Update README.md

added updated citation and cleaned

README.md

@@ -7,29 +7,32 @@ tags:
 pipeline_tag: zero-shot-classification
 library_name: transformers
 ---
-# deberta-v3-base-zeroshot-v1
 
+# deberta-v3-base-zeroshot-v1
 ## Model description
 The model is designed for zero-shot classification with the Hugging Face pipeline. 
 The model should be substantially better at zero-shot classification than my other zero-shot models on the
 Hugging Face hub: https://huggingface.co/MoritzLaurer.
 
-The model can do one universal task: determine whether a hypothesis is `true` or `not_true` given a text.  
+The model can do one universal task: determine whether a hypothesis is `true` or `not_true`
+given a text (also called `entailment` vs. `not_entailment`).  
 This task format is based on the Natural Language Inference task (NLI).
-This task is so universal that any classification task can be reformulated into this true vs. false task.
+The task is so universal that any classification task can be reformulated into the task.
 
+## Training data
 The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format.
 1.   26 classification tasks with ~400k texts:
-['amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
+'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
 'emotiondair', 'emocontext', 'empathetic',
 'financialphrasebank', 'banking77', 'massive',
 'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate', 
 'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
 'agnews', 'yahootopics',
-'trueteacher', 'spam', 'wellformedquery',]
-2.   Five NLI datasets with ~885k texts: ["mnli", "anli", "fever", "wanli", "ling"]
+'trueteacher', 'spam', 'wellformedquery'
+2.   Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
 
-Note that compared to other NLI models, this model predicts two classes (true vs. not_true) as opposed to three classes (entailment/neutral/contradiction)
+Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
+as opposed to three classes (entailment/neutral/contradiction)
 
 
 ### How to use the model
@@ -44,7 +47,6 @@ print(output)
 ```
 
 ### Details on data and training
-
 The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main
 
 ## Limitations and bias
@@ -53,7 +55,23 @@ The model can only do text classification tasks.
 Please consult the original DeBERTa paper and the papers for the different datasets for potential biases. 
 
 ## 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.
+If you use this model, please cite: 
+```
+@article{laurer_less_2023,
+	title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
+	issn = {1047-1987, 1476-4989},
+	shorttitle = {Less {Annotating}, {More} {Classifying}},
+	url = {https://www.cambridge.org/core/product/identifier/S1047198723000207/type/journal_article},
+	doi = {10.1017/pan.2023.20},
+	language = {en},
+	urldate = {2023-06-20},
+	journal = {Political Analysis},
+	author = {Laurer, Moritz and Van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
+	month = jun,
+	year = {2023},
+	pages = {1--33},
+}
+```
 
 ### 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/)