README.md

metadata

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
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        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

The MERIT Dataset :school_satchel::page_with_curl::trophy:

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 and you can access the generation pipeline on GitHub.

Introduction :information_source:

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.

Hardware :gear:

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 :muscle:

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