Self-Supervised-Learning-with-LoRA-Fine-Tuning

This notebook demonstrates a complete pipeline for:

Self-Supervised Learning (SSL) - Pre-training a model using denoising autoencoder
LoRA (Low-Rank Adaptation) - Parameter-efficient fine-tuning
Evaluation - Comparing SSL+LoRA against baseline models

Approach Overview

Phase 1: SSL pre-training on CNN/DailyMail dataset using denoising autoencoder
Phase 2: LoRA fine-tuning on DialogSum dataset with minimal labeled data
Phase 3: Evaluation using ROUGE, BLEU, and BERTScore metrics

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README.md		README.md
SSL_LORA.ipynb		SSL_LORA.ipynb
Self_Supervised_Learning_with_LoRA.ipynb		Self_Supervised_Learning_with_LoRA.ipynb