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🔌 prodigy-whisper v0.1.0Nov 12, 2023

Audio transcription with OpenAI’s Whisper model in the loop

Introducing Prodigy-HF

Introducing Prodigy-HF Hugging Face Blog

Last week, Explosion introduced Prodigy-HF, a new Prodigy plugin offering code recipes that directly integrate with the Hugging Face stack.

Prodigy-ANN for Image Retrieval via CLIP

Prodigy-ANN for Image Retrieval via CLIP

Dealing with a huge bucket of images that you want to annotate? The new image retrieval features in Prodigy-ANN (approximate nearest neighbors) might help!

Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City

Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City Brunila, LaViolette, CH-Wang, Verma, Féré, McKenzie (2023), EMNLP 2023

All annotation was performed using Prodigy following an initial training session where annotators collaboratively annotated a randomly chosen set of samples.

🦙 spacy-llm v0.6.0Oct 5, 2023

PaLM, Azure OpenAI, Mistral & fixed OS model responses

🔌 prodigy-ann v0.1.0Oct 5, 2023

Use ANN techniques to fetch relevant data subsets to label

scispacy v0.5.3

scispacy v0.5.3

A Python package containing spaCy models for processing biomedical, scientific or clinical text, developed by AI2.

🦙 spacy-llm v0.5.0Sep 8, 2023

Improved user API and novel Chain-of-Thought prompting for more accurate NER

Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts

Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts Dannenfelser, Zhong, Zhang, Yao (2023), NeurIPS

Tissue, cell type, tool, and method were annotated using the Prodigy software tool developed by Explosion AI for easy tracking of token-level tags.

🦦 weasel v0.3.0Aug 14, 2023

Updates for requirements checks

Task Routers in Prodigy

Task Routers in Prodigy

How to use the new task routers to customize how examples are assigned in multi-annotator workflows.

🦙 spacy-llm v0.4.0Jul 6, 2023

Falcon, sentiment analysis, summarization, backend refactoring

Concepts and measures of bureaucratic constraints in European Union laws from hand-coding to machine-learning

Concepts and measures of bureaucratic constraints in European Union laws from hand-coding to machine-learning Franchino, Migliorati, Pagano, Vignoli (2023)

The models “learn” the relations between the text tokens and the entity categories from two randomly selected samples of sentences that are extracted from a pre-processed corpus and have been manually annotated using the Python-implemented platform “Prodigy”.

🦦 weasel v0.1.0Jun 14, 2023

A small and easy workflow system

Large Language Models: From Prototype to Production

Large Language Models: From Prototype to Production PyData London Keynote

Efficient Information Extraction From Text With spaCy

Efficient Information Extraction From Text With spaCy JetBrains PyCharm

This webinar takes you through building a spaCy project that uses a named entity recognition (NER) model to extract entities of interest from restaurant reviews, like prices, opening hours and ratings.

State-of-the-Art Transformer Pipelines in spaCy

State-of-the-Art Transformer Pipelines in spaCy aiGrunn

In this talk, we will show you how you can use transformer models (from pretrained models such as XLM-RoBERTa to large language models like Llama2) to create state-of-the-art annotation pipelines for text annotation tasks such as named entity recognition.

Explosion, NLP, Generative AI, Entrepreneurship

Explosion, NLP, Generative AI, Entrepreneurship Learning from Machine Learning

✨ prodigy v1.14.5Oct 24, 2023

Toggle for character vs. token highlighting, CSS and JS from local and remote paths

Identifying Signs and Symptoms of Urinary Tract Infection from Emergency Department Clinical Notes Using Large Language Models

Identifying Signs and Symptoms of Urinary Tract Infection from Emergency Department Clinical Notes Using Large Language Models Iscoe, Socrates, Gilson, Chi, Li, Huang, Kearns, Perkins, Khandjian, Taylor (2023)

For annotation we employed Prodigy, a scriptable annotation tool designed to maximize efficiency, enabling data scientists to perform the annotation tasks themselves and facilitating rapid iterative development in natural language processing (NLP) projects.

🤖 curated-transformers v1.3.0Oct 2, 2023

Custom model repositories, NVTX Ranges, store config in models

🔌 prodigy-pdf v0.1.0Oct 5, 2023

Annotate and segment PDF files and perform OCR

MP Interests Tracker: Utilising GenAI to uncover insights in the UK Register of Financial Interest

MP Interests Tracker: Utilising GenAI to uncover insights in the UK Register of Financial Interest JournalismAI Blog

Project from teams at The Times and BBC using spacy-llm to make complex financial interests data more accessible.

✨ prodigy v1.13.2Sep 7, 2023

New LLM recipes for terms generation and prompt engineering

Models as annotators in Prodigy

Models as annotators in Prodigy

How to use models and LLMs as annotators to find disagreements and prioritize examples to annotate first.

🔮 thinc v8.2.0Aug 11, 2023

Updates for automatic imports

ACL LAW Workshop Poster

ACL LAW Workshop Poster ACL 2023

✨ prodigy v1.12.0Jul 5, 2023

LLM-assisted workflows for annotation and prompt engineering, task routing for multi-annotator setups

spaCy: a customizable NLP toolkit designed for developers

spaCy: a customizable NLP toolkit designed for developers ODSC Europe

SpanCat with spaCy and Prodigy on real data

SpanCat with spaCy and Prodigy on real data

YouTube series by WJB Mattingly showing an end-to-end project, from cultivating and annotating data to training, testing and visualizing a model.

🦙 spacy-llm v0.2.0May 30, 2023

REL and spancat tasks, reading prompt templates from file

spaCy Plugin for VSCode

spaCy Plugin for VSCode

The spaCy VSCode Extension provides additional tooling and features for working with spaCy’s config files. Version 1.0.0 includes hover descriptions for registry functions, variables, and section names within the config as an installable extension.

