new family model

app.py

@@ -23,17 +23,17 @@ import os
 with open('family_labels.pkl', 'rb') as filefam:
     yfam = pickle.load(filefam)
 
-tokenizerfam = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") #facebook/esm2_t33_650M_UR50D
+tokenizerfam = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D") #facebook/esm2_t33_650M_UR50D
 label_encoderfam = LabelEncoder()
 encoded_labelsfam = label_encoderfam.fit_transform(yfam)
 labelsfam = torch.tensor(encoded_labelsfam)
 device = 'cpu'
 device
 
-modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t33_650M_UR50D", num_labels=len(set(labelsfam.tolist())))
+modelfam = EsmForSequenceClassification.from_pretrained("facebook/esm2_t12_35M_UR50D", num_labels=len(set(labelsfam.tolist())))
 modelfam = modelfam.to('cpu')
 
-modelfam.load_state_dict(torch.load("model_650M.pth", map_location=torch.device('cpu')))
+modelfam.load_state_dict(torch.load("family.pth", map_location=torch.device('cpu')))
 modelfam.eval()
 
 x_testfam = ["""MAEVLRTLAGKPKCHALRPMILFLIMLVLVLFGYGVLSPRSLMPGSLERGFCMAVREPDH

model_650M.pth → family.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:36a565e96eefc30f1809be4afcba0bed656fc81ed6d34d78cb58bafc9f1d7c4d
-size 2610018370
+oid sha256:03dcff847ada129cd2889ea3f62071b666b009087829dabf85301210c7fe8382
+size 136199341

family_labels.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9a22d6c50aaaffe9306de56cf40283a2b74107bed475de9674ac279ae1c44446
-size 20257
+oid sha256:c3e8fe9ddb883008ab377fba3837200626ee609fbe892950b1fada9ff078eca4
+size 4559