Hi.
Admittedly I am a beginner to HuggingFace, though I do have some Python experience and general programming experience.
I am using


transformers version: 3.5.1
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 copy-pasted the following code (see at the bottom of the post) from the Transformers doc on Summary of the tasks — transformers 4.0.0 documentation (huggingface.co) 1
However this code (I made no changes) returns with the following error:
Traceback (most recent call last):
  File "c:/Workspace/py-conda-workspaces/py36-conda-speechDemo/text-question-answering.py", line 21, in <module>
    answer_start_scores = outputs.start_logits
AttributeError: 'tuple' object has no attribute 'start_logits'```

Here is the code:
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch
tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
text = r"""
🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between
TensorFlow 2.0 and PyTorch.
"""
questions = [
    "How many pretrained models are available in 🤗 Transformers?",
    "What does 🤗 Transformers provide?",
    "🤗 Transformers provides interoperability between which frameworks?",
]
for question in questions:
    inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")
    input_ids = inputs["input_ids"].tolist()[0]
    text_tokens = tokenizer.convert_ids_to_tokens(input_ids)
    outputs = model(**inputs)
    answer_start_scores = outputs.start_logits
    answer_end_scores = outputs.end_logits
    answer_start = torch.argmax(
        answer_start_scores
    )  # Get the most likely beginning of answer with the argmax of the score
    answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score
    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
    print(f"Question: {question}")
    print(f"Answer: {answer}")


Please help. What did I do wrong?