Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings... (read more)
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GLUE
SST
Universal Dependencies
QNLI
MRPC
CoLA
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