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. It was then fine tuned on the XNLI dataset, which contains hypothesis premise pairs from 15 languages, as well as the English MNLI dataset. As of December 2021, mDeBERTa base is the best performing multilingual base sized transformer model, introduced by Microsoft in this paper. If you are looking for a smaller, faster (but less performant) model, you can try multilingual MiniLMv2 L6 mnli xnli. How to use the model Simple zero shot classification pipeline NLI use case Training data This model was trained on the XNLI development dataset and the MNLI train dataset. The XNLI development set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see this paper). Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages, but due to quality issues with these machine translations, thi…
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