Dontopedia

tensor device transfer

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tensor device transfer has 5 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

5 facts·2 predicates·2 sources·2 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Other facts (4)

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4 facts
PredicateValueRef
Rdf:typeHardware Optimization[1]
Rdf:typeTensor Device Transfer[2]
Applied toInputs[2]
Applied toLabels[2]

Timeline

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typebeam/dec138b8-3361-428f-b049-8ef1e4b6719e
ex:HardwareOptimization
labelbeam/dec138b8-3361-428f-b049-8ef1e4b6719e
tensor device transfer
typebeam/d722ad53-d442-458e-b561-cab7e12fcbbf
ex:TensorDeviceTransfer
appliedTobeam/d722ad53-d442-458e-b561-cab7e12fcbbf
ex:inputs
appliedTobeam/d722ad53-d442-458e-b561-cab7e12fcbbf
ex:labels

References (2)

2 references
  1. ctx:claims/beam/dec138b8-3361-428f-b049-8ef1e4b6719e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/dec138b8-3361-428f-b049-8ef1e4b6719e
      Show excerpt
      labels = batch['labels'].to(device) outputs = model(input_ids, attention_mask=attention_mask, labels=labels) _, predicted = torch.max(outputs.scores, dim=1) total_correct += (predicted == lab
  2. ctx:claims/beam/d722ad53-d442-458e-b561-cab7e12fcbbf
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d722ad53-d442-458e-b561-cab7e12fcbbf
      Show excerpt
      optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running

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