In the Materials and Methods section of your paper, it is mentioned that you used mean pooling of amino-acid embeddings along the length of the sequence to get sequence embedding. However, in the code ppi/modeling.py and symmetry/modeling.py , you used the first token embedding (pooler_output) as the sequence embedding.
p1_embedding = self.language_model(p1_tokens, p1_attention)[1]
p2_embedding = self.language_model(p2_tokens, p2_attention)[1]
mean_embedding = torch.div(torch.add(p1_embedding, p2_embedding), 2)
Which method did you actually use for the results you showed on the paper?
In the Materials and Methods section of your paper, it is mentioned that you used mean pooling of amino-acid embeddings along the length of the sequence to get sequence embedding. However, in the code ppi/modeling.py and symmetry/modeling.py , you used the first token embedding (pooler_output) as the sequence embedding.
Which method did you actually use for the results you showed on the paper?