Preprocessing Input for BERT Question Answering

This guide outlines the essential steps for preparing input data for a Question Answering (QA) task using the BERT (Bidirectional Encoder Representations from Transformers) model. Proper input formatting is crucial for BERT to accurately understand the question and the context it needs to search for an answer.

Core Concepts

BERT relies on specific input formats, including special tokens and segment IDs, to process text effectively.

• Special Tokens: These tokens have predefined meanings for BERT.
  • [CLS] (Classification Token): Typically placed at the beginning of the input sequence. In QA, it can also help in downstream tasks like classification.
  • [SEP] (Separator Token): Used to distinguish between different segments of text. In QA, it separates the question from the paragraph.
• Segment IDs (Token Type IDs): These identify which segment a token belongs to. For QA, this typically means distinguishing tokens from the question versus tokens from the context paragraph.

Steps for Input Preprocessing

Follow these steps to prepare your question and paragraph for BERT:

1. Define Question and Paragraph

Start by defining your question and the context paragraph.

question = "What is the immune system?"
paragraph = "The immune system is a system of many biological structures and processes within an organism that protects against disease. To function properly, an immune system must detect a wide variety of agents, known as pathogens, from viruses to parasitic worms, and distinguish them from the organism's own healthy tissue."

2. Add Special Tokens

Append the [CLS] and [SEP] tokens to structure the input correctly for BERT.

• Add [CLS] at the beginning of the question.
• Add [SEP] at the end of the question.
• Add [SEP] at the end of the paragraph.

# Example using Python string formatting
formatted_question = '[CLS] ' + question + ' [SEP]'
formatted_paragraph = paragraph + ' [SEP]'

3. Tokenize the Input

Tokenize both the formatted question and paragraph into individual words or sub-word units using a pre-trained tokenizer (e.g., from the transformers library).

# Assuming 'tokenizer' is an instance of a BERT tokenizer
question_tokens = tokenizer.tokenize(formatted_question)
paragraph_tokens = tokenizer.tokenize(formatted_paragraph)

4. Combine Tokens and Convert to Input IDs

Concatenate the tokenized question and paragraph tokens. Then, convert these tokens into their corresponding numerical IDs (input IDs) using the tokenizer's vocabulary.

all_tokens = question_tokens + paragraph_tokens
input_ids = tokenizer.convert_tokens_to_ids(all_tokens)

Important Note on Token Length: BERT has a maximum input length (typically 512 tokens). If the combined length of the question and paragraph exceeds this limit, you will need to truncate either the question or, more commonly, the paragraph.

5. Define Segment IDs

Create a list of segment IDs (also known as token type IDs). This list helps BERT differentiate between the question and the paragraph.

• Assign 0 to all tokens belonging to the question.
• Assign 1 to all tokens belonging to the paragraph.

segment_ids = [0] * len(question_tokens) + [1] * len(paragraph_tokens)

6. Convert to Tensors

Convert the input_ids and segment_ids into PyTorch tensors (or TensorFlow tensors, depending on your framework) to be fed into the BERT model.

# For PyTorch
import torch
input_ids_tensor = torch.tensor([input_ids])
segment_ids_tensor = torch.tensor([segment_ids])

With these tensors prepared, you can now feed them into the BERT model for question answering.

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Interview Questions

• What are the special tokens used when preparing input for BERT in question-answering tasks?
• Why do we use segment IDs in BERT for QA tasks?
• How are input IDs generated from tokens in BERT?
• What is the role of [CLS] and [SEP] tokens in BERT question-answering?
• How do you differentiate between question and paragraph tokens in BERT input?
• Why do we convert tokenized inputs into PyTorch tensors before feeding into BERT?
• What happens if the combined token length exceeds the maximum limit for BERT?
• What does the tokenizer do in the preprocessing step?
• Can segment IDs be omitted in BERT QA tasks?
• How does BERT use input_ids and segment_ids to extract the answer span?