device,
'gelu'
)
sp.Load(swedish_model)
criterion = nn.CrossEntropyLoss(ignore_index=sp.pad_id())
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
load_model = False
if load_model == True:
checkpoint = torch.load(model_path, map_location='cpu')
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
model.to(device)
sentence = "Sámediggi lea sámiid álbmotválljen orgána Norggas."
sentence2 = "Deaŧalaš lea gozihit álgoálbmotoli nationála ja riikkaidgaskasaš forain."
scores = []
e_losses = []
e_val_losses = []
e_ppl = []
e_val_ppl = []
threshold = 5
step = 5
max_len, device, 'gelu')
# [30] END
# [31] START
sp.Load(swedish_model)
criterion = nn.CrossEntropyLoss(ignore_index=sp.pad_id())
optimizer = optim.Adam(model_synth_swe.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
# [31] END
# [32] START
if load_model == True:
checkpoint = torch.load(model_path, map_location='cpu')
model_synth_swe.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
model_synth_swe.to(device)
# [32] END
# [33] START
translate_sentence(model_synth_swe, sent1, device, sami_model, swedish_model)
# [33] END
# [34] START
translate_sentence(model_synth_swe, sent2, device, sami_model, swedish_model)
# [34] END
# [35] START
translate_sentence(model_synth_swe, sent3, device, sami_model, swedish_model)
# [35] END
# [36] START