ESM-1v T33 650M UR90S
| Property | Value |
|---|---|
| Parameter Count | 650 Million |
| Model Type | Protein Language Model |
| Architecture | Transformer-based |
| Developer | Facebook AI Research |
| Model URL | https://huggingface.co/facebook/esm1v_t33_650M_UR90S_1 |
What is esm1v_t33_650M_UR90S_1?
ESM-1v is a powerful protein language model developed by Facebook AI Research, designed specifically for protein sequence analysis and representation learning. This variant features 33 transformer layers and 650 million parameters, trained on the UniRef90 dataset. It represents a significant advancement in protein sequence modeling and analysis capabilities.
Implementation Details
The model implements a transformer-based architecture optimized for protein sequence understanding. It leverages deep learning techniques to capture complex patterns and relationships in protein sequences, enabling various downstream applications in computational biology.
- 33-layer transformer architecture
- 650 million parameters for deep representation learning
- Trained on UniRef90 sequence database
- Optimized for protein sequence analysis
Core Capabilities
- Protein sequence representation learning
- Structure prediction assistance
- Evolutionary relationship analysis
- Protein function prediction
- Sequence-based property prediction
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its large parameter count (650M) and specialized training on protein sequences, making it particularly effective for protein-related tasks and biological sequence analysis.
Q: What are the recommended use cases?
The model is ideal for protein sequence analysis, structure prediction, function prediction, and other computational biology applications requiring deep understanding of protein sequences.
