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Dataset Summary
Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).
We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com.
Supported Tasks and Leaderboards
Product reviews sentiment analysis. https://klejbenchmark.com/leaderboard/
Languages
Polish
Dataset Structure
Data Instances
Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text).
Data Fields
- text: a product review of at least 50 words
- rating: product rating of a scale of one (negative review) to five (positive review)
Data Splits
Data is splitted in train/dev/test split.
Dataset Creation
Curation Rationale
This dataset is one of nine evaluation tasks to improve polish language processing.
Source Data
Initial Data Collection and Normalization
The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl).
Who are the source language producers?
Customers of an e-commerce marketplace.
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Allegro Machine Learning Research team [email protected]
Licensing Information
Dataset licensed under CC BY-SA 4.0
Citation Information
@inproceedings{rybak-etal-2020-klej, title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding", author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.111", pages = "1191--1201", }
Contributions
Thanks to @abecadel for adding this dataset.
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