UIE-base-en Information Extraction Model
| Property | Value |
|---|---|
| Developer | PaddlePaddle |
| License | Apache-2.0 |
| Paper | UIE Research Paper |
| Language | English |
What is uie-base-en?
UIE-base-en is a sophisticated information extraction model that implements a unified text-to-structure generation framework. Built on ERNIE 3.0, it's specifically designed for English language processing and can handle various information extraction tasks through a universal approach.
Implementation Details
The model employs a schema-based prompt mechanism called structural schema instructor, which enables adaptive generation of extraction structures. It's trained on large-scale information extraction datasets and uses a text-to-structure architecture to uniformly process different extraction tasks.
- Unified framework for multiple IE tasks
- Schema-based prompt mechanism
- Built on ERNIE 3.0 architecture
- Supports zero-shot and few-shot learning
Core Capabilities
- Entity extraction and recognition
- Relation extraction between entities
- Event extraction and classification
- Sentiment analysis and opinion mining
- Adaptable to various domains including finance, healthcare, and internet
Frequently Asked Questions
Q: What makes this model unique?
UIE-base-en stands out for its ability to handle multiple IE tasks without task-specific architectural changes, using a unified framework that can adapt to different extraction needs through prompting.
Q: What are the recommended use cases?
The model is ideal for English text processing tasks including named entity recognition, relationship extraction, event detection, and sentiment analysis. It performs particularly well in both zero-shot and few-shot scenarios, making it suitable for applications with limited training data.
