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/LAiSER-Software/extract-module.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-0.2.4.tar.gz (13.9 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-0.2.4-py3-none-any.whl (17.7 kB view details)

Uploaded Python 3

File details

Details for the file dev_laiser-0.2.4.tar.gz.

File metadata

  • Download URL: dev_laiser-0.2.4.tar.gz
  • Upload date:
  • Size: 13.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.0

File hashes

Hashes for dev_laiser-0.2.4.tar.gz
Algorithm Hash digest
SHA256 1c30eaebf5136dbf0a75393c7f052fb2deac2667cf803d48433dca9574f78b4a
MD5 c1b915d23d2d280ef15027c931e2d5fc
BLAKE2b-256 043fd48f7dac09b3adfa276b37e716442cefe64f020e018252fc1769ce5364e8

See more details on using hashes here.

File details

Details for the file dev_laiser-0.2.4-py3-none-any.whl.

File metadata

  • Download URL: dev_laiser-0.2.4-py3-none-any.whl
  • Upload date:
  • Size: 17.7 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-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 e9619f69eb89459e5f14809eeec405f69081467a33cb52778120fd7024f1b45f
MD5 d4814771fd77ddfae6edbcfa6fc261d8
BLAKE2b-256 332517e13c9499fc57b87986a8412eb9372e9dac85b7fabede0af357d01890b3

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