bert-xsmall-dummy
| Property | Value |
|---|---|
| Author | julien-c |
| Model Type | BERT |
| Architecture | Minimal BERT with 10 layers, 20 attention heads |
| Model URL | Hugging Face Hub |
What is bert-xsmall-dummy?
bert-xsmall-dummy is a minimal implementation of the BERT architecture designed for testing and educational purposes. It features a significantly reduced architecture with just 10 layers and 20 attention heads, making it perfect for development and debugging scenarios.
Implementation Details
The model is implemented using the Hugging Face Transformers library, supporting both PyTorch and TensorFlow frameworks. It's built with a custom configuration using BertConfig with minimal parameters: 10 layers, 20 attention heads, and a vocabulary size of 40 tokens.
- Implements both PyTorch (BertForMaskedLM) and TensorFlow (TFBertForMaskedLM) versions
- Uses minimal configuration parameters for lightweight deployment
- Includes save and load functionality for both model variants
Core Capabilities
- Masked Language Modeling (MLM) functionality
- Cross-platform compatibility (PyTorch and TensorFlow)
- Minimal memory footprint
- Suitable for testing and prototyping
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its minimal configuration, making it perfect for testing BERT implementations without the computational overhead of full-scale models.
Q: What are the recommended use cases?
The model is ideal for development environments, testing pipelines, and educational purposes where a lightweight BERT implementation is needed.
