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# Multilingual mDeBERTa-v3-base-mnli-xnli
## Model description
This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying model was pre-trained by Microsoft on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf).
This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual
zero-shot classification. The underlying model was pre-trained by Microsoft on the
[CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model,
introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf).
If you are looking for a smaller, faster (but less performant) model, you can
try [multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli).
### How to use the model
#### Simple zero-shot classification pipeline