Feature/text modality

README.md

@@ -1,63 +1 @@
-# MLOps UvA Bachelor AI Course: Medical Image Classification Skeleton Code
-
-![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)
-![Build Status](https://github.com/yourusername/mlops_course/actions/workflows/ci.yml/badge.svg)
-![Code Style: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)
-
-A repo exemplifying **MLOps best practices**: modularity, reproducibility, automation, and experiment tracking.
-
-This project implements a standardized workflow for training neural networks on medical data (PCAM/TCGA). 
-
-The idea is that you fill in the repository with the necessary functions so you can execute the ```train.py``` function. Please also fill in this ```README.md``` clearly to setup, install and run your code. 
-
-Don't forget to setup CI and linting!
-
----
-
-## 🚀 Quick Start
-
-### 1. Installation
-Clone the repository and set up your isolated environment.
-
-```bash
-# 1. Create a virtual environment
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-
-# 2. Install the package in "Editable" mode
-pip install -e .
-
-# 3. Install pre-commit hooks
-pre-commit install
-```
-
-### 2. Verify Setup
-```bash
-pytest tests/
-```
-
-### 3. Run an Experiment
-```bash
-python experiments/train.py --config experiments/configs/train_config.yaml
-```
-
----
-
-## 📂 Project Structure
-
-```text
-.
-├── src/ml_core/          # The Source Code (Library)
-│   ├── data/             # Data loaders and transformations
-│   ├── models/           # PyTorch model architectures
-│   ├── solver/           # Trainer class and loops
-│   └── utils/            # Loggers and experiment trackers
-├── experiments/          # The Laboratory
-│   ├── configs/          # YAML files for hyperparameters
-│   ├── results/          # Checkpoints and logs (Auto-generated)
-│   └── train.py          # Entry point for training
-├── scripts/              # Helper scripts (plotting, etc)
-├── tests/                # Unit tests for QA
-├── pyproject.toml        # Config for Tools (Ruff, Pytest)
-└── setup.py              # Package installation script
-```
+## Here you find the TCGA TITAN visual embeddings, raw text reports for each patient, and the vLLM code you need to use