This package solves task of splitting product title string into components, like
article (or SKU or product code or you name it).
Imagine classic named entity recognition, but recognition done on product titles.
revizor requires python 3.8+ version on Linux or macOS, Windows isn't supported now, but contributions are welcome.
from revizor.tagger import ProductTagger tagger = ProductTagger() product = tagger.predict("Смартфон Apple iPhone 12 Pro 128 gb Gold (CY.563781.P273)") assert product.type == "Смартфон" assert product.brand == "Apple" assert product.model == "iPhone 12 Pro" assert product.article == "CY.563781.P273"
Actually, just output from flair training log:
Corpus: "Corpus: 138959 train + 15440 dev + 51467 test sentences" Results: - F1-score (micro) 0.8843 - F1-score (macro) 0.8766 By class: ARTICLE tp: 9893 - fp: 1899 - fn: 3268 - precision: 0.8390 - recall: 0.7517 - f1-score: 0.7929 BRAND tp: 47977 - fp: 2335 - fn: 514 - precision: 0.9536 - recall: 0.9894 - f1-score: 0.9712 MODEL tp: 35187 - fp: 11824 - fn: 9995 - precision: 0.7485 - recall: 0.7788 - f1-score: 0.7633 TYPE tp: 25044 - fp: 637 - fn: 443 - precision: 0.9752 - recall: 0.9826 - f1-score: 0.9789
Model was trained on automatically annotated corpus. Since it may be affected by DMCA, we'll not publish it.
But we can give hint on how to obtain it, don't we?
Dataset can be created by scrapping any large marketplace, like goods, yandex.market or ozon.
We extract product title and table with product info, then we parse brand and model strings from product info table.
Now we have product title, brand and model. Then we can split product title by brand string, e.g.:
product_title = "Смартфон Apple iPhone 12 Pro 128 Gb Space Gray" brand = "Apple" model = "iPhone 12 Pro" product_type, product_model_plus_some_random_info = product_title.split(brand) product_type # => 'Смартфон' product_model_plus_some_random_info # => 'iPhone 12 Pro 128 Gb Space Gray'
This package is licensed under MIT license.