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| Blazing Text |
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| The Amazon SageMaker BlazingText algorithm provides highly optimized implementations of the Word2vec and text classification algorithms. The Word2vec algorithm is useful for many downstream natural language processing (NLP) |
| tasks, such as sentiment analysis, named entity recognition, machine translation, etc. Text classification is an important task for applications that perform web searches, information retrieval, ranking, and document classification. |
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| The Word2vec algorithm maps words to high-quality distributed vectors. The resulting vector representation of a word is called a word embedding. Words that are semantically similar correspond to vectors that are close together. |
| That way, word embeddings capture the semantic relationships between words. |
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| Many natural language processing (NLP) applications learn word embeddings by training on large collections of documents. These pretrained vector representations provide information about semantics and word distributions that |
| typically improves the generalizability of other models that are later trained on a more limited amount of data. Most implementations of the Word2vec algorithm are not optimized for multi-core CPU architectures. This makes it |
| difficult to scale to large datasets. |
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| With the BlazingText algorithm, you can scale to large datasets easily. Similar to Word2vec, it provides the Skip-gram and continuous bag-of-words (CBOW) training architectures. BlazingText's implementation of the supervised |
| multi-class, multi-label text classification algorithm extends the fastText text classifier to use GPU acceleration with custom `CUDA <https://docs.nvidia.com/cuda/index.html>`__ |
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| kernels. You can train a model on more than a billion words in a couple of minutes using a multi-core CPU or a GPU. And, you achieve performance on par with the state-of-the-art deep learning text classification algorithms. |
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| The BlazingText algorithm is not parallelizable. For more information on parameters related to training, see `Docker Registry Paths for SageMaker Built-in Algorithms <https://docs.aws.amazon.com/en_us/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html>`__. |
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| For a sample notebook that uses the SageMaker BlazingText algorithm to train and deploy supervised binary and multiclass classification models, see |
| `Blazing Text classification on the DBPedia dataset <https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia/blazingtext_text_classification_dbpedia.html>`__. |
| For instructions for creating and accessing Jupyter notebook instances that you can use to run the example in SageMaker, see `Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. |
| After creating and opening a notebook instance, choose the SageMaker Examples tab to see a list of all the SageMaker examples. The topic modeling example notebooks that use the Blazing Text are located in the Introduction to Amazon |
| algorithms section. To open a notebook, choose its Use tab, then choose Create copy. |
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