Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API. FastAPI helped us quickly build a fast and robust machine learning API serving NLP models. Let me tell you why we made such a choice, and show you how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER). Simple FastAPI spaCy API for NER Each entity is made up of the position of the first character of the entity, the last position of the entity, the type of the entity, and the text of the entity itself. As you can see, creating an API with FastAPI is dead simple, and the validation with Pydantic makes the code very expressive (and then needs less documentation in return) and less error-prone.

FastAPI is a new Python API framework that is more and more used in production today. We are using FastAPI under the hood behind NLP Cloud. NLP Cloud is an API based on spaCy and HuggingFace transformers in order to propose Named Entity Recognition (NER), sentiment analysis, text classification, summarization, and much more. FastAPI helped us quickly build a fast and robust machine learning API serving NLP models. Let me tell you why we made such a choice, and show you how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER).