| --- |
| license: apache-2.0 |
| base_model: google/flan-t5-small |
| tags: |
| - resume |
| - chatbot |
| - flan-t5 |
| - text2text-generation |
| - question-answering |
| - fine-tuning |
| library_name: transformers |
| pipeline_tag: text2text-generation |
| --- |
| |
| # Vaishnavi ResumeBot - FLAN-T5 |
|
|
| This repository contains a personal ResumeBot built by fine-tuning `google/flan-t5-small` on resume-based question-answer pairs. |
|
|
| ## About |
|
|
| The bot is designed to answer questions about Vaishnavi V M's academic background, technical skills, projects, and contact information. |
|
|
| ## Base Model |
|
|
| `google/flan-t5-small` |
|
|
| ## Main Topics |
|
|
| - MSc Data Science at Dayananda Sagar University |
| - BSc Mathematics and Computer Science |
| - Python, Machine Learning, Deep Learning, NLP, SQL |
| - Medical X-ray Abnormality Localization using Attention Mechanisms |
| - Driver Drowsiness Detection |
| - Phishing Website Detection |
| - Language Identification and Accent Detection |
| - Social Media Addiction Risk Prediction |
|
|
| ## Repository Files |
|
|
| - `Vaishnavi_ResumeBot_FLAN_T5.ipynb` - complete Colab notebook |
| - `resume_qa_dataset.csv` - training question-answer pairs |
| - `app.py` - Gradio ResumeBot interface |
| - `inference.py` - command-line prediction example |
| - `push_to_hub.py` - upload your trained model to Hugging Face |
| - `requirements.txt` - required libraries |
| - `.gitignore` - files to ignore in Git |
| - `MODEL_FILES_README.txt` - explains how trained model files are created |
|
|
| ## How to Train |
|
|
| Open `Vaishnavi_ResumeBot_FLAN_T5.ipynb` in Google Colab. |
|
|
| Use: |
|
|
| **Runtime -> Change runtime type -> T4 GPU** |
|
|
| Then run the notebook from top to bottom. |
|
|
| After training, save the model using: |
|
|
| ```python |
| model.save_pretrained("vaishnavi-resumebot-flan-t5") |
| tokenizer.save_pretrained("vaishnavi-resumebot-flan-t5") |
| ``` |
|
|
| This produces files such as: |
|
|
| - `config.json` |
| - `generation_config.json` |
| - `model.safetensors` |
| - tokenizer files |
| - `spiece.model` |
|
|
| ## Run the App Locally |
|
|
| ```bash |
| pip install -r requirements.txt |
| python app.py |
| ``` |
|
|
| ## Example Questions |
|
|
| - What is your educational background? |
| - What are your technical skills? |
| - Tell me about your medical X-ray project. |
| - Which algorithms did you use for phishing detection? |
| - What is your language identification project? |
| - What is your email address? |
|
|
| ## Important |
|
|
| The final fine-tuned `model.safetensors` is not included in the starter repository because it must be generated by actually training the model. |
|
|
