Machine Learning Use Case
Named Entity Recognition
Named Entity Recognition (NER) is an NLP task that involves using machine learning to identify a predefined set of text entities within an input text. NER can be used in many applications like corpus annotations for inline term definitions or enhancing question and answering systems. Learn how to build an end-to-end NER pipeline to identify molecular biology terminology.
The Predibase Solution
Extract useful information from unstructured text with NER
- Rapidly preprocess unstructured text - in this use case, molecular biology texts
- Finely tune a series of neural networks with little-to-no-code
- Automate text entity recognition to power downstream use cases
Train your first NER model in <20 lines of a config file
At the core of Predibase is Ludwig, an open-source declarative ML framework, that automates complex model development with a simple configuration file. Predibase builds on these capabilities with a collaborative, easy-to-use, and fully managed ML platform in the cloud.
Resources to Get Started
Read the Tutorial
Check out our deep dive tutorial to learn how to build an end-to-end deep learning pipeline for NER on a corpus of biological data.
Run the Notebook
Get started with named entity recognition using this open-source Ludwig notebook and sample code.
Visit the Resource Center
Visit the Predibase resource center to see our collection of ebooks and webinars on topics from multimodal ML to computer vision.