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
We’re back to running Explosion as a smaller, independent-minded and self-sufficient company. spaCy and Prodigy will stay stable and sustainable, maintained by their original authors. We’ll keep updating our stack with the latest technologies, without changing its core identity or purpose.
This blog post presents practical solutions for using the latest state-of-the-art models in real-world applications and distilling their knowledge into smaller and faster components that you can run and maintain in-house.
Interactive app by the Canton of Zurich, Switzerland, using LLMs and spaCy to analyze and simplify institutional communication and make bureaucratic German more inclusive.
New out-of-the-box Prodigy integration in ZenML for LLMs and beyond, to make data development and annotation a core part of your MLOps lifecycle.
With the latest advancements in NLP and LLMs, and big companies like OpenAI dominating the space, many people wonder: Are we heading further into a black box era with larger and larger models, obscured behind APIs controlled by big tech monopolies?
A case study on S&P Global’s efficient information extraction pipelines for real-time commodities trading insights in a high-security environment.
Large Language Models (LLMs) offer a lot of value for modern NLP and can typically achieve surprisingly good accuracy on predictive NLP tasks. But can we do even better than that? In this workshop we show how to use LLMs at development time to create high-quality datasets and train specific, smaller, private and more accurate models for your business problems.
Open-source initiatives are pivotal in democratizing AI technology, offering transparent, extensible tools that empower users. Daniel Dominguez summarizes the key takeaways from Ines’ recent talk for InfoQ.
Muted can leverage any transformer-based HAP-classification model to identify toxic spans, without further fine-tuning. In addition, we use the spaCy library to identify the specific targets and arguments for the words predicted by the attention heatmaps.
We have made a ton of new updates in Prodigy this year with v1.12, v1.13, and v1.14 releases. So we decided to write a post about them.