Quality Classifier DeBERTa
| Property | Value |
|---|---|
| Parameter Count | 184M |
| Model Type | Text Classification |
| Architecture | DeBERTa V3 Base |
| License | Apache-2.0 |
| Paper | Research Paper |
What is quality-classifier-deberta?
Quality-classifier-deberta is a sophisticated text classification model developed by NVIDIA that evaluates document quality by classifying text into three categories: High, Medium, and Low. Built on the DeBERTa V3 Base architecture, it processes text with a context length of 1024 tokens and has been trained on 22,828 Common Crawl text samples.
Implementation Details
The model leverages the powerful DeBERTa architecture and incorporates comprehensive quality assessment factors including content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness. It achieves an impressive accuracy of 82.52% on evaluation data where all three annotators agreed on the labels.
- Trained on human-annotated dataset of 22.8K samples
- Context length of 1024 tokens
- Uses PyTorch framework with Safetensors support
- Achieves 83.25% precision for Medium quality content
Core Capabilities
- Qualitative data annotation and filtering
- Quality-specific content blending
- Automated metadata tagging
- Real-time quality assessment of text content
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its ability to provide objective quality assessments based on multiple factors, trained on human-annotated data. It's particularly valuable as part of NVIDIA's NeMo Curator for content filtering and quality control.
Q: What are the recommended use cases?
The model is ideal for content curation, educational material assessment, automated content filtering systems, and quality control in content management systems. It's particularly useful when implementing quality-based content organization or filtering pipelines.
