📁 Project Structure

Resume_parser/
│
├── README.md                      # Project documentation
├── Resume_parser_final.ipynb      # Main notebook for development and experimentation
│
├── base_config.cfg                # Base configuration for NLP model
├── config.cfg                     # Final training configuration file
│
├── jz_skill_patterns.jsonl        # Custom skill patterns for entity recognition
├── train_data.pk                  # Serialized training dataset
├── train_new.spacy                # Trained spaCy model data

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📂 File Descriptions

📘 README.md

Contains project overview, setup instructions, and usage guide.

📓 Resume_parser_final.ipynb

Jupyter Notebook used for:

• Data preprocessing
• Model training
• Testing and evaluation

⚙️ base_config.cfg

Initial configuration file used to define the NLP pipeline structure before training.

⚙️ config.cfg

Final configuration file used for training the spaCy model with optimized parameters.

🧠 jz_skill_patterns.jsonl

Contains predefined skill patterns used for:

• Named Entity Recognition (NER)
• Skill extraction from resumes

📦 train_data.pk

Pickle file containing the processed training dataset.

🤖 train_new.spacy

Trained spaCy model file used for:

• Extracting entities
• Running inference on resumes

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📝 Notes

• The project uses spaCy for building and training the NLP model
• Custom NER is implemented for better skill extraction
• Training data can be extended to improve model accuracy