Lilt Xlm Roberta Base Finetuned With DocLayNet Base At Paragraphlevel Ml512

🚀 Document Understanding model (finetuned LiLT base at paragraph level on DocLayNet base)

This Document Understanding model is a fine - tuned version of [nielsr/lilt - xlm - roberta - base](https://huggingface.co/nielsr/lilt - xlm - roberta - base) using the [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet - base) dataset. It offers high - performance token classification and object detection capabilities, which are crucial for document layout analysis and understanding.

🚀 Quick Start

To get started with this model, you can test it using the provided APP in Hugging Face Spaces: [Inference APP for Document Understanding at paragraph level (v1)](https://huggingface.co/spaces/pierreguillou/Inference - APP - Document - Understanding - at - paragraphlevel - v1). You can also run the corresponding notebook: [Document AI | Inference APP at paragraph level with a Document Understanding model (LiLT fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb).

✨ Features

• Multilingual Support: Supports multiple languages including English, German, French, and Japanese.
• High - Accuracy Metrics: Achieves high precision, recall, F1 score, and accuracy in token classification tasks.
• Diverse Datasets: Trained on the DocLayNet base dataset, which covers a wide range of document categories.
• Visualization: Provides visualizations such as accuracy at the paragraph level and confusion matrices.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Evaluation Results

This model achieves the following results on the evaluation set:

• Loss: 0.4104
• Precision: 0.8634
• Recall: 0.8634
• F1: 0.8634
• Token Accuracy: 0.8634
• Paragraph Accuracy: 0.6815

Accuracy at paragraph level

• Paragraph Accuracy: 68.15%
• Accuracy by label
  • Caption: 22.82%
  • Footnote: 0.0%
  • Formula: 97.33%
  • List - item: 8.42%
  • Page - footer: 98.77%
  • Page - header: 77.81%
  • Picture: 39.16%
  • Section - header: 76.17%
  • Table: 37.7%
  • Text: 86.78%
  • Title: 0.0%



References

Blog posts

• Layout XLM base
  • (03/05/2023) Document AI | Inference APP and fine - tuning notebook for Document Understanding at line level with LayoutXLM base
• LiLT base
  • (02/16/2023) [Document AI | Inference APP and fine - tuning notebook for Document Understanding at paragraph level](https://medium.com/@pierre_guillou/document - ai - inference - app - and - fine - tuning - notebook - for - document - understanding - at - paragraph - level - c18d16e53cf8)
  • (02/14/2023) [Document AI | Inference APP for Document Understanding at line level](https://medium.com/@pierre_guillou/document - ai - inference - app - for - document - understanding - at - line - level - a35bbfa98893)
  • (02/10/2023) [Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset](https://medium.com/@pierre_guillou/document - ai - document - understanding - model - at - line - level - with - lilt - tesseract - and - doclaynet - dataset - 347107a643b8)
  • (01/31/2023) [Document AI | DocLayNet image viewer APP](https://medium.com/@pierre_guillou/document - ai - doclaynet - image - viewer - app - 3ac54c19956)
  • (01/27/2023) [Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](https://medium.com/@pierre_guillou/document - ai - processing - of - doclaynet - dataset - to - be - used - by - layout - models - of - the - hugging - face - hub - 308d8bd81cdb)

Notebooks (paragraph level)

• LiLT base
  • [Document AI | Inference APP at paragraph level with a Document Understanding model (LiLT fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)
  • [Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)
  • [Document AI | Fine - tune LiLT on DocLayNet base in any language at paragraph level (chunk of 512 tokens with overlap)](https://github.com/piegu/language - models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_paragraphlevel_ml_512.ipynb)

Notebooks (line level)

• Layout XLM base
  • [Document AI | Inference at line level with a Document Understanding model (LayoutXLM base fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/inference_on_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
  • [Document AI | Inference APP at line level with a Document Understanding model (LayoutXLM base fine - tuned on DocLayNet base dataset)](https://github.com/piegu/language - models/blob/master/Gradio_inference_on_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
  • [Document AI | Fine - tune LayoutXLM base on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language - models/blob/master/Fine_tune_LayoutXLM_base_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
• LiLT base
  • [Document AI | Inference at line level with a Document Understanding model (LiLT fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
  • [Document AI | Inference APP at line level with a Document Understanding model (LiLT fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
  • [Document AI | Fine - tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language - models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
  • [DocLayNet image viewer APP](https://github.com/piegu/language - models/blob/master/DocLayNet_image_viewer_APP.ipynb)
  • Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)

APP

You can test this model with this APP in Hugging Face Spaces: [Inference APP for Document Understanding at paragraph level (v1)](https://huggingface.co/spaces/pierreguillou/Inference - APP - Document - Understanding - at - paragraphlevel - v1).


DocLayNet dataset

DocLayNet dataset (IBM) provides page - by - page layout segmentation ground - truth using bounding - boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.

Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:

• direct links: [doclaynet_core.zip](https://codait - cos - dax.s3.us.cloud - object - storage.appdomain.cloud/dax - doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait - cos - dax.s3.us.cloud - object - storage.appdomain.cloud/dax - doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
• Hugging Face dataset library: dataset DocLayNet

Paper: DocLayNet: A Large Human - Annotated Dataset for Document - Layout Analysis (06/02/2022)

Model description

The model was finetuned at paragraph level on chunk of 512 tokens with overlap of 128 tokens. Thus, the model was trained with all layout and text data of all pages of the dataset.

At inference time, a calculation of best probabilities give the label to each paragraph bounding boxes.

Inference

See notebook: [Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine - tuned on DocLayNet dataset)](https://github.com/piegu/language - models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)

Training and evaluation data

See notebook: [Document AI | Fine - tune LiLT on DocLayNet base in any language at paragraph level (chunk of 512 tokens with overlap)](https://github.com/piegu/language - models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_paragraphlevel_ml_512.ipynb)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2

Other models

• Line level
  • [Document Understanding model (finetuned LiLT base at line level on DocLayNet base)](https://huggingface.co/pierreguillou/lilt - xlm - roberta - base - finetuned - with - DocLayNet - base - at - linelevel - ml384) (accuracy | tokens: 85.84% - lines: 91.97%)
  • [Document Understanding model (finetuned LayoutXLM base at line level on DocLayNet base)](https://huggingface.co/pierreguillou/layout - xlm - base - finetuned - with - DocLayNet - base - at - linelevel - ml384) (accuracy | tokens: 93.73% - lines: ...)
• Paragraph level
  • [Document Understanding model (finetuned LiLT base at paragraph level on DocLayNet base)](https://huggingface.co/pierreguillou/lilt - xlm - roberta - base - finetuned - with - DocLayNet - base - at - paragraphlevel - ml512) (accuracy | tokens: 86.34% - paragraphs: 68.15%)
  • [Document Understanding model (finetuned LayoutXLM base at paragraph level on DocLayNet base)](https://huggingface.co/pierreguillou/layout - xlm - base - finetuned - with - DocLayNet - base - at - paragraphlevel - ml512) (accuracy | tokens: 96.93% - paragraphs: 86.55%)

📄 License

This model is released under the MIT license.