DebertaForSequenceClassification documents examples report RuntimeError: Index tensor must have the same number of dimensions as input tensor

Environment info

transformers version:
Platform: macos
Python version: 3.8.3
PyTorch version (GPU?): no
Tensorflow version (GPU?): no
Using GPU in script?: no
Using distributed or parallel set-up in script?: no

Who can help

Information

Model I am using (Bert, XLNet ...): Deberta

The problem arises when using:

[x] the official example scripts: (give details below)
[ ] my own modified scripts: (give details below)

The tasks I am working on is:

[ ] an official GLUE/SQUaD task: (give the name)
[ ] my own task or dataset: (give details below)

To reproduce

from transformers import DebertaTokenizer, DebertaForSequenceClassification
import torch
tokenizer = DebertaTokenizer.from_pretrained('microsoft/deberta-base')
model = DebertaForSequenceClassification.from_pretrained('microsoft/deberta-base')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0) 
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
print(loss)
print(logits)

Exception like below:

Traceback (most recent call last): File "/Users/admin/git/transformers/myexample4/deberta_MLM.py", line 65, in sequence_classify() File "/Users/admin/git/transformers/myexample4/deberta_MLM.py", line 45, in sequence_classify outputs = model(*inputs, labels=labels) File "/Users/admin/virtulenv/py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(input, **kwargs) File "/Users/admin/git/transformers/src/transformers/models/deberta/modeling_deberta.py", line 1169, in forward labels = torch.gather(labels, 0, label_index.view(-1)) RuntimeError: Index tensor must have the same number of dimensions as input tensor

Process finished with exit code 1

Steps to reproduce the behavior:

1. 2. 3.

huggingface / transformers