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huggingface_dataset/Dataset_Card/Aisha_BAAD6.md ADDED
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+ ---
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+ annotations_creators:
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+ - found
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+ - crowdsourced
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+ - expert-generated
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+ language_creators:
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+ - found
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+ - crowdsourced
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+ language:
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+ - bn
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+ license:
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+ - cc-by-4.0
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+ multilinguality:
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+ - monolingual
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+ pretty_name: 'BAAD6: Bangla Authorship Attribution Dataset (6 Authors)'
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+ size_categories:
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+ - unknown
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - text-classification
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+ task_ids:
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+ - multi-class-classification
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+ ---
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+
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+ ## Description
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+
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+ **BAAD6** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by Hemayet et al [[1]](https://ieeexplore.ieee.org/document/8631977). The data was obtained from different online posts and blogs. This dataset is balanced among the 6 Authors with 350 sample texts per author. This is a relatively small dataset but is noisy given the sources it was collected from and its cleaning procedure. Nonetheless, it may help evaluate authorship attribution systems as it resembles texts often available on the Internet. Details about the dataset are given in the table below.
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+
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+ | Author | Samples | Word count | Unique word |
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+ | ------ | ------ | ------ | ------ |
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+ |fe|350|357k|53k|
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+ | ij | 350 | 391k | 72k
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+ | mk | 350 | 377k | 47k
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+ | rn | 350 | 231k | 50k
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+ | hm | 350 | 555k | 72k
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+ | rg | 350 | 391k | 58k
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+ **Total** | 2,100 | 2,304,338 | 230,075
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+ **Average** | 350 | 384,056.33 | 59,006.67
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+
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+ ## Citation
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+
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+ If you use this dataset, please cite the paper [A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature](https://ieeexplore.ieee.org/document/8631977).
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+
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+ ```
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+ @INPROCEEDINGS{BAAD6Dataset,
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+ author={Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Islam, Md. Saiful},
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+ booktitle={2018 21st International Conference of Computer and Information Technology (ICCIT)},
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+ title={A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature},
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+ year={2018},
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+ volume={},
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+ number={},
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+ pages={1-6},
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+ doi={10.1109/ICCITECHN.2018.8631977}
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+ }
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+ ```
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+
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+ This dataset is also available in Mendeley: [BAAD6 dataset](https://data.mendeley.com/datasets/w9wkd7g43f/5). Always make sure to use the latest version of the dataset. Cite the dataset directly by:
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+
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+ ```
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+ @misc{BAAD6Dataset,
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+ author = {Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Khatun, Aisha and Islam, Md. Saiful},
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+ title = {BAAD6: Bangla Authorship Attribution Dataset},
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+ year={2018},
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+ doi = {10.17632/w9wkd7g43f.5},
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+ howpublished= {\url{https://data.mendeley.com/datasets/w9wkd7g43f/5}}
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+ }
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+ ```
huggingface_dataset/Dataset_Card/Baybars_parla_text_corpus.md ADDED
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+ ---
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+ annotations_creators:
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+ - no-annotation
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+ language_creators:
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+ - various
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+ language:
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+ - ca
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+ license:
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+ - cc-by-4.0
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+ multilinguality:
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+ - monolingual
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+ pretty_name: ParlaTextCorpus
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+ size_categories:
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+ - 100k<n<1M
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+ source_datasets:
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+ - found
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+ task_categories:
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+ - sequence-modeling
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+ task_ids:
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+ - language-modeling
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+ tags:
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+ - robust-speech-event
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+ ---
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+ # ParlaTextCorpus
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+ Spoken text corpus for Catalan. Derived and cleaned from three sources. OpenSubtitles, Tv3Parla and Festcat.
huggingface_dataset/Dataset_Card/BeIR_scidocs-generated-queries.md ADDED
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+ ---
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+ annotations_creators: []
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+ language_creators: []
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+ language:
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+ - en
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+ license:
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+ - cc-by-sa-4.0
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+ multilinguality:
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+ - monolingual
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+ paperswithcode_id: beir
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+ pretty_name: BEIR Benchmark
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+ size_categories:
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+ msmarco:
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+ - 1M<n<10M
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+ trec-covid:
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+ - 100k<n<1M
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+ nfcorpus:
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+ - 1K<n<10K
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+ nq:
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+ - 1M<n<10M
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+ hotpotqa:
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+ - 1M<n<10M
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+ fiqa:
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+ - 10K<n<100K
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+ arguana:
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+ - 1K<n<10K
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+ touche-2020:
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+ - 100K<n<1M
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+ cqadupstack:
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+ - 100K<n<1M
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+ quora:
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+ - 100K<n<1M
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+ dbpedia:
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+ - 1M<n<10M
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+ scidocs:
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+ - 10K<n<100K
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+ fever:
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+ - 1M<n<10M
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+ climate-fever:
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+ - 1M<n<10M
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+ scifact:
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+ - 1K<n<10K
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+ source_datasets: []
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+ task_categories:
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+ - text-retrieval
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+ - zero-shot-retrieval
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+ - information-retrieval
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+ - zero-shot-information-retrieval
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+ task_ids:
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+ - passage-retrieval
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+ - entity-linking-retrieval
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+ - fact-checking-retrieval
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+ - tweet-retrieval
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+ - citation-prediction-retrieval
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+ - duplication-question-retrieval
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+ - argument-retrieval
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+ - news-retrieval
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+ - biomedical-information-retrieval
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+ - question-answering-retrieval
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+ ---
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+
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+ # Dataset Card for BEIR Benchmark
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
86
+ - [Contributions](#contributions)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://github.com/UKPLab/beir
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+ - **Repository:** https://github.com/UKPLab/beir
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+ - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
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+ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
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+ - **Point of Contact:** nandan.thakur@uwaterloo.ca
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+
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+ ### Dataset Summary
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+
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+ BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
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+
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+ - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
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+ - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
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+ - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
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+ - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
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+ - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
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+ - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
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+ - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
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+ - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
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+ - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
109
+
110
+ All these datasets have been preprocessed and can be used for your experiments.
111
+
112
+
113
+ ```python
114
+
115
+ ```
116
+
117
+ ### Supported Tasks and Leaderboards
118
+
119
+ The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
120
+
121
+ The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
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+
123
+ ### Languages
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+
125
+ All tasks are in English (`en`).
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+
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+ ## Dataset Structure
128
+
129
+ All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
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+ - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
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+ - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
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+ - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
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+
134
+ ### Data Instances
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+
136
+ A high level example of any beir dataset:
137
+
138
+ ```python
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+ corpus = {
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+ "doc1" : {
141
+ "title": "Albert Einstein",
142
+ "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
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+ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
144
+ its influence on the philosophy of science. He is best known to the general public for his massรขโ‚ฌโ€œenergy \
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+ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
146
+ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
147
+ of the photoelectric effect', a pivotal step in the development of quantum theory."
148
+ },
149
+ "doc2" : {
150
+ "title": "", # Keep title an empty string if not present
151
+ "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
152
+ malted barley. The two main varieties are German Weiรƒลธbier and Belgian witbier; other types include Lambic (made\
153
+ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
154
+ },
155
+ }
156
+
157
+ queries = {
158
+ "q1" : "Who developed the mass-energy equivalence formula?",
159
+ "q2" : "Which beer is brewed with a large proportion of wheat?"
160
+ }
161
+
162
+ qrels = {
163
+ "q1" : {"doc1": 1},
164
+ "q2" : {"doc2": 1},
165
+ }
166
+ ```
167
+
168
+ ### Data Fields
169
+
170
+ Examples from all configurations have the following features:
171
+
172
+ ### Corpus
173
+ - `corpus`: a `dict` feature representing the document title and passage text, made up of:
174
+ - `_id`: a `string` feature representing the unique document id
175
+ - `title`: a `string` feature, denoting the title of the document.
176
+ - `text`: a `string` feature, denoting the text of the document.
177
+
178
+ ### Queries
179
+ - `queries`: a `dict` feature representing the query, made up of:
180
+ - `_id`: a `string` feature representing the unique query id
181
+ - `text`: a `string` feature, denoting the text of the query.
182
+
183
+ ### Qrels
184
+ - `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
185
+ - `_id`: a `string` feature representing the query id
186
+ - `_id`: a `string` feature, denoting the document id.
187
+ - `score`: a `int32` feature, denoting the relevance judgement between query and document.
