Explosion builds developer tools for AI, Machine Learning and Natural Language Processing.
Project
Topics
Category
Tasks
Select...Code Generation, Coreference Resolution, Dependency Parsing, Distillation, Embeddings & Vectors, Entity Linking, Evaluation, Image Classification, Image Segmentation, Layout Analysis, Lemmatization, Named Entity Recognition, Object Detection, Optical Character Recognition (OCR), Part-of-Speech Tagging, PII Anonymization, Question Answering, Relation Extraction, Retrieval-Augmented Generation (RAG), Rule-Based Matching, Span Categorization, Text Classification, Text Generation, Tokenization, Weak Supervision
Authors
Select...Adriane Boyd, Ákos Kádár, Basile Dura, Chung-Fan Tsai, Damian Romero, Daniel de Kok, Duygu Altinok, Edward Schmuhl, Helena Steckmeister, India Kerle, Ines Montani, Kabir Khan, Lj Miranda, Madeesh Kannan, Magdalena Anioł, Matthew Honnibal, Paul O’Leary McCann, Peter Baumgartner, Philip Vollet, Raphael Mitsch, Rehan Ahmed, Richard Hudson, Ryan Wesslen, Sofie Van Landeghem, Victoria Slocum, Vincent D. Warmerdam, Vinit Ravishankar, Walter Henry
What if we could take learnings from AI-powered coding agents and apply them to solving real-world NLP problems? In this talk, I’ll show how we’ve built a powerful virtual NLP assistant to help developers create practical and modular solutions that are small, fast and fully data-private.
With all the hype around Generative AI, many are led to believe it’s the solution to everything. So how can you, as a developer, communicate the nuances and advocate for new and modular solutions that are better, easier and cheaper?
This talk presents approaches for bootstrapping NLP pipelines and retrieval via information extraction, including tips for training, modelling and data annotation.
This talk presents pragmatic and practical approaches for how to use LLMs beyond just chat bots, how to ship more successful NLP projects from prototype to production and how to use the latest state-of-the-art models in real-world applications.
How the Guardian uses spaCy and Prodigy to train a custom coreference resolution model.
You can create custom annotation layouts in Prodigy using the annotation widgets that Prodigy provides by using the blocks feature. This video explains how to use this feature by building a custom interface that can manually annotate and transcribe audio.
In this video, we’ll show you how to use set up Prodigy to find bad labels in text classification tasks. While many of the techniques are applied to text classification, they can also be used for classification tasks in general.
How the Guardian uses spaCy and Prodigy to train a machine learning model that helps extract quotes from news articles and match them to the correct source.
Machine learning systems are built from both code and data. It's easy to reuse the code but hard to reuse the data, so building AI mostly means doing annotation. This is good, because the examples are how you program the behaviour – the learner itself is really just a compiler. What's not good is the current technology for creating the examples. That's why we're pleased to introduce Prodigy, a downloadable tool for radically efficient machine teaching.
This blog post collects tips and advice for how to build efficient human-in-the-loop data development workflows, break down business problems into actionable annotation steps and make the most of automation and model assistance.
A case study on Love Without Sound’s innovative AI-powered tools for the music industry and law firms specializing in royalty negotiations.