Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

Code

• github.com/machamp-nlp/machamp
  OfficialIn paperpytorch★ 91
• github.com/anouck96/parsingfrisian
  none★ 0

Abstract

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.

Tasks

Reproductions