ESM-2 Protein Language Model Explained – Architecture & Training
In this video, we introduce the ESM-2 protein language model, one of the most widely used transformer-based models for sequence-based protein prediction tasks.
We explain the model architecture, Rotary Positional Embeddings (RoPE), and the self-supervised masked language modeling objective that makes ESM-2 so powerful for downstream protein property prediction.
📚References
ESM-2 paper (Lin et al., Science)
“Evolutionary-scale prediction of atomic-level protein structure with a language model”
DOI: https://doi.org/10.1126/science.ade2574
ESM-2 Python code walkthrough & fine-tuning (Part 2 video)
• Using ESM-2 in Python: Protein Embeddings ...
Jupyter notebook for ESM-2 fine-tuning & analysis
https://github.com/ProteinVision/ESM2...
🎵Credits
Intro music: “Unbreakable” – Pixabay Music
https://pixabay.com/music/main-title-...
🧬About ProteinVision
We are ProteinVision, and we develop state-of-the-art models for protein prediction tasks.
🌐 Homepage: https://protein.vision
📧 Contact: [email protected]
If you find this video helpful, please like, subscribe, and let us know in the comments which protein models or tasks you’d like to see next!