| Property | Value |
|---|---|
| Parameter Count | 35M parameters |
| Model Type | Protein Language Model |
| License | MIT |
| Framework | PyTorch, TensorFlow |
| Author |
What is esm2_t12_35M_UR50D?
ESM2_t12_35M_UR50D is a lightweight variant of the ESM-2 protein language model family, designed for efficient protein sequence analysis. With 12 layers and 35 million parameters, it offers a balanced compromise between computational efficiency and performance for protein-related tasks.
Implementation Details
The model implements a transformer-based architecture with masked language modeling capabilities, specifically optimized for protein sequences. It's available in both PyTorch and TensorFlow frameworks, making it accessible across different deep learning ecosystems.
- 12-layer transformer architecture
- 35M parameter efficient design
- Masked language modeling objective
- Compatible with PyTorch and TensorFlow
Core Capabilities
- Protein sequence analysis
- Masked protein residue prediction
- Feature extraction for downstream tasks
- Transfer learning for protein-specific applications
Frequently Asked Questions
Q: What makes this model unique?
This model represents the efficient end of the ESM-2 family, offering a good balance between model size and performance. Its 12-layer architecture makes it suitable for scenarios where computational resources are limited but protein language modeling capabilities are needed.
Q: What are the recommended use cases?
The model is ideal for protein sequence analysis tasks, particularly when computational resources are constrained. It's suitable for transfer learning, protein feature extraction, and basic protein property prediction tasks.