bart-large-mnli

bart-large-mnli This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset. Additional information about this model: The bart-large model page BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension BART fairseq implementation NLI-based Zero Shot Text Classification Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of `This text is about politics.`. The probabilities for entailment and contradiction are then converted to label probabilities. This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code. With the zero-shot classification pipeline The model can be loaded with the `zero-shot-classification` pipeline like so: You can then use this pipeline to classify sequences into any of the class names you specify. If more than one candidate label can be correct, pass `multi_label=True` to calculate each class independently: With manual PyTorch