Skip to main content

Text Generation Using Keywords

Project description

keytotext

pypi Version Downloads Open In Colab Streamlit App API Call Docker Call HuggingFace Documentation Status Code style: black

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: HuggingFace

  • 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: Open In Colab

Example Notebooks can be found in the Notebooks Folder

pip install keytotext

carbon (3)

Trainer:

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: Open In Colab

from keytotext import trainer

image

UI:

UI: Streamlit App

pip install streamlit-tags

This uses a custom streamlit component built by me: GitHub

image

API:

API: API Call Docker Call

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"]

k2t_json

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

Articles about keytotext:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

keytotext-1.3.7.tar.gz (721.4 kB view details)

Uploaded Source

Built Distribution

keytotext-1.3.7-py3-none-any.whl (9.4 kB view details)

Uploaded Python 3

File details

Details for the file keytotext-1.3.7.tar.gz.

File metadata

  • Download URL: keytotext-1.3.7.tar.gz
  • Upload date:
  • Size: 721.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.25.1

File hashes

Hashes for keytotext-1.3.7.tar.gz
Algorithm Hash digest
SHA256 eb1e4290bb8103a82929a009957b1cff94d9adae48359883a43aa7fc52fc4861
MD5 9cba3412c27a36ae280d729a474ac15e
BLAKE2b-256 2e5b04ca1a8a79bafc0b52b6f64b5a167fae108a82fcb438b6c0b3f79d7917ac

See more details on using hashes here.

File details

Details for the file keytotext-1.3.7-py3-none-any.whl.

File metadata

  • Download URL: keytotext-1.3.7-py3-none-any.whl
  • Upload date:
  • Size: 9.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.25.1

File hashes

Hashes for keytotext-1.3.7-py3-none-any.whl
Algorithm Hash digest
SHA256 0ace1e1522990c5ec39d253f54d4eaf2f07b24aa6f0b887050b87a84b0e46486
MD5 4f377a54bab965d4f052449a83d97c0b
BLAKE2b-256 f9ccfdaa691044562956742f251ffe4715ec9d227e0f3f9108b8814330ec1f6c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page