Notes

Yes, that could indeed be the reason for the error. If the padding token (e.g., '[PAD]') is not included in your vocabulary, then attempting to pad sequences with this token will result in indexing errors. The padding token is used to fill sequences to a consistent length during batching, and it needs to have a corresponding index in your vocabulary.

To fix this issue, you should ensure that the padding token is included in your vocabulary and assigned an index. For example:

```python
# Build vocabulary
def build_vocabulary(data):
vocab = set()
for sentence in data:
tokens = nltk.word_tokenize(sentence)
vocab.update(tokens)
vocab = list(vocab)
vocab.insert(0, START_TOKEN)
vocab.append(PADDING_TOKEN) # Include padding token in the vocabulary
vocab.append(END_TOKEN)
return vocab

# Assign an index to the padding token
PADDING_INDEX = len(vocabulary) # The index after adding padding token to vocabulary
vocabulary[PADDING_TOKEN] = PADDING_INDEX

# Create question and answer vocabularies
question_vocabulary = build_vocabulary(questions)
answer_vocabulary = build_vocabulary(answers)
```

With the padding token included in the vocabulary, you should no longer encounter indexing errors related to tensor(851, device=cuda:0).