chinese roberta wwm ext large

Introduction

The Chinese-RoBERTa-wwm-ext-large model is a pre-trained language model tailored for Chinese natural language processing using the Whole Word Masking (WWM) approach. It is developed by the Joint Laboratory of HIT and iFLYTEK Research (HFL) and is part of the broader Chinese BERT series.

Architecture

Chinese-RoBERTa-wwm-ext-large is based on the BERT architecture with modifications for Whole Word Masking. This approach enhances the model's understanding of Chinese by masking entire words during training rather than sub-word units, leading to improved contextual embedding for the language.

Training

The model is pre-trained using a technique called Whole Word Masking, which is known to enhance the performance of BERT models, particularly for languages like Chinese. The training methodology and detailed performance metrics are discussed in the associated research papers:

• "Pre-Training with Whole Word Masking for Chinese BERT" arXiv:1906.08101
• "Revisiting Pre-Trained Models for Chinese Natural Language Processing" arXiv:2004.13922

Guide: Running Locally

1. Install Dependencies: Ensure you have transformers and torch libraries installed. You can install them via pip:

   pip install transformers torch
   

2. Load the Model: Use the Hugging Face transformers library to load the model:

   from transformers import BertTokenizer, BertForMaskedLM
   
   tokenizer = BertTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
   model = BertForMaskedLM.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
   

3. Inference: Prepare your input text, tokenize it, and perform inference:

   inputs = tokenizer("这是一个 [MASK] 例子。", return_tensors="pt")
   outputs = model(**inputs)
   

4. Cloud GPUs: For improved performance, consider using a cloud-based GPU service such as Google Colab, AWS, or Azure.

License

The Chinese-RoBERTa-wwm-ext-large model is released under the Apache 2.0 License, allowing for both personal and commercial use.