Usage and Output

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.

HuggingFace Setup

• Follow this article to create an account in HuggingFace and activate access tokens to access the models.

Request Gemma Access

• Open this link to request access for the google/gemma-2b-it model.

• Follow through the instructions provided in the page and send access request by filling out the requested details.

Create Colab Secret Keys

• Click the keys (can be found in the below image) button.

• Fill HF_TOKEN in the Name field and your huggingface access token in the value field.

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:

  python main.py

CAUTION

• If you encounter any *.dll file missing errors, make sure you downgrade the pytorch version to 2.2.2.

    pip install pytorch=2.2.2
  

Output

Output Column Descriptions:

• Research ID - Unique ID of each job description.
• Raw Skill - Skill Extracted by the model.
• Skill Tag - Unique ID of taxonomy skill that matches the Raw Skill.
• Correlation Coefficient - Describes the closeness of Raw Skill and Skill Tag.