MiniLM L12 H384 uncased for Extractive QA Overview Language model: microsoft/MiniLM L12 H384 uncased Language: English 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 with the official eval script. Authors Vaishali Pal: vaishali.pal@deepset.ai Branden Chan: branden.chan@deepset.ai Timo Möller: timo.moeller@deepset.ai Malte Pietsch: malte.pietsch@deepset.ai Tanay Soni: tanay.soni@deepset.ai About us deepset is the company behind the production ready open source AI framework Haystack. Some of our other work: Distilled roberta base squad2 (aka "tinyroberta squad2") German BERT, GermanQuAD and German…
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