Update README.md

README.md

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 ---
 license: mit
+tags:
+  - chemistry
+  - raman-spectroscopy
+  - deep-learning
+  - self-supervised-learning
+  - time-series
 ---
+
+# GEMS: Multi-Source Raman Spectral Dataset
+
+This repository hosts the comprehensive, multi-domain Raman spectral datasets utilized in the **GEMS** framework. 
+
+The data is meticulously structured to support our proposed multi-stage training methodology, encompassing initial pre-training, contrastive learning, and diverse downstream fine-tuning tasks across different domains.
+
+## Data Pre-processing & Format
+All spectral data across the sub-directories have been uniformly pre-processed. They are **ready for direct model input** without requiring additional transformations:
+* **Spectral Range:** 0 – 3500 cm⁻¹
+* **Sequence Length:** Exactly 3,500 data points per spectrum.
+* **Normalization:** Min-Max normalization has been applied to all samples.
+* **Format:** Standard array formats ready for PyTorch/NumPy ingestion.
+
+## Dataset Structure & Training Stages
+
+The repository is organized into 7 distinct subsets. Each directory serves a highly specific role in the GEMS pipeline, ensuring continuous model optimization and rigorous experimental validation.
+
+### Core Training Stages
+
+| Directory | Stage | Primary Purpose |
+| :--- | :--- | :--- |
+| `QMe14S` | **Stage 1** | **Foundation Pre-training.** Used to train the initial foundational encoder. |
+| `RRUFF_CL` | **Stage 2** | **Contrastive Learning.** Directly inherits the parameters from Stage 1 for continued training, aiming to build robust, generalized feature representations. |
+
+### Downstream Fine-Tuning & Analytical Experiments
+The remaining datasets leverage the pre-trained weights from Stage 2 for specific downstream applications and model evaluation:
+
+| Directory | Experimental Focus / Task |
+| :--- | :--- |
+| `RRUFF_FT` | **Architecture & Hyperparameter Optimization.** Used as the benchmark to optimize the model's structural design and training configurations. |
+| `Bacteria_ID` | **Few-Shot Learning Study.** Evaluates the model's generalization capabilities and performance stability when fine-tuned on highly limited annotated data. |
+| `skincancer` | **Interpretability Analysis.** Investigates which specific spectral wavebands and features the deep learning model focuses on for medical diagnostics. |
+| `Mutant_wheat` | **Downstream Fine-Tuning.** Agricultural domain classification task. |
+| `microplastic` | **Downstream Fine-Tuning.** Environmental monitoring and material classification task. |
+
+## Usage 
+
+You can easily download the entire dataset or specific sub-directories using the official Hugging Face CLI:
+
+```bash
+# Download the entire dataset
+hf download YourUsername/YourDatasetName --repo-type dataset --local-dir ./data
+
+# Or download a specific subset (e.g., Stage 1 Pre-training data)
+hf download YourUsername/YourDatasetName QMe14S/* --repo-type dataset --local-dir ./data/QMe14S