Update README.md

README.md

@@ -1,41 +1,140 @@
 ---
-license: cc-by-nc-sa-4.0
+language:
+- multilingual
+- en
+- de
+- fr
+- ja
+license: mit
 tags:
+- object-detection
+- vision
 - generated_from_trainer
+- DocLayNet
+- LayoutXLM
+- COCO
+- PDF
+- IBM
+- Financial-Reports
+- Finance
+- Manuals
+- Scientific-Articles
+- Science
+- Laws
+- Law
+- Regulations
+- Patents
+- Government-Tenders
+- object-detection
+- image-segmentation
+- token-classification
+inference: false
+datasets:
+- pierreguillou/DocLayNet-base
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: layout-xlm-base-finetuned-DocLayNet-base_paragraphs_ml512-v6
-  results: []
+- name: pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    metrics:
+    - name: f1
+      type: f1
+      value: 0.7739
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Document Understanding model (finetuned LiLT base at paragraph level on DocLayNet base)
 
-# layout-xlm-base-finetuned-DocLayNet-base_paragraphs_ml512-v6
-
-This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the None dataset.
+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.
 It achieves the following results on the evaluation set:
+
 - Loss: 0.1796
 - Precision: 0.8062
 - Recall: 0.7441
 - F1: 0.7739
 - Accuracy: 0.9693
 
+## References
+
+### Other models
+
+- LayoutXLM base
+  - [Document Understanding model (at paragraph level)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512)
+  - [Document Understanding model (at paragraph level)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384)
+- LiLT base
+  - [Document Understanding model (at line level)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384)
+  - [Document Understanding model (at paragraph level)](https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512)
+  - [Document Understanding model (at line level)](https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384)
+
+### Blog posts
+
+- Layout XLM base
+    - (03/31/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at paragraph level with LayoutXLM base]()
+    - (03/25/2023) [Document AI | APP to compare the Document Understanding LiLT and LayoutXLM (base) models at line level](https://medium.com/@pierre_guillou/document-ai-app-to-compare-the-document-understanding-lilt-and-layoutxlm-base-models-at-line-1c53eb481a15)
+    - (03/05/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at line level with LayoutXLM base](https://medium.com/@pierre_guillou/document-ai-inference-app-and-fine-tuning-notebook-for-document-understanding-at-line-level-with-b08fdca5f4dc)
+- 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)
+- Layout XLM base
+- 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 APP at line level with 2 Document Understanding models (LiLT and LayoutXLM base fine-tuned on DocLayNet base dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_&_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
+    - [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)](processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb)
+
+## APP
+
+You can test this model with this APP in Hugging Face Spaces: [Inference APP for Document Understanding at paragraph level (v2)](https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-paragraphlevel-v2).
+
+![Inference APP for Document Understanding at paragraph level (v2)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512/resolve/main/app_lilt_document_understanding_AI_paragraphlevel_v2.png)
+
+You can run as well the corresponding notebook: [Document AI | Inference APP at paragraph level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet dataset)]()
+
+## DocLayNet dataset
+
+[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. 
+
+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](https://huggingface.co/datasets/ds4sd/DocLayNet)
+
+Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
+
 ## Model description
 
-More information needed
+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.
 
-## Intended uses & limitations
+## Inference
 
-More information needed
+See notebook: [Document AI | Inference at paragraph level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet dataset)]()
 
 ## Training and evaluation data
 
-More information needed
+See notebook: [Document AI | Fine-tune LayoutXLM base on DocLayNet base in any language at paragraph level (chunk of 512 tokens with overlap)]()
 
 ## Training procedure