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The Smart Identity Proof of Concept was produced by ETSI Special Committee USER Group and is described in ETSI TR 103 875-2. It is intended to demonstrate the feasibility of the Smart Identity as it is defined in TR 103 875-1
## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
It defines, for a specific use case (e-health) the Smart Identity (ID) and provides an associated Proof of Concept (PoC)
## Integrate with your tools
-[ ] [Set up project integrations](https://forge.etsi.org/rep/user/tr-103-875-2/smartid-poc/-/settings/integrations)
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-[ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
## Getting started
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
The Smart Identity Proof of Concept is run in the Google Colaboratory notebook (https://colab.research.google.com/).
***
The `POC_SmartID_v3.ipynb` file is to be uploaded to Google Colaboratory and the PoC is executed from there.
# Editing this README
## Further details
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
The Smart Identity Proof of Concept is documented in ETSI TR 103 875-2.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
For the creation of AI models for Smart ID, a pre-trained neural network model based on Transformers was used. It is called CamemBERT™.
## Name
Choose a self-explaining name for your project.
The Camembert™-Base-XNLI zero-stroke pre-trained transfer learning algorithm was used because classical machine learning algorithms did not give accurate results during training on the dataset.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
Camembert™-base-XNLI is a transformer-based natural language processing model written in Python®. It was trained on XNLI (Multilingual Natural Language Inference) which was published by Facebook. It is mainly used to determine the probability of a corpus of text belonging to a predefined class.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
To implement the Camembert-Base-XNLI algorithm for data entry and resource prediction, the following tools used are:
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
* Python® 3.7
* Transformers 4.24.0
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
Library for downloading and training pre-trained natural language processing models.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
* Tensorflow®-Text 2.9.0
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
TensorFlow® library to perform operations on text for pre-processing.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
* Pandas™ 1.3.5
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For managing datasets using dataframes
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
* Google Colab® 1.0.0
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
A cloud service offered by Google®, based on Jupyter Notebook and allowing to train ML models directly online, without the need to install anything.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
## License
For open source projects, say how it is licensed.
For a better visualization of the results of the main model, web interfaces have been developed with the Gradio API version 3.12.1.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
