AI Story Generator

AI Story Generator – Best Software For Generating Scripts, Articles and Editing Copies

AI Story Generator - Best Software in 2020 For Generating Scripts, Articles and Editing Copies

Muhammad Imran


February 11, 2020


AI story generator apps will help you to produce cutting-edge content and streamline content creation across different departments

AI Story Generator

Keeping up with the growing demand for content has become easier with AI story generators. These AI story generator apps powered by machine learning solutions can create content at unparalleled speed, whereby enabling streamlining of the work and both time & money-saving.

Machine learning as a service allow companies to process large datasets more efficiently than humans. With NLP implementation, thousands of narratives can be created, where humans can only write a single piece at a time.

Best AI Story Generator Software

1) Grammarly

Grammarly is a great tool that helps create content that is concise, readable and error-free. It identifies and fixes writing mistakes in basic to complex material, depending on the plan. It finds problem areas and makes word & sentence structuring suggestions. It also allows you to add new words to the dictionary and receive reports on writing mistakes you make regularly. Premium plans start from $0 to $11.66 a month and business plan that can be shared with up to 100 people, costs $15/mo.

2) Textio

Textio is a good tool that enables augmentation of your writing to produce effective job listings. It scores writing skills and suggests the phrases that might be more apt for recruiting the most qualified applicants. It also reports on the key characteristics of your applicant pool, such as gender and diversity. Gmail and LinkedIn Recruiter integration allows you to write and post from anywhere, while also enabling you to share documents with other teams within your organisation.

The Textio Hire Package that facilitates job and email communication subscription ranges around $17-27k for a 12 month period. The final figure is based on the number of months, job postings and business size.

3) Articoolo

Articoolo is another great tool that is used by writers to create greater volumes of content in a more efficient manner. It helps find the best material, message and keywords to help you bring your project to life. While you pick the topic and length, it takes the material and utilizes NLP to rewrite it in order to produce unique material that can be used as a starting point. Moreover, it has a WordPress plug-in that can find images that go with the articles. The price is based on pay per use: 10 articles are $19,  50 articles are $75, 100 articles are $99. 30 articles per month can be subscribed for $29/mo, 100 articles per month at $49/mo and 250 articles per month at $99/mo.

4) ProWriting Aid

ProWriting Aid works as an online editor, writing and style guide - all in one. It identifies issues in your writing that are likely to go unnoticed such as overused or abstract words. The free version allows you to upload copies of your documents and receive up to 20 in-depth writing reports. These can be utilized to strengthen and bring clarity to your text.  ProWritingAid smoothly integrates with numerous other software, such as MS Word, GoogleDocs, Scrivener, Open Office, or Chrome. This means you can edit wherever you write.

5) Article Forge

Article Forge platform utilizes powerful algorithms that can automatically review existing articles similar to how humans do. The intelligent algorithms have the capability to conduct research and go through an infinite number of articles in order to write articles in their own words. It can also generate search engine optimized pieces. The tool allows for easy scheduling and automation of WordPress postings. You can pay $297 yearly or $47 monthly, plus it comes with a 30 days no risk and money back guarantee.

Key Difference Between Speech to Text and AI Generated Stories App

Speech to text software is a computer program that utilizes linguistic algorithms to understand auditory signals and converting it into words by using Unicode characters. In simple words, this  automatic speech recognition (ASR) software listens to audio and delivers precise and editable transcripts.

AI generated stories app is based on NLG, which allows generation of written narratives from data. This software is already being used for business intelligence dashboards, personalized email, data reports and more. Structured or raw data is processed by the software through the "conditional logic" to produce narratives that sound like human-generated content.

Folio3.Ai Offer Custom AI Story Generator Solution

Enable greater time savings and more control with our AI Story Generator Solution. Utilize Folio3’s AI story generator software to create unique, SEO optimized and information-densed content. Our Speech to text software also allows you to reword existing articles and add value. We have the expertise to generate custom article software with exceptional text generation abilities. We offer just the right coding expertise to help you utilize our text AI in your autoblogging projects. Our AI story generator can be used as a good source of inspiration and ideas.


Can AI write a story without plagiarism?

Most AI-generated stories are based on existing templates. These templates are semi-automatically generated sentences or stories that the machine uses according to the context that it is working in. These slots are then filled with relevant material to create an AI-generated story. Even though Predictive analytics has improved a lot over time, it still cannot generate human-like prose or produce fluid paraphrases. Due to this, AI stories often resemble works that already exist. Human intervention is therefore needed to make the AI-generated stories smoother and more original.

What is GPT2 for beginners?

GPT-2 stands for “Generative Pretrained Transformer 2”. Generative refers to the model’s automated predictive capabilities. This means that the model is trained to identify statistical features in a given data-set, raw or otherwise, to create more text. “Pretrained” refers to the enhancements made to the language model’s abilities to carry out specific tasks. This large and powerful model has become quite popular and we will see an upward trend moving forward. “Transformer” means that it is based on the transformer architecture, while “2” simply means this is not the first time GPT is being tried out.

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Fairseq – All You Need to Know About Features, How to Install and Where to Use

Fairseq - All You Need to Know About Features, How to Install and Where to Use

Muhammad Imran


February 11, 2020


Artificial Intelligence (AI) is the new center of attraction in technology. The field is gaining ground, followed by tremendous research. To deploy AI more conveniently, the introduction of new modes, methods, and libraries is becoming standard. Machine Learning (ML) is one of the trending topics in AI. Although we have hundreds of famous libraries and frameworks for AI and ML, there is always a chance of improvement. Apart from other popular frameworks such as TensorFlow, Theano, and PyTorch, Fairseq has also emerged as an optimal machine learning solution. It is gaining popularity and is used by over 1.7k developers worldwide.

