deberta v3 large 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 (also called entailment vs. not entailment ). This task format is based on the Natural Language Inference task (NLI). 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', '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', 'w…
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