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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