roberta-base-finetuned-dianping-chinese

Maintained By
uer

RoBERTa Base Dianping Chinese

PropertyValue
AuthorUER
Downloads12,457
FrameworkPyTorch
PaperUER-py Paper

What is roberta-base-finetuned-dianping-chinese?

This is a specialized Chinese language model based on RoBERTa architecture, fine-tuned specifically for sentiment analysis of Dianping restaurant reviews. It's part of a larger collection of Chinese text classification models developed using the UER-py framework.

Implementation Details

The model was fine-tuned using the UER-py toolkit on Tencent Cloud infrastructure. The training process involved three epochs with a sequence length of 512, building upon the pre-trained chinese_roberta_L-12_H-768 model. The fine-tuning utilized a learning rate of 3e-5 and a batch size of 32.

  • Built on RoBERTa base architecture
  • Optimized for Chinese text classification
  • Trained on Dianping restaurant review dataset
  • Implements advanced sentiment analysis capabilities

Core Capabilities

  • Sentiment analysis for Chinese text
  • Restaurant review classification
  • Real-time inference support
  • Integration with HuggingFace transformers pipeline

Frequently Asked Questions

Q: What makes this model unique?

This model specializes in Chinese sentiment analysis, particularly for restaurant reviews from Dianping. It combines RoBERTa's robust architecture with domain-specific training, making it highly effective for Chinese language understanding in the food service context.

Q: What are the recommended use cases?

The model is ideal for analyzing Chinese restaurant reviews, customer feedback analysis, sentiment classification in the food service industry, and general Chinese text classification tasks. It can be easily integrated into existing NLP pipelines using the HuggingFace transformers library.

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