keytotext

Idea is to build a model which will take keywords as inputs and generate sentences as outputs.

Potential use case can include:

• Marketing

• Search Engine Optimization

• Topic generation etc.

• Fine tuning of topic modeling models

Model:

Keytotext is based on the Amazing T5 Model:

• k2t: Model

• k2t-tiny: Model

• k2t-base: Model

• mrm8488/t5-base-finetuned-common_gen (by Manuel Romero): Model

Training Notebooks can be found in the Training Notebooks Folder

Note: To add your own model to keytotext Please read Models Documentation

Usage:

Example usage:

Example Notebooks can be found in the Notebooks Folder

pip install keytotext

Finetune Model:

Keytotext now has a trainer class than be used to train and finetune any T5 based model on new data. Trainer docs here: Docs

Trainer example here:

from keytotext import KeytotextTrainer

UI:

UI:

pip install streamlit-tags

This uses a custom streamlit component built by me: GitHub

API:

API:

The API is hosted in the Docker container and it can be run quickly. Follow instructions below to get started

docker pull gagan30/keytotext

docker run -dp 8000:8000 gagan30/keytotext

This will start the api at port 8000 visit the url below to get the results as below:

http://localhost:8000/api?data=["India","Capital","New Delhi"]

Note: The Hosted API is only available on demand

BibTex:

To quote keytotext please use this citation

@misc{bhatia, 
      title={keytotext},
      url={https://github.com/gagan3012/keytotext}, 
      journal={GitHub}, 
      author={Bhatia, Gagan}
}

References:

• https://towardsdatascience.com/data-to-text-generation-with-t5-building-a-simple-yet-advanced-nlg-model-b5cce5a6df45 (Mathew Alexander)

• https://github.com/patil-suraj/question_generation (Suraj Patil)

• Amazing Video by 1LittleCoder here: https://www.youtube.com/watch?v=I0iBzP-SxFY about keytotext

• https://github.com/Shivanandroy/simpleT5 (Shivanand Roy)