188
+
189
+
190
+ ### Data Splits
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+
192
+ | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
193
+ | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
194
+ | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
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+ | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
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+ | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
197
+ | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
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+ | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
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+ | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
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+ | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
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+ | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
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+ | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
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+ | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
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+ | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
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+ | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
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+ | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
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+ | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
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+ | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
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+ | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
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+ | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
211
+ | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
212
+ | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
213
+
214
+
215
+ ## Dataset Creation
216
+
217
+ ### Curation Rationale
218
+
219
+ [Needs More Information]
220
+
221
+ ### Source Data
222
+
223
+ #### Initial Data Collection and Normalization
224
+
225
+ [Needs More Information]
226
+
227
+ #### Who are the source language producers?
228
+
229
+ [Needs More Information]
230
+
231
+ ### Annotations
232
+
233
+ #### Annotation process
234
+
235
+ [Needs More Information]
236
+
237
+ #### Who are the annotators?
238
+
239
+ [Needs More Information]
240
+
241
+ ### Personal and Sensitive Information
242
+
243
+ [Needs More Information]
244
+
245
+ ## Considerations for Using the Data
246
+
247
+ ### Social Impact of Dataset
248
+
249
+ [Needs More Information]
250
+
251
+ ### Discussion of Biases
252
+
253
+ [Needs More Information]
254
+
255
+ ### Other Known Limitations
256
+
257
+ [Needs More Information]
258
+
259
+ ## Additional Information
260
+
261
+ ### Dataset Curators
262
+
263
+ [Needs More Information]
264
+
265
+ ### Licensing Information
266
+
267
+ [Needs More Information]
268
+
269
+ ### Citation Information
270
+
271
+ Cite as:
272
+ ```
273
+ @inproceedings{
274
+ thakur2021beir,
275
+ title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
276
+ author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
277
+ booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
278
+ year={2021},
279
+ url={https://openreview.net/forum?id=wCu6T5xFjeJ}
280
+ }
281
+ ```
282
+
283
+ ### Contributions
284
+
285
+ Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
huggingface_dataset/Dataset_Card/Davis_Swahili-tweet-sentiment.md ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ ---
4
+ A new Swahili tweet dataset for sentiment analysis.
5
+
6
+
7
+ ## Issues โš ๏ธ
8
+
9
+ Incase you have any difficulties or issues while trying to run the script
10
+ you can raise it on the issues section.
11
+
12
+ ## Pull Requests ๐Ÿ”ง
13
+
14
+ If you have something to add or new idea to implement, you are welcome to create a pull requests on improvement.
15
+
16
+ ## Give it a Like ๐Ÿ‘
17
+
18
+ If you find this dataset useful, give it a like so as many people can get to know it.
19
+
20
+ ## Credits
21
+
22
+ All the credits to [Davis David ](https://twitter.com/Davis_McDavid), [Zephania Reuben](https://twitter.com/nsomazr) & [Eliya Masesa](https://twitter.com/eliya_masesa)
huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-another-test-name__1655982268.md ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ benchmark: gem
3
+ type: prediction
4
+ submission_name: This is another test name
5
+ tags:
6
+ - evaluation
7
+ - benchmark
8
+ ---
9
+ # GEM Submission
10
+
11
+ Submission name: This is another test name
12
+
huggingface_dataset/Dataset_Card/Kaludi_data-food-category-classification.md ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ task_categories:
3
+ - image-classification
4
+
5
+ ---
6
+ # Dataset for project: food-category-classification
7
+
8
+ ## Dataset Description
9
+
10
+ This dataset is for project food-category-classification.
11
+
12
+ ### Languages
13
+
14
+ The BCP-47 code for the dataset's language is unk.
15
+
16
+ ## Dataset Structure
17
+
18
+ ### Data Instances
19
+
20
+ A sample from this dataset looks as follows:
21
+
22
+ ```json
23
+ [
24
+ {
25
+ "image": "<512x512 RGB PIL image>",
26
+ "target": 0
27
+ },
28
+ {
29
+ "image": "<512x512 RGB PIL image>",
30
+ "target": 0
31
+ }
32
+ ]
33
+ ```
34
+
35
+ ### Dataset Fields
36
+
37
+ The dataset has the following fields (also called "features"):
38
+
39
+ ```json
40
+ {
41
+ "image": "Image(decode=True, id=None)",
42
+ "target": "ClassLabel(names=['Bread', 'Dairy product', 'Dessert', 'Egg', 'Fried food', 'Meat', 'Noodles-Pasta', 'Rice', 'Seafood', 'Soup', 'Vegetable-Fruit'], id=None)"
43
+ }
44
+ ```
45
+
46
+ ### Dataset Splits
47
+
48
+ This dataset is split into a train and validation split. The split sizes are as follow:
49
+
50
+ | Split name | Num samples |
51
+ | ------------ | ------------------- |
52
+ | train | 1210 |
53
+ | valid | 275 |
huggingface_dataset/Dataset_Card/NbAiLab_NCC_small_100.md ADDED
@@ -0,0 +1,658 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Dataset Card for NBAiLab/NCC_small_100
2
+
3
+
4
+ annotations_creators:
5
+ - no-annotation
6
+ language_creators:
7
+ - found
8
+ languages:
9
+ - en,nb,no,nn,se,dk,is,fo
10
+ licenses:
11
+ - odc-by-1.0
12
+ multilinguality:
13
+ - multilingual
14
+ pretty_name: NCC
15
+ size_categories:
16
+ - 2G<n<1B
17
+ source_datasets:
18
+ - original
19
+ task_categories:
20
+ - sequence-modeling
21
+ task_ids:
22
+ - language-modeling
23
+
24
+
25
+ ## Table of Contents
26
+ - [Dataset Description](#dataset-description)
27
+ - [Dataset Summary](#dataset-summary)
28
+ - [Data Fields](#data-fiels)
29
+ - [Dataset Creation](#dataset-creation)
30
+ - [Statistics](#statistics)
31
+ - [Document Types](#document-types)
32
+ - [Languages](#languages)
33
+ - [Publish Periode](#publish-periode)
34
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
35
+ - [Social Impact of Dataset](#social-impact-of-dataset)
36
+ - [Discussion of Biases](#discussion-of-biases)
37
+ - [Other Known Limitations](#other-known-limitations)
38
+ - [Additional Information](#additional-information)
39
+ - [Dataset Curators](#dataset-curators)
40
+ - [Licensing Information](#licensing-information)
41
+ - [Citation Information](#citation-information)
42
+
43
+ ## Dataset Description
44
+ - **Homepage:** https://github.com/NBAiLab/notram
45
+ - **Repository:** https://github.com/NBAiLab/notram
46
+ - **Paper:** https://arxiv.org/abs/2104.09617
47
+ - **Point of Contact:** [Freddy Wetjen](mailto:freddy.wetjen@nb.no)
48
+
49
+ The Norwegian Colossal Corpus is a collection of multiple smaller Norwegian corpuses suitable for training large language models. We have done extensive cleaning on the datasets, and have made them available in a common format. The total size of the NCC is currently 45GB.
50
+
51
+ ## How to Use
52
+ ```python
53
+ from datasets import load_dataset
54
+ data = load_dataset("NBAiLab/NCC_small_100", streaming=True)
55
+ ```
56
+ ## Download Data
57
+ If you do not want to use the HuggingFace Dataset-library for training, or if you want to do additional pre-processing, it is also possible to download the files locally.
