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---
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 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
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
You may contact the lead ML developer [Iván R. Gázquez](mailto:[email protected]) |