openprotein.embeddings.EmbeddingsAPI

class openprotein.embeddings.EmbeddingsAPI(session)

Embeddings API providing the interface for creating embeddings using protein language models.

You can access all our models either via get_model() or directly through the session’s embedding attribute using the model’s ID and the desired method. For example, to use the attention method on the protein sequence model, you would use session.embedding.prot_seq.attn().

Examples

Accessing a model’s method:

# To call the attention method on the protein sequence model:
import openprotein
session = openprotein.connect(username="user", password="password")
session.embedding.prot_seq.attn()

Using the get_model method:

# Get a model instance by name:
import openprotein
session = openprotein.connect(username="user", password="password")
# list available models:
print(session.embedding.list_models() )
# init model by name
model = session.embedding.get_model('prot-seq')

Parameters:

session (APISession)

__init__(session)

Parameters:

session (APISession)

Methods

__init__(session)
get_model(name)	Get model by model_id.
list_models()	list models available for creating embeddings of your sequences

Attributes

poet2	PoET-2 model
poet	PoET model
prot_seq	Prot-seq model
rotaprot_large_uniref50w	Rotaprot model trained on UniRef50
rotaprot_large_uniref90_ft	Rotaprot model trained on UniRef90
poet_2
esm1b	ESM1b model
esm1b_t33_650M_UR50S
esm1v	ESM1v model
esm1v_t33_650M_UR90S_1
esm1v_t33_650M_UR90S_2
esm1v_t33_650M_UR90S_3
esm1v_t33_650M_UR90S_4
esm1v_t33_650M_UR90S_5
esm2	ESM2 model
esm2_t12_35M_UR50D
esm2_t30_150M_UR50D
esm2_t33_650M_UR50D
esm2_t36_3B_UR50D
esm2_t6_8M_UR50D