Update README.md

README.md

@@ -24,11 +24,17 @@ The model only has 22 million parameters and is 51 MB small, providing a signifi
 This model was trained to provide a very small and highly efficient zeroshot option, 
 especially for edge devices or in-browser use-cases with transformers.js.
 
+## Usage and other details
+For usage instructions and other details refer to 
+this model card [MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33)
+and this [paper](https://arxiv.org/pdf/2312.17543.pdf).
 
 ## Metrics:
 
 I didn't not do zeroshot evaluation for this model to save time and compute. 
-The table below shows standard accuracy for all datasets the model was trained on.
+The table below shows standard accuracy for all datasets the model was trained on (note that the NLI datasets are binary).
+
+General takeaway: the model is much more efficient than its larger sisters, but it performs less well. 
 
 |Datasets|mnli_m|mnli_mm|fevernli|anli_r1|anli_r2|anli_r3|wanli|lingnli|wellformedquery|rottentomatoes|amazonpolarity|imdb|yelpreviews|hatexplain|massive|banking77|emotiondair|emocontext|empathetic|agnews|yahootopics|biasframes_sex|biasframes_offensive|biasframes_intent|financialphrasebank|appreviews|hateoffensive|trueteacher|spam|wikitoxic_toxicaggregated|wikitoxic_obscene|wikitoxic_identityhate|wikitoxic_threat|wikitoxic_insult|manifesto|capsotu|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |