Create README.md

README.md

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+---
+language:
+- en
+tags:
+- text-classification
+- zero-shot-classification
+pipeline_tag: zero-shot-classification
+library_name: transformers
+---
+# 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.  
+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 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',
+'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"]
+
+Note that compared to other NLI models, this model predicts two classes (true vs. not_true) as opposed to three classes (entailment/neutral/contradiction)
+
+
+### How to use the model
+#### Simple zero-shot classification pipeline
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1")
+sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
+candidate_labels = ["politics", "economy", "entertainment", "environment"]
+output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
+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
+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.
+
+### 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 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.