Cappy
A small scorer to improve the performance of large multi-task language models
Product Details
Cappy is a novel method designed to improve the performance and efficiency of large multi-task language models. It is a lightweight pre-trained scorer based on RoBERTa with only 360 million parameters. Cappy can solve classification tasks independently or be used as an auxiliary component to improve language model performance. Fine-tuning Cappy in downstream tasks can effectively integrate supervision information, improve model performance, and does not require backpropagation to language model parameters, reducing memory requirements. Cappy is suitable for open source and closed source language models and is an efficient model fine-tuning method.
Main Features
Target Users
Cappy can be used to improve the performance of large language models on various natural language processing tasks, such as question answering, sentiment analysis, summarization, etc. It is especially suitable for scenarios that cannot be succinctly defined with instructions, require personalization or are complex tasks.
Examples
Use Cappy in customer service bots to improve conversation quality
Combine Cappy with search engines to improve the relevance of query results
Use Cappy to optimize large language model output for tasks such as content summarization and text generation
Quick Access
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