How to uncover and avoid structural biases in evaluating your Machine Learning/NLP projectsPyData LondonThis talk highlights common pitfalls that occur when evaluating ML and NLP approaches. It provides comprehensive advice on how to set up a solid evaluation procedure in general, and dives into a few specific use-cases to demonstrate artificial bias that unknowingly can creep in.
scispacy v0.5.3A Python package containing spaCy models for processing biomedical, scientific or clinical text, developed by AI2.
Constructing a knowledge base with spaCy and spacy-llmMantisNLP BlogThis blog post shows how to use spaCy and LLMs to extract entities and relationships from text and quickly tackle the complex problem of constructing a knowledge base graph from a corpus.
The Nesta Skills Extractor LibraryEconomic Statistics Centre of ExcellenceA new library for extracting skills from job adverts and mapping them to a taxonomy of your choice, built on top of spaCy.
KAZU v1.5A biomedical NLP framework designed to handle production workloads, built by AstraZeneca and Korea University and using spaCy under the hood.
Introducing spaCy v3.5spaCy v3.5 introduces new CLI commands, fuzzy matching, improvements for entity linking and more.
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.
