How the transformer model is used for question-answering

Key takeaways:

• Transformer models replace RNNs/LSTMs in NLP, using attention mechanisms for efficiency. They consist of an encoder for input processing and a decoder for output generation.

• Transformer pretrained models like BERT answer questions by extracting answers from context.

• For question-answering, input text is tokenized, structured with special tokens, and converted to IDs for processing. The model predicts start and end indices to extract and display answers.

• Transformers are scalable, versatile, and essential for modern NLP tasks.

The Transformer model is a type of deep learning neural network that is used as an efficient replacement of recurrent neural network (RNN) and long short-term memory (LSTM) for various natural language processing (NLP) tasks. It was developed by Google and proposed in the groundbreaking paper "Attention Is All You Need" in 2017 based on the multi-head attention mechanism. It is designed in a way to handle the sequential data more efficiently as compared to the previous modules.

We’ll see how a transformer model helps to implement question-answers using a pretrained model.

Workflow

Let’s understand how the transformer model works. It has two main components: an encoder and a decoder. The encoder processes the input data and passes information about the representation of the input data to the decoder. The decoder receives the representation sent by the encoder and generates the output sentence in the sequence to generate the answer.

Here’s the explanation of the code with steps:

Question answering with transformer model

Suppose we have a question and a relevant paragraph. We want to extract the answer from the paragraph using a Transformer model. Let’s go through the steps:

1. Import libraries

Import necessary Python libraries and modules needed for text processing and question answering.

2. Load the tokenizer and model

We use the pretrained bert-large-uncased-whole-word-masking-finetuned-squad model, fine-tuned on SQuAD v1.1. This model is case-insensitive and trained to answer questions using a context. The tokenizer is used to process input text into tokens.

3. Define the question and paragraph

Input the question and the paragraph from which the answer will be extracted.

4. Process the input data

Special tokens are added to mark the beginning of the input ([CLS]), separate the question and paragraph ([SEP]), and end the input.

5. Tokenize and generate IDs

The question and paragraph are tokenized into subwords, combined, and converted into numerical IDs that the model can process.

6. Generate segment IDs

Create a list of segment IDs to differentiate tokens from the question (0) and the paragraph (1).

7. Prepare tensors

Convert token IDs and segment IDs into PyTorch tensors to prepare them for the model.

8. Use the model to get scores

Pass the tensors to the model to get start_logits (score for the start of the answer) and end_logits (score for the end of the answer).

9. Get the starting and ending index of the answer

Find the indexes of the tokens with the highest scores, representing the start and end of the answer.

10. Display the answer

Using the starting and ending indexes, extract and display the answer from the tokens.

Output

When you run the code, the model processes the input and outputs:

This detailed breakdown explains the working of the Transformer model for question answering using the bert-large-uncased pretrained model.

Code: Full implementation

Here is the complete implementation of the steps we discussed above.

In conclusion, the transformer model represents a prominent advancement in natural language processing because of its scalability, efficiency, and versatility. Developers can leverage this state-of-the-art deep neural architecture for NLP tasks and other areas of machine learning.