A text labelizing tool build using TensorFlow!
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Whilst learning tensorflow I stumbled upon the concept of embedding which I found quite interesting and taking the motivation through it, here I tried to create an NLP model that categorises abstract statements according to their function (e.g., objective, techniques, results, etc.) so that researchers can diversify through the literature and dig further in depth when needed.
The problem statement :
- The number of RCT papers published is growing, and those without organised abstracts can be difficult to read, slowing researchers' progress through the literature.
Solution :
- Creation of an NLP model that will do teh work of diversification of the input texts we provide and output the labels those suggest the intuitive information behind those chunks of literature.
Precisely, when we implement this in our own devices We'll be recreating and replicating better version of the deep learning model used in PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts, which was published in the year 2017.
Link of the paper for reference : PubMed 200k RCT
Follow through to get started!
This section is the list of any major frameworks/libraries used to bootstrap the subsequent project. Leave any add-ons/plugins for the acknowledgements section. Here are a few assets to work with :
- ReactJS
- Tensorflow
- Scikitlearn
- Keras
- Python3
- numpy
- Matplotlib
- Pandas(DF)
- Local GPU(For optimum acceleration)
Following are the instructions on setting up your project locally. To get a local copy up and running, follow these simple steps.
This is an example of how to list things you need to use the software and how to install them.
- pip
python -m pip install --upgrade pip
- npm
npm install npm@latest -g
Install TensorFlow with pip TensorFlow 2 packages are available
-
tensorflow —Latest stable release with CPU and GPU support (Ubuntu and Windows)
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tf-nightly —Preview build (unstable). Ubuntu and Windows include GPU support. Older versions of TensorFlow For TensorFlow 1.x, CPU and GPU packages are separate:
- tensorflow==1.15 —Release for CPU-only
- tensorflow-gpu==1.15 —Release with GPU support (Ubuntu and Windows) System requirements
- Python 3.7–3.9
- Python 3.9 support requires TensorFlow 2.5 or later.
- Python 3.8 support requires TensorFlow 2.2 or later.
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Clone the repo
git clone https://github.com/DrCybernotix/eSKIMo.git
After cloning just open the project folder in any IDE, (Recommend: Pycharm or Visual Studio) Run the test.py first or either you can do this from the terminal.
For terminal :
cd (paste the directory location where you inported the repository) python test py
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Install packages
npm install
Useful examples of how the model actually works and what does it do when provided costum inputs
For more examples, please refer to the (coming soon!) Documentation
- Add Changelog
- Add back to top links
- Add Additional Templates w/ Examples
- Add "components" document to easily copy & paste sections of the readme
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create! Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Twitter/Email - @DrCybernotix - 12shreyashh@gmail.com
Project Link: eSKIMo
The list of resources I found helpful whilst makingg this project and would like to give credit to :