Skip to main content

LAiSER (Leveraging Artificial Intelligence for Skill Extraction & Research) is a tool designed to help learners, educators, and employers extract and share trusted information about skills. It uses a fine-tuned language model to extract raw skill keywords from text, then aligns them with a predefined taxonomy. You can find more technical details in the project’s paper.md and an overview in the README.md.

Project description

Leveraging ​Artificial ​Intelligence for ​Skill ​Extraction &​ Research (LAiSER)

Contents

LAiSER is a tool that helps learners, educators and employers share trusted and mutually intelligible information about skills​.

About

Requirements

  • Python version >= Python 3.12.
  • A GPU with atelast 15GB video memory is essential for running this tool on large datasets.

Setup and Installation

i. Download the repository

Before proceeding to LAiSER, you'd want to follow the steps below to install the required dependencies:

  • Clone the repository using
    git clone https://github.com/Micah-Sanders/LAiSER.git
    
    or download the zip(link) file and extract it.

ii. Install the dependencies

[!NOTE] If you intend to use the Jupyter Notebook interface, you can skip this step as the dependencies will be installed seperately in the Google Colab environment.

Install the required dependencies using the command below:

  pip install -r requirements.txt

NOTE: Python 3.9 or later, preferably 3.12, is expected to be installed on your system. If you don't have Python installed, you can download it from here.

Usage

As of now LAiSER can be used a command line tool or from the Jupyter notebook(Google Colab). The steps to setup the tool are as follows:

Google Colab Setup(preferred)

LAiSER's Jupyter notebook is, currently, the fastest way to get started with the tool. You can access the notebook here.

  • Once the notebook is imported in google colaboratory, connect to a GPU-accelerated runtime(T4 GPU) and run the cells in the notebook.

Command Line Setup

To use LAiSER as a command line tool, follow the steps below:

  • Navigate to the root directory of the repository and run the command below:

    pip install dev-laiser
    
  • Once the installation is complete, you can run the tool using the command below:

    TODO: add an example of importing and initiating the skillExtractor class
    

Funding

Authors

Partners


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

dev_laiser-0.2.3.tar.gz (13.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dev_laiser-0.2.3-py3-none-any.whl (17.2 kB view details)

Uploaded Python 3

File details

Details for the file dev_laiser-0.2.3.tar.gz.

File metadata

  • Download URL: dev_laiser-0.2.3.tar.gz
  • Upload date:
  • Size: 13.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.0

File hashes

Hashes for dev_laiser-0.2.3.tar.gz
Algorithm Hash digest
SHA256 eb091c9973b81fec7113b96cbae5c4e8681bdf51986b0b3801c71880dbc1bfef
MD5 fefa6641e6543f883cd38e346bfe5c45
BLAKE2b-256 b59cc5a7a7b5d76b1f46b0e52a8790169f9c7f2e5d20b7f29e3e6d0768cb4237

See more details on using hashes here.

File details

Details for the file dev_laiser-0.2.3-py3-none-any.whl.

File metadata

  • Download URL: dev_laiser-0.2.3-py3-none-any.whl
  • Upload date:
  • Size: 17.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.0

File hashes

Hashes for dev_laiser-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 c3457f9379b372579bf4c1176713283502f1736107f28ba161235c38f2dca37c
MD5 18b41da48504737193892af800451bf4
BLAKE2b-256 d819ee32da4b1615a938f71eda068dcec5720b5539a605f819e31f339d53ab15

See more details on using hashes here.

Supported by

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