Update README.md

README.md

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-language: de
+---
+license: cc-by-sa-4.0
+language:
+- de
+tags:
+- text complexity
+---
+# Model Card for DistilBERT German Text Complexity
 
-This is a version of "distilbert-base-german-cased" fine-tuned for text complexity prediction on a scale between 1 and 7.
+This model is version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) fine-tuned for text complexity prediction on a scale between 1 and 7.
+
+### Direct Use
+To use this model, use our [eval_distilbert.py](https://github.com/MiriUll/text_complexity/blob/master/eval_distilbert.py) script.
+
+## Training Details
+
+The model is a fine-tuned version of the [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) and a contribution to the GermEval 2022 shared task on text complexity prediction. 
+It was fine-tuned on the dataset by [Naderi et al, 2019](https://arxiv.org/abs/1904.07733).
+For further details, visit our [KONVENS paper](https://aclanthology.org/2022.germeval-1.4/).
+
+
+
+## Citation
+
+Please cite our [INLG 2023 paper](https://arxiv.org/abs/2307.13989), if you use our model. 
+**BibTeX:**
+```bibtex
+@inproceedings{anschutz-groh-2022-tum,
+    title = "{TUM} Social Computing at {G}erm{E}val 2022: Towards the Significance of Text Statistics and Neural Embeddings in Text Complexity Prediction",
+    author = {Ansch{\"u}tz, Miriam  and
+      Groh, Georg},
+    booktitle = "Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text",
+    month = sep,
+    year = "2022",
+    address = "Potsdam, Germany",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.germeval-1.4",
+    pages = "21--26",
+}