updated readme, ruff formatted all files

Cleaned up code, added multiseed training wrapper, PyTorch profiler training option, updated gradio demo, made changes to research paper to match new changes and new training results from adding new training techniques, architecture.md now explains all designs and decisions

Medium training run

Major improvements: real titles, anti-overfitting, better demo UX

Add English language filter to all data downloads

Fixed compiling issue, added legnth penalty, and atttempting freezing encoder layers 0-5 to lower parameters and preserve T5's langauge understanding.

Clean up codebase and fix training bugs

Update LexiMind: improved training, model architecture, and evaluation

Expand datasets: GoEmotions (28 emotions), Yahoo Answers (10 topics), BookSum

Full training run, code cleanup, mypy/ruff fixes

Style: Apply ruff formatting

Style: Fix linting errors and organize imports (ruff & mypy)

Summarization fix through pipeline

chore: snapshot current refinements