Explore powerful examples showcasing how to leverage DirectML for cutting-edge Natural Language Processing tasks on Windows.
Implement and optimize Transformer models for tasks like text summarization, translation, and question answering using DirectML.
// Example snippet (conceptual)
winrt::Microsoft::AI::DirectML::DMLDevice device = ...;
auto transformerModel = winrt::Microsoft::AI::DirectML::Graph::LoadModel(L"path/to/transformer.onnx");
auto inputTensor = winrt::Microsoft::AI::DirectML::Tensor::CreateFromBuffer(device, DML_TENSOR_DATA_TYPE_FLOAT32, {1, 128}, inputBuffer);
auto outputTensor = device.ExecuteGraph(transformerModel, {inputTensor});
Utilize DirectML with models like BERT or RoBERTa to analyze the sentiment of text with low latency, ideal for real-time applications.
// Example snippet (conceptual)
winrt::Microsoft::AI::DirectML::DMLCompiledOperator compiledOperator = ...;
winrt::Microsoft::AI::DirectML::DMLOperatorOperatorDesc operatorDesc = ...;
auto graph = winrt::Microsoft::AI::DirectML::Graph::CreateGraph(device, {operatorDesc});
auto inferenceSession = winrt::Microsoft::AI::DirectML::InferenceSession::Create(device, graph);
auto results = inferenceSession.ProcessInput({textTensor});
Discover how to efficiently identify and categorize named entities (people, organizations, locations) in text using DirectML-accelerated models.
// Example snippet (conceptual)
winrt::Microsoft::AI::DirectML::DMLBindingProperties properties = ...;
auto descriptorHeap = device.CreateDescriptorHeap(properties.DescriptorCount);
auto commandList = device.CreateCommandList();
commandList.AddOperator(nerOperator);
commandList.Close();
device.Submit(commandList);
Learn the steps to integrate custom NLP models, trained with popular frameworks, into your DirectML applications for optimized performance.
// Example snippet (conceptual)
winrt::Microsoft::AI::DirectML::Graph::LoadGraph(device, L"path/to/your/model.xml");
// ... configure tensors and execute ...