58
+ ```bash
59
+ # Download all files in one batch operation
60
+
61
+ for i in $(seq -f "%04g" 1 100): do wget https://huggingface.co/datasets/NbAiLab/NCC_small_100/blob/main/data/train-shard-$i-of-0100.json.gz &; done
62
+ # Create one large training file of all shards without unpacking
63
+ cat *.gz > onefile.json.gz
64
+ ```
65
+
66
+ <details>
67
+ <summary>List of all the files.</summary>
68
+
69
+
70
+ * [train-shard-0001-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0001-of-0100.json.gz)
71
+ * [train-shard-0002-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0002-of-0100.json.gz)
72
+ * [train-shard-0003-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0003-of-0100.json.gz)
73
+ * [train-shard-0004-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0004-of-0100.json.gz)
74
+ * [train-shard-0005-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0005-of-0100.json.gz)
75
+ * [train-shard-0006-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0006-of-0100.json.gz)
76
+ * [train-shard-0007-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0007-of-0100.json.gz)
77
+ * [train-shard-0008-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0008-of-0100.json.gz)
78
+ * [train-shard-0009-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0009-of-0100.json.gz)
79
+ * [train-shard-0010-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0010-of-0100.json.gz)
80
+ * [train-shard-0011-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0011-of-0100.json.gz)
81
+ * [train-shard-0012-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0012-of-0100.json.gz)
82
+ * [train-shard-0013-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0013-of-0100.json.gz)
83
+ * [train-shard-0014-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0014-of-0100.json.gz)
84
+ * [train-shard-0015-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0015-of-0100.json.gz)
85
+ * [train-shard-0016-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0016-of-0100.json.gz)
86
+ * [train-shard-0017-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0017-of-0100.json.gz)
87
+ * [train-shard-0018-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0018-of-0100.json.gz)
88
+ * [train-shard-0019-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0019-of-0100.json.gz)
89
+ * [train-shard-0020-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0020-of-0100.json.gz)
90
+ * [train-shard-0021-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0021-of-0100.json.gz)
91
+ * [train-shard-0022-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0022-of-0100.json.gz)
92
+ * [train-shard-0023-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0023-of-0100.json.gz)
93
+ * [train-shard-0024-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0024-of-0100.json.gz)
94
+ * [train-shard-0025-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0025-of-0100.json.gz)
95
+ * [train-shard-0026-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0026-of-0100.json.gz)
96
+ * [train-shard-0027-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0027-of-0100.json.gz)
97
+ * [train-shard-0028-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0028-of-0100.json.gz)
98
+ * [train-shard-0029-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0029-of-0100.json.gz)
99
+ * [train-shard-0030-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0030-of-0100.json.gz)
100
+ * [train-shard-0031-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0031-of-0100.json.gz)
101
+ * [train-shard-0032-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0032-of-0100.json.gz)
102
+ * [train-shard-0033-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0033-of-0100.json.gz)
103
+ * [train-shard-0034-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0034-of-0100.json.gz)
104
+ * [train-shard-0035-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0035-of-0100.json.gz)
105
+ * [train-shard-0036-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0036-of-0100.json.gz)
106
+ * [train-shard-0037-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0037-of-0100.json.gz)
107
+ * [train-shard-0038-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0038-of-0100.json.gz)
108
+ * [train-shard-0039-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0039-of-0100.json.gz)
109
+ * [train-shard-0040-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0040-of-0100.json.gz)
110
+ * [train-shard-0041-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0041-of-0100.json.gz)
111
+ * [train-shard-0042-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0042-of-0100.json.gz)
112
+ * [train-shard-0043-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0043-of-0100.json.gz)
113
+ * [train-shard-0044-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0044-of-0100.json.gz)
114
+ * [train-shard-0045-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0045-of-0100.json.gz)
115
+ * [train-shard-0046-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0046-of-0100.json.gz)
116
+ * [train-shard-0047-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0047-of-0100.json.gz)
117
+ * [train-shard-0048-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0048-of-0100.json.gz)
118
+ * [train-shard-0049-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0049-of-0100.json.gz)
119
+ * [train-shard-0050-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0050-of-0100.json.gz)
120
+ * [train-shard-0051-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0051-of-0100.json.gz)
121
+ * [train-shard-0052-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0052-of-0100.json.gz)
122
+ * [train-shard-0053-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0053-of-0100.json.gz)
123
+ * [train-shard-0054-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0054-of-0100.json.gz)
124
+ * [train-shard-0055-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0055-of-0100.json.gz)
125
+ * [train-shard-0056-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0056-of-0100.json.gz)
126
+ * [train-shard-0057-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0057-of-0100.json.gz)
127
+ * [train-shard-0058-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0058-of-0100.json.gz)
128
+ * [train-shard-0059-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0059-of-0100.json.gz)
129
+ * [train-shard-0060-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0060-of-0100.json.gz)
130
+ * [train-shard-0061-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0061-of-0100.json.gz)
131
+ * [train-shard-0062-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0062-of-0100.json.gz)
132
+ * [train-shard-0063-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0063-of-0100.json.gz)
133
+ * [train-shard-0064-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0064-of-0100.json.gz)
134
+ * [train-shard-0065-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0065-of-0100.json.gz)
135
+ * [train-shard-0066-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0066-of-0100.json.gz)
136
+ * [train-shard-0067-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0067-of-0100.json.gz)
137
+ * [train-shard-0068-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0068-of-0100.json.gz)
138
+ * [train-shard-0069-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0069-of-0100.json.gz)
139
+ * [train-shard-0070-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0070-of-0100.json.gz)
140
+ * [train-shard-0071-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0071-of-0100.json.gz)
141
+ * [train-shard-0072-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0072-of-0100.json.gz)
142
+ * [train-shard-0073-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0073-of-0100.json.gz)
143
+ * [train-shard-0074-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0074-of-0100.json.gz)
144
+ * [train-shard-0075-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0075-of-0100.json.gz)
145
+ * [train-shard-0076-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0076-of-0100.json.gz)
146
+ * [train-shard-0077-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0077-of-0100.json.gz)
147
+ * [train-shard-0078-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0078-of-0100.json.gz)
148
+ * [train-shard-0079-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0079-of-0100.json.gz)
149
+ * [train-shard-0080-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0080-of-0100.json.gz)
150
+ * [train-shard-0081-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0081-of-0100.json.gz)
151
+ * [train-shard-0082-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0082-of-0100.json.gz)
152
+ * [train-shard-0083-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0083-of-0100.json.gz)
153
+ * [train-shard-0084-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0084-of-0100.json.gz)
154
+ * [train-shard-0085-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0085-of-0100.json.gz)
155
+ * [train-shard-0086-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0086-of-0100.json.gz)
156
+ * [train-shard-0087-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0087-of-0100.json.gz)
157
+ * [train-shard-0088-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0088-of-0100.json.gz)
158
+ * [train-shard-0089-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0089-of-0100.json.gz)
159
+ * [train-shard-0090-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0090-of-0100.json.gz)
160
+ * [train-shard-0091-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0091-of-0100.json.gz)
161
+ * [train-shard-0092-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0092-of-0100.json.gz)
162
+ * [train-shard-0093-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0093-of-0100.json.gz)
163
+ * [train-shard-0094-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0094-of-0100.json.gz)
164
+ * [train-shard-0095-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0095-of-0100.json.gz)
165
+ * [train-shard-0096-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0096-of-0100.json.gz)
166
+ * [train-shard-0097-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0097-of-0100.json.gz)
167
+ * [train-shard-0098-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0098-of-0100.json.gz)
168
+ * [train-shard-0099-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0099-of-0100.json.gz)
169
+ * [train-shard-0100-of-0100](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/train-shard-0100-of-0100.json.gz)
170
+ * [validation-shard-0001-of-0001](https://huggingface.co/datasets/NbAiLab/NCC_small_100/resolve/main/data/validation-shard-0001-of-0001.json.gz)
171
+
172
+
173
+ </details>
174
+
175
+ ### Dataset Summary
176
+ The NCC_small_100 dataset contains json lines with language training data. Here is an example json line:
177
+ ```json
178
+ {
179
+ "id": "1006205",
180
+ "doc_type": "cc100",
181
+ "publish_year": 2021,
182
+ "lang_fasttext": "nn",
183
+ "lang_fasttext_conf": "0.641",
184
+ "text": "Eg har ein PLAN! KOS deg og ha ei fin helg"
185
+ }
186
+ ```
187
+ ## Data Fields
188
+ |**id:** | String with id to source of line and a unique identifier|
189
+ |:-----------|:------------|
190
+ |**doc_type ** | String describing type of media text extracted from (I.e. book,newspaper etc)|
191
+ |**publish_year ** | Integer. The year text published. When year is undetermined it is set to 2021.|
192
+ |**lang_fasttext ** | String. Language of text identified by FastText|
193
+ |**lang_fasttext_conf ** | String. Confidence calculated by FastText|
194
+ |**text ** | String. The complete utf-8 document. If longer than 1M characters it is split.|
195
+
196
+ ### Dataset Creation
197
+ We are providing a **train** and a **validation** split. The standard size of the validation is a single 1GB file, while train is sharded in 1GB chunks.
198
+ All files are gzipped.
199
+
200
+ Build date: 03122021
201
+
202
+ #### Initial Data Collection and Curation
203
+ The procedure for the dataset creation is described in detail in our paper.
