Problem
Legal text is dense, multilingual, and costly to triage manually.
NLP & ML Research · 2024
Legal Clarity
Fine-tuned multilingual transformer summarization and translation pipeline for legal text, packaged as a Flask service.
Approach
I fine-tuned transformer models for summarization and translation, then wrapped the workflow in a reusable Flask service.
Result
The project translated research-style NLP work into a more deployable document simplification workflow.
Step 1 of 4: Legal document
Highlights
- Fine-tuned transformer models (Pegasus, T5, IndicBART) in PyTorch to summarize and translate long-form, multilingual legal text for faster review.
- Packaged the pipeline into a Flask service for repeatable inference and downstream integration.
Summary
This project focuses on making legal text easier to process through summarization and translation workflows.
What stands out
- Fine-tuned Pegasus, T5, and IndicBART variants.
- Worked across multilingual legal content.
- Packaged the workflow behind Flask endpoints for easier integration.