What is Fairseq?

Fairseq PyTorch is an opensource machine learning library based on a sequence modeling toolkit. It allows the researchers to train custom models for fairseq summarization transformer, language, translation, and other generation tasks. It supports distributed training across multiple GPUs and machines. GitHub hosts its repository.

Fairseq Features in 2020

Fairseq provides researchers with smooth implementation of sequence to sequence models. It supports various models. Some of them include:

Convolutional Neural Networks (CNN)

Convolutional Neural Networks are a form of deep neural networks commonly used for visual imagery. They are useful in areas such as object detection, image recognition and other computer vision stuff. Fairseq is handy with the following:

1) Fairseq Language Modelling with Gated CNN

2) Classical Structured Prediction Losses

3) Hierarchical Neural Story Generation

4) Unsupervised Learning for Speech Recognition using predictive analytics solution

LightConv and DynamicConv Models

This model contains some pre-trained dataset and as well as the instructions on training the new model. It includes models without graphics library hence making it faster. You can quickly get the fairseq-preprocess datasets for languages such as English, Chinese, German, and French with fairseq-train paper.

Long Short-Term Memory (LSTM) Networks

LSTM is an artificial recurrent neural network (RNN) that are well-suited for classification and making predictions on time series data. It is convenient to use for unsegmented handwriting recognition, speech recognition, and anomaly detection in network traffic. Fairseq provides a practical approach to solve Attention-based Neural Machine Translation.

Transformer (self-attention) Networks

In place of CNN and RNN, many researchers prefer to use transformer networks. They implement encoder and decoder as self-attention networks to draw global dependencies between input and output. It works well in:

1) Scaling Neural Machine Fairseq Translation

2) Understanding Back-Translation

3) Mixture Models for Diverse Machine Translation

4) Input Representations for Neural Language Modeling

Non-autoregressive Transformers

Non-autoregressive Transformers or NAT removes the dependencies from the inputs of the decoder on the previous target token with fairseq bart. It helps to achieve:

1) Non-autoregressive Neural Machine Translation

2) Neural Sequence Modeling Iterative Refinement

3) Flexible Sequence Generation by Fairseq Insertion Transformer Model

4) Mask-Predict: Conditional Masked Language Models Parallel Decoding.

Apart from all these supported models and techniques by Fairseq, it also has other advantages. You can do multi-GPU training either on one machine or multiple machines. One can quickly implement them on both CPU and GPU with search algorithms. With its mixed-precision training, you can train models while consuming lesser GPU memory. It is extensible and makes registering of new models, tasks, and optimizers convenient.

FairSeq GitHub

The GitHub repository of Fairseq is at this link. It has 1128 commits with eight branches and 11 releases. Over six thousand people have starred it while 1.7k forked it. It has about 132 contributors with an active community backing it up.

How to Use FairSeq - Installation Requirements and Prerequisite

1) As Fairseq is an ML library in python, so you need python with version 3.6 or onwards.

2) PyTorch is also necessary before proceeding with Fairseq. You will require version 1.2.0 or onwards.

3) For training models, you will need an NVIDIA GPU. For better and efficient results, use NCCL.

4) Install NVIDIA’s apex library for faster training with the following two commands. 



5) After fulfilling all the requirements, install Fairseq. You can either clone it by ‘git clone’ or use the command ‘pip install fairseq.’

After successfully installing the fairseq, you can view its documentation here to get started. You even get pre-trained models and datasets with which you can get familiarization with the new library. Each pre-trained model has its READMEs as well for your convenience.

How to Install Fairseq - Interactive Installation Guide

There are a few simple steps to get started with fairseq. Follow the sequence:

1) First, you need python installed on your machine. Make sure its version is either 3.6 or higher. You can get python for your computer here.

2) After getting python, you need PyTorch. The underlying technology behind fairseq is PyTorch. You need version 1.2.0 or higher. To get PyTorch, you can clone it by the command ‘git clone’ You can install it from Anaconda or Chocolatey based installed. Here is the documentation.

3) Get fairseq by typing the following commands on the terminal.
git clone

cd fairseq

pip install -r requirements.txt

python build develop

Download pre-trained models and get acquainted with the syntax.

Start working on new projects and models.

Fairseq Machine Translation Youtube

This video takes you through the fairseq documentation tutorial and demo. If you are a newbie with fairseq, this might help you out.


1) Why is the dictionary required in fairseq? Dictionaries are the base of machine learning. One important aspect is that you train data using a separate function and then return the results. These results can be effectively stored in dictionaries and can be retrieved efficiently. You can save multiple values in a single dictionary with unique key-value pairs. 2) How to get a specific module out of fairseq? There are several modules defined in fairseq. All of them have the same naming convention that starts with ‘fairseq.modules.’ To get a specific module, you need to retrieve its name and place it at the end of fairseq.modules. For example,
  • fairseq.modules.AdaptiveInput (AdaptiveInput is the module name)
  • fairseq.modules.AdaptiveSoftmax (AdaptiveSoftmax is the module name)
fairseq.modules.BeamableMM (BeamableMM is the module name)

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We are the Pioneers in the Cognitive Arena - Do you want to become a pioneer yourself ?
Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

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