Erlangshen-Roberta-110M-Sentiment
Maintainer: IDEA-CCNL
53
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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 Erlangshen-Roberta-110M-Sentiment model is a fine-tuned version of the Chinese RoBERTa-wwm-ext-base model, trained on several Chinese sentiment analysis datasets. It is part of the Fengshenbang language model series developed by the IDEA-CCNL research group. The model has 110M parameters and is designed for sentiment analysis tasks.
Similar models include the DistilRoBERTa-financial-sentiment model, which is fine-tuned on financial news sentiment data, and the Wenzhong2.0-GPT2-3.5B-chinese model, a large-scale Chinese GPT-2 model developed by IDEA-CCNL.
Model inputs and outputs
Inputs
- Text: The model accepts single sentences or short paragraphs as input.
Outputs
- Sentiment score: The model outputs a sentiment score for the input text, ranging from 0 (negative) to 1 (positive).
Capabilities
The Erlangshen-Roberta-110M-Sentiment model is capable of accurately classifying the sentiment of Chinese text. It has been evaluated on several benchmark datasets, achieving high performance on tasks like ASAP-SENT, ASAP-ASPECT, and ChnSentiCorp.
What can I use it for?
The Erlangshen-Roberta-110M-Sentiment model can be used for a variety of Chinese sentiment analysis applications, such as:
- Customer feedback analysis: Analyze customer reviews, comments, or survey responses to understand sentiment and identify areas for improvement.
- Social media monitoring: Track sentiment around brands, products, or topics on Chinese social media platforms.
- Content moderation: Detect and filter out negative or toxic content in online forums, chat rooms, or other user-generated content.
Things to try
One interesting aspect of the Erlangshen-Roberta-110M-Sentiment model is its ability to handle different types of sentiment-bearing text, including short and colloquial expressions. You could try using the model to analyze a range of Chinese text, from formal essays to informal social media posts, and observe how it performs across different domains and styles of writing.
Additionally, you could experiment with using the model as a feature extractor, feeding the sentiment scores into other machine learning models for tasks like topic classification, recommendation systems, or sentiment-driven content generation.
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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