204
+
205
+ ### Summary
206
+ | Words | Documents | Words/Document |
207
+ |------------:|------------:|-----------------:|
208
+ | 152,828,370 | 452,838 | 337 |
209
+
210
+ ### Document Types
211
+ | Source | Words | Documents | Words/Document |
212
+ |--------------------------------------:|-----------:|------------:|-----------------:|
213
+ | newspaper_ocr | 42,875,141 | 214,047 | 200 |
214
+ | parliament | 27,906,014 | 201 | 138,835 |
215
+ | books | 19,806,532 | 546 | 36,275 |
216
+ | newspapers_online_nb | 10,681,495 | 75,216 | 142 |
217
+ | maalfrid_regjeringen | 7,884,471 | 20,124 | 391 |
218
+ | maalfrid_ssb | 6,101,595 | 18,502 | 329 |
219
+ | maalfrid_uio | 3,891,127 | 16,548 | 235 |
220
+ | government_nb | 3,399,192 | 100 | 33,991 |
221
+ | wikipedia_download_nbo | 2,520,875 | 11,481 | 219 |
222
+ | maalfrid_fylkesmannen | 2,234,608 | 10,150 | 220 |
223
+ | publicreports | 2,198,220 | 82 | 26,807 |
224
+ | maalfrid_nve | 1,430,464 | 6,458 | 221 |
225
+ | maalfrid_patentstyret | 1,367,518 | 4,551 | 300 |
226
+ | maalfrid_ntnu | 1,266,953 | 4,378 | 289 |
227
+ | newspapers_online_nn | 913,353 | 3,679 | 248 |
228
+ | maalfrid_fhi | 729,409 | 3,167 | 230 |
229
+ | maalfrid_vegvesen | 710,333 | 3,545 | 200 |
230
+ | maalfrid_norad | 708,294 | 2,017 | 351 |
231
+ | lovdata_cd_odelsting_2005 | 696,273 | 43 | 16,192 |
232
+ | maalfrid_skatteetaten | 679,125 | 1,753 | 387 |
233
+ | maalfrid_uib | 615,724 | 2,483 | 247 |
234
+ | wikipedia_download_nno | 592,536 | 3,181 | 186 |
235
+ | maalfrid_forskningsradet | 526,472 | 1,604 | 328 |
236
+ | maalfrid_nasjonalparkstyre | 467,226 | 2,013 | 232 |
237
+ | maalfrid_nmbu | 406,053 | 1,578 | 257 |
238
+ | maalfrid_domstol | 382,629 | 1,121 | 341 |
239
+ | maalfrid_oslomet | 380,272 | 1,002 | 379 |
240
+ | maalfrid_nav | 363,539 | 1,670 | 217 |
241
+ | maalfrid_banenor | 338,844 | 1,479 | 229 |
242
+ | maalfrid_landbruksdirektoratet | 293,549 | 1,048 | 280 |
243
+ | maalfrid_helsedirektoratet | 289,269 | 1,076 | 268 |
244
+ | government_nn | 281,763 | 25 | 11,270 |
245
+ | maalfrid_udir | 228,128 | 874 | 261 |
246
+ | maalfrid_nokut | 226,072 | 877 | 257 |
247
+ | maalfrid_norges-bank | 224,812 | 829 | 271 |
248
+ | maalfrid_vkm | 214,855 | 683 | 314 |
249
+ | maalfrid_nbim | 214,235 | 417 | 513 |
250
+ | maalfrid_hi | 209,748 | 848 | 247 |
251
+ | maalfrid_ngu | 209,454 | 794 | 263 |
252
+ | maalfrid_miljodirektoratet | 208,346 | 757 | 275 |
253
+ | maalfrid_distriktssenteret | 204,538 | 847 | 241 |
254
+ | maalfrid_ptil | 204,458 | 753 | 271 |
255
+ | maalfrid_nord | 193,119 | 961 | 200 |
256
+ | maalfrid_difi | 172,807 | 788 | 219 |
257
+ | maalfrid_fiskeridir | 172,337 | 714 | 241 |
258
+ | maalfrid_hivolda | 168,122 | 564 | 298 |
259
+ | maalfrid_mattilsynet | 165,226 | 616 | 268 |
260
+ | maalfrid_havarikommisjonen | 164,555 | 555 | 296 |
261
+ | maalfrid_kulturradet | 151,989 | 472 | 322 |
262
+ | maalfrid_kystverket | 151,671 | 686 | 221 |
263
+ | maalfrid_ks | 149,000 | 563 | 264 |
264
+ | maalfrid_udi | 141,628 | 429 | 330 |
265
+ | maalfrid_uia | 134,214 | 535 | 250 |
266
+ | maalfrid_hjelpemiddeldatabasen | 129,178 | 764 | 169 |
267
+ | maalfrid_dsb | 127,197 | 423 | 300 |
268
+ | maalfrid_khrono | 124,208 | 432 | 287 |
269
+ | maalfrid_helsetilsynet | 123,804 | 397 | 311 |
270
+ | lovdata_cd_somb_rundskriv_2005 | 121,983 | 68 | 1,793 |
271
+ | maalfrid_veiviseren | 116,185 | 388 | 299 |
272
+ | lovdata_cd_sentrale_forskrifter_2005 | 114,379 | 251 | 455 |
273
+ | maalfrid_moreforsk | 114,223 | 451 | 253 |
274
+ | maalfrid_husbanken | 112,257 | 359 | 312 |
275
+ | maalfrid_forsvarsbygg | 109,309 | 441 | 247 |
276
+ | maalfrid_imdi | 108,090 | 357 | 302 |
277
+ | maalfrid_jernbanedirektoratet | 107,264 | 435 | 246 |
278
+ | maalfrid_konkurransetilsynet | 106,330 | 296 | 359 |
279
+ | maalfrid_inn | 102,298 | 613 | 166 |
280
+ | maalfrid_legemiddelverket | 100,455 | 452 | 222 |
281
+ | maalfrid_dsa | 100,141 | 353 | 283 |
282
+ | maalfrid_hiof | 99,743 | 528 | 188 |
283
+ | maalfrid_vetinst | 97,390 | 312 | 312 |
284
+ | maalfrid_ehelse | 95,975 | 496 | 193 |
285
+ | maalfrid_arkivverket | 94,310 | 360 | 261 |
286
+ | maalfrid_sdir | 94,192 | 311 | 302 |
287
+ | maalfrid_klagenemndssekretariatet | 87,830 | 258 | 340 |
288
+ | maalfrid_dibk | 84,106 | 336 | 250 |
289
+ | maalfrid_nhh | 81,294 | 317 | 256 |
290
+ | maalfrid_sprakradet | 80,918 | 315 | 256 |
291
+ | maalfrid_toll | 79,364 | 305 | 260 |
292
+ | maalfrid_politiet | 78,471 | 240 | 326 |
293
+ | maalfrid_vestlandfylke | 77,600 | 304 | 255 |
294
+ | maalfrid_riksrevisjonen | 77,117 | 225 | 342 |
295
+ | maalfrid_met | 76,310 | 400 | 190 |
296
+ | maalfrid_artsdatabanken | 76,117 | 200 | 380 |
297
+ | maalfrid_kartverket | 75,468 | 397 | 190 |
298
+ | maalfrid_bufdir | 75,375 | 262 | 287 |
299
+ | maalfrid_nibio | 74,594 | 386 | 193 |
300
+ | maalfrid_nkom | 63,734 | 215 | 296 |
301
+ | maalfrid_npd | 63,605 | 260 | 244 |
302
+ | maalfrid_nlr | 61,251 | 364 | 168 |
303
+ | maalfrid_aldringoghelse | 58,322 | 147 | 396 |
304
+ | maalfrid_uis | 57,580 | 190 | 303 |
305
+ | maalfrid_custompublish | 56,876 | 219 | 259 |
306
+ | maalfrid_nyemetoder | 56,634 | 245 | 231 |
307
+ | maalfrid_sykkelbynettverket | 53,461 | 230 | 232 |
308
+ | maalfrid_arbeidstilsynet | 52,903 | 127 | 416 |
309
+ | maalfrid_luftfartstilsynet | 51,929 | 221 | 234 |
310
+ | maalfrid_seniorporten | 50,569 | 159 | 318 |
311
+ | maalfrid_bioteknologiradet | 49,956 | 129 | 387 |
