Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 language: 
 - en
+license: mit
 tags:
 - text-classification
 - zero-shot-classification
@@ -22,11 +23,14 @@ Note that the model was trained on binary NLI to predict either "entailment" or
 
 The base model is [DeBERTa-v3-xsmall from Microsoft](https://huggingface.co/microsoft/deberta-v3-xsmall). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see the [DeBERTa-V3 paper](https://arxiv.org/abs/2111.09543). 
 
+For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli.
+
 ## Intended uses & limitations
 #### How to use the model
 ```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-xsmall-mnli-fever-anli-ling-binary"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -71,11 +75,11 @@ speed (text/sec, GPU Tesla P100, 128 batch) | 473 | 487 | 230 | 390 | 340 | 586
 ## Limitations and bias
 Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. 
 
-### BibTeX entry and citation info
-If you want to cite this model, please cite the original DeBERTa paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. 
+## 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/)
 
 ### Debugging and issues
-Note that DeBERTa-v3 was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues. 
+Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.