Update README.md

README.md

@@ -10,4 +10,48 @@ size_categories:
 - 1K<n<10K
 task_categories:
 - translation
----
+---
+
+## Dataset Description
+GAND (Gender-Ambiguous Natural Data) is a benchmarking resource for evaluating gender (bias) in machine translation or downstream NLP tasks. 
+The data stems purely from natural data resources (OpenSubtitles from the [OPUS project](https://opus.nlpl.eu/) and [C4](https://huggingface.co/datasets/allenai/c4)).
+The data has been meticulously (automatically + manually) filtered to ensure complete gender ambiguity with respect to a specific referent.
+More information on the compilation of GAND can be found on [GitHub](https://github.com/jhacken/GAND/tree/main).
+
+## Usage
+```python
+from datasets import load_dataset
+ds = load_dataset("jhacken/GAND")
+```
+
+## Train, dev, test split
+- Train set: 4037 rows
+- Dev set: 505 rows
+- Test set: 505 rows
+
+## Dataset Structure
+| referent | EN_source_sentence | referent_embedding | sentence_source | 
+|----------|--------------------|--------------------|-----------------| 
+| assistant | No one' s permitted to enter the library... other than myself and my assistant. | female_embedding_list | OpenSubtitles |
+| specialist | As a social media specialist with a million things on your plate, you might not have been aware that citrus was all the rage atm. | LLM_neutral_list | C4 |
+
+## Cite this dataset
+@dataset{hackenbuchner_2026_20324375,
+  author       = {Hackenbuchner, Janiça and
+                  Degraeuwe, Jasper and
+                  Tezcan, Arda and
+                  Daems, Joke},
+  title        = {GAND Dataset: Gender-Ambiguous Natural Data},
+  month        = may,
+  year         = 2026,
+  publisher    = {Zenodo},
+  version      = {v1.0.0},
+  doi          = {10.5281/zenodo.20324375},
+  url          = {https://doi.org/10.5281/zenodo.20324375},
+}
+
+## Acknowledgements
+GAND was developed as part of a strategic basic PhD research (1SH5V24N) fully funded by The Research Foundation – Flanders (FWO) for the time span of four years, 
+from 01.11.2023 until 31.10.2027, and hosted within the Language and Translation Technology Team (LT3) at Ghent University. 
+The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), 
+funded by Ghent University, FWO and the Flemish Government - department EWI.