How to run a massive inference of multilingual text sentences using a powerful pre-trained model from the TensorFlow Hub.
Tensorflow HUB makes available a variety of pre-trained models ready to use for inference. A very powerful model is the (Multilingual) Universal Sentence Encoder that allows embedding bodies of text written in any language into a common numerical vector representation.
Embedding text is a very powerful natural language processing (NLP) technique for extracting features from text fields. Those features can be used for training other models or for data analysis takes such as clustering documents or search engines based on word semantics.
Unfortunately, if we have billions of text data to encode it might take several days to run on a single machine.
In this tutorial, I will show how to leverage Spark. In particular, we will use the AWS-managed Elastic MapReduce (EMR) service to apply the sentence encoder to a large dataset and complete it in a matter of a couple of hours.
Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge.NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition
In this article, I’ll walk you through my experience to code a model that will learn some Ed Sheeran songs and try to create some first sentences for a song.
NLP: Detecting Spam Messages with TensorFlow. Prevent over-fitting when building a spam detection model. A recurrent neural network in TensorFlow was used to detect spam text messages.
This is a tutorial to walk through the NLP model preparation pipeline: tokenization, sequence padding, word embeddings, and Embedding layer setups. Hands-on NLP Deep Learning Model Preparation in TensorFlow 2.X
In this article we will write an algorithm that classifies movie reviews: positive or negative, we will train it on an already labeled comment dataset. Tensorflow has included databases ready to be playing with.