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Build NLP Paraphrase Generator

by kandikits Updated: Oct 20, 2022


Instantly build Python Paraphrase Generator for NLP


Paraphrase Generator is used to build NLP training data in minutes with this fully editable source code that comes along with the Kandi 1-Click Solution kit. The entire solution is available as a package to download from the source code repository.


  • Generate paraphrases for text using this application. The trained model for Google PAWS, ParaNMT, Quora question pairs, SNIPS commands, and MSRP Frames
  • Use Paraphrasers to augment training data for Natural Language Understanding models to build conversational engines.
  • Modify source code to customize as per your requirement.

Deployment Information

Paraphrase Generator created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.


For Windows OS,

  1. Download, extract and double-click the kit installer file to install the kit. Do ensure to extract the zip file before running it.
  2. The installation may take from 2 to 10 minutes based on bandwidth.
  3. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
  4. To run the kit manually, press 'N' and locate the zip file 'parrot-paraphrase-generator.zip'
  5. Extract the zip file and navigate to the directory 'parrot-paraphrase-generator'
  6. Open the command prompt in the extracted directory 'parrot-paraphrase-generator' and run the command 'jupyter notebook'


For other Operating System,

  1. Click here to install python
  2. Click here to download the repository
  3. Extract the zip file and navigate to the directory 'parrot-paraphrase-generator-main'
  4. Open terminal in the extracted directory 'parrot-paraphrase-generator-main'
  5. Install dependencies by executing the command 'pip install -r requirements.txt'
  6. Run the command ‘jupyter notebook’ and select the notebook ‘paraphrase_generator.ipynb’ on the browser window.


Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Paraphrase Generator App.

Instructions to Run

Follow the below instructions to run the solution.

  1. Locate and open the 'paraphrase_generator.ipynb' notebook from the Jupyter Notebook browser window.
  2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar.
  3. Once all the cells of the notebook are executed, the last interactive cell (Interactive shell simulation) will be active where input data can be given.


Input data

Enter a phrase (To quit enter exit): How do I become an astronaut?
Do you want phrasal diversity (y or n): n


Output data

Input_phrase:  How do I become an astronaut?
----------------------------------------------------------------------------------------------------
('how can i become a successful astronaut?', 26)
('how can one become an astronaut?', 16)
('how should i become an astronaut?', 14)
('how do i become an astronaut?', 12)


For any support, you can reach us at FAQ & Support

Troubleshooting

  1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools
  2. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.
  3. During kit installer, if you encounter Windows security alert, click Allow.
  4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8 GB.


If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

  1. Click here to install python
  2. Click here to download the repository
  3. Extract the zip file and navigate to the directory 'parrot-paraphrase-generator-main'
  4. Open terminal in the extracted directory 'parrot-paraphrase-generator-main'
  5. Install dependencies by executing the command 'pip install -r requirements.txt'
  6. Run the command ‘jupyter notebook’ and select the notebook ‘paraphrase_generator.ipynb’ on the browser window.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

vscodeby microsoft

TypeScript star image 130477 Version:1.66.2

License: Permissive (MIT)

Visual Studio Code

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vscodeby microsoft

TypeScript star image 130477 Version:1.66.2 License: Permissive (MIT)

Visual Studio Code
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jupyterby jupyter

Python star image 12379 Version:Current

License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat

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jupyterby jupyter

Python star image 12379 Version:Current License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat
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Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

pandasby pandas-dev

Python star image 33259 Version:v1.4.1

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python star image 33259 Version:v1.4.1 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language.

spaCyby explosion

Python star image 23063 Version:v3.1.6

License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python

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spaCyby explosion

Python star image 23063 Version:v3.1.6 License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python
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fuzzywuzzyby seatgeek

Python star image 8138 Version:0.18.0

License: Strong Copyleft (GPL-2.0)

Fuzzy String Matching in Python

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fuzzywuzzyby seatgeek

Python star image 8138 Version:0.18.0 License: Strong Copyleft (GPL-2.0)

Fuzzy String Matching in Python
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Machine Learning

The library offers state-of-the-art pre-trained models for Natural Language Processing (NLP).

transformersby huggingface

Python star image 61400 Version:v4.18.0

License: Permissive (Apache-2.0)

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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transformersby huggingface

Python star image 61400 Version:v4.18.0 License: Permissive (Apache-2.0)

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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sentence-transformersby UKPLab

Python star image 5944 Version:v2.0.0

License: Permissive (Apache-2.0)

Multilingual Sentence & Image Embeddings with BERT

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sentence-transformersby UKPLab

Python star image 5944 Version:v2.0.0 License: Permissive (Apache-2.0)

Multilingual Sentence & Image Embeddings with BERT
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Kit Solution Source

Parrot_Paraphraserby kandi1clickkits

Python star image 0 Version:v1.0.0

License: Permissive (Apache-2.0)

A practical and feature-rich paraphrasing framework to augment human intents in text form to build robust NLU models for conversational engines. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

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Parrot_Paraphraserby kandi1clickkits

Python star image 0 Version:v1.0.0 License: Permissive (Apache-2.0)

A practical and feature-rich paraphrasing framework to augment human intents in text form to build robust NLU models for conversational engines. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
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Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

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