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
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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
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-[ ] [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.
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-[ ] [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
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The Smart Identity Proof of Concept is documented in ETSI TR 103 875-2.
## Suggestions for a good README
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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
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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
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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
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To implement the Camembert-Base-XNLI algorithm for data entry and resource prediction, the following tools used are:
## Visuals
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* Python® 3.7
* Transformers 4.24.0
## Installation
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Library for downloading and training pre-trained natural language processing models.
## Usage
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* Tensorflow®-Text 2.9.0
## Support
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TensorFlow® library to perform operations on text for pre-processing.
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* Pandas™ 1.3.5
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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
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## License
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For a better visualization of the results of the main model, web interfaces have been developed with the Gradio API version 3.12.1.
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