Add NLP Model Fine-Tuning for Better Resume Parsing

[sriraamav]

Description:

The current resume parsing process relies on basic text cleaning and skill extraction. This issue focuses on fine-tuning a pre-trained NLP model (e.g., BERT, RoBERTa) specifically for resume parsing to better handle ambiguous or missing skills.

Tips for the issue:

• Use the Hugging Face transformers library to fine-tune a pre-trained model on a custom resume dataset.
• Train the model to recognize skill-related phrases and context from resumes.
• Test the fine-tuned model against the current rule-based skill extraction method to compare performance.

To do:

• Ask us to assign the issue.
• Once the issue is assigned, you can start working on it.
• Create a PR.

Resource:

• Hugging Face transformers library
• Pre-trained BERT, RoBERTa models

Notes:
The task is assigned on a first-come, first-serve basis, and the contributor must report progress every 3 days to ensure active development.