Metadata-Version: 2.2
Name: dev-laiser
Version: 0.2.2
Summary: 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.
Home-page: https://github.com/LAiSER-Software/extract-module
Author: Satya Phanindra Kumar Kalaga, Bharat Khandelwal, Prudhvi Chekuri
Author-email: phanindra.connect@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: psutil
Requires-Dist: scikit_learn
Requires-Dist: skillNer
Requires-Dist: spacy
Requires-Dist: transformers
Requires-Dist: accelerate
Requires-Dist: bitsandbytes
Requires-Dist: datasets
Requires-Dist: huggingface_hub
Requires-Dist: peft
Requires-Dist: torch
Requires-Dist: trl
Requires-Dist: ipython
Requires-Dist: python-dotenv
Requires-Dist: vllm
Requires-Dist: tqdm
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

<div align="center">
<img src="https://i.imgur.com/XznvjNi.png" width="70%"/>
<h2>Leveraging â€‹Artificial â€‹Intelligence for â€‹Skill â€‹Extraction &â€‹ Research (LAiSER)</h2>
</div>

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

- [About](#about)
- [Requirements](#requirements)
- [Setup and Installation](#setup-and-installation)
  - [i. Download the repository](#i-download-the-repository)
  - [ii. Install the dependencies](#ii-install-the-dependencies)
- [Usage](#usage)
  - [Google Colab Setup(preferred)](#google-colab-setuppreferred)
  - [Command Line Setup](#command-line-setup)
- [Funding](#funding)
- [Authors](#authors)
- [Partners](#partners)
<!-- - [Examples](#examples) -->
- [Funding](#funding)
- [Authors](#authors)
- [Partners](#partners)

## 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 
  ```shell
  git clone https://github.com/Micah-Sanders/LAiSER.git
  ```
  or download the [zip(link)](https://github.com/Micah-Sanders/LAiSER/archive/refs/heads/main.zip) 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:
  ```shell
    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](https://www.python.org/downloads/).


## 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](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.

### 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:
  ```shell
  pip install laiser-dev
  ```

- 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 -->
  ```shell
  TODO: add an example of importing and initiating the skillExtractor class
  ```

<!-- > [!CAUTION]
> - If you encounter any `*.dll` file missing errors, make sure you downgrade the pytorch version to `2.2.2`.
```shell
pip install pytorch=2.2.2
``` -->


<!-- ## Examples -->


## 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>



</br>
<!-- <p align='center'> <b> Made with PassionðŸ’–, Data ScienceðŸ“Š, and a little magic!ðŸª„ </b></p> -->
