I am working on a Multi head regression problem where for each text I want to predict 5 scores. You can do this by setting `problem_type = 'regression'`

as given in transformers code

Issue is that when I run my model with `Trainer`

, it gives an error like:

# Error:

```
raise ValueError(
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`labels` in this case) have excessive nesting (inputs type `list` where type `int` is expected).
```

It worked with `num_classes = 1`

but when I do it with 5, it throws this error. Below are the minimal code for my model, data.

# Model

```
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased",
num_labels=5,
problem_type = "regression")
```

# Custom DataLoader:

```
class MultiRegressionDataset(torch.utils.data.Dataset):
def __init__(self, texts, labels):
self.labels = labels
self.texts = texts
def __getitem__(self, idx, sanity_check = False):
output = tokenizer(self.texts[idx], truncation=True,
padding="max_length",
max_length = 128) # This returns a dict
output['labels'] = torch.tensor(self.labels[idx])
return output
data = MultiRegressionDataset(["text1", "text2"], [[1,2,3,4,5], [5,4,3,2,1]])
data.__getitem__(0) # Gives a value
```

Tried doing it with

`output['labels'] = torch.tensor(self.labels[idx]).unsqueeze(-1)`

- Combination of
`return_tensors = "pt"`

with the above

Nothing worked. What am I doing wrong here?