What is YODA
YODA is a series of models for Google Feed product optimization. We aim to increase the market reach for ecommerce by augmenting and improving certain metadata like short titles, colors, measures and more. YODA is being used in production by +300 companies with +3.5M products.
What we use NER for
We have trained a NER model for product feature extraction. We retrieve data like colors, sizes, brands and energy labels. Trained with +3M lines of product metadata, the model returns the next scores:
- F-score (micro) 0.972
- F-score (macro) 0.9692
- Accuracy 0.9461
Demo: How to use in Flair
- Flair (
pip install flair)
from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("lighthousefeed/yoda-ner") # make example sentence sentence = Sentence("Jean Paul Gaultier Classique - 50 ML Eau de Parfum Damen Parfum.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity)
Contact the lead ML developer Iván R. Gázquez for any inquiry. We love hearing what you used this model for!
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This model can be loaded on the Inference API on-demand.