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+ ---
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+ license: cdla-permissive-2.0
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+ task_categories:
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+ - text-generation
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+ tags:
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+ - ocr
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+ - chart
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+ pretty_name: SynthChartNet
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+ size_categories:
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+ - 1M<n<10M
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+ ---
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+ # SynthChartNet
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+
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+ <div style="display: flex; justify-content: center; align-items: center;">
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/663e1254887b6f5645a0399f/EhyekK2QdOe9PFID8PK4R.png" alt="Chart Example" style="width: 500px; height: auto">
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+ </div>
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+
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+ **SynthChartNet** is a multimodal dataset designed for training the **SmolDocling** model on chart-based document understanding tasks. It consists of **1,981,157** synthetically generated samples, where each image depicts a chart (e.g., line chart, bar chart, pie chart, stacked bar chart), and the associated ground truth is given in **OTSL** format.
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+
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+ Charts were rendered using a diverse set of visualization libraries: **Matplotlib**, **Seaborn**, and **Pyecharts**, enabling visual variability in layout, style, and color schemes.
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+
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+ ---
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+
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+ ## Dataset Statistics
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+
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+ * **Total samples**: 1,981,157
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+
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+ * **Training set**: 1,981,157
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+
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+ * **Modalities**: Image, Text (OTSL format)
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+
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+ * **Chart Types**: Line, Bar, Pie, Stacked Bar
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+
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+ * **Rendering Engines**: Matplotlib, Seaborn, Pyecharts
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+
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+ ---
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+
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+ ## Data Format
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+
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+ Each dataset entry is structured as follows:
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+
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+ ```json
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+ {
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+ "images": [PIL Image],
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+ "texts": [
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+ {
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+ "assistant": "<loc_x0><loc_y0><loc_x1><loc_y1><_Chart_>OTSL_REPRESENTATION</chart>",
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+ "source": "SynthChartNet",
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+ "user": "<chart>"
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+ }
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+ ]
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+ }
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+ ```
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+
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+ ---
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+
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+ ## Intended Use
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+
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+ * Training multimodal models for **chart understanding**, specifically:
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+
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+ * Chart parsing and transcription to structured formats (OTSL)
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+
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+ ---
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+
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+ ## Citation
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+
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+ If you use SynthChartNet, please cite:
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+
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+ ```bibtex
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+ @article{nassar2025smoldocling,
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+ title={SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion},
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+ author={Nassar, Ahmed and Marafioti, Andres and Omenetti, Matteo and Lysak, Maksym and Livathinos, Nikolaos and Auer, Christoph and Morin, Lucas and de Lima, Rafael Teixeira and Kim, Yusik and Gurbuz, A Said and others},
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+ journal={arXiv preprint arXiv:2503.11576},
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+ year={2025}
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+ }
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+ ```