Build a Sentiment & Entity Detection API with FastAPI (2/2)
Part 2/2 of our tutorial on using FastAPI, Flair, Spacy to create a powerful NLP sentiment and entity detection API with Python in less than 70 lines of code.
Part 2/2 of our tutorial on using FastAPI, Flair, Spacy to create a powerful NLP sentiment and entity detection API with Python in less than 70 lines of code.
Sentiment analysis and entity detection are key elements of NLP pipelines today. In this tutorial we’ll build a FastAPI based API that can process text passages from HTTP requests to detect sentiment and entities in submitted passages.
This post shows how to load, use, and make your own word embeddings using Python. Use the Gensim and Spacy libraries to load pre-trained word vector models from Google and Facebook, or train custom models using your own data and the Word2Vec algorithm. This post is a direct follow-on from the introductory Word Embeddings post, and will show you how to get started using word vectors with your own models and systems.
This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of natural language processing (NLP). We examine training, use, and properties of word embeddings models, and look at how and why you should look to use word embeddings over older bag-of-words techniques in your data science and language modelling tasks.