Update README.md

README.md

@@ -1,3 +1,42 @@
 ---
 license: cc-by-4.0
+datasets:
+- jagoldz/gahd
+- Paul/hatecheck-german
+language:
+- de
+metrics:
+- f1
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- hate-speech-detection
+- hate-speech
 ---
+
+# Model Card
+
+## Model Description
+
+We fine-tuned this [gelectra-large model](https://huggingface.co/deepset/gelectra-large) for four rounds of dynamic adversarial data collection to create the GAHD dataset. In each round annotators created examples by trying to trick the model into a misclassification. We explored different ways of supporting annotators in finding model-tricking examples during the data collection. This is the final model (R4) in our paper. The model classifies text into "hate speech" (1) or "not-hate speech" (0).
+
+Please check out our [paper](https://arxiv.org/abs/2403.19559) for further details about the training procedure (Appendix C) or evaluation (Section 4).
+
+- paper: https://arxiv.org/abs/2403.19559
+- GAHD dataset on Huggingface: https://huggingface.co/datasets/jagoldz/gahd
+- GAHD dataset on GitHub: https://github.com/jagol/gahd
+
+## Citation
+
+When using this model or the GAHD dataset, please cite our preprint on Arxiv:
+
+```
+@misc{goldzycher2024improving,
+      title={Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset}, 
+      author={Janis Goldzycher and Paul Röttger and Gerold Schneider},
+      year={2024},
+      eprint={2403.19559},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```