Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: texts
-    sequence: string
-  - name: bboxes_block
-    sequence:
-      sequence: int64
-  - name: bboxes_line
-    sequence:
-      sequence: int64
-  - name: categories
-    sequence:
-      class_label:
-        names:
-          '0': Caption
-          '1': Footnote
-          '2': Formula
-          '3': List-item
-          '4': Page-footer
-          '5': Page-header
-          '6': Picture
-          '7': Section-header
-          '8': Table
-          '9': Text
-          '10': Title
-  - name: page_hash
-    dtype: string
-  - name: original_filename
-    dtype: string
-  - name: page_no
-    dtype: int32
-  - name: num_pages
-    dtype: int32
-  - name: original_width
-    dtype: int32
-  - name: original_height
-    dtype: int32
-  - name: coco_width
-    dtype: int32
-  - name: coco_height
-    dtype: int32
-  - name: collection
-    dtype: string
-  - name: doc_category
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 773564178
-    num_examples: 69103
-  - name: validation
-    num_bytes: 74492844
-    num_examples: 6480
-  - name: test
-    num_bytes: 58899926
-    num_examples: 4994
-  download_size: 196812287
-  dataset_size: 906956948
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- en
+- de
+- fr
+- ja
+annotations_creators:
+- crowdsourced
+license: other
+pretty_name: DocLayNet large
+size_categories:
+- 10K<n<100K
+tags:
+- DocLayNet
+- COCO
+- PDF
+- IBM
+- Financial-Reports
+- Finance
+- Manuals
+- Scientific-Articles
+- Science
+- Laws
+- Law
+- Regulations
+- Patents
+- Government-Tenders
+- object-detection
+- image-segmentation
+- token-classification
+task_categories:
+- object-detection
+- image-segmentation
+- token-classification
+task_ids:
+- instance-segmentation
 ---
+
+# Dataset Card for DocLayNet large without image
+
+## About this card (02/14/2024)
+
+### Property and license
+
+All information from this page but the content of this paragraph "About this card (02/14/2025)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
+
+DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information). 
+
+I do not claim any rights to the data taken from this dataset and published on this page.
+
+# Dataset Card for DocLayNet
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Annotations](#annotations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
+- **Repository:** https://github.com/DS4SD/DocLayNet
+- **Paper:** https://doi.org/10.1145/3534678.3539043
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
+
+1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
+2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
+3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
+4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
+5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
+
+### Supported Tasks and Leaderboards
+
+We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
+
+## Dataset Structure
+
+### Data Fields
+
+DocLayNet provides four types of data assets:
+
+1. Bounding-box annotations in COCO format for each PNG image
+2. Extra: Single-page PDF files matching each PNG image
+3. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
+
+The COCO image record are defined like this example
+
+```js
+    ...
+    {
+      "id": 1,
+      "width": 1025,
+      "height": 1025,
+      "file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
+
+      // Custom fields:
+      "doc_category": "financial_reports" // high-level document category
+      "collection": "ann_reports_00_04_fancy", // sub-collection name
+      "doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
+      "page_no": 9, // page number in original document
+      "precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
+    },
+    ...
+```
+
+The `doc_category` field uses one of the following constants:
+
+```
+financial_reports,
+scientific_articles,
+laws_and_regulations,
+government_tenders,
+manuals,
+patents
+```
+
+
+### Data Splits
+
+The dataset provides three splits
+- `train`
+- `val`
+- `test`
+
+## Dataset Creation
+
+### Annotations
+
+#### Annotation process
+
+The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
+
+
+#### Who are the annotators?
+
+Annotations are crowdsourced.
+
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
+You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
+
+Curators:
+- Christoph Auer, [@cau-git](https://github.com/cau-git)
+- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
+- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
+- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
+
+### Licensing Information
+
+License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
+
+
+### Citation Information
+
+
+```bib
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
+  doi = {10.1145/3534678.353904},
+  url = {https://doi.org/10.1145/3534678.3539043},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022},
+  isbn = {9781450393850},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
+  pages = {3743–3751},
+  numpages = {9},
+  location = {Washington DC, USA},
+  series = {KDD '22}
+}
+```
+
+### Contributions
+
+Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.