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--- |
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language: |
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- multilingual |
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- en |
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- de |
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- fr |
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- ja |
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license: mit |
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tags: |
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- object-detection |
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- vision |
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- generated_from_trainer |
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- DocLayNet |
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- COCO |
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- PDF |
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- IBM |
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- Financial-Reports |
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- Finance |
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- Manuals |
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- Scientific-Articles |
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- Science |
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- Laws |
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- Law |
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- Regulations |
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- Patents |
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- Government-Tenders |
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- object-detection |
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- image-segmentation |
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- token-classification |
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inference: false |
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datasets: |
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- pierreguillou/DocLayNet-base |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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metrics: |
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- name: f1 |
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type: f1 |
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value: 0.7336 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Document Understanding model (finetuned LayoutXLM base at line level on DocLayNet base) |
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This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) with the [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2364 |
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- Precision: 0.7260 |
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- Recall: 0.7415 |
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- F1: 0.7336 |
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- Accuracy: 0.9373 |
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## References |
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### Blog posts |
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- Layout XLM base |
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- (03/05/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at line level with LayoutXLM base]() |
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- LiLT base |
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- (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) |
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- (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) |
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- (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) |
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- (01/31/2023) [Document AI | DocLayNet image viewer APP](https://medium.com/@pierre_guillou/document-ai-doclaynet-image-viewer-app-3ac54c19956) |
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- (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) |
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### Notebooks (paragraph level) |
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- LiLT base |
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- [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) |
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- [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) |
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- [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) |
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### Notebooks (line level) |
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- Layout XLM base |
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- [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) |
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- [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) |
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- [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) |
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- LiLT base |
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- [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) |
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- [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) |
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- [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) |
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- [DocLayNet image viewer APP](https://github.com/piegu/language-models/blob/master/DocLayNet_image_viewer_APP.ipynb) |
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- [Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) |
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### APP |
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You can test this model with this APP in Hugging Face Spaces: [Inference APP for Document Understanding at line level (v2)](https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v2). |
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### DocLayNet dataset |
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[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (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. |
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Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets: |
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- 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) |
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- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet) |
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Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022) |
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## Model description |
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The model was finetuned at **line level on chunk of 384 tokens with overlap of 128 tokens**. Thus, the model was trained with all layout and text data of all pages of the dataset. |
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At inference time, a calculation of best probabilities give the label to each line bounding boxes. |
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## Inference |
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See notebook: [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) |
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## Training and evaluation data |
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See notebook: [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) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| No log | 0.12 | 300 | 0.8413 | 0.1311 | 0.5185 | 0.1437 | 0.1205 | |
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| 0.9231 | 0.25 | 600 | 0.8751 | 0.5031 | 0.4108 | 0.4637 | 0.5498 | |
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| 0.9231 | 0.37 | 900 | 0.8887 | 0.5206 | 0.3911 | 0.5076 | 0.5343 | |
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| 0.369 | 0.5 | 1200 | 0.8724 | 0.5365 | 0.4118 | 0.5094 | 0.5667 | |
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| 0.2737 | 0.62 | 1500 | 0.8960 | 0.6033 | 0.3328 | 0.6046 | 0.6020 | |
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| 0.2737 | 0.75 | 1800 | 0.9186 | 0.6404 | 0.2984 | 0.6062 | 0.6787 | |
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| 0.2542 | 0.87 | 2100 | 0.9163 | 0.6593 | 0.3115 | 0.6324 | 0.6887 | |
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| 0.2542 | 1.0 | 2400 | 0.9198 | 0.6537 | 0.2878 | 0.6160 | 0.6962 | |
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| 0.1938 | 1.12 | 2700 | 0.9165 | 0.6752 | 0.3414 | 0.6673 | 0.6833 | |
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| 0.1581 | 1.25 | 3000 | 0.9193 | 0.6871 | 0.3611 | 0.6868 | 0.6875 | |
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| 0.1581 | 1.37 | 3300 | 0.9256 | 0.6822 | 0.2763 | 0.6988 | 0.6663 | |
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| 0.1428 | 1.5 | 3600 | 0.9287 | 0.7084 | 0.3065 | 0.7246 | 0.6929 | |
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| 0.1428 | 1.62 | 3900 | 0.9194 | 0.6812 | 0.2942 | 0.6866 | 0.6760 | |
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| 0.1025 | 1.74 | 4200 | 0.9347 | 0.7223 | 0.2990 | 0.7315 | 0.7133 | |
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| 0.1225 | 1.87 | 4500 | 0.9360 | 0.7048 | 0.2729 | 0.7249 | 0.6858 | |
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| 0.1225 | 1.99 | 4800 | 0.9396 | 0.7222 | 0.2826 | 0.7497 | 0.6966 | |
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| 0.108 | 2.12 | 5100 | 0.9301 | 0.7193 | 0.3071 | 0.7022 | 0.7372 | |
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| 0.108 | 2.24 | 5400 | 0.9334 | 0.7243 | 0.2999 | 0.7250 | 0.7237 | |
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| 0.0799 | 2.37 | 5700 | 0.9382 | 0.7254 | 0.2710 | 0.7310 | 0.7198 | |
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| 0.0793 | 2.49 | 6000 | 0.9329 | 0.7228 | 0.3201 | 0.7352 | 0.7108 | |
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| 0.0793 | 2.62 | 6300 | 0.9373 | 0.7336 | 0.3035 | 0.7260 | 0.7415 | |
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| 0.0696 | 2.74 | 6600 | 0.9374 | 0.7275 | 0.3137 | 0.7313 | 0.7237 | |
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| 0.0696 | 2.87 | 6900 | 0.9381 | 0.7253 | 0.3242 | 0.7369 | 0.7142 | |
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| 0.0866 | 2.99 | 7200 | 0.2473 | 0.7439 | 0.7207 | 0.7321 | 0.9407 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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## Other models |
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- Line level |
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- [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%) |
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- [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: ...) |
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- Paragraph level |
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- [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%) |
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- [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%) |
