cv_matcher

Description about files:

1. data/data-list of resumes downloaded from Kaggle resume dataset.
   
2. parsed_resumes - Extracted JSON data from CV PDFs using pyPDF2 and pyresparser.
   
3. job_description_records - Extracted JSON data of job descriptions from the Huggingface dataset (as prescribed).
   
4. res.py - A Python script to extract details like skills and experience from PDFs.
   
5. data_gather.py - A Python script used to obtain 15 job descriptions from the Huggingface dataset.
   
6. parsed_resume.json - A sample JSON file describing the extracted data from a single CV PDF.
   
7. Embedding_and_similarity.ipynb - An IPython Notebook file used for tokenizing, performing embeddings on the obtained JSON files, and calculating cosine similarities.

Models and tools utilized:

1. Pyresparser- A Python library for parsing and extracting information from resumes (CVs). It's a powerful tool designed to efficiently extract essential details from resumes, including:
   

   Candidate's name
   Contact information
   Skills
   Work experience
   Educational background
   In some cases, Pyresparser may not extract education details as accurately as required. To address this limitation, we've included custom code to concurrently extract education details in the desired format.

2. SentenceTransformer - A versatile Python library that allows you to obtain embeddings for sentences or entire documents. It leverages pre-trained models to represent text data as dense vectors. One of the models I've used is paraphrase-MiniLM-L6-v2, which is particularly effective for various natural language processing tasks.

Steps to run the run the project:

1. Install the requirements:

pip install -r requirements.txt

2. Run the res.py file:
   

python res.py

3. Run the data_gather.py file:
   

python data_gather.py

4. Run the Embedding_and_similarity.ipynb file.