Model description: deberta v3 base zeroshot v1.1 all 33 The model is designed for zero shot classification with the Hugging Face pipeline. The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text ( 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 this task. A detailed description of how the model was trained and how it can be used is available in this paper. Training data The model was trained on a mixture of 33 datasets and 387 classes that have been reformatted into this universal format. 1. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling" 2. 28 classification tasks reformatted into the universal NLI format. ~51k cleaned texts were used to avoid overfitting: 'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes', 'emotiondair', 'emocontext', 'empathetic', 'financialphrasebank', 'banking77', 'massive', 'wikitoxic toxicaggregated', 'wikitoxic obscene', 'wikitoxic threat', 'wikitoxic insult', 'wikitoxic identityhate', 'hateoffensive', 'hatexpla…
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