UIE-Base Information Extraction Model
| Property | Value |
|---|---|
| Parameter Count | 118M |
| License | Apache-2.0 |
| Paper | Universal Information Extraction |
| Tensor Type | F32 |
What is uie-base?
UIE-base is a universal information extraction model developed based on ERNIE 3.0, specifically designed for Chinese language processing. Published by researchers including Yaojie Lu at ACL-2022, it represents a unified framework for various extraction tasks including entity recognition, relation extraction, event extraction, and sentiment analysis.
Implementation Details
The model implements a unified framework based on ERNIE 3.0 knowledge-enhanced pre-training architecture. It's implemented in PyTorch and uses the transformers library for easy integration. The model operates using F32 tensor types and comprises 118M parameters.
- Zero-shot learning capabilities for quick cold start
- Excellent few-shot fine-tuning abilities
- Unified framework for multiple extraction tasks
- Based on ERNIE 3.0 architecture
Core Capabilities
- Entity Extraction: Identifies and extracts named entities from text
- Relation Extraction: Determines relationships between entities
- Event Extraction: Captures event-related information including time, location, and participants
- Sentiment Analysis: Analyzes emotional context and opinion orientation
- Domain-Agnostic: Works across various industry domains without specific limitations
Frequently Asked Questions
Q: What makes this model unique?
UIE-base's uniqueness lies in its universal approach to information extraction, allowing it to handle multiple extraction tasks within a single framework. Its zero-shot learning capability means it can begin extracting information without task-specific training data.
Q: What are the recommended use cases?
The model is particularly well-suited for Chinese text processing tasks including named entity recognition, relationship extraction, event extraction, and sentiment analysis. It's especially valuable in scenarios where training data is limited or when quick deployment is needed across different domains.
