dummy-unknown
| Property | Value |
|---|---|
| Downloads | 33,620 |
| Framework Support | PyTorch, TensorFlow |
| Task | Fill-Mask |
| Author | julien-c |
What is dummy-unknown?
dummy-unknown is a lightweight RoBERTa-based model specifically designed for continuous integration (CI) and unit testing purposes. It implements a minimal version of the RoBERTa architecture with just 10 layers and 20 attention heads, making it ideal for testing pipelines and development workflows.
Implementation Details
The model is implemented using both PyTorch and TensorFlow frameworks, featuring a compact vocabulary of just 20 tokens. It utilizes a RoBERTa configuration with minimal parameters: 10 layers, 20 attention heads, and a vocabulary size of 40 tokens.
- Dual framework implementation (PyTorch and TensorFlow)
- Minimalistic vocabulary system with basic merges
- Lightweight architecture optimized for testing
Core Capabilities
- Masked Language Modeling (MLM) task support
- Cross-framework compatibility testing
- CI/CD pipeline validation
- Quick model loading and inference testing
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its purposeful simplicity, designed specifically for testing scenarios rather than production use. Its minimal architecture makes it perfect for validating ML pipelines and framework compatibility.
Q: What are the recommended use cases?
The model is recommended for developers working on ML infrastructure, testing new features in transformers libraries, and validating CI/CD pipelines. It's not intended for production or real-world NLP tasks.
