Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- token-classification
+language:
+- nl
+tags:
+- ner
+- named-entity-extraction
+- dutch
+- historical-documents
+- archival-texts
+- relabeling
+- datasets
+- 17th-century
+- 18th-century
+- 19th-century
+pretty_name: Dutch Historical Notarial NER Dataset (Tag de Tekst)
+---
+
+# Dutch Historical Notarial NER Dataset
+
+This dataset is a relabeled version of the "AI-trainingset voor Named Entity Recognition (NER)" created during the crowdsourcing project [**"Tag de Tekst"** on VeleHanden.nl](https://taalmaterialen.ivdnt.org/download/aitrainingset1-0/) in 2020. It has been adapted for use in Named Entity Recognition (NER) tasks, with relabeling conducted using **[Google Deepmind's Gemini 2.0 Flash](https://ai.google.dev/gemini-api/docs/models/gemini-v2)**  model.
+
+## Dataset Overview
+
+- **Original Source**: Transcriptions of Dutch notarial texts from the 17th to 19th centuries.
+- **Annotations**: Annotated by ~150 volunteers and reviewed by super users.
+- **Relabeling**: Automatic relabeling into 4 entity classes:
+  - `persoon` (person names)
+  - `locatie` (locations)
+  - `datum` (dates)
+  - `organisatie` (organizations)
+- **Sources**: Includes material from:
+  - Stadsarchief Amsterdam
+  - Nationaal Archief
+  - Noord-Hollands Archief
+  - Other regional historical centers
+- **Total Scans**: 10,567
+- **Language**: Dutch
+
+## Format
+
+This dataset is formatted for use with [GLiNER](https://github.com/urchade/GLiNER). Each sample includes:
+- `text`: The full text.
+- `tokenized_text`: The text split into full-word tokens.
+- `ner`: A list of annotated entities with start and end token indices and entity types.
+
+Example:
+```json
+{
+  "text": "Henrick Cardamon Op huijden ...",
+  "tokenized_text": ["Henrick", "Cardamon", "Op", "huijden", ...],
+  "ner": [
+    {
+      "start": 0,
+      "end": 1,
+      "label": "persoon"
+    },
+    ...
+  ]
+}
+```
+
+## Usage
+
+To load this dataset with the [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) library, run:
+
+```py
+from datasets import load_dataset
+
+dataset = load_dataset("TimKoornstra/dutch-notarial-ner")
+```
+
+## Data Preprocessing and Relabeling
+
+The original dataset, created during the **"Tag de Tekst"** project, was annotated by a large group of volunteers. While this collaborative effort provided a valuable starting point, the annotations were often inconsistent and contained inaccuracies. Common issues included:
+
+- Mislabeling of entities (e.g., locations marked as persons).
+- Overlapping or incomplete entity spans.
+- Inconsistent application of annotation guidelines.
+
+To address these challenges, the dataset was automatically relabeled using **[Google Deepmind's Gemini 2.0 Flash](https://ai.google.dev/gemini-api/docs/models/gemini-v2)** model. This process mapped all annotations into a simplified schema with four entity types:
+- `persoon` (person names),
+- `locatie` (locations),
+- `datum` (dates),
+- `organisatie` (organizations).
+
+Additionally:
+- **Inconsistent spans** were corrected to ensure uniformity.
+- The data was reformatted for compatibility with modern tools like [GLiNER](https://github.com/urchade/GLiNER) and the Hugging Face `datasets` library.
+
+These preprocessing steps ensure that the dataset is more accurate and consistent for training and evaluating Named Entity Recognition (NER) models.
+
+## Limitations
+
+Despite preprocessing and relabeling, the dataset has some limitations:
+
+- **Incomplete Entity Coverage**: While many errors were corrected, there may still be missed entities or incorrect spans, especially in complex cases.
+- **Model-Induced Bias**: The relabeling process relied on **[Google Deepmind's Gemini 2.0 Flash](https://ai.google.dev/gemini-api/docs/models/gemini-v2)**  model, which may introduce biases inherent to the model's training data.
+- **Historical Context Challenges**: The dataset consists of historical Dutch texts (17th–19th century) with archaic language and formatting, which may pose additional challenges for modern models.
+- **Potential Noise**: Due to the automatic relabeling process, there may still be minor inconsistencies or errors in the annotations.
+- **HTR Artifacts**: The dataset is based on handwritten text recognition (HTR) outputs, so any transcription errors from the HTR process remain in the data. This limitation is consistent with the original dataset.
+
+Users of this dataset should carefully evaluate its performance on their specific use case and consider further fine-tuning or validation if needed.
+
+## License
+
+This dataset respects the original license: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+## Citation
+
+If you use this dataset in your research, please cite the original dataset and this repository:
+
+```bibtex
+@misc{dutch_notarial_ner,
+  author = {Tim Koornstra},
+  title = {Dutch Historical Notarial NER Dataset},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/TimKoornstra/dutch-notarial-ner}},
+  note = {Relabeled with Gemini 2.0 Flash model}
+}
+```
+
+## Acknowledgements
+
+This dataset was originally developed as part of the projects:
+
+- "De IJsberg zichtbaar maken" ([zoekintranscripties.nl](https://www.zoekintranscripties.nl/))
+- "Slimmer zoeken in archieven" ([archieveninbeeld.nl](https://archieveninbeeld.nl/))
+
+Special thanks to the volunteers of the "Tag de Tekst" project and the organizations contributing archival material.