chinese lert small

Introduction

LERT is a linguistically-motivated pre-trained language model developed by the Joint Laboratory of HIT and iFLYTEK Research. It is particularly designed for applications in the Chinese language using the BERT architecture and is compatible with popular machine learning frameworks like PyTorch and TensorFlow.

Architecture

LERT is based on the BERT architecture, which is widely used for its effectiveness in natural language processing tasks. The model is designed to incorporate linguistic features, enhancing its performance on language understanding tasks in Chinese.

Training

The training details and methodology of LERT are documented in the corresponding research paper available on arXiv. The model is pre-trained on large datasets to capture linguistic patterns and nuances specific to the Chinese language.

Guide: Running Locally

1. Clone the Repository:
   Clone the LERT repository from GitHub to access the model files and dependencies.

   git clone https://github.com/ymcui/LERT.git
   

2. Install Dependencies:
   Ensure you have either PyTorch or TensorFlow installed, as LERT is compatible with both. You may also need to install the transformers library from Hugging Face.

   pip install torch transformers  # For PyTorch
   pip install tensorflow transformers  # For TensorFlow
   

3. Load the Model:
   Use the transformers library to load the LERT model.

   from transformers import BertTokenizer, BertModel
   
   tokenizer = BertTokenizer.from_pretrained('hfl/chinese-lert-small')
   model = BertModel.from_pretrained('hfl/chinese-lert-small')
   

4. Inference:
   Prepare your input and perform inference using the model.

   inputs = tokenizer("你好，世界", return_tensors="pt")
   outputs = model(**inputs)
   

Cloud GPUs: For large-scale inference or training, consider using cloud-based GPU services like AWS, Google Cloud, or Azure to leverage their computational efficiency.

License

LERT is released under the Apache 2.0 License, which allows for both personal and commercial use, modification, and distribution of the model and its components.