Update README.md

README.md

@@ -54,12 +54,13 @@ tags:
 pretty_name: Beyond Words
 size_categories:
 - 1K<n<10K
+language:
+- en
 ---
 
 # Dataset Card for Beyond Words
 
 ## 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)
@@ -74,125 +75,129 @@ size_categories:
   - [Annotations](#annotations)
   - [Personal and Sensitive Information](#personal-and-sensitive-information)
 - [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Social Impact](#social-impact)
   - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
+  - [Limitations](#limitations)
 - [Additional Information](#additional-information)
   - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
+  - [Licensing](#licensing)
+  - [Citation](#citation)
   - [Contributions](#contributions)
 
 ## Dataset Description
 
-- **Homepage:** https://labs.loc.gov/
+- **Homepage:** https://labs.loc.gov/work/experiments/beyond-words/
 - **Repository:** https://github.com/LibraryOfCongress/newspaper-navigator
 - **Paper:** https://arxiv.org/abs/2005.01583
-- **Leaderboard:**
-- **Point of Contact:** [email protected]
+- **Contact:** [email protected]
 
 ### Dataset Summary
 
-[More Information Needed]
+The **Beyond Words** dataset is a crowdsourced collection of bounding box annotations on World War I-era historical newspaper pages from the Library of Congress’s Chronicling America collection. Volunteers marked seven types of visual content — photographs, illustrations, maps, comics, editorial cartoons, headlines, and advertisements — enabling the training of the visual content recognition model behind the Newspaper Navigator project. It serves as a foundational dataset for large-scale document layout analysis in historical archives.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+- Object detection
+- Visual content classification
+- Document layout analysis
 
 ### Languages
 
-[More Information Needed]
+- English (used in transcribed captions and OCR content)
 
 ## Dataset Structure
 
 ### Data Instances
 
-[More Information Needed]
+Each instance is an image of a historic newspaper page, annotated with bounding boxes around regions containing visual content.
 
 ### Data Fields
 
-[More Information Needed]
+- `image_id`: Unique identifier for the image.
+- `image`: Full page image from Chronicling America.
+- `width`, `height`: Image dimensions.
+- `objects`: List of annotations, each including:
+  - `bw_id`: Unique Beyond Words annotation ID.
+  - `category_id`: One of 7 class labels (Photograph, Illustration, etc.).
+  - `image_id`: Reference to source image.
+  - `id`: Object instance ID.
+  - `area`: Area of the bounding box.
+  - `bbox`: Bounding box coordinates (x, y, width, height).
+  - `iscrowd`: Crowd label (for COCO format compatibility).
 
 ### Data Splits
 
-[More Information Needed]
+- **Train:** 2,846 examples
+- **Validation:** 712 examples
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
+To train models that can detect and classify visual content in historic newspapers at scale, the Library of Congress launched the Beyond Words crowdsourcing initiative in 2017. The data produced is designed to support machine learning workflows and historical content analysis.
 
 ### Source Data
 
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
+- Scanned WWI-era newspapers from Chronicling America
+- Public domain metadata and images
+- OCR from METS/ALTO XML files
 
 ### Annotations
 
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
+- Collected via the Beyond Words crowdsourcing platform
+- Up to 6 volunteers per annotation; verified by consensus
+- Categories include photograph, illustration, map, comic/cartoon, editorial cartoon
+-
+-  Additional headline and advertisement annotations added by project team
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+None known. All pages are from historical public domain newspapers.
 
 ## Considerations for Using the Data
 
-### Social Impact of Dataset
+### Social Impact
 
-[More Information Needed]
+- Democratizes access to historical newspaper content
+- Supports digital humanities, education, and public history initiatives
 
 ### Discussion of Biases
 
-[More Information Needed]
+- Limited to WWI-era newspapers
+- Class distribution skewed toward some categories (e.g. headlines, ads)
+- Some annotations (headlines, ads) not crowd-verified
 
-### Other Known Limitations
+### Limitations
 
-[More Information Needed]
+- Visual content from pre-1875 newspapers may yield lower model performance
+- Quality of annotations can vary due to the experimental nature of the crowdsourcing workflow
 
 ## Additional Information
 
 ### Dataset Curators
 
-[More Information Needed]
+- Benjamin Charles Germain Lee
+- Jaime Mears, Eileen Jakeway, Meghan Ferriter, Chris Adams, Nathan Yarasavage, Deborah Thomas, Kate Zwaard (Library of Congress)
+- Daniel Weld (University of Washington)
 
-### Licensing Information
+### Licensing
 
-[More Information Needed]
+- **License:** CC0 1.0 Universal (Public Domain Dedication)
 
-### Citation Information
+### Citation
 
 ```bibtex
 @inproceedings{10.1145/3340531.3412767,
-author = {Lee, Benjamin Charles Germain and Mears, Jaime and Jakeway, Eileen and Ferriter, Meghan and Adams, Chris and Yarasavage, Nathan and Thomas, Deborah and Zwaard, Kate and Weld, Daniel S.},
-title = {The Newspaper Navigator Dataset: Extracting Headlines and Visual Content from 16 Million Historic Newspaper Pages in Chronicling America},
-year = {2020},
-isbn = {9781450368599},
-publisher = {Association for Computing Machinery},
-address = {New York, NY, USA},
-url = {https://doi.org/10.1145/3340531.3412767},
-doi = {10.1145/3340531.3412767},
-abstract = {Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic American newspapers. Over 16 million pages have been digitized to date, complete with high-resolution images and machine-readable METS/ALTO OCR. Of considerable interest to Chronicling America users is a semantified corpus, complete with extracted visual content and headlines. To accomplish this, we introduce a visual content recognition model trained on bounding box annotations collected as part of the Library of Congress's Beyond Words crowdsourcing initiative and augmented with additional annotations including those of headlines and advertisements. We describe our pipeline that utilizes this deep learning model to extract 7 classes of visual content: headlines, photographs, illustrations, maps, comics, editorial cartoons, and advertisements, complete with textual content such as captions derived from the METS/ALTO OCR, as well as image embeddings. We report the results of running the pipeline on 16.3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model, and all source code are placed in the public domain for unrestricted re-use.},
-booktitle = {Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management},
-pages = {3055–3062},
-numpages = {8},
-keywords = {digital humanities, dataset, chronicling america, newspaper navigator, document analysis, information retrieval, digital libraries and archives, public domain, historic newspapers},
-location = {Virtual Event, Ireland},
-series = {CIKM '20}
+  author = {Lee, Benjamin Charles Germain and Mears, Jaime and Jakeway, Eileen and Ferriter, Meghan and Adams, Chris and Yarasavage, Nathan and Thomas, Deborah and Zwaard, Kate and Weld, Daniel S.},
+  title = {The Newspaper Navigator Dataset: Extracting Headlines and Visual Content from 16 Million Historic Newspaper Pages in Chronicling America},
+  year = {2020},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  doi = {10.1145/3340531.3412767},
+  url = {https://doi.org/10.1145/3340531.3412767}
 }
-```
+
 
 ### Contributions
 
-Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
+Thanks to @davanstrien for adding this dataset to Hugging Face.