![\[PE(pos,2i)=sin(pos100002i/d),PE(pos,2i+1)=cos(pos100002i/d)\text{PE}(pos, 2i) = \sin\left(\frac{pos}{10000^{2i/d}}\right), \quad \text{PE}(pos, 2i+1) = \cos\left(\frac{pos}{10000^{2i/d}}\right)PE(pos,2i)=sin(100002i/dpos),PE(pos,2i+1)=cos(100002i/dpos)\]](https://esm3academy.com/wp-content/ql-cache/quicklatex.com-f5003b914e4915a10c1f26acc929b940_l3.png "Rendered by QuickLaTeX.com")
![\[\text{Attention}(Q, K, V) = \text{softmax} \left( \frac{QK^T}{\sqrt{d_k}} \right) V\]](https://esm3academy.com/wp-content/ql-cache/quicklatex.com-c999fe7b687e6762a39098cbfb6a1684_l3.png "Rendered by QuickLaTeX.com")
from esm import pretrained, Alphabet
# Load the ESM3 model
model, alphabet = pretrained.esm3_model()
batch_converter = alphabet.get_batch_converter()
# Example sequence
sequences = [("Protein1", "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAG"),
("Protein2", "GDVAKGEPVQLVCDNGSGLVQINKLKCLIEKFTKDYGVKTKQIKLHGLENVRDYLIP")]
# Preprocess sequences
batch_labels, batch_strs, batch_tokens = batch_converter(sequences)
# Predict outputs
with torch.no_grad():
results = model(batch_tokens)
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