Prodigy-Segment for Pixel SegmentationUse Meta’s “Segment Anything” model in Prodigy to help you select the right pixels in images.
Task Routers in ProdigyHow to use the new task routers to customize how examples are assigned in multi-annotator workflows.
Finetuning and Bulk Labelling Images with Prodigy In this video, we’ll show how you might be able to improve the annotation experience by using bulk labelling for image classification.
Finding Bad Labels for Text Classification with Jupyter and Prodigy 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.
spaCy v3: Custom trainable relation extraction componentspaCy v3.0 features new transformer-based pipelines that get spaCy’s accuracy right up to the current state-of-the-art, and a new training config and workflow system to help you take projects from prototype to production. In this video, Sofie shows you how to apply all these new features when implementing a custom trainable component from scratch.
Image Captioning with Prodigy & PyTorchIn this video, we’ll show you how you can use Prodigy to script fully custom annotation workflows in Python, how to plug in your own machine learning models and how to mix and match different interfaces for your specific use case.
FAQ #1: Tips & tricks for NLP, annotation & training with Prodigy and spaCyIn this video, Ines talks about a few frequently asked questions and shares some general tips and tricks for how to structure your NLP annotation projects, how to design your label schemes and how to solve common problems.
Prodigy-ANN for Image Retrieval via CLIPDealing with a huge bucket of images that you want to annotate? The new image retrieval features in Prodigy-ANN (approximate nearest neighbors) might help!
Finding Video Games with Sense2VecIn this video, we’ll show how you can improve the annotation experience by leveraging sense2vec to pre-fill named entities.
Finding Bad Image Data using UMAP and ProdigyIn this video, we’ll show you how to use Prodigy to find bad examples in the Google QuickDraw dataset. We will be leveraging a technique that involves UMAP to find strange images semi-automatically.
spaCy v3: Design concepts explained (behind the scenes)In this video, Ines shows you some of the new design concepts and explain what’s going on under the hood, how we’ve implemented them and most importantly, why.
Training a Named Entity Recognition Model with Prodigy and Transfer LearningIn this video, we’ll show you how to use Prodigy to train a named entity recognition model from scratch, by taking advantage of semi-automatic annotation and modern transfer learning techniques.
Training a new entity type with Prodigy – annotation powered by active learningIn this video, we’ll show you how to use Prodigy to train a phrase recognition system for a new concept. Specifically, we’ll train a model to detect references to drugs, using text from Reddit.
Prodigy-PDF for PDF annotation and OCRWant to annotate PDF files? Our new Prodigy plugin can help with that! To explain how to use PDF segmentation and OCR, Vincent made a small demo video.
Custom Interfaces with blocksYou 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.
Bulk Labelling and ProdigyIn this video, we’ll show a bulk labelling technique that can help you prepare data for Prodigy.
Finding Duplicates in Tabular Data with Jupyter and ProdigyIn this video, we’ll show you how to use Prodigy to train a named entity recognition model from scratch, by taking advantage of semi-automatic annotation and modern transfer learning techniques.
spaCy’s entity recognition model: incremental parsing with Bloom embeddings & residual CNNsspaCy v2.0’s Named Entity Recognition system features a sophisticated word embedding strategy using subword features and "Bloom" embeddings, a deep convolutional neural network with residual connections, and a novel transition-based approach to named entity parsing.
Models as annotators in ProdigyHow to use models and LLMs as annotators to find disagreements and prioritize examples to annotate first.
Coreference Resolution in spaCyIn everyday conversation, we use pronouns or other expressions to refer to entities in many different ways, but we effortlessly understand these references. In NLP this is a challenging problem known as Coreference Resolution. In this video, we’ll show how to train spaCy’s new component for Coreference Resolution and how to apply the pipeline to resolve references in a text.
Introducing Span Categorization in Prodigy and spaCyIn this video, we’ll show you how to use Prodigy for spaCy’s Span Categorizer. We’ll be annotating food recipes and looking into ways to help with consistent annotations and speed up the process with patterns and temporary models.
Prodigy v1.10: Dependencies, relations, audio, video & moreVersion 1.10 of Prodigy includes tons of new features, including manual dependency and relation annotation, audio and video annotation, a new and improved image UI, new recipe callbacks, more settings for manual NER, plus various new config options and settings.
Training a custom entity linking model with spaCyIn this video, we show you how to create a custom Entity Linking model in spaCy to disambiguate different mentions of the person “Emerson” to unique identifiers in a knowledge base.
Intro to NLP with spaCy (1): Detecting programming languagesIn this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text.
Training an insults classifier with Prodigy in ~1 hourIn this video, we’ll show you how to use Prodigy to train a classifier to detect disparaging or insulting comments. Prodigy makes text classification particularly powerful, because you can try out new ideas very quickly.
