roberta base wechsel chinese

Introduction

The RoBERTa-base-WECHSEL-Chinese model is a language model trained using the WECHSEL method for effective initialization of subword embeddings, facilitating cross-lingual transfer of monolingual language models. The model supports the Chinese language and is built on the RoBERTa architecture using PyTorch.

Architecture

RoBERTa-base-WECHSEL-Chinese is based on the RoBERTa architecture, a robustly optimized BERT approach. The model uses the WECHSEL method to initialize subword embeddings for effective cross-lingual transfer. It employs a tokenizer adapted from English to Chinese, using multilingual static word embeddings to maintain semantic similarity.

Training

The model has been evaluated with the following performance metrics compared to other models:

• NLI (Natural Language Inference) Score: 78.32
• NER (Named Entity Recognition) Score: 80.55
• Average Score: 79.44

These scores demonstrate its competitive performance against other models, such as bert-base-chinese.

Guide: Running Locally

1. Install Dependencies:

   • Ensure Python and PyTorch are installed.
   • Install the Transformers library from Hugging Face: pip install transformers.

2. Load and Use the Model:

   from transformers import AutoTokenizer, AutoModelForMaskedLM
   
   tokenizer = AutoTokenizer.from_pretrained("benjamin/roberta-base-wechsel-chinese")
   model = AutoModelForMaskedLM.from_pretrained("benjamin/roberta-base-wechsel-chinese")
   
   inputs = tokenizer("你的文本在这里", return_tensors="pt")
   outputs = model(**inputs)
   

3. Suggest Cloud GPUs:

   • Consider using cloud services like AWS, Google Cloud, or Azure with GPU instances for faster processing and training.

License

The RoBERTa-base-WECHSEL-Chinese model is released under the MIT License, allowing for wide usage and modification with minimal restrictions.