4/14/2023 0 Comments Microsoft toolkit![]() ![]() ![]() Anthology ID: 2020.acl-demos.16 Volume: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations Month: July Year: 2020 Address: Online Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 118–126 Language: URL: DOI: 10.18653/v1/2020.acl-demos.16 Bibkey: liu-etal-2020-microsoft Cite (ACL): Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, and Jianfeng Gao. ![]() The software and pre-trained models will be publicly available at. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). Abstract We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. ![]()
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