---
language:
- en
license: cc-by-nc-4.0
tags:
- news
---

- Based on the ukr_pravda dataset: https://huggingface.co/datasets/shamotskyi/ukr_pravda_2y
- Sister dataset: https://huggingface.co/datasets/shamotskyi/ukr_pravda_titles_ukr (same but in Ukrainian)

For each article, its text and titles are given, as well as _masked_ text and title (with all digits replaced with "X").
The indexes of the similar articles refer to the ids in the ukr_pravda_2y dataset (TODO check if this is factually correct).

Then, as ML eval task, a choice of 10 masked titles from _similar_ articles are given (including the 'real' one). The `label` column points to the index of the correct masked title.

Similarity of articles is a dead-simple cosine distance over binary vectors of the articles tags:
- a vector is built using spacy CountVectorizer, with 0 if the tag is absent and 1 if present
- similarity is cosine distance between these vectors of two articles
- the 10 most similar articles' titles are taken

NB this simple similarity may be suboptimal, because there are MANY UP articles with the exact same tags (ergo all with similarity 1.0 to the source), and there may be more similar articles (by human intuition as well as by any more reasonable metric) than the ones actually chosen. 

This is done in the context of my Master's thesis, better documentation will follow soon.