Multilingual XLM RoBERTa base distilled for Extractive QA on various languages Haystack's distillation feature was used for training. deepset/xlm roberta large squad2 was used as the teacher model. Overview Language model: deepset/xlm roberta base squad2 distilled Language: Multilingual Downstream task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See an example extractive QA pipeline built with Haystack Infrastructure : 1x Tesla v100 Hyperparameters Usage In Haystack Haystack is an AI orchestration framework to build customizable, production ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. In Transformers Performance Evaluated on the SQuAD 2.0 dev set Authors Timo Möller: timo.moeller@deepset.ai Julian Risch: julian.risch@deepset.ai Malte Pietsch: malte.pietsch@deepset.ai Michel Bartels: michel.bartels@deepset.ai About us deepset is the company behind the production ready open source AI framework Haystack. Some of…
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