Getting the Answer from a BERT Question-Answering Model

After preparing your input tensors ( input_ids and segment_ids) and feeding them to a fine-tuned BERT model, the model outputs two sets of scores: start_scores and end_scores. These scores represent the model's confidence that a particular token is the start or end of the answer span within the provided context.

Understanding the Output

The BERT model will return:

• start_scores: A tensor where each element corresponds to a token in the input sequence, indicating the probability that the token is the start of the answer.
• end_scores: A tensor where each element corresponds to a token in the input sequence, indicating the probability that the token is the end of the answer.

start_scores, end_scores = model(input_ids, token_type_ids=segment_ids)

Determining the Answer Span

To find the most likely answer span, you need to identify the tokens with the highest start and end scores. This is typically done using torch.argmax() which returns the index of the maximum value in a tensor.

start_index = torch.argmax(start_scores)
end_index = torch.argmax(end_scores)

Explanation:

• torch.argmax(start_scores): This finds the index of the token that the model predicts as the most probable start of the answer.
• torch.argmax(end_scores): This finds the index of the token that the model predicts as the most probable end of the answer.

Extracting the Answer Text

Once you have the start_index and end_index, you can extract the answer by selecting the tokens from your original tokenized input that fall within this range. Remember that Python slicing is exclusive of the end index, so you'll need to add 1 to end_index to include the token at that position.

# Assuming 'tokens' is a list of the tokenized words from your input
answer_tokens = tokens[start_index : end_index + 1]
answer = ' '.join(answer_tokens)
print(answer)

Example Output:

If the tokens list contained the following:

['a', 'system', 'of', 'many', 'biological', 'structures', 'and', 'processes', 'within', 'an', 'organism', 'that', 'protects', 'against', 'disease']

And start_index was 0 and end_index was 14, the output would be:

a system of many biological structures and processes within an organism that protects against disease

This process concludes the fundamental steps of fine-tuning BERT for Question Answering. The next logical step would be to explore fine-tuning BERT for other Natural Language Processing tasks, such as Named Entity Recognition (NER).

Interview Questions Related to BERT QA

• What do the start_scores and end_scores represent in BERT question-answering?
• How do you determine the answer span in a QA task using BERT?
• Why do we use torch.argmax() on the output of the BERT model in QA tasks?
• How does BERT extract the most probable answer from a paragraph?
• What is the significance of start and end token indices in BERT-based QA?
• How is the predicted answer span converted back into a readable string?
• Can the start_index be after the end_index in BERT QA predictions? How do you handle this scenario?
• What would happen if the predicted span includes special tokens like [SEP] or [PAD] tokens? How would you typically handle such cases?
• What are the key steps to visualize or debug a wrongly predicted answer in BERT?
• How do tokenization and token alignment affect the final answer extraction in BERT?