Document Answering Model

Building a Document Question Answering System with ktrain

This guide demonstrates how to build a document question answering (Q&A) system using the ktrain library. You will learn to index a collection of documents and retrieve answers to questions based on their content.

Introduction to Document Question Answering

In a document question answering task, the model is provided with a set of documents and asked to answer questions derived from the information contained within those documents. This is a powerful technique for creating intelligent search and Q&A applications.

Prerequisites

• Python installed

• Basic understanding of Python and its package management (pip)

Steps to Build the Q&A System

Step 1: Install and Import Required Libraries

First, ensure you have ktrain installed. Then, import the necessary modules.

!pip install ktrain

from ktrain import text
import os
import shutil

Step 2: Download and Extract the BBC News Dataset

We will use the BBC News dataset, which comprises 2,225 news articles categorized into business, entertainment, politics, sports, and technology.

!wget http://mlg.ucd.ie/files/datasets/bbc-fulltext.zip
!unzip bbc-fulltext.zip

Step 3: Set Working Directory

Navigate into the extracted dataset directory to work with the documents.

os.chdir(os.getcwd() + '/bbc')

Step 4: Initialize Document Index Directory

ktrain uses a designated directory to store indices for efficient document searching. We initialize this directory.

text.SimpleQA.initialize_index('index')

Step 5: Index the Documents

We will index documents from one of the subfolders. For this example, we'll use the entertainment folder. The index_from_folder() method processes the documents within the specified folder and creates the necessary index.

text.SimpleQA.index_from_folder(folder_path='entertainment', index_dir='index')

Explanation: The index_from_folder function scans the specified directory, processing each document. It extracts text, potentially performs preprocessing (like tokenization and cleaning), and then creates an index that allows for fast retrieval of relevant document passages based on a query. The index_dir parameter specifies where this index will be stored.

Step 6: Create an Instance of the SimpleQA Class

Instantiate the SimpleQA class, providing the path to the initialized index directory. This object will be used to query the indexed documents.

qa = text.SimpleQA('index')

Step 7: Ask Questions and Retrieve Answers

Use the ask() method to query the indexed documents. The qa object will search the index for documents relevant to your question and extract the most likely answer.

answers = qa.ask('who had a global hit with where is the love?')

To display the top 5 answers, use the display_answers() method.

qa.display_answers(answers[:5])

The output for each answer typically includes:

• Candidate Answers: The text segment identified as the answer.

• Context: The surrounding text from the document that provides context for the answer.

• Confidence Score: A score indicating the model's certainty about the answer.

• Source Document Name: The name of the document from which the answer was retrieved.

Example Output Structure:

Candidate answers:
    Who had a global hit with Where Is The Love?
Context:
    Justin Timberlake had a global hit with Where Is The Love?
Confidence score: 0.95
Source document name: news/entertainment/2005/01/20/justin-timberlake-had-a-global-hit-with-where-is-the-love/

Step 8: Try Another Example

Let's test with another question.

answers = qa.ask('who win at mtv europe awards?')
qa.display_answers(answers[:5])

Conclusion

ktrain simplifies the process of building a robust document-based question answering system. With just a few lines of code, you can create powerful Q&A capabilities that can be applied across various domains, including news analysis, legal document retrieval, and research content exploration.

Applications

Document Q&A systems are valuable for:

• Building intelligent search engines.

• Creating customer support bots.

• Analyzing large volumes of text data.

• Facilitating research by quickly finding answers within documents.

Further Exploration

• Indexing Multiple Categories: To index documents from multiple categories, you can call index_from_folder() for each category or combine documents before indexing.

• Performance Tuning: For larger datasets, consider optimizing indexing and retrieval processes.

• Customization: Explore ktrain's advanced features for fine-tuning models and customizing the Q&A pipeline.

Related Concepts

• Semantic Search: Understanding the meaning behind queries to find relevant documents.

• Document Indexing: Creating searchable data structures from text documents.

• Natural Language Processing (NLP): The field of computer science focused on enabling computers to understand and process human language.

Interview Questions

1. What is the purpose of the SimpleQA class in ktrain? The SimpleQA class in ktrain is designed to facilitate the creation and querying of document question answering systems. It handles the indexing of documents and provides an interface for asking questions and retrieving relevant answers.

2. How does ktrain index documents for question answering? ktrain indexes documents by processing them and creating a searchable data structure, often based on techniques like TF-IDF or embeddings, allowing for efficient retrieval of relevant passages when a question is asked.

3. What kind of datasets are suitable for document question answering tasks? Datasets containing a collection of text documents with factual information are suitable. This includes news articles, research papers, legal documents, customer support logs, books, and internal company knowledge bases.

4. Explain the process of setting up an index for documents in ktrain. You initialize an index directory using text.SimpleQA.initialize_index(). Then, you populate this index by calling text.SimpleQA.index_from_folder() or index_from_file() for your documents. This process analyzes the text and creates the necessary structures for efficient retrieval.

5. How does ktrain retrieve answers from indexed documents? When a question is posed using qa.ask(), ktrain uses the indexed documents to find passages that are semantically related to the question. It then employs a question-answering model (often based on transformers) to extract the most relevant answer from these passages.

6. What information is included in the output when you ask a question using SimpleQA? The output typically includes candidate answers (the extracted text), the context from which the answer was extracted, a confidence score for the answer's relevance, and the name of the source document.

7. How can document question answering systems be applied in real-world scenarios? They can be used for building chatbots that answer questions from a knowledge base, creating intelligent search tools for internal company documents, automating customer support by answering FAQs, and aiding researchers in finding information within large document collections.

8. What are the advantages of using ktrain for building Q&A systems compared to other NLP libraries? ktrain aims to simplify the machine learning workflow, providing a high-level API that makes it easier and faster to build and deploy NLP models, including Q&A systems, with less boilerplate code compared to more fundamental libraries like TensorFlow or PyTorch alone.

9. Describe the workflow from document ingestion to answer retrieval in ktrain’s Q&A pipeline. The workflow involves:

   • Ingestion: Downloading or specifying the location of your documents.

   • Initialization: Creating an index directory.

   • Indexing: Processing and storing document content in the index using index_from_folder or similar methods.

   • Instanciation: Creating a SimpleQA object with the index path.

   • Querying: Submitting a question using the ask() method.

   • Retrieval: The system searches the index, retrieves relevant passages, and uses an underlying QA model to extract the answer.

   • Display: Presenting the answer(s) with context and confidence scores.

10. How would you extend this Q&A system to handle multiple document categories? To handle multiple categories, you could:

    • Index each category into separate subdirectories within the main index_dir.

    • When initializing SimpleQA, point it to the root index_dir that contains these sub-indices.

    • Alternatively, you could create a single index that includes metadata about the category for each document, allowing you to filter results by category after querying.