312
+ | maalfrid_riksantikvaren | 49,722 | 187 | 265 |
313
+ | maalfrid_sjt | 47,006 | 230 | 204 |
314
+ | maalfrid_dfo | 46,582 | 206 | 226 |
315
+ | maalfrid_hvl | 46,544 | 202 | 230 |
316
+ | lovdata_cd_lokaleforskrifter_2005 | 46,482 | 476 | 97 |
317
+ | maalfrid_forbrukerradet | 44,620 | 157 | 284 |
318
+ | maalfrid_himolde | 43,761 | 226 | 193 |
319
+ | maalfrid_kompetansenorge | 43,626 | 213 | 204 |
320
+ | maalfrid_ldo | 41,409 | 153 | 270 |
321
+ | lovdata_cd_norgeslover_2005 | 40,450 | 32 | 1,264 |
322
+ | maalfrid_forskningsetikk | 39,574 | 127 | 311 |
323
+ | maalfrid_naku | 37,039 | 107 | 346 |
324
+ | maalfrid_usn | 35,982 | 154 | 233 |
325
+ | maalfrid_godeidrettsanlegg | 35,482 | 145 | 244 |
326
+ | maalfrid_naturfag | 34,881 | 132 | 264 |
327
+ | maalfrid_matematikksenteret | 34,258 | 158 | 216 |
328
+ | maalfrid_medietilsynet | 33,904 | 145 | 233 |
329
+ | maalfrid_diskrimineringsnemnda | 33,264 | 89 | 373 |
330
+ | maalfrid_nupi | 31,508 | 121 | 260 |
331
+ | maalfrid_miljopakken | 31,029 | 140 | 221 |
332
+ | lovdata_cd_rtv_rundskriv_2005 | 30,518 | 222 | 137 |
333
+ | maalfrid_dirmin | 30,360 | 117 | 259 |
334
+ | maalfrid_diku | 29,246 | 135 | 216 |
335
+ | maalfrid_arbeidsretten | 27,492 | 92 | 298 |
336
+ | maalfrid_fellesstudentsystem | 27,029 | 197 | 137 |
337
+ | maalfrid_kriminalitetsforebygging | 26,971 | 104 | 259 |
338
+ | maalfrid_statsbygg | 26,256 | 102 | 257 |
339
+ | maalfrid_nb | 25,375 | 94 | 269 |
340
+ | maalfrid_nih | 25,036 | 112 | 223 |
341
+ | maalfrid_folketrygdfondet | 25,027 | 91 | 275 |
342
+ | maalfrid_npolar | 24,843 | 62 | 400 |
343
+ | maalfrid_valgdirektoratet | 23,205 | 205 | 113 |
344
+ | maalfrid_lottstift | 22,736 | 78 | 291 |
345
+ | maalfrid_naturfagsenteret | 22,618 | 95 | 238 |
346
+ | maalfrid_samordnaopptak | 22,400 | 56 | 400 |
347
+ | maalfrid_sykehuspartner | 21,855 | 108 | 202 |
348
+ | maalfrid_unit | 21,305 | 135 | 157 |
349
+ | lovdata_cd_rundskriv_lovavdeling_2005 | 21,295 | 10 | 2,129 |
350
+ | maalfrid_anskaffelser | 21,097 | 104 | 202 |
351
+ | maalfrid_barneombudet | 20,092 | 65 | 309 |
352
+ | maalfrid_mareano | 19,922 | 91 | 218 |
353
+ | maalfrid_datatilsynet | 19,845 | 55 | 360 |
354
+ | maalfrid_fiskeridirektoratet | 18,831 | 60 | 313 |
355
+ | maalfrid_spesialenheten | 18,550 | 47 | 394 |
356
+ | maalfrid_xn--miljlftet-o8ab | 18,447 | 78 | 236 |
357
+ | lovdata_cd_skatt_rundskriv_2005 | 18,316 | 7 | 2,616 |
358
+ | maalfrid_skrivesenteret | 17,951 | 102 | 175 |
359
+ | maalfrid_khio | 16,924 | 63 | 268 |
360
+ | maalfrid_bibliotekutvikling | 16,631 | 89 | 186 |
361
+ | maalfrid_helsenorge | 15,431 | 60 | 257 |
362
+ | maalfrid_sykehusinnkjop | 15,204 | 92 | 165 |
363
+ | maalfrid_spk | 13,824 | 44 | 314 |
364
+ | maalfrid_aho | 13,268 | 78 | 170 |
365
+ | maalfrid_matportalen | 12,756 | 51 | 250 |
366
+ | maalfrid_nfi | 12,696 | 36 | 352 |
367
+ | maalfrid_samas | 12,650 | 62 | 204 |
368
+ | maalfrid_kunstkultursenteret | 12,307 | 35 | 351 |
369
+ | maalfrid_nhn | 12,156 | 77 | 157 |
370
+ | maalfrid_pasientsikkerhetsprogrammet | 11,892 | 91 | 130 |
371
+ | maalfrid_ceres | 11,310 | 44 | 257 |
372
+ | maalfrid_nysgjerrigper | 11,177 | 63 | 177 |
373
+ | maalfrid_une | 11,036 | 23 | 479 |
374
+ | maalfrid_nynorsksenteret | 10,822 | 45 | 240 |
375
+ | maalfrid_natursekken | 10,060 | 73 | 137 |
376
+ | maalfrid_nidsenter | 9,996 | 34 | 294 |
377
+ | maalfrid_nsm | 9,926 | 39 | 254 |
378
+ | maalfrid_justervesenet | 9,847 | 29 | 339 |
379
+ | maalfrid_giek | 9,769 | 39 | 250 |
380
+ | maalfrid_digdir | 9,675 | 54 | 179 |
381
+ | maalfrid_stami | 9,518 | 22 | 432 |
382
+ | maalfrid_sshf | 9,488 | 37 | 256 |
383
+ | maalfrid_kriminalomsorgen | 9,126 | 32 | 285 |
384
+ | maalfrid_vinmonopolet | 9,094 | 22 | 413 |
385
+ | maalfrid_nodnett | 8,738 | 50 | 174 |
386
+ | maalfrid_gjenopptakelse | 8,249 | 30 | 274 |
387
+ | maalfrid_fordelingsutvalget | 8,242 | 28 | 294 |
388
+ | maalfrid_kjonnsforskning | 8,010 | 22 | 364 |
389
+ | maalfrid_nasjonalmuseet | 7,935 | 21 | 377 |
390
+ | maalfrid_forsvaret | 7,614 | 27 | 282 |
391
+ | maalfrid_ombudsmann | 7,496 | 12 | 624 |
392
+ | maalfrid_forbrukereuropa | 7,260 | 28 | 259 |
393
+ | maalfrid_romsenter | 7,219 | 27 | 267 |
394
+ | maalfrid_ovf | 6,699 | 28 | 239 |
395
+ | maalfrid_beccle | 6,686 | 33 | 202 |
396
+ | maalfrid_forbrukertilsynet | 6,440 | 19 | 338 |
397
+ | maalfrid_helfo | 5,746 | 21 | 273 |
398
+ | maalfrid_politietssikkerhetstjeneste | 5,570 | 16 | 348 |
399
+ | maalfrid_geonorge | 5,228 | 35 | 149 |
400
+ | maalfrid_realfagsloyper | 5,155 | 22 | 234 |
401
+ | maalfrid_opplaringslovutvalget | 5,062 | 11 | 460 |
402
+ | maalfrid_vea-fs | 5,026 | 28 | 179 |
403
+ | maalfrid_energimerking | 4,842 | 25 | 193 |
404
+ | maalfrid_jernbanemagasinet | 4,663 | 14 | 333 |
405
+ | maalfrid_traumebevisst | 4,456 | 50 | 89 |
406
+ | maalfrid_politihogskolen | 4,434 | 27 | 164 |
407
+ | maalfrid_universell | 4,138 | 37 | 111 |
408
+ | maalfrid_nafkam | 4,096 | 11 | 372 |
409
+ | maalfrid_koro | 3,781 | 10 | 378 |
410
+ | maalfrid_npe | 3,744 | 21 | 178 |
411
+ | maalfrid_regionaleforskningsfond | 3,512 | 23 | 152 |
412
+ | maalfrid_denkulturelleskolesekken | 3,375 | 7 | 482 |
413
+ | maalfrid_squarespace | 3,310 | 12 | 275 |
414
+ | maalfrid_riksteatret | 3,143 | 12 | 261 |
415
+ | maalfrid_riksmekleren | 2,936 | 15 | 195 |
416
+ | maalfrid_pkh | 2,927 | 9 | 325 |
417
+ | maalfrid_konfliktraadet | 2,918 | 9 | 324 |
