How to Generate Quality FAQs & FAQPage Schemas Automatically with Python

During my SEJ eSummit presentation, I launched the thought of producing high quality content material at scale by answering questions robotically.

I launched a course of the place we researched questions utilizing in style instruments.

But, what if we don’t even want to analysis the questions?

In this column, we’re taking that course of a big step additional.

We are going to find out how to generate high-quality query/reply pairs (and their corresponding schema) robotically.

Here is the technical plan:

  • We will fetch content material from an instance URL.
  • We will feed that content material right into a T5-based query/reply generator.
  • We will generate a FAQPage schema object with the questions and solutions.
  • We will validate the generated schema and produce a preview to verify it really works as anticipated.
  • We will go over the ideas that make this doable.

Generating FAQs from Existing Text

Let’s begin with an instance.

Make a duplicate of this Colab pocket book I created to present this method.

  • Change the runtime to GPU and click on join.
  • Feel free to change the URL within the kind. For illustration functions, I’m going to give attention to this latest article about Google Ads hiding key phrase information.
  • The second enter within the kind is a CSS selector we’re utilizing to mixture textual content paragraphs. You would possibly want to change it relying on the web page you utilize to check.
  • After you hit Runtime > Run all, you need to get a block of HTML that you could copy and paste into the Rich Results Test instrument.


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Here is what the wealthy outcomes preview appears for that instance web page:

How to Generate Quality FAQs & FAQPage Schemas Automatically with Python

Completely automated.

How cool is that, proper?

Now, let’s step via the Python code to perceive how the magic is occurring.

Fetching Article Content

We can use the dependable Requests-HTML library to pull content material from any web page, even when the content material is rendered utilizing JavaScript.

We simply want a URL and a CSS selector to extract solely the content material that we want.


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We want to set up the library with:

!pip set up requests-html

Now, we will proceed to use it as follows.

from requests_html import HTMLSession
session = HTMLSession()

with session.get(url) as r:

    paragraph =, first=False)

    textual content = " ".be part of([ p.text for p in paragraph])

I made some minor adjustments from what I’ve used prior to now.

I request a listing of DOM nodes after I specify first=False, after which I be part of the listing by combining every paragraph by areas.

I’m utilizing a easy selector, p, which can return all paragraphs with textual content.

This works properly for Search Engine Journal, however you would possibly want to use a special selector and textual content extraction technique for different websites.

After I print the extracted textual content, I get what I anticipated.

How to Generate Quality FAQs & FAQPage Schemas Automatically with Python

The textual content is clear of HTML tags and scripts.

Now, let’s get to probably the most thrilling half.

We are going to construct a deep studying mannequin that may take this textual content and switch it into FAQs! 🤓

Google T5 for Question & Answer Generation

I launched Google’s T5 (Text-to-Text Transfer Transformer) in my article about high quality title and meta description era.

T5 is a pure language processing mannequin that may carry out any kind of activity, so long as it takes enter as textual content and output as textual content, supplied you may have the suitable dataset.

I additionally coated it throughout my SEJ eSummit discuss, after I talked about that the designers of the algorithm really misplaced on a trivia contest!

How to Generate Quality FAQs & FAQPage Schemas Automatically with Python

Now, we’re going to leverage the wonderful work of researcher Suraj Patil.


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He put collectively a high-quality GitHub repository with T5 fine-tuned for query era utilizing the SQuAD dataset.

The repo contains directions on how to practice the mannequin, however as he already did that, we’ll leverage his pre-trained fashions.

This will saves us vital time and expense.

Let’s evaluate the code and steps to arrange the FAQ era mannequin.

First, we want to obtain the T5 weights.

!python -m nltk.downloader punkt

This will trigger the python library nltk to obtain some recordsdata.

[nltk_data] Downloading package deal punkt to /root/nltk_data... [nltk_data] Unzipping tokenizers/

Clone the repository.

!git clone
%cd question_generation

At the time of penning this, I confronted a bug and within the pocket book I utilized a brief patch. See if the problem has been closed and you’ll skip it.

Next, let’s set up the transformers library.

!pip set up transformers

We are going to import a module that mimics the transformers pipelines to maintain issues tremendous easy.

from pipelines import pipeline

Now we get to the thrilling half that takes simply two traces of code!

nlp = pipeline("multitask-qa-qg")
faqs = nlp(textual content)

Here are the generated questions and solutions for the article we scraped.

How to Generate Quality FAQs & FAQPage Schemas Automatically with Python
Look on the unbelievable high quality of the questions and the solutions.

They are various and complete.


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And we didn’t even have to learn the article to do that!

I used to be in a position to do that rapidly by leveraging open supply code that’s freely accessible.

As spectacular as these fashions are, I strongly advocate that you just evaluate and edit the content material generated for high quality and accuracy.

You would possibly want to take away query/reply pairs or make corrections to maintain them factual.

Let’s generate a FAQPage JSON-LD schema utilizing these generated questions.

Generating FAQPage Schema

In order to generate the JSON-LD schema simply, we’re going to borrow an thought from considered one of my early articles.

