2 research outputs found
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. 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.Comment: https://machamp-nlp.github.io