Update README.md

README.md

@@ -6,4 +6,52 @@ configs:
   data_files:
   - split: train
     path: "vb-mols-test.parquet"
----
+---
+
+[![](https://img.shields.io/badge/arXiv-2503.07014-c72c2c.svg)](https://arxiv.org/abs/2503.07014)
+[![](https://img.shields.io/badge/huggingface-ckpt--of--vib2mol-dd9029)](https://huggingface.co/xinyulu/vib2mol)
+[![](https://img.shields.io/badge/huggingface-vibench-dd9029)](https://huggingface.co/datasets/xinyulu/vibench)
+[![](https://img.shields.io/badge/figshare-10.6084/m9.figshare.28579832-2243da)](https://doi.org/10.6084/m9.figshare.28579832)
+
+# introduction
+This dataset contains the datasets used in our paper, ***Vib2Mol: from vibrational spectra to molecular structures—a versatile deep learning model***. 
+The full paper provides more details and is available on [arXiv](https://arxiv.org/abs/2503.07014).<br>
+All datasets are pre-split for training, validation, and testing. 
+To reproduce our results, you can find detailed steps, codes, and training logs in our [GitHub repository](https://github.com/X1nyuLu/vib2mol). <br>
+A copy of the full dataset is also available for download on [Figshare](https://figshare.com/articles/dataset/README_md/28579832).
+
+# Demonstration
+
+For demonstration purposes, this dataset card provides a copy of the **VB-mols test set**. 
+Please note that this is a **small demo set**, not the entire dataset used for our research.
+
+# How to Use
+
+After downloading the datasets, you can use the following Python code to visualize the data.
+
+```python
+import lmdb
+import pickle
+import pandas as pd
+from tqdm import tqdm
+
+# Open the database
+db = lmdb.open('path/to/lmdb/data', subdir=False, lock=False, map_size=int(1e11))
+
+# Load data and convert to DataFrame
+with db.begin() as txn:
+    data = list(txn.cursor())
+
+df = pd.DataFrame([pickle.loads(item[1]) for item in tqdm(data)])
+
+# Now you can work with the DataFrame `df`
+print(df.head())
+```
+
+# Acknowledgements
+This work was supported by the National Natural Science Foundation (Grant No: 22227802, 22021001, 22474117 and 22272139) of China and the Fundamental Research Funds for the Central Universities (20720220009 and 20720250005) and Shanghai Innovation Institute.
+
+# Contact
+Welcome to contact us or raise issues if you have any questions. 
+Email: xinyulu@stu.xmu.edu.cn
+