MPNet, developed by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu, introduces a novel pre-training method for language understanding tasks. This method addresses the challenges faced by MLM (masked language modeling) in BERT and PLM (permuted language modeling) in XLNet, delivering enhanced accuracy. The team has recently updated their pre-trained models.
Features of MPNet
- Unified View and Implementation: MPNet offers a consolidated view and implementation of various pre-training models, including BERT, XLNet, MPNet, and more.
- Pre-training and Fine-tuning Code: The repository provides code for both pre-training and fine-tuning across a range of language understanding tasks such as GLUE, SQuAD, RACE, etc.
- Installation and Setup: MPNet is built upon the fairseq codebase. The installation process involves a series of pip installations, including the installation of
- Pre-training MPNet: The model is pre-trained using the bert dictionary. A script named
encode.pyand a dictionary file
dict.txtare provided to tokenize the corpus. The WikiText-103 dataset serves as a demo for the pre-training process.
- Fine-tuning MPNet: The repository provides guidance on fine-tuning MPNet for downstream tasks, including GLUE and SQuAD.
- Pre-trained Models: The team has updated the final pre-trained MPNet model for fine-tuning. Users can easily load this pre-trained model using the provided instructions.
- Acknowledgements and References: The MPNet code is based on fairseq-0.8.0. The repository also provides a citation for those who find the toolkit beneficial in their research.
- Related Works: The repository mentions other related works, such as MASS and LightPAFF, which are also significant contributions to the field of language understanding.