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Replace the default Kenlm language model with a user-trained or smaller model for resource-constrained environments.
Distinct from Language Modeling Data Loading: Distinct from Language Modeling Data Loading: focuses on loading custom language models for inference, not data loading for training.
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pycorrector is an open-source toolkit for detecting and correcting spelling and grammar errors in Chinese text. It combines multiple correction approaches, including rule-based methods using Kenlm n-gram language models and confusion sets, as well as deep learning correctors built on BERT, GPT, and T5 models. The toolkit also provides a command-line interface for batch processing Chinese text files with configurable detection and output options. The project distinguishes itself by offering a range of correction strategies that can be mixed and matched. Rule-based correction uses character-lev
Replaces the default Kenlm language model with a user-trained or smaller model for resource-constrained environments.