chinese electra base discriminator

Introduction

The Chinese ELECTRA is a pre-trained model developed by the Joint Laboratory of HIT and iFLYTEK Research (HFL) for Chinese natural language processing. It is based on Google's ELECTRA model, designed to offer competitive performance with a more compact size compared to BERT. The model has been optimized for Chinese language tasks and can achieve similar or better results using significantly fewer parameters.

Architecture

Chinese ELECTRA applies the ELECTRA architecture, which uses a smaller model to replace the masked tokens in input data and a discriminator to predict if each token is replaced. This model efficiently uses parameters, allowing it to achieve high performance on various tasks with a reduced model size.

Training

The model was trained using the official ELECTRA codebase. It is suggested to use ElectraForPreTraining for the discriminator and ElectraForMaskedLM for the generator if re-training the models is necessary. The Chinese ELECTRA models have been shown to perform well on several NLP tasks with only a fraction of the parameters required by BERT models.

Guide: Running Locally

1. Installation: Clone the ELECTRA repository and install the required dependencies.

   git clone https://github.com/google-research/electra
   cd electra
   pip install -r requirements.txt
   

2. Model Loading: Use the Hugging Face Transformers library to load the model.

   from transformers import ElectraTokenizer, ElectraForPreTraining
   
   tokenizer = ElectraTokenizer.from_pretrained("hfl/chinese-electra-base-discriminator")
   model = ElectraForPreTraining.from_pretrained("hfl/chinese-electra-base-discriminator")
   

3. Inference: Tokenize input text and feed it through the model for inference.

4. Cloud GPUs: For efficient training and inference, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The Chinese ELECTRA model is released under the Apache 2.0 license, allowing for both personal and commercial use with proper attribution.