readme: ---
  language: 
    - es
  license: isc
  library_name: flair
  tags:
  - flair
  - token-classification
  metrics:
  - f1
  - precision
  - recall
  - accuracy
  widget:
  - text: "Jean Paul Gaultier Classique - 50 ML Eau de Parfum  Damen Parfum"
  ---
  
  # 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:
  
  Results:
  - F-score (micro) 0.972
  - F-score (macro) 0.9692
  - Accuracy 0.9461
  
  By class:
  |        | precision | recall | f1-score | support |
  | ------ | --------- | ------ | -------- | ------- |
  | size   | 0.9734    | 0.9793 | 0.9764   | 26707   |
  | brand  | 0.9618    | 0.9788 | 0.9702   | 15621   |
  | color  | 0.9566    | 0.9612 | 0.9589   | 6785    |
  | energy | 0.9444    | 1.0000 | 0.9714   | 119     |
  
  |              | precision | recall | f1-score | support |
  | ------------ | --------- | ------ | -------- | ------- |
  | micro avg    | 0.9673    | 0.9767 | 0.9720   | 49232   |
  | macro avg    | 0.9591    | 0.9798 | 0.9692   | 49232   |
  | weighted avg | 0.9674    | 0.9767 | 0.9720   | 49232   |
  
  ### Demo: How to use in Flair
  Requires:
  - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  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
  
  Contact the lead ML developer [Iván R. Gázquez](mailto:ivan@gazquez.com) for any inquiry. We love hearing what you used this model for!