Update README.md

Add the figure of data visualization

README.md

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 Model reviews and evaluations related to this dataset can be directly accessed and used via the LLM4Mol link: [LLM4Mol](https://github.com/AI-HPC-Research-Team/LLM4Mol).
 
+## Data Visualization
+
+We employ visualization techniques to analyze the **suitability** of data sources for language models and **chemical space coverage**. The figure below illustrates our use of different visualization methods to analyze text length distributions and token counts generated by each model's tokenizer across various text data types. This approach evaluates the adaptability of language models to the textual characteristics of our dataset.
+
+![Data Visualization](data_visualization.png)
+
+We also focus on the top 10 scaffolds within the dataset, counting the number of molecules for each scaffold. Here, semi-transparent bars represent the total count, while solid bars indicate the quantity in the training set. On the other hand, for the analysis of \textbf{chemical space coverage}, we choose molecular weight (MW), LogP, the number of aromatic rings, and the Topological Polar Surface Area (TPSA) as descriptors. We examine the distribution and correlation of these descriptors within the dataset, providing insights into the chemical diversity and complexity present in our data.
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 ## Acknowledgments
 
 The development of the ChEBI-20-MM dataset was inspired by the ChEBI-20 in molecule generation and captioning initiated by MolT5. Additional data information supplements are derived from PubChem.