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Pre-training has become an essential part for NLP tasks and has led to remarkable improvements. UER-py (Universal Encoder Representations) is a toolkit for pre-training on general-domain corpus and fine-tuning on downstream task. UER-py maintains model modularity and supports research extensibility. It facilitates the use of different pre-training models (e.g. BERT, GPT, ELMO), and provides interfaces for users to further extend upon. With UER-py, we build a model zoo which contains pre-trained models based on different corpora, encoders, and targets.