error.My model:model = LongformerForSequenceClassification.from_pretrained('yikuan8/Clinical-Longformer',num_labels = len(cols))
ltokenizer = LongformerTokenizerFast.from_pretrained('yikuan8/Clinical-Longformer')This is how I perform predictions:# Put model in evaluation mode
device = "cuda"
model = trainer.model.to("cuda") # moving model to cuda
model.eval()

# Tracking variables
pred_outs, true_labels = [], []
#i=0
# Predict
for batch in validation_dataset:
    # Unpack the inputs from our dataloader
    #batch = tuple(batch[t].to(device,) for t in batch)
    #b_input_ids, b_attn_mask, 
    b_labels = batch['labels']

    with torch.no_grad():
        # Forward pass, calculate logit predictions
        pred_out = trainer.model(input_ids=batch['input_ids'],
            attention_mask=batch['attention_mask'])#,labels=batch['labels'])
        pred_out = torch.sigmoid(pred_out)
        # Move predicted output and labels to CPU
        pred_out = pred_out.detach().cpu().numpy()
        label_ids = b_labels.to('cpu').numpy()
        #i+=1
        # Store predictions and true labels
        #print(i)
        #print(outputs)
        #print(logits)
        #print(label_ids)
    pred_outs.append(pred_out)
    true_labels.append(label_ids)My error:---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[127], line 18
     14 b_labels = batch['labels']
     16 with torch.no_grad():
     17     # Forward pass, calculate logit predictions
---> 18     pred_out = trainer.model(input_ids=batch['input_ids'],
     19         attention_mask=batch['attention_mask'])#,labels=batch['labels'])
     20     pred_out = torch.sigmoid(pred_out)
     21     # Move predicted output and labels to CPU

File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/conda/lib/python3.10/site-packages/accelerate/utils/operations.py:632, in convert_outputs_to_fp32.<locals>.forward(*args, **kwargs)
    631 def forward(*args, **kwargs):
--> 632     return model_forward(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/accelerate/utils/operations.py:620, in ConvertOutputsToFp32.__call__(self, *args, **kwargs)
    619 def __call__(self, *args, **kwargs):
--> 620     return convert_to_fp32(self.model_forward(*args, **kwargs))

File /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:14, in autocast_decorator.<locals>.decorate_autocast(*args, **kwargs)
     11 @functools.wraps(func)
     12 def decorate_autocast(*args, **kwargs):
     13     with autocast_instance:
---> 14         return func(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/accelerate/utils/operations.py:632, in convert_outputs_to_fp32.<locals>.forward(*args, **kwargs)
    631 def forward(*args, **kwargs):
--> 632     return model_forward(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/accelerate/utils/operations.py:620, in ConvertOutputsToFp32.__call__(self, *args, **kwargs)
    619 def __call__(self, *args, **kwargs):
--> 620     return convert_to_fp32(self.model_forward(*args, **kwargs))

File /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:14, in autocast_decorator.<locals>.decorate_autocast(*args, **kwargs)
     11 @functools.wraps(func)
     12 def decorate_autocast(*args, **kwargs):
     13     with autocast_instance:
---> 14         return func(*args, **kwargs)

    [... skipping similar frames: ConvertOutputsToFp32.__call__ at line 620 (10 times), autocast_decorator.<locals>.decorate_autocast at line 14 (10 times), convert_outputs_to_fp32.<locals>.forward at line 632 (10 times)]

File /opt/conda/lib/python3.10/site-packages/accelerate/utils/operations.py:632, in convert_outputs_to_fp32.<locals>.forward(*args, **kwargs)
    631 def forward(*args, **kwargs):
--> 632     return model_forward(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/accelerate/utils/operations.py:620, in ConvertOutputsToFp32.__call__(self, *args, **kwargs)
    619 def __call__(self, *args, **kwargs):
--> 620     return convert_to_fp32(self.model_forward(*args, **kwargs))

File /opt/conda/lib/python3.10/site-packages/torch/amp/autocast_mode.py:14, in autocast_decorator.<locals>.decorate_autocast(*args, **kwargs)
     11 @functools.wraps(func)
     12 def decorate_autocast(*args, **kwargs):
     13     with autocast_instance:
---> 14         return func(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py:1932, in LongformerForSequenceClassification.forward(self, input_ids, attention_mask, global_attention_mask, head_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
   1930     global_attention_mask = torch.zeros_like(input_ids)
   1931     # global attention on cls token
-> 1932     global_attention_mask[:, 0] = 1
   1934 outputs = self.longformer(
   1935     input_ids,
   1936     attention_mask=attention_mask,
   (...)
   1944     return_dict=return_dict,
   1945 )
   1946 sequence_output = outputs[0]

IndexError: too many indices for tensor of dimension 1and this is other parts of my code:test_encodings = ltokenizer(ds_test.text.tolist(), add_special_tokens=True,
            max_length= 1024,
            padding = 'max_length',
            return_token_type_ids= False,
            return_attention_mask= True,
            truncation= True)

class ReadDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.labels)

train_dataset = ReadDataset(train_encodings, ds_train[cols].values)
validation_dataset = ReadDataset(validation_encodings, ds_val[cols].values)
test_dataset = ReadDataset(test_encodings, ds_test[cols].values)

class MultilabelTrainer(Trainer):
    def compute_loss(self, model, inputs, return_outputs=False):
        labels = inputs.get("labels")
        outputs = model(input_ids=inputs['input_ids'],
            attention_mask=inputs['attention_mask'])
        logits = outputs.get('logits')
        loss_fct = nn.BCEWithLogitsLoss()
        loss = loss_fct(logits.view(-1, self.model.config.num_labels).float(),
                        labels.float().view(-1, self.model.config.num_labels))
        return (loss, outputs) if return_outputs else loss

def sigmoid(x):  
    return np.exp(-np.logaddexp(0, -x))

def compute_metrics(eval_preds):
    logits, labels = eval_preds
    res = {}
    for i, d in enumerate(cols):
        res[d] = roc_auc_score(labels[:,i], sigmoid(logits[:,i]))
    return res

training_args = TrainingArguments(
    output_dir='result',          # output directory
    num_train_epochs=6,              # total number of training epochs
    per_device_train_batch_size=4,  # batch size per device during training
    per_device_eval_batch_size=8,   # batch size for evaluation
    evaluation_strategy='epoch',
    learning_rate=2e-5,
    fp16=True,
    fp16_backend="amp"    
)

trainer = MultilabelTrainer(
    model=model,                         # the instantiated 🤗 Transformers model to be trained
    args=training_args,                  # training arguments, defined above
    train_dataset=train_dataset,         # training dataset
    eval_dataset=validation_dataset,
    compute_metrics=compute_metrics,
)

trainer.train()Thanks
2023-09-08T17:38:26Z
trainer.predict(test_dataset)after many searches I found out I should do it in this waythis is the linkhttps://huggingface.co/learn/nlp-course/chapter3/3?fw=pt