Built-in SageMaker Algorithms for Text Data - Amazon SageMaker

Built-in SageMaker Algorithms for Text Data

SageMaker provides algorithms that are tailored to the analysis of textual documents used in natural language processing, document classification or summarization, topic modeling or classification, and language transcription or translation.

  • BlazingText algorithm—a highly optimized implementation of the Word2vec and text classification algorithms that scale to large datasets easily. It is useful for many downstream natural language processing (NLP) tasks.

  • Latent Dirichlet Allocation (LDA) Algorithm—an algorithm suitable for determining topics in a set of documents. It is an unsupervised algorithm, which means that it doesn't use example data with answers during training.

  • Neural Topic Model (NTM) Algorithm—another unsupervised technique for determining topics in a set of documents, using a neural network approach.

  • Object2Vec Algorithm—a general-purpose neural embedding algorithm that can be used for recommendation systems, document classification, and sentence embeddings.

  • Sequence-to-Sequence Algorithm—a supervised algorithm commonly used for neural machine translation.

  • Text Classification - TensorFlow—a supervised algorithm that supports transfer learning with available pretrained models for text classification.

Algorithm name Channel name Training input mode File type Instance class Parallelizable
BlazingText train File or Pipe Text file (one sentence per line with space-separated tokens) GPU (single instance only) or CPU No
LDA train and (optionally) test File or Pipe recordIO-protobuf or CSV CPU (single instance only) No
Neural Topic Model train and (optionally) validation, test, or both File or Pipe recordIO-protobuf or CSV GPU or CPU Yes
Object2Vec train and (optionally) validation, test, or both File JSON Lines GPU or CPU (single instance only) No
Seq2Seq Modeling train, validation, and vocab File recordIO-protobuf GPU (single instance only) No
Text Classification - TensorFlow training and validation File CSV CPU or GPU Yes (only across multiple GPUs on a single instance)