--- language: - en - es license: mit task_categories: - token-classification - image-to-text dataset_info: - config_name: en-digital-seq features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 3422825072.42 num_examples: 7324 - name: test num_bytes: 1800300619.069 num_examples: 4349 - name: validation num_bytes: 867013113.894 num_examples: 1831 download_size: 6044707011 dataset_size: 6090138805.383 - config_name: en-render-seq features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 19131026017.588 num_examples: 7324 - name: test num_bytes: 11101342722.574 num_examples: 4349 - name: validation num_bytes: 4749558423.85 num_examples: 1831 download_size: 34947880371 dataset_size: 34981927164.012 - config_name: es-digital-seq features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 3515604711.065 num_examples: 8115 - name: test num_bytes: 2068684395.052 num_examples: 4426 - name: validation num_bytes: 880373678.928 num_examples: 2028 download_size: 6392517545 dataset_size: 6464662785.045 - config_name: es-render-seq features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 20956369016.935 num_examples: 8115 - name: test num_bytes: 11530001568.862 num_examples: 4426 - name: validation num_bytes: 5264019060.636 num_examples: 2028 download_size: 37775576850 dataset_size: 37750389646.433 configs: - config_name: en-digital-seq data_files: - split: train path: en-digital-seq/train-* - split: test path: en-digital-seq/test-* - split: validation path: en-digital-seq/validation-* - config_name: en-render-seq data_files: - split: train path: en-render-seq/train-* - split: test path: en-render-seq/test-* - split: validation path: en-render-seq/validation-* - config_name: es-digital-seq data_files: - split: train path: es-digital-seq/train-* - split: test path: es-digital-seq/test-* - split: validation path: es-digital-seq/validation-* - config_name: es-render-seq data_files: - split: train path: es-render-seq/train-* - split: test path: es-render-seq/test-* - split: validation path: es-render-seq/validation-* tags: - synthetic ---

Visual Abstract

# The MERIT Dataset 🎒📃🏆 The MERIT Dataset is a multimodal dataset (image + text + layout) designed for training and benchmarking Large Language Models (LLMs) on Visually Rich Document Understanding (VrDU) tasks. It is a fully labeled synthetic dataset generated using our opensource pipeline available on [GitHub](https://github.com/nachoDRT/MERIT-Dataset). You can explore more details about the dataset and pipeline reading our [paper](https://arxiv.org/abs/2409.00447). ## Introduction ℹī¸ AI faces some dynamic and technical issues that push end-users to create and gather their own data. In addition, multimodal LLMs are gaining more and more attention, but datasets to train them might be improved to be more complex, more flexible, and easier to gather/generate. In this research project, we identify school transcripts of records as a suitable niche to generate a synthetic challenging multimodal dataset (image + text + layout) for Token Classification or Sequence Generation.

demo

## Hardware ⚙ī¸ We ran the dataset generator on an MSI Meg Infinite X 10SF-666EU with an Intel Core i9-10900KF and an Nvidia RTX 2080 GPU, running on Ubuntu 20.04. Energy values in the table refer to 1k samples, and time values refer to one sample. | Task | Energy (kWh) | Time (s) | |------------------------------|--------------|----------| | Generate digital samples | 0.016 | 2 | | Modify samples in Blender | 0.366 | 34 | ## Benchmark đŸ’Ē We train the LayoutLM family models on Token Classification to demonstrate the suitability of our dataset. The MERIT Dataset poses a challenging scenario with more than 400 labels. We benchmark on three scenarios with an increasing presence of Blender-modified samples. + Scenario 1: We train and test on digital samples. + Scenario 2: We train with digital samples and test with Blender-modified samples. + Scenario 3: We train and test with Blender-modified samples. | | **Scenario 1** | **Scenario 2** | **Scenario 3** | **FUNSD/** | **Lang.** | **(Tr./Val./Test)** | |------------------|----------------|----------------|----------------|------------|-----------|----------------------| | | Dig./Dig. | Dig./Mod. | Mod./Mod | XFUND | | | | | F1 | F1 | F1 | F1 | | | | **LayoutLMv2** | 0.5536 | 0.3764 | 0.4984 | 0.8276 | Eng. | 7324 / 1831 / 4349 | | **LayoutLMv3** | 0.3452 | 0.2681 | 0.6370 | 0.9029 | Eng. | 7324 / 1831 / 4349 | | **LayoutXLM** | 0.5977 | 0.3295 | 0.4489 | 0.7550 | Spa. | 8115 / 2028 / 4426 | ## Citation If you find our research interesting, please cite our work. 📄✏ī¸ ```bibtex @article{de2024merit, title={The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts}, author={de Rodrigo, I and Sanchez-Cuadrado, A and Boal, J and Lopez-Lopez, AJ}, journal={arXiv preprint arXiv:2409.00447}, year={2024} }