dev-anket-laiser 2.2.18
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.
Requires Python: <3.12,>=3.9

> [!CAUTION]
> <h3>LAiSER is currently in development mode, features could be experimental. Use with caution!</h3>


<div align="center">
<img src="https://i.imgur.com/XznvjNi.png" width="70%"/>
<h1>Leveraging ​Artificial ​Intelligence for ​Skill ​Extraction &​ Research (LAiSER)</h1>
</div>

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

- [About](#about)
- [Requirements](#requirements)
- [Setup and Installation](#setup-and-installation)
- [Usage](#usage)
  - [Google Colab Setup](#google-colab-setup)
- [Funding](#funding)
- [Authors](#authors)
- [Partners](#partners)

## About

LAiSER is an innovative tool that harnesses the power of artificial intelligence to simplify the extraction and analysis of skills. It is designed for learners, educators, and employers who want to gain reliable insights into skill sets, ensuring that the information shared is both trusted and mutually intelligible across various sectors.

By leveraging state-of-the-art AI models, LAiSER automates the process of identifying and classifying skills from diverse data sources. This not only saves time but also enhances accuracy, making it easier for users to discover emerging trends and in-demand skills.

The tool emphasizes standardization and transparency, offering a common framework that bridges the communication gap between different stakeholders. With LAiSER, educators can better align their teaching methods with industry requirements, and employers can more effectively identify the competencies required for their teams. The result is a more efficient and strategic approach to skill development, benefiting the entire ecosystem.

## Requirements
- Python version >= Python 3.9. 
- A GPU with atleast 15GB video memory is essential for running this tool on large datasets.

## Setup and Installation

- Install LAiSER using pip:

  # For GPU support (recommended if you have a CUDA-capable GPU):
  ```shell
  pip install laiser[gpu]
  ```
  # For CPU-only environments:
  ```shell
  pip install laiser[cpu]
  ```
  # By default, torch and vllm GPU dependencies are included. Only when using the [cpu] extra will these GPU dependencies be excluded.

**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](https://www.python.org/downloads/).

You can check if your machine has a GPU available with:
```shell
python -c "import torch; print(torch.cuda.is_available())"
```

## Usage

As of now LAiSER can be used a python package in Google Colab or a local machine with GPU access. The steps to setup the tool are as follows:

### Google Colab Setup
LAiSER's Jupyter notebook is, currently, the fastest way to get started with the tool. You can access the notebook [here](https://github.com/LAiSER-Software/extract-module/blob/main/dev_space/Extract%20Function%20Colab%20Execution.ipynb).

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

- Sample code to import and verify laiser module
  ```shell
  from laiser.skill_extractor import Skill_Extractor
  print('\n\nInitializing the Skill Extractor...')
  # Replace 'your_model_id' and 'your_hf_token' with your actual credentials.
  AI_MODEL_ID = "your_model_id"  # e.g., "bert-base-uncased"
  HF_TOKEN = "your_hf_token"
  use_gpu = True  # Change to False if you are not using a GPU
  se = Skill_Extractor(AI_MODEL_ID=AI_MODEL_ID, HF_TOKEN=HF_TOKEN, use_gpu=use_gpu)
  print('The Skill Extractor has been initialized successfully!\n')
  print("LAiSER package loaded successfully!")
  ```


## Funding
<div align="center">
<img src="https://i.imgur.com/XtgngBz.png" width="100px"/>
<img src="https://i.imgur.com/a2SNYma.jpeg" width="130px"/>
</div>

## Authors
<a href="https://github.com/LAiSER-Software/extract-module/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=LAiSER-Software/extract-module" />
</a>

## Partners
<div align="center">
<img src="https://i.imgur.com/hMb5n6T.png" width="120px"/>
<img src="https://i.imgur.com/dxz2Udo.png" width="70px"/>
<img src="https://i.imgur.com/5O1EuFU.png" width="100px"/>
</div>



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