We used a Jinja2 template to generate XML sitemaps and we will use the identical trick to generate JSON-LD and HTML.

We first want to set up jinja2.

!pip set up jinja2

This is the jinja2 template that we’ll use to do the era.

faqpage_template="""<script type="software/ld+json">


"@context": "",

"@type": "FAQPage",

"mainEntity": [

% for faq in faqs %


"@type": "Question",

"name": tojson,






I need to spotlight a few tips I wanted to use to make it work.

The first problem with our questions is that they embrace quotes (“), for instance:


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Who introduced that search queries with out a "vital" quantity of knowledge will not present in question studies?

This is an issue as a result of the quote is a separator in JSON.

Instead of quoting the values manually, I used a jinja2 filter, tojson to do the quoting for me and in addition escape any quotes.

It converts the instance above to:

"Who announced that search queries without a "vital" amount of data will no longer show in query reports?"

The different problem was that including the comma after every query/reply pair works properly for all however the final one, the place we’re left with a dangling comma.

I discovered one other StackOverflow thread with a sublime resolution for this.

It solely provides the comma if it isn’t the final loop iteration.

Once you may have the template and the listing of distinctive FAQs, the remainder is simple.

from jinja2 import Template


That is all we want to generate our JSON-LD output.

You can discover it right here.

Finally, we will copy and paste it into the Rich Results Test instrument, validate that it really works and preview how it will look within the SERPs.

How to Generate Quality FAQs &#038; FAQPage Schemas Automatically with Python



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Deploying the Changes to Cloudflare with RankSense

Finally, in case your web site makes use of the Cloudflare CDN, you can use the RankSense app content material guidelines to add the FAQs to the positioning with out involving builders. (Disclosure: I’m the CEO and founding father of RankSense.)

Before we will add FAQPage schema to the pages, we want to create the corresponding FAQs to keep away from any penalties.

According to Google’s normal structured information pointers:

Don’t mark up content material that’s not seen to readers of the web page. For instance, if the JSON-LD markup describes a performer, the HTML physique ought to describe that very same performer.”

We can merely adapt our jinja2 template so it outputs HTML.

faqpage_template=""" <div id="FAQTab">

% for faq in faqs %

<div id="Question">  </div>

<div id="Answer"> <sturdy> </sturdy></div>

</div> """

Here is what the HTML output appears like.

How to Generate Quality FAQs &#038; FAQPage Schemas Automatically with Python

Now, that we will generate FAQs and FAQPage schemas for any URLs, we will merely populate a Google Sheet with the adjustments.


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My group shared a tutorial with code you need to use to robotically populate sheets right here.

Your homework is to adapt it to populate the FAQs HTML and JSON-LD we generated.

In order to replace the pages, we want to present Cloudflare-supported CSS selectors to specify the place within the DOM we wish to make the insertions.

We can insert the JSON-LD within the HTML head, and the FAQ content material on the backside of the Search Engine Journal article.

As you’re making HTML adjustments and may probably break the web page content material, it will be a good suggestion to preview the adjustments utilizing the RankSense Chrome extension.

In case you’re questioning, RankSense makes these adjustments immediately within the HTML with out utilizing client-side JavaScript.

They occur within the Cloudflare CDN and are seen to each customers and serps.

Now, let’s go over the ideas that make this work so properly.

How Does This T5-Based Model Generate These Quality FAQs?

The researcher is utilizing an answer-aware, neural query era strategy.


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This strategy typically requires three fashions:

  • One to extract potential solutions from the textual content.
  • Another to generate questions given solutions and the textual content.
  • Finally, a mannequin to take the questions and the context and produce the solutions.

Here’s a helpful clarification from Suraj Patil.

How to Generate Quality FAQs &#038; FAQPage Schemas Automatically with Python

One easy strategy to extract solutions from textual content is to use Name Entity Recognition (NER) or information from a customized information graph like we in-built my final column.

A query era mannequin is principally a question-answering mannequin however with the enter and goal reversed.

An issue-answering mannequin takes questions+context and outputs solutions, whereas a question-generation mannequin takes solutions+context and outputs questions.


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Both sorts of fashions will be educated utilizing the identical dataset – in our case, the available SQuAD dataset.

Make certain to learn my put up for the Bing Webmaster Tools Blog.

I supplied a reasonably in-depth clarification of how transformer-based, question-answering fashions work.

It contains easy analogies and a few minimal Python code.

One of the core strengths of the T5 mannequin is that it may well carry out a number of duties, so long as they take textual content and output textual content.

This enabled the researcher to practice only one mannequin to carry out all duties as a substitute of three fashions.

Resources & Community Projects

The Python search engine optimization neighborhood retains rising with extra rising stars showing each month. 🐍🔥

Here are some thrilling initiatives and new faces I realized about on Twitter.


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Keyword Density and Entity Calculator (Python + Knowledge Base API)

Get probably the most out of PageSpeed Insights API with Python

More Resources:


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Image Credits

All screenshots taken by creator, September 2020

Source hyperlink search engine optimization

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