Joe Davison

Model · Zero-shot classification

xlm-roberta-large-xnli

Joe Davison

This model takes xlm-roberta-large and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face ZeroShotClassificationPipeline. This model is intended to be used for zero-shot text classification, especially in languages other than English. It is fine-tuned on XNLI, which is a multilingual NLI dataset. The model can therefore be used with any of the languages in the XNLI corpus: Since the base model was pre-trained trained on 100 different languages, the model has shown some effectiveness in languages beyond those listed above as well. See the full list of pre-trained languages in appendix A of the…

Model · Zero-shot classification

bart-large-mnli-yahoo-answers

Joe Davison

This model takes facebook/bart-large-mnli and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before. You can play with an interactive demo of this zero-shot technique with this model, as well as the non-finetuned facebook/bart-large-mnli, here. This model was fine-tuned on topic classification and will perform best at zero-shot topic classification. Use hypothesistemplate="This text is about {}." as this is the template used during fine-tuning. For settings other than topic classification, you can use any model pre-trained on MNLI such as facebook/bart-large-mnli…