roberta-base-finetuned-dianping-chinese
Maintainer: uer
42
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The roberta-base-finetuned-dianping-chinese model is one of a set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py, a toolkit introduced in this paper. These models could also be fine-tuned using TencentPretrain, which extends UER-py to support models with over one billion parameters and adds a multimodal pre-training framework.
The Dianping model is fine-tuned on a dataset of user reviews with different sentiment polarities, while the other models in this set are fine-tuned on datasets like JD full, JD binary, Ifeng, and Chinanews. All of these datasets were collected by the Glyph project, as discussed in this paper.
Model inputs and outputs
Inputs
- Text inputs for classification tasks
Outputs
- Predicted class labels and corresponding confidence scores
Capabilities
The roberta-base-finetuned-dianping-chinese model is capable of performing sentiment analysis on Chinese text. It can classify user reviews as having positive or negative sentiment with high accuracy. This makes it useful for applications like product review analysis, customer service, and social media monitoring.
What can I use it for?
The Dianping model, and the other models in this set, can be used for a variety of Chinese text classification tasks. Potential use cases include:
- Product review analysis: Classifying customer reviews on e-commerce sites to gauge sentiment and identify trends.
- Customer service: Automatically analyzing chat logs or support tickets to detect and route issues based on sentiment.
- Social media monitoring: Monitoring social media posts to detect brand sentiment, identify influential users, and respond to customer feedback.
Things to try
When using this model, you can experiment with different input text lengths and see how that affects the classification performance. You could also try fine-tuning the model further on a specific domain or application to improve its accuracy for your use case. Additionally, you could explore using the model in conjunction with other NLP techniques like named entity recognition or aspect-based sentiment analysis to gain deeper insights from your text data.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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