Github | Dataset(ModelScope) | Model | Paper
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks. However, interpreting charts with textual descriptions often leads to information loss, as it fails to fully capture the dense information embedded in charts. In contrast, parsing charts into code provides lossless representations that can… See the full description on the dataset page:
https://huggingface.co/datasets/xxxllz/Chart2Code-160k.