418
+ | maalfrid_aasentunet | 2,713 | 8 | 339 |
419
+ | maalfrid_radetfordyreetikk | 2,579 | 12 | 214 |
420
+ | maalfrid_generaladvokaten | 2,428 | 7 | 346 |
421
+ | maalfrid_lanekassen | 2,237 | 7 | 319 |
422
+ | maalfrid_okokrim | 2,184 | 10 | 218 |
423
+ | maalfrid_kulturminnefondet | 2,157 | 10 | 215 |
424
+ | maalfrid_whocc | 2,143 | 13 | 164 |
425
+ | maalfrid_brreg | 2,140 | 13 | 164 |
426
+ | maalfrid_polarhistorie | 2,016 | 7 | 288 |
427
+ | maalfrid_unknown | 2,015 | 11 | 183 |
428
+ | maalfrid_ffi | 2,010 | 6 | 335 |
429
+ | maalfrid_finansportalen | 1,967 | 7 | 281 |
430
+ | maalfrid_digidel | 1,701 | 10 | 170 |
431
+ | maalfrid_sismo | 1,685 | 6 | 280 |
432
+ | maalfrid_nlb | 1,665 | 5 | 333 |
433
+ | maalfrid_lektor2 | 1,397 | 8 | 174 |
434
+ | maalfrid_sivilforsvaret | 1,365 | 8 | 170 |
435
+ | maalfrid_konkursradet | 1,309 | 4 | 327 |
436
+ | maalfrid_varsom | 1,281 | 10 | 128 |
437
+ | maalfrid_informasjonskompetanse | 1,254 | 8 | 156 |
438
+ | maalfrid_skattefunn | 1,171 | 3 | 390 |
439
+ | maalfrid_sivilrett | 1,166 | 3 | 388 |
440
+ | maalfrid_uit | 1,112 | 16 | 69 |
441
+ | maalfrid_yrkesfisker | 1,110 | 10 | 111 |
442
+ | maalfrid_nbsk | 1,098 | 8 | 137 |
443
+ | maalfrid_lokforerskolen | 1,075 | 7 | 153 |
444
+ | maalfrid_laudim | 1,069 | 8 | 133 |
445
+ | maalfrid_nyinorge | 1,064 | 2 | 532 |
446
+ | maalfrid_transport21 | 1,030 | 4 | 257 |
447
+ | maalfrid_openaccess | 953 | 3 | 317 |
448
+ | maalfrid_sinn | 924 | 5 | 184 |
449
+ | maalfrid_htu | 881 | 4 | 220 |
450
+ | maalfrid_yr | 865 | 12 | 72 |
451
+ | maalfrid_akkreditert | 856 | 4 | 214 |
452
+ | maalfrid_helseklage | 855 | 3 | 285 |
453
+ | maalfrid_ssn | 841 | 5 | 168 |
454
+ | maalfrid_fug | 816 | 2 | 408 |
455
+ | maalfrid_matogindustri | 780 | 6 | 130 |
456
+ | maalfrid_fordelingsutvalet | 772 | 2 | 386 |
457
+ | maalfrid_dekom | 764 | 18 | 42 |
458
+ | maalfrid_lokalhistorie | 753 | 3 | 251 |
459
+ | maalfrid_unesco | 749 | 4 | 187 |
460
+ | maalfrid_omsorgsforskning | 711 | 5 | 142 |
461
+ | maalfrid_pts | 651 | 2 | 325 |
462
+ | maalfrid_valg | 638 | 2 | 319 |
463
+ | maalfrid_forbrukerklageutvalget | 626 | 2 | 313 |
464
+ | maalfrid_miljoklagenemnda | 625 | 3 | 208 |
465
+ | maalfrid_regjeringsadvokaten | 616 | 2 | 308 |
466
+ | maalfrid_iearth | 552 | 3 | 184 |
467
+ | maalfrid_skeivtarkiv | 552 | 4 | 138 |
468
+ | maalfrid_xn--kvinneligomskjring-1ub | 514 | 1 | 514 |
469
+ | maalfrid_haldenfengsel | 469 | 1 | 469 |
470
+ | maalfrid_hjelpelinjen | 466 | 2 | 233 |
471
+ | maalfrid_sevuppt | 429 | 1 | 429 |
472
+ | maalfrid_norec | 376 | 1 | 376 |
473
+ | maalfrid_kk-utvalget | 348 | 1 | 348 |
474
+ | maalfrid_ah | 346 | 1 | 346 |
475
+ | maalfrid_lykillinn | 331 | 1 | 331 |
476
+ | maalfrid_vergemal | 319 | 1 | 319 |
477
+ | maalfrid_riksadvokaten | 315 | 2 | 157 |
478
+ | maalfrid_global | 301 | 1 | 301 |
479
+ | maalfrid_webhuset | 280 | 1 | 280 |
480
+ | maalfrid_xn--tilbakefring-2jb | 267 | 2 | 133 |
481
+ | maalfrid_oslofengsel | 266 | 1 | 266 |
482
+ | maalfrid_nasjonaleturistveger | 227 | 1 | 227 |
483
+ | maalfrid_kulturped | 172 | 1 | 172 |
484
+ | maalfrid_altinn | 170 | 2 | 85 |
485
+ | maalfrid_shiprep | 165 | 2 | 82 |
486
+ | maalfrid_kulturoghelse | 161 | 4 | 40 |
487
+ | maalfrid_kantinekurset | 145 | 1 | 145 |
488
+ | maalfrid_designavgang | 145 | 1 | 145 |
489
+ | maalfrid_memu | 126 | 2 | 63 |
490
+ | maalfrid_alleteller | 123 | 1 | 123 |
491
+ | maalfrid_havmiljo | 118 | 1 | 118 |
492
+ | maalfrid_fmfiavo@fylkesmannen | 81 | 2 | 40 |
493
+ | maalfrid_okopark | 61 | 1 | 61 |
494
+ | maalfrid_nynorskbok | 52 | 1 | 52 |
495
+ | maalfrid_uh-it | 47 | 2 | 23 |
496
+ | maalfrid_bastoyfengsel | 46 | 1 | 46 |
497
+ | maalfrid_overgangsbolig | 40 | 1 | 40 |
498
+ | maalfrid_spinn-inn | 37 | 2 | 18 |
499
+ | maalfrid_karriereveiledning | 31 | 1 | 31 |
500
+ | maalfrid_norskpetroleum | 15 | 2 | 7 |
501
+ | maalfrid_feide | 9 | 1 | 9 |
502
+
503
+ ### Languages
504
+ | Language | Words | Documents | Words/Document |
505
+ |-----------:|------------:|------------:|-----------------:|
506
+ | no | 110,561,181 | 373,475 | 296 |
507
+ | da | 22,054,103 | 12,507 | 1,763 |
508
+ | en | 10,551,361 | 33,082 | 318 |
509
+ | nn | 6,400,816 | 21,583 | 296 |
510
+ | fr | 1,150,970 | 2,354 | 488 |
511
+ | de | 848,915 | 1,804 | 470 |
512
+ | sv | 290,653 | 2,578 | 112 |
513
+ | es | 238,453 | 910 | 262 |
514
+ | fi | 138,410 | 984 | 140 |
515
+ | et | 71,255 | 507 | 140 |
516
+ | cs | 57,634 | 465 | 123 |
517
+ | oc | 51,457 | 109 | 472 |
518
+ | pt | 49,471 | 326 | 151 |
519
+ | nl | 38,024 | 266 | 142 |
520
+ | la | 36,388 | 20 | 1,819 |
521
+ | uk | 31,820 | 107 | 297 |
522
+ | zh | 27,640 | 181 | 152 |
523
+ | eu | 25,582 | 74 | 345 |
524
+ | it | 24,134 | 199 | 121 |
525
+ | ru | 24,022 | 149 | 161 |
526
+ | pl | 23,919 | 216 | 110 |
527
+ | ca | 23,748 | 84 | 282 |
528
+ | gu | 16,739 | 1 | 16,739 |
529
+ | fa | 11,657 | 49 | 237 |
530
+ | hu | 10,583 | 173 | 61 |
531
+ | is | 10,225 | 37 | 276 |
532
+ | ja | 9,563 | 109 | 87 |
533
+ | el | 5,320 | 20 | 266 |
534
+ | id | 5,254 | 44 | 119 |
535
+ | ar | 4,268 | 20 | 213 |
536
+ | so | 3,343 | 13 | 257 |
537
+ | sl | 3,243 | 47 | 69 |
538
+ | vi | 3,077 | 22 | 139 |
539
+ | sr | 2,022 | 29 | 69 |
540
+ | hr | 1,947 | 23 | 84 |
541
+ | tr | 1,802 | 41 | 43 |
542
+ | gl | 1,709 | 17 | 100 |
543
+ | mn | 1,575 | 1 | 1,575 |
544
+ | lt | 1,442 | 15 | 96 |
545
+ | am | 1,405 | 6 | 234 |
