Prodigy is a modern annotation tool for collecting training data for machine learning models, developed by the makers of spaCy. In 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.
STEP BY STEP
03:24 – Create a phrase list and match patterns for ingredients
09:24 – Label all ingredients in a sample of texts from r/Cooking with the help of match patterns
19:25 – Train and evaluate a first model to see if we're on the right track
24:44 – Label more examples by correcting the model's predictions
31:56 – Train a new model with improved accuracy
34:11 – Run model over 2m+ Reddit comments and count the mentions over time
37:00 – Select interesting results and visualize them
PRODIGY
● Website & docs: https://prodi.gy
● Live demo: https://prodi.gy/demo
● Forum: https://support.prodi.gy
● Recipe scripts: https://github.com/explosion/prodigy-recipes
THIS TUTORIAL
● Code & data: https://github.com/explosion/projects/tree/master/ner-food-ingredients
● Visualization: https://public.flourish.studio/visualisation/1532208/
● Download Reddit comments: https://files.pushshift.io/reddit/comments/
● spaCy documentation: https://spacy.io
FOLLOW US
● Ines Montani: https://twitter.com/_inesmontani
● Explosion: https://twitter.com/explosion_ai
CREDITS
● Food emoji: https://github.com/twitter/twemoji
