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Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
2021
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
unpublished
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. The benefits of MACHAMP are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from
doi:10.18653/v1/2021.eacl-demos.22
fatcat:dojyfdq475bilavthy24q2nfau