Hi.
I am using


transformers version: 4
Platform: Windows-10-10.0.18362-SP0
Python version: 3.6.12
PyTorch version (GPU?): 1.7.0 (False)
Tensorflow version (GPU?): not installed (NA)
Using GPU in script?: No
Using distributed or parallel set-up in script?: No

I am running the pipeline example for question answering from the doc. It throws the following error:
Traceback (most recent call last):
  File "c:/Workspace/py-conda-workspaces/py36-conda-speechDemo/text-question-answering.py", line 9, in <module>
    result = nlp(question="What is extractive question answering?", context=context)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\transformers\pipelines.py", line 1874, in __call__
    start, end = self.model(**fw_args)[:2]
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\transformers\models\distilbert\modeling_distilbert.py", line 706, in forward
    return_dict=return_dict,
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\transformers\models\distilbert\modeling_distilbert.py", line 480, in forward
    inputs_embeds = self.embeddings(input_ids)  # (bs, seq_length, dim)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\transformers\models\distilbert\modeling_distilbert.py", line 107, in forward
    word_embeddings = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\torch\nn\modules\sparse.py", line 126, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "C:\Users\Gilad\miniconda3\envs\speechDemoEnv_NMT\lib\site-packages\torch\nn\functional.py", line 1852, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but got torch.IntTensor instead (while checking arguments for embedding)

Here is the code, it is copy-pasted from Summary of the tasks — transformers 4.0.0 documentation (huggingface.co) 1
from transformers import pipeline
nlp = pipeline("question-answering")
context = r"""
Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
a model on a SQuAD task, you may leverage the examples/question-answering/run_squad.py script.
"""

result = nlp(question="What is extractive question answering?", context=context)
print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
result = nlp(question="What is a good example of a question answering dataset?", context=context)
print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")

If it helps, I yesterday upgraded from v3.5.1 to v4, using pip install --upgrade transformers. Since this upgrade, the environment seems damaged also in other places, for example transformer-cli --help is broken (see a related issue at The question-answering example in the doc throws an AttributeError exception. Please help - Beginners - Hugging Face Forums  )
Help will be appreciated. Thanks!