---
title: GEMS Generalized Raman Classifier
emoji: 📊
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
---

# 📊 GEMS: Generalized Raman Classifier Webserver

Welcome to the official interactive web application for **GEMS (Foundation Models for Universal, Data-efficient Classification of Raman Spectra)**.

This Space provides a highly convenient, no-code graphical interface. It empowers researchers and practitioners to seamlessly perform fine-tuning, automated evaluation, and rapid predictions using the powerful GEMS foundation model.

## 🔗 Quick Links

* **Source Code:** Explore the core implementation, model architecture, and local deployment instructions in our [GitHub Repository](https://github.com/JunhanCai/GEMS).
* **Datasets:** Access the complete, pre-processed multi-stage datasets (including pre-training, contrastive learning, and diverse downstream tasks) at our [Hugging Face Dataset Repo](https://huggingface.co/datasets/JunhanCai/GEMS-Raman-Dataset).

## 🚀 How to Test it Out?

You can test this web server immediately without preparing your own data! We have provided several pre-processed downstream datasets that are 100% compatible with this interface.

**Step-by-step Guide:**
1. Visit our [GEMS-Raman-Dataset](https://huggingface.co/datasets/JunhanCai/GEMS-Raman-Dataset) page.
2. Download any downstream task subset that interests you (e.g., the `.npy` files from the `microplastic`, `skincancer`, or `Bacteria_ID` folders).
3. Return to this Space and upload the downloaded `spectral.npy`, `labels.npy`, and `wavenumbers.npy` files into the **Fine-tune** panel.
4. *(Optional)* Leave the `Pretrained Model` field empty! The server will automatically load our built-in foundation weights.
5. Click **Start Fine-Tuning Job** to witness the automated AutoML pipeline and get your classification reports.

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**Note:** Due to the hardware constraints of the free Hugging Face CPU tier, training may take some time. For production-level speed, we highly recommend pulling the Docker image or source code from our GitHub and deploying it locally on a GPU-enabled machine.