This model takes [xlm-roberta-large](https://huggingface.co/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](https://huggingface.co/transformers/master/main_classes/pipelines.html#transformers.ZeroShotClassificationPipeline).
You can play with an interactive demo of this zero-shot technique with this model [here](https://huggingface.co/zero-shot/).
## Inteded Usage
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:
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@@ -46,13 +44,14 @@ This model is intended to be used for zero-shot text classification, especially
- Swahili
- Urdu
Since the base model was pre-trained trained on 100 different languages (see the full list in appendix A of the [XLM
Roberata paper](https://arxiv.org/abs/1911.02116)), the model may have some limited effectiveness in other languages as
well.
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
