cleaning readme

README.md

@@ -5,34 +5,36 @@ title: "Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Lear
 
 ## Overview
 
-This repository contains a collection of embedding datasets for evaluating quantum-classical support vector machines (QSVMs) using pre-trained classical models.  
-Each dataset is named using the convention:
+This repository provides a collection of embedding datasets for evaluating quantum-classical support vector machines (QSVMs) using embeddings from pre-trained classical models. Each dataset follows the naming convention:
 
+```
 <model_name>_<embedding_dim>.csv
+```
 
 Where:
+- `model_name`: the architecture used to generate the embeddings (e.g., `vit_b_16`, `efficientnet`, `vit_l_14@336px`)
+- `embedding_dim`: the dimensionality of the embedding vectors
+- The **last column** in each CSV represents the **class label**
 
-- `model_name`: the architecture used to generate the embeddings (e.g., `vit_b_16`, `efficientnet`, `vit_l_14@336px`, etc.)
-- `embedding_dim`: the dimensionality of the embedding vectors.
-- The **last column** in each CSV corresponds to the **class label**.
+Each CSV file is structured as a table:
+- Rows correspond to distilled training or testing samples
+- Columns represent embedding values followed by the class label
 
-Each file includes a tabular structure where:
-- Rows represent distilled training or testing samples.
-- Columns represent the embedding dimensions followed by a label column.
-
-These datasets are compatible with quantum kernel methods and hybrid learning pipelines as implemented in [QuantumVE](https://github.com/sebasmos/QuantumVE).
+These datasets are designed to support quantum kernel methods and hybrid pipelines, as implemented in [QuantumVE](https://github.com/sebasmos/QuantumVE).
 
 ---
 
 ## Repository Structure
 
-All datasets are stored in `.csv` format and follow the naming scheme:
+Datasets are stored in `.csv` format and organized into folders named according to the embedding configuration:
 
+```
 <model_name>_<embedding_dim>/
-├── train.csv   # Training embeddings + labels
-└── test.csv    # Testing embeddings + labels
+├── train.csv   # Training set with embeddings and labels
+└── test.csv    # Testing set with embeddings and labels
+```
 
-Example folders:
+### Example Folders:
 - `efficientnet_1536/`
 - `vit_b_16_512/`
 - `vit_l_14@336px_768/`
@@ -41,23 +43,23 @@ Example folders:
 
 ## Loading Datasets
 
-You can load and preview a dataset using Python:
+Example code to load and preview a dataset:
 
 ```python
 import pandas as pd
 
 df = pd.read_csv("vit_b_16_512/train.csv")
-X = df.iloc[:, :-1].values  # Embeddings
-y = df.iloc[:, -1].values   # Labels
-
+X = df.iloc[:, :-1].values  # Embedding features
+y = df.iloc[:, -1].values   # Class labels
+```
 
+---
 
-⸻
-
-Citation
+## Citation
 
 If you use this dataset collection, please cite:
 
+```bibtex
 @misc{cajas_quantumve_2025,
   author       = {Sebastián Andrés Cajas Ordóñez, Luis Torres, Mario Bifulco, Carlos Duran, Cristian Bosch, Ricardo Simon Carbajo},
   title        = {Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning},
@@ -66,18 +68,19 @@ If you use this dataset collection, please cite:
   note         = {GitHub repository},
   version      = {v1.0},
   howpublished = {\url{https://github.com/sebasmos/QuantumVE}},
-}
+```
 
+---
 
+## License
 
-⸻
+QuantumVE is free and open source, released under the MIT License.
 
-License
+---
 
-QuantumVE is free and open source, released under the MIT License.
+## Contact & Contributions
 
-⸻
+This dataset collection is maintained as part of the QuantumVE project.
 
-Contact & Contributions
+For questions or contributions, feel free to reach out or submit a pull request via the [GitHub repository](https://github.com/sebasmos/QuantumVE).
 
-This dataset is part of the QuantumVE project.