模型:
facebook/esm2_t36_3B_UR50D
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper . You may also be interested in some demo notebooks ( PyTorch , TensorFlow ) which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:
Checkpoint name | Num layers | Num parameters |
---|---|---|
esm2_t48_15B_UR50D | 48 | 15B |
esm2_t36_3B_UR50D | 36 | 3B |
esm2_t33_650M_UR50D | 33 | 650M |
esm2_t30_150M_UR50D | 30 | 150M |
esm2_t12_35M_UR50D | 12 | 35M |
esm2_t6_8M_UR50D | 6 | 8M |