546
+ | ko | 1,301 | 29 | 44 |
547
+ | sq | 1,265 | 8 | 158 |
548
+ | ro | 1,214 | 13 | 93 |
549
+ | kk | 1,092 | 2 | 546 |
550
+ | ur | 1,003 | 5 | 200 |
551
+ | ml | 986 | 6 | 164 |
552
+ | sh | 939 | 5 | 187 |
553
+ | eo | 755 | 14 | 53 |
554
+ | th | 550 | 12 | 45 |
555
+ | ta | 505 | 6 | 84 |
556
+ | sw | 468 | 3 | 156 |
557
+ | sk | 442 | 12 | 36 |
558
+ | war | 369 | 3 | 123 |
559
+ | tl | 340 | 2 | 170 |
560
+ | bg | 327 | 1 | 327 |
561
+ | pnb | 276 | 1 | 276 |
562
+ | bs | 230 | 2 | 115 |
563
+ | ceb | 196 | 6 | 32 |
564
+ | cy | 182 | 2 | 91 |
565
+ | ku | 175 | 1 | 175 |
566
+ | ga | 102 | 6 | 17 |
567
+ | my | 82 | 1 | 82 |
568
+ | hy | 66 | 2 | 33 |
569
+ | ast | 59 | 1 | 59 |
570
+ | ms | 53 | 13 | 4 |
571
+ | be | 40 | 1 | 40 |
572
+ | nds | 30 | 6 | 5 |
573
+ | lv | 30 | 3 | 10 |
574
+ | als | 22 | 3 | 7 |
575
+ | mk | 21 | 2 | 10 |
576
+ | as | 17 | 1 | 17 |
577
+ | br | 16 | 3 | 5 |
578
+ | af | 13 | 1 | 13 |
579
+ | tt | 12 | 2 | 6 |
580
+ | si | 10 | 1 | 10 |
581
+ | su | 8 | 1 | 8 |
582
+ | bn | 8 | 1 | 8 |
583
+ | hsb | 6 | 1 | 6 |
584
+ | jv | 5 | 1 | 5 |
585
+ | fy | 5 | 2 | 2 |
586
+ | az | 5 | 1 | 5 |
587
+ | pms | 4 | 1 | 4 |
588
+ | jbo | 4 | 1 | 4 |
589
+ | lb | 3 | 1 | 3 |
590
+ | io | 3 | 1 | 3 |
591
+ | he | 1 | 1 | 1 |
592
+
593
+ ### Publish Periode
594
+ | Decade | Words | Documents | Words/Document |
595
+ |---------:|-----------:|------------:|-----------------:|
596
+ | 2020 | 90,368,489 | 238,255 | 568 |
597
+ | 2010 | 7,706,272 | 52,464 | 1,483 |
598
+ | 2000 | 10,118,391 | 36,978 | 3,135 |
599
+ | 1990 | 16,379,779 | 54,636 | 2,989 |
600
+ | 1980 | 3,378,092 | 11,838 | 2,845 |
601
+ | 1970 | 4,041,362 | 17,805 | 2,261 |
602
+ | 1960 | 3,523,333 | 17,974 | 1,971 |
603
+ | 1950 | 2,128,506 | 10,387 | 2,058 |
604
+ | 1940 | 2,662,606 | 12,271 | 2,521 |
605
+ | 1930 | 964,846 | 20 | 383,978 |
606
+ | 1920 | 744,560 | 16 | 328,756 |
607
+ | 1910 | 1,701,319 | 31 | 527,445 |
608
+ | 1900 | 1,183,273 | 24 | 414,972 |
609
+ | 1890 | 2,246,433 | 40 | 461,126 |
610
+ | 1880 | 1,059,838 | 19 | 490,702 |
611
+ | 1870 | 999,024 | 15 | 521,165 |
612
+ | 1860 | 842,042 | 17 | 533,772 |
613
+ | 1850 | 1,408,491 | 25 | 434,091 |
614
+ | 1840 | 627,004 | 10 | 398,914 |
615
+ | 1830 | 695,289 | 11 | 475,094 |
616
+ | 1820 | 49,421 | 2 | 49,421 |
617
+
618
+ ## Considerations for Using the Data
619
+ This corpus contains data under copyright and is not allowed to be used outide the National Library of Norway. The dataset should not be distributed.
620
+
621
+ ### Discussion of Biases
622
+ Please refer to our paper.
623
+
624
+ ### Dataset Curators
625
+ Freddy.wetjen@nb.no
626
+ Per.Kummervold@nb.no
627
+
628
+ ## License
629
+ Various licences applies to different parts of the corpus. Every document in the corpus has a tag telling what **"doc_type"** it belongs to. If you are unable to accept any of the licenses, you should filter out the **"doc_type"** with a conflicting license.
630
+
631
+ | Doc_type | License |
632
+ | :-------- | :------------- |
633
+ | government_nb, government_nn, parliament, publicreports, lovdata_cd_\*, maalfrid_\* | [NLOD 2.0](https://data.norge.no/nlod/en/2.0/)|
634
+ | newspapers_ocr, newspapers_pdf, books| [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)|
635
+ | newspapers_online_nb, newspapers_online_nn | [CC BY-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/)|
636
+ | opensubtitles, wikipedia | [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)
637
+ |
638
+
639
+ ### Citation Information
640
+ We are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:
641
+ ```
642
+ @inproceedings{kummervold-etal-2021-operationalizing,
643
+ title = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
644
+ author = {Kummervold, Per E and
645
+ De la Rosa, Javier and
646
+ Wetjen, Freddy and
647
+ Brygfjeld, Svein Arne",
648
+ booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
649
+ year = "2021",
650
+ address = "Reykjavik, Iceland (Online)",
651
+ publisher = {Link{"o}ping University Electronic Press, Sweden},
652
+ url = "https://aclanthology.org/2021.nodalida-main.3",
653
+ pages = "20--29",
654
+ abstract = "In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
655
+ The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
656
+ in several token and sequence classification tasks for both Norwegian Bokm{aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.",
657
+ }
658
+ ```
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-indonli-indonli-717ea6-1995866375.md ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ type: predictions
3
+ tags:
4
+ - autotrain
5
+ - evaluation
6
+ datasets:
7
+ - indonli
8
+ eval_info:
9
+ task: natural_language_inference
10
+ model: w11wo/indonesian-roberta-base-indonli
11
+ metrics: []
12
+ dataset_name: indonli
13
+ dataset_config: indonli
14
+ dataset_split: test_expert
15
+ col_mapping:
16
+ text1: premise
17
+ text2: hypothesis
18
+ target: label
19
+ ---
20
+ # Dataset Card for AutoTrain Evaluator
21
+
22
+ This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
23
+
24
+ * Task: Natural Language Inference
25
+ * Model: w11wo/indonesian-roberta-base-indonli
26
+ * Dataset: indonli
27
+ * Config: indonli
28
+ * Split: test_expert
29
+
30
+ To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
31
+
32
+ ## Contributions
33
+
34
+ Thanks to [@afaji](https://huggingface.co/afaji) for evaluating this model.
