FAQ #1: Tips & tricks for NLP, annotation & training with Prodigy and spaCy

Prodigy is an annotation tool for creating training data for machine learning models. In this video, I'll be talking about a few frequently asked questions and share 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
● Website: https://prodi.gy
● Forum: https://support.prodi.gy
● Recipes repo: https://github.com/explosion/prodigy-recipes

THIS VIDEO

[0:46] Binary of manual annotation?
● ner.teach vs. ner.match https://support.prodi.gy/t/877
● Best practices for validation sets https://support.prodi.gy/t/693

[3:34] Accept or reject partial suggestions?
● How to score incompletely highlighted entities https://support.prodi.gy/t/625
● Should I reject or accept partially correct predictions? https://support.prodi.gy/t/945

[5:35] Reject example or skip it?
● Reject or skip examples for text classifier annotations https://support.prodi.gy/t/998
● Ignored sentences for text classification https://support.prodi.gy/t/1183

[7:30] What if I need to label long texts?
Dealing with sparse data https://support.prodi.gy/t/518
Text categorization at document level https://support.prodi.gy/t/1160

[9:24] Fine-tune pre-trained model or start from scratch?
● Pre-trained model vs training a model from scratch https://support.prodi.gy/t/631/4
● Fact extraction for earnings news https://support.prodi.gy/t/1023
● Extracting current and prior company affiliations from bios https://support.prodi.gy/t/1176
● NER or PhraseMatcher https://support.prodi.gy/t/686

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● Explosion AI: https://twitter.com/explosion_ai
● Ines Montani: https://twitter.com/_inesmontani
● Matthew Honnibal: https://twitter.com/honnibal

Видео FAQ #1: Tips & tricks for NLP, annotation & training with Prodigy and spaCy канала Explosion