Transfer learning, particularly approaches that combine multi-task learning with pretrained 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 text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.
|Titel||Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations|
|Forlag||Association for Computational Linguistics|
|Status||Udgivet - 2021|