README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - pl
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-scoring
  - text-scoring
paperswithcode_id: allegro-reviews
pretty_name: Allegro Reviews
dataset_info:
  features:
    - name: text
      dtype: string
    - name: rating
      dtype: float32
  splits:
    - name: train
      num_bytes: 4899539
      num_examples: 9577
    - name: test
      num_bytes: 514527
      num_examples: 1006
    - name: validation
      num_bytes: 515785
      num_examples: 1002
  download_size: 2314847
  dataset_size: 5929851

Dataset Card for [Dataset Name]

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://klejbenchmark.com/
• Repository: https://github.com/allegro/klejbenchmark-allegroreviews
• Paper: KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz)
• Leaderboard: https://klejbenchmark.com/leaderboard/
• Point of Contact: klejbenchmark@allegro.pl

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 klejbenchmark@allegro.pl

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.