![]() ![]() Distributed Data Parallel in PyTorch - Video Tutorials.Distributed and Parallel Training Tutorials.(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA).Inductor CPU backend debugging and profiling.Getting Started - Accelerate Your Scripts with nvFuser.Grokking PyTorch Intel CPU performance from first principles (Part 2).Grokking PyTorch Intel CPU performance from first principles.(beta) Static Quantization with Eager Mode in PyTorch.(beta) Quantized Transfer Learning for Computer Vision Tutorial.(beta) Dynamic Quantization on an LSTM Word Language Model.Extending dispatcher for a new backend in C++.Registering a Dispatched Operator in C++.Extending TorchScript with Custom C++ Classes.Extending TorchScript with Custom C++ Operators.Fusing Convolution and Batch Norm using Custom Function.Jacobians, Hessians, hvp, vhp, and more: composing function transforms.Forward-mode Automatic Differentiation (Beta).(beta) Channels Last Memory Format in PyTorch.(beta) Building a Simple CPU Performance Profiler with FX.(beta) Building a Convolution/Batch Norm fuser in FX.Real Time Inference on Raspberry Pi 4 (30 fps!).(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime.Deploying PyTorch in Python via a REST API with Flask.Reinforcement Learning (PPO) with TorchRL Tutorial.Preprocess custom text dataset using Torchtext.Language Translation with nn.Transformer and torchtext.Text classification with the torchtext library.NLP From Scratch: Translation with a Sequence to Sequence Network and Attention.NLP From Scratch: Generating Names with a Character-Level RNN.NLP From Scratch: Classifying Names with a Character-Level RNN.Fast Transformer Inference with Better Transformer.Language Modeling with nn.Transformer and torchtext.Optimizing Vision Transformer Model for Deployment.Transfer Learning for Computer Vision Tutorial.TorchVision Object Detection Finetuning Tutorial.Visualizing Models, Data, and Training with TensorBoard.Deep Learning with PyTorch: A 60 Minute Blitz.Introduction to PyTorch - YouTube Series.Java is a registered trademark of Oracle and/or its affiliates. For details, see the Google Developers Site Policies. end_offsets: A RaggedTensor of the tokens' ending byte offset.Įxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License.start_offsets: A RaggedTensor of the tokens' starting byte offset.tokens: A RaggedTensor of tokenized text.Example: splitter = WhitespaceTokenizer() pieces, starts, ends = splitter.tokenize_with_offsets("a bb ccc") print(pieces.numpy(), starts.numpy(), ends.numpy()) Ī RaggedTensoror Tensor of UTF-8 strings with any shape.Ī tuple (tokens, start_offsets, end_offsets) where: Input tensor with an added ragged dimension for tokens of each string. ![]() Example: WhitespaceTokenizer().tokenize("small medium large") Ī RaggedTensor or Tensor of UTF-8 strings with any shape.Ī RaggedTensor of tokenized text. ![]() The strings are split on ICU defined whitespace characters. split_with_offsetsĪlias for TokenizerWithOffsets.tokenize_with_offsets. Converting TensorFlow Text operators to TensorFlow LiteĪlias for Tokenizer.tokenize.Inherits From: TokenizerWithOffsets, Tokenizer, SplitterWithOffsets, Splitter text.WhitespaceTokenizer() Tokenizes a tensor of UTF-8 strings on whitespaces. ![]()
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