Update README.md

README.md

@@ -109,6 +109,7 @@ The foundation model is [DeBERTa-v3-large from Microsoft](https://huggingface.co
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
 model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -157,8 +158,8 @@ The model achieves state-of-the-art performance on each dataset. Surprisingly, i
 ## Limitations and bias
 Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data. 
 
-### BibTeX entry and citation info
-If you want to cite this model, please cite my [preprint on low-resource text classification](https://osf.io/74b8k/) and the original DeBERTa-v3 paper.
+## Citation
+If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.
 
 ### Ideas for cooperation or questions?
 If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)