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--- |
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language: |
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- es |
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license: isc |
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library_name: flair |
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tags: |
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- flair |
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- token-classification |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- accuracy |
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widget: |
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- text: "Jean Paul Gaultier Classique - 50 ML Eau de Parfum Damen Parfum" |
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--- |
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# What is YODA |
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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. |
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## What we use NER for |
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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: |
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Results: |
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- F-score (micro) 0.972 |
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- F-score (macro) 0.9692 |
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- Accuracy 0.9461 |
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By class: |
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precision recall f1-score support |
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size 0.9734 0.9793 0.9764 26707 |
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brand 0.9618 0.9788 0.9702 15621 |
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color 0.9566 0.9612 0.9589 6785 |
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energy 0.9444 1.0000 0.9714 119 |
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micro avg 0.9673 0.9767 0.9720 49232 |
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macro avg 0.9591 0.9798 0.9692 49232 |
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weighted avg 0.9674 0.9767 0.9720 49232 |
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### Demo: How to use in Flair |
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Requires: |
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- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# load tagger |
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tagger = SequenceTagger.load("lighthousefeed/yoda-ner") |
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# make example sentence |
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sentence = Sentence("Jean Paul Gaultier Classique - 50 ML Eau de Parfum Damen Parfum.") |
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# predict NER tags |
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tagger.predict(sentence) |
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# print sentence |
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print(sentence) |
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# print predicted NER spans |
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print('The following NER tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('ner'): |
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print(entity) |
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``` |
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## Contact |
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Contact the lead ML developer [Iván R. Gázquez](mailto:[email protected]) for any inquiry. We love hearing what you used this model for! |