Half hour of labeling power: Can we beat GPT?

Half hour of labeling power: Can we beat GPT?PyData NYC

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.

How many Labelled Examples do you need for a BERT-sized Model to Beat GPT-4 on Predictive Tasks?

How many Labelled Examples do you need for a BERT-sized Model to Beat GPT-4 on Predictive Tasks?Generative AI Summit

How does in-context learning compare to supervised approaches on predictive tasks? How many labelled examples do you need on different problems before a BERT-sized model can beat GPT-4 in accuracy? The answer might surprise you: models with fewer than 1b parameters are actually very good at classic predictive NLP, while in-context learning struggles on many problem shapes.

🔌 prodigy-hf v0.1.0Oct 23, 2023

Train Hugging Face models with Prodigy annotations

DaCy v2.7.2

DaCy v2.7.2

State-of-the-Art Danish NLP pipelines for spaCy

💫 spacy v3.7.0Oct 2, 2023

Trained pipelines using Curated Transformers and support for Python 3.12

✨ prodigy v1.14.1Sep 29, 2023

Custom event hooks for custom UI interactivity

Panel: Large Language Models

Panel: Large Language Models Big PyData BBQ

with Ines, Alejandro Saucedo (Zalando, Institute for Ethical AI & ML), Alina Lehnhard (Cerence), Michael Gerz (Heidelberg University), Alexander CS Hendorf (Königsweg)

✨ prodigy v1.13.1Aug 23, 2023

Use models and LLMs as annotators to find disagreements

How to Host Your Own API of Open Language Models For Free

Powered by Explosion’s curated-transformers, FastAPI and ngrok.

🦦 weasel v0.2.0Aug 4, 2023

Support for Pydantic v2 and cloudpathlib

Introducing spaCy v3.6

Introducing spaCy v3.6

spaCy v3.6 introduces the span finder component and trained pipelines for Slovenian.

🍬 confection v0.1.0Jun 29, 2023

Improved JSON parsing, updated utils and warnings

Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records

Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records Oommen, Howlett-Prieto, Carrithers, Hier (2023)

Prodigy was used to annotate neurologic concepts in the EHR physician notes.

Large Disagreement Modelling

Large Disagreement Modelling

“In this blogpost I’d like to talk about large language models. There’s a bunch of hype, sure, but there’s also an opportunity to revisit one of my favourite machine learning techniques: disagreement.”

🦙 spacy-llm v0.1.0May 11, 2023

Integrating LLMs into structured NLP pipelines

Implementing a custom trainable component for relation extraction

Implementing a custom trainable component for relation extraction

Relation extraction refers to the process of predicting and labeling semantic relationships between named entities. In this blog post, we'll go over the process of building a custom relation extraction component using spaCy and Thinc. We'll also add a Hugging Face transformer to improve performance at the end of the post. You'll see how you can utilize Thinc's flexible and customizable system to build an NLP pipeline for biomedical relation extraction.

GERNERMED++: Semantic annotation in German medical NLP through transfer-learning, translation and word alignment

GERNERMED++: Semantic annotation in German medical NLP through transfer-learning, translation and word alignment Frei, Frei-Stuber, Kramer (2023), Journal of Biomedical Informatics

The training of our entity recognition model employs the entity recognition parser from the spaCy library which follows a transducer-based parsing approach with a BILOU scheme instead of a state-agnostic token tagging approach.

Prodigy-PDF for PDF annotation and OCR

Prodigy-PDF for PDF annotation and OCR

Want 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.

Natural Language Processing and Python

Natural Language Processing and Python The Python Show

✨ prodigy v1.14.3Oct 6, 2023

Inter-annotator agreement for document-level and token-level annotations, new plugins

🔌 prodigy-lunr v0.1.0Oct 5, 2023

Document search via LUNR to fetch relevant data subsets to label

Natural Intelligence is All You Need[tm]

Natural Intelligence is All You Need[tm]PyData Amsterdam Keynote

🛸 spacy-transformers v1.3.1Sep 26, 2023

Support for newer versions of Transformers

✨ prodigy v1.13.0Aug 15, 2023

LLM support for NER, text classification and span categorization

🤖 curated-transformers v1.0.0Aug 3, 2023

Lightweight, composable PyTorch transformers

Large Language Models: From Prototype to Production

Large Language Models: From Prototype to Production EuroPython Keynote

Large Language Models (LLMs) have shown some impressive capabilities and their impact is the topic of the moment. In this talk, Ines presents visions for NLP in the age of LLMs and a pragmatic, practical approach for how to use Large Language Models to ship more successful NLP projects from prototype to production today.

Prodigy v1.12: OpenAI integration, prompt engineering, task routers, deployment docs and more

Prodigy v1.12: OpenAI integration, prompt engineering, task routers, deployment docs and more

🦙 spacy-llm v0.3.0Jun 14, 2023

Cohere, Anthropic, OpenLLaMa, StableLM, logging, streamlit demo, lemmatization task

How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?

How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?Ahmed, Nath, Regan, Pollins, Krishnaswamy, Martin (2023)

Figure 6 illustrates the interface design of the annotation methodology on the popular model-in-the-loop annotation tool - Prodigy. We use this tool for the simplicity it offers in plugging in the various ranking methods we explained.

Against LLM maximalism

Against LLM maximalism

LLMs are not a direct solution to most of the NLP use-cases companies have been working on. They are extremely useful, but if you want to deliver reliable software you can improve over time, you can't just write a prompt and call it a day. Once you're past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability.

You are what you read: Building a personal internet front-page with spaCy and Prodigy

You are what you read: Building a personal internet front-page with spaCy and Prodigy PyCon DE & PyData Berlin