Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (13.5%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Keywords to Sentences
Basic Info
- Host: GitHub
- Owner: gagan3012
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://share.streamlit.io/gagan3012/keytotext/UI/app.py
- Size: 4.04 MB
Statistics
- Stars: 452
- Watchers: 12
- Forks: 57
- Open Issues: 19
- Releases: 19
Topics
Metadata Files
README.md
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:
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
shell script
pip install keytotext
Trainer:
Keytotext now has a trainer class than be used to train and finetune any T5 based model on new data. Updated Trainer docs here: Docs
Trainer example here:
python
from keytotext import trainer
UI:
UI:
shell script
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
```shell script 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
bibtex
@misc{bhatia,
title={keytotext},
url={https://github.com/gagan3012/keytotext},
journal={GitHub},
author={Bhatia, Gagan}
}
References
- https://github.com/Shivanandroy/simpleT5 (Shivanand Roy)
- https://github.com/patil-suraj/question_generation (Suraj Patil)
- https://github.com/MathewAlexander/T5_nlg (Mathew Alexander)
Articles about keytotext:
- https://towardsdatascience.com/data-to-text-generation-with-t5-building-a-simple-yet-advanced-nlg-model-b5cce5a6df45 (Mathew Alexander)
- Amazing Video by 1LittleCoder here: https://www.youtube.com/watch?v=I0iBzP-SxFY about keytotext
- https://medium.com/mlearning-ai/generating-sentences-from-keywords-using-transformers-in-nlp-e89f4de5cf6b (Prakhar Mishra)
Owner
- Name: Gagan Bhatia
- Login: gagan3012
- Kind: user
- Website: https://www.linkedin.com/in/gbhatia30/
- Twitter: gaganbhatiaml
- Repositories: 135
- Profile: https://github.com/gagan3012
NLP Research | MLE
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Bhatia" given-names: "Gagan" orcid: "https://orcid.org/0009-0003-1972-501X" title: "Keytotext: Training LLMs for guided storytelling (Keywords to Sentences)" version: 1.5.0 date-released: 2021-05-01 url: "https://github.com/gagan3012/keytotext"
GitHub Events
Total
- Watch event: 9
- Pull request event: 1
Last Year
- Watch event: 9
- Pull request event: 1
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Gagan Bhatia | 4****2 | 1,573 |
| deepsource-autofix[bot] | 6****] | 4 |
| anath2110benten | 1****n | 2 |
| Johannes Rieke | j****e@g****m | 1 |
| DeepSource Bot | b****t@d****o | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 36
- Total pull requests: 55
- Average time to close issues: 4 days
- Average time to close pull requests: 7 days
- Total issue authors: 19
- Total pull request authors: 5
- Average comments per issue: 1.08
- Average comments per pull request: 0.04
- Merged pull requests: 53
- Bot issues: 0
- Bot pull requests: 5
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- gagan3012 (18)
- huyremy (1)
- RuiFeiHe (1)
- haibin-chen (1)
- skintflickz (1)
- creatonce (1)
- Thriliriel (1)
- TazeemKhan9 (1)
- pendekarcode (1)
- drscotthawley (1)
- aishwaryapisal9 (1)
- gaito-20 (1)
- ChunxuYang (1)
- varunakk (1)
- avaughan0 (1)
Pull Request Authors
- gagan3012 (47)
- deepsource-autofix[bot] (5)
- saied71 (2)
- jrieke (1)
- anath2110benten (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 243 last-month
- Total docker downloads: 34
- Total dependent packages: 0
- Total dependent repositories: 7
- Total versions: 68
- Total maintainers: 1
pypi.org: keytotext
Text Generation Using Keywords
- Homepage: https://github.com/gagan3012/keytotext
- Documentation: https://keytotext.readthedocs.io/
- License: MIT
-
Latest release: 2.3.2
published almost 4 years ago
Rankings
Maintainers (1)
Dependencies
- keytotext *
- streamlit *
- streamlit_tags *
- fastapi *
- keytotext *
- uvicorn *
- myst_parser *
- keytotext *
- pandas *
- torch *
- torch_xla *
- python 3.8.1-slim build