Resume Filter is a machine learning project that predicts the role of a candidate based on their resume. The model utilizes the Hugging Face pre-trained model fazni/distilbert-base-uncased-career-path-prediction and is trained on the fazni/role-based-on-skills-2.0 dataset.
The model is deployed and running on the Hugging Face model hub in the fazni/Resume-filter-plus-QA-documents space. You can check out the live model and explore its predictions.
- Pre-trained Model: fazni/distilbert-base-uncased-career-path-prediction
- Training Dataset: fazni/role-based-on-skills-2.0
- Live Model: Resume Filter on Hugging Face
In addition to the role prediction model, the project also incorporates a set of Q&A documents using the OpenAI API. This feature enhances the understanding and interaction capabilities of the model, providing more context and information related to candidate resumes.
To use the project locally, follow these steps:
-
Clone the repository:
git clone https://github.com/farookfazni/Resume_filter.git
-
Install dependencies:
pip install -r requirements.txt
-
Run the Streamlit application:
streamlit run app.py
This will launch the application locally, and you can access it in your web browser.
Visit the local URL provided by Streamlit (usually http://localhost:8501) in your web browser. Upload a resume, and the model will predict the candidate's role.
If you'd like to contribute to the project, feel free to fork the repository and submit a pull request.
If you encounter any issues or bugs, please open an issue on the GitHub Issues page.
This project is licensed under the MIT License.
Happy filtering!