t5 base Chinese

Introduction

The T5-BASE-CHINESE model is a variant of the mt5-base designed for Text2Text generation, with its vocabulary and word embedding tailored specifically to retain only Chinese and English characters. This model is compatible with the Transformers library and supports both PyTorch and JAX frameworks.

Architecture

The model architecture is based on the mt5-base, which is a multilingual version of the T5 model. The architecture has been modified by truncating the vocabulary and word embeddings so that it focuses on Chinese and English languages. This makes it efficient for tasks involving these two languages while maintaining the original architecture's capabilities.

Training

Details on the exact training process are not specified, but it is likely to follow the standard fine-tuning methods used for T5 models, with datasets that emphasize Chinese and English text.

Guide: Running Locally

To run this model locally, follow these steps:

1. Install the Transformers Library: Ensure you have the latest version of the Hugging Face Transformers library installed.

   pip install transformers
   

2. Download the Model: Use the Hugging Face Model Hub to download the T5-BASE-CHINESE model.

   from transformers import T5Tokenizer, T5ForConditionalGeneration
   
   tokenizer = T5Tokenizer.from_pretrained("lemon234071/t5-base-Chinese")
   model = T5ForConditionalGeneration.from_pretrained("lemon234071/t5-base-Chinese")
   

3. Run Inference: Tokenize your input text and generate outputs.

   input_text = "Your input text here"
   input_ids = tokenizer(input_text, return_tensors="pt").input_ids
   outputs = model.generate(input_ids)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Consider Cloud GPUs: For better performance, especially with large datasets or models, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

Refer to the GitHub repository for detailed licensing information. Ensure compliance with the license terms when using and distributing this model.