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-kmfoda__booksum-kmfoda__booksum-228ea1-1466053986.md ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ type: predictions
3
+ tags:
4
+ - autotrain
5
+ - evaluation
6
+ datasets:
7
+ - kmfoda/booksum
8
+ eval_info:
9
+ task: summarization
10
+ model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
11
+ metrics: []
12
+ dataset_name: kmfoda/booksum
13
+ dataset_config: kmfoda--booksum
14
+ dataset_split: test
15
+ col_mapping:
16
+ text: chapter
17
+ target: summary_text
18
+ ---
19
+ # Dataset Card for AutoTrain Evaluator
20
+
21
+ This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
22
+
23
+ * Task: Summarization
24
+ * Model: pszemraj/long-t5-tglobal-base-16384-booksum-V12
25
+ * Dataset: kmfoda/booksum
26
+ * Config: kmfoda--booksum
27
+ * Split: test
28
+
29
+ To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
30
+
31
+ ## Contributions
32
+
33
+ Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-lener_br-lener_br-14b0f6-1886164288.md ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ type: predictions
3
+ tags:
4
+ - autotrain
5
+ - evaluation
6
+ datasets:
7
+ - lener_br
8
+ eval_info:
9
+ task: entity_extraction
10
+ model: Luciano/xlm-roberta-large-finetuned-lener_br-finetuned-lener-br
11
+ metrics: []
12
+ dataset_name: lener_br
13
+ dataset_config: lener_br
14
+ dataset_split: train
15
+ col_mapping:
16
+ tokens: tokens
17
+ tags: ner_tags
18
+ ---
19
+ # Dataset Card for AutoTrain Evaluator
20
+
21
+ This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
22
+
23
+ * Task: Token Classification
24
+ * Model: Luciano/xlm-roberta-large-finetuned-lener_br-finetuned-lener-br
25
+ * Dataset: lener_br
26
+ * Config: lener_br
27
+ * Split: train
28
+
29
+ To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
30
+
31
+ ## Contributions
32
+
33
+ Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-samsum-db063b78-12135617.md ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ type: predictions
3
+ tags:
4
+ - autotrain
5
+ - evaluation
6
+ datasets:
7
+ - samsum
8
+ eval_info:
9
+ task: summarization
10
+ model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2
11
+ metrics: []
12
+ dataset_name: samsum
13
+ dataset_config: samsum
14
+ dataset_split: test
15
+ col_mapping:
16
+ text: dialogue
17
+ target: summary
18
+ ---
19
+ # Dataset Card for AutoTrain Evaluator
20
+
21
+ This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
22
+
23
+ * Task: Summarization
24
+ * Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2
25
+ * Dataset: samsum
26
+ * Config: samsum
27
+ * Split: test
28
+
29
+ To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
30
+
31
+ ## Contributions
32
+
33
+ Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
huggingface_dataset/Dataset_Card/code_x_glue_cc_code_to_code_trans.md ADDED
@@ -0,0 +1,184 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language_creators:
5
+ - found
6
+ language:
7
+ - code
8
+ license:
9
+ - c-uda
10
+ multilinguality:
11
+ - other-programming-languages
12
+ size_categories:
13
+ - 10K<n<100K
14
+ source_datasets:
15
+ - original
16
+ task_categories:
17
+ - translation
18
+ task_ids: []
19
+ pretty_name: CodeXGlueCcCodeToCodeTrans
20
+ tags:
21
+ - code-to-code
22
+ dataset_info:
23
+ features:
24
+ - name: id
25
+ dtype: int32
26
+ - name: java
27
+ dtype: string
28
+ - name: cs
29
+ dtype: string
30
+ splits:
31
+ - name: train
32
+ num_bytes: 4372657
33
+ num_examples: 10300
34
+ - name: validation
35
+ num_bytes: 226415
36
+ num_examples: 500
37
+ - name: test
38
+ num_bytes: 418595
39
+ num_examples: 1000
40
+ download_size: 4876035
41
+ dataset_size: 5017667
42
+ ---
43
+ # Dataset Card for "code_x_glue_cc_code_to_code_trans"
44
+
45
+ ## Table of Contents
46
+ - [Dataset Description](#dataset-description)
47
+ - [Dataset Summary](#dataset-summary)
48
+ - [Supported Tasks and Leaderboards](#supported-tasks)
49
+ - [Languages](#languages)
50
+ - [Dataset Structure](#dataset-structure)
51
+ - [Data Instances](#data-instances)
52
+ - [Data Fields](#data-fields)
53
+ - [Data Splits](#data-splits-sample-size)
54
+ - [Dataset Creation](#dataset-creation)
55
+ - [Curation Rationale](#curation-rationale)
56
+ - [Source Data](#source-data)
57
+ - [Annotations](#annotations)
58
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
59
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
60
+ - [Social Impact of Dataset](#social-impact-of-dataset)
61
+ - [Discussion of Biases](#discussion-of-biases)
62
+ - [Other Known Limitations](#other-known-limitations)
63
+ - [Additional Information](#additional-information)
64
+ - [Dataset Curators](#dataset-curators)
65
+ - [Licensing Information](#licensing-information)
66
+ - [Citation Information](#citation-information)
67
+ - [Contributions](#contributions)
68
+
69
+ ## Dataset Description
70
+
71
+ - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans
72
+
73
+ ### Dataset Summary
74
+
75
+ CodeXGLUE code-to-code-trans dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans
76
+
77
+ The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
78
+ We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplicates and functions with the empty body, we split the whole dataset into training, validation and test sets.
79
+
80
+ ### Supported Tasks and Leaderboards
81
+
82
+ - `machine-translation`: The dataset can be used to train a model for translating code in Java to C# and vice versa.
83
+
84
+ ### Languages
85
+
86
+ - Java **programming** language
87
+ - C# **programming** language
88
+
89
+ ## Dataset Structure
90
+
91
+ ### Data Instances
92
+
93
+ An example of 'validation' looks as follows.
94
+ ```
95
+ {
96
+ "cs": "public DVRecord(RecordInputStream in1){_option_flags = in1.ReadInt();_promptTitle = ReadUnicodeString(in1);_errorTitle = ReadUnicodeString(in1);_promptText = ReadUnicodeString(in1);_errorText = ReadUnicodeString(in1);int field_size_first_formula = in1.ReadUShort();_not_used_1 = in1.ReadShort();_formula1 = NPOI.SS.Formula.Formula.Read(field_size_first_formula, in1);int field_size_sec_formula = in1.ReadUShort();_not_used_2 = in1.ReadShort();_formula2 = NPOI.SS.Formula.Formula.Read(field_size_sec_formula, in1);_regions = new CellRangeAddressList(in1);}\n",
97
+ "id": 0,
98
+ "java": "public DVRecord(RecordInputStream in) {_option_flags = in.readInt();_promptTitle = readUnicodeString(in);_errorTitle = readUnicodeString(in);_promptText = readUnicodeString(in);_errorText = readUnicodeString(in);int field_size_first_formula = in.readUShort();_not_used_1 = in.readShort();_formula1 = Formula.read(field_size_first_formula, in);int field_size_sec_formula = in.readUShort();_not_used_2 = in.readShort();_formula2 = Formula.read(field_size_sec_formula, in);_regions = new CellRangeAddressList(in);}\n"
99
+ }
100
+ ```
101
+
102
+ ### Data Fields
103
+
104
+ In the following each data field in go is explained for each config. The data fields are the same among all splits.
105
+
106
+ #### default
107
+
108
+ |field name| type | description |
109
+ |----------|------|-----------------------------|
110
+ |id |int32 | Index of the sample |
111
+ |java |string| The java version of the code|
112
+ |cs |string| The C# version of the code |
113
+
114
+ ### Data Splits
115
+
116
+ | name |train|validation|test|
117
+ |-------|----:|---------:|---:|
118
+ |default|10300| 500|1000|
119
+
120
+ ## Dataset Creation
121
+
122
+ ### Curation Rationale
123
+
124
+ [More Information Needed]
125
+
126
+ ### Source Data
127
+
128
+ #### Initial Data Collection and Normalization
129
+
130
+ [More Information Needed]
131
+
132
+ #### Who are the source language producers?
133
+
134
+ [More Information Needed]
135
+
136
+ ### Annotations
137
+
138
+ #### Annotation process
139
+
140
+ [More Information Needed]
141
+
142
+ #### Who are the annotators?
143
+
144
+ [More Information Needed]
145
+
146
+ ### Personal and Sensitive Information
147
+
148
+ [More Information Needed]
149
+
150
+ ## Considerations for Using the Data
151
+
152
+ ### Social Impact of Dataset
153
+
154
+ [More Information Needed]
155
+
156
+ ### Discussion of Biases
157
+
158
+ [More Information Needed]
159
+
160
+ ### Other Known Limitations
161
+
162
+ [More Information Needed]
163
+
164
+ ## Additional Information
165
+
166
+ ### Dataset Curators
167
+
168
+ https://github.com/microsoft, https://github.com/madlag
169
+
170
+ ### Licensing Information
171
+
172
+ Computational Use of Data Agreement (C-UDA) License.
173
+
174
+ ### Citation Information
175
+
176
+ ```
177
+ @article{CodeXGLUE,
178
+ title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
179
+ year={2020},}
180
+ ```
181
+
182
+ ### Contributions
183
+
184
+ Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
huggingface_dataset/Dataset_Card/flax-sentence-embeddings_Gender_Bias_Evaluation_Set.md ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ **This dataset has been created as part of the Flax/JAX community week for testing the [flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) Sentence Similarity models for Gender Bias but can be used for other use-cases as well related to evaluating Gender Bias.**
2
+
3
+ The Following Dataset has been created for Evaluating Gender Bias for different models, based on various stereotypical occupations.
4
+
5
+ * The Structure of the dataset is of the following type:
6
+
7
+ Base Sentence | Occupation | Steretypical_Gender | Male Sentence | Female Sentence
8
+ ------------ | ------------- | ------------- | ------------- | -------------
9
+ The lawyer yelled at the nurse because he did a bad job. | nurse | female | The lawyer yelled at him because he did a bad job. | The lawyer yelled at her because she did a bad job.
10
+
11
+ * The Base Sentence has been taken from the WinoMT (Anti_Steretypical) dataset [@Stanovsky2019ACL](https://arxiv.org/abs/1906.00591).
12
+
13
+ **Dataset Fields**
14
+
15
+ Fields | Description |
16
+ ------------ | ------------- |
17
+ Base Sentence | Sentence comprising of an anti-stereotypical gendered occupation |
18
+ Occupation | The occupation in the base sentence on which gender bias is being evaluated |
19
+ Steretypical_Gender | Stereotypical gