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| Model | Params Trained | Accuracy | F1 (macro) | MCC |
|---|---|---|---|---|
| ESM-2 8M linear probe | 100% (head only) | 69.6% | 0.581 | 0.614 |
| ESM-2 35M full fine-tune | 100% | 74.3% | 0.647 | 0.677 |
| ESM-2 150M full fine-tune | 100% | 76.6% | 0.696 | 0.706 |
| ESM-2 650M LoRA | 2.4% | 76.5% | 0.668 | 0.704 |
1import torch
2from transformers import AutoTokenizer, EsmModel
3from peft import LoraConfig, get_peft_model
4
5# Load base model + LoRA weights
6tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
7# See full inference code in the repository| ID | Location |
|---|---|
| 0 | Cytoplasm |
| 1 | Nucleus |
| 2 | Extracellular |
| 3 | Cell membrane |
| 4 | Mitochondrion |
| 5 | Endoplasmic reticulum |
| 6 | Membrane |
| 7 | Golgi apparatus |
| 8 | Lysosome/Vacuole |
| 9 | Peroxisome |
1@article{lin2023evolutionary,
2 title={Evolutionary-scale prediction of atomic-level protein structure with a language model},
3 author={Lin, Zeming and Akin, Halil and Rao, Roshan and others},
4 journal={Science},
5 year={2023}
6}