It’s actually pretty simple if you know how the embedding model is defined.
Initialize a new model with the same configuration of layers as the embedding model.
And say, siameseNetwork is the network with the embedding model within it as a layer, called “embedding_model”. You can find this by print(siameseNetwork). Then do something like:
embedding_model = EmbeddingModel()
embedding_model.load_state_dict(siameseNetwork.embedding_model.state_dict())

Now embedding_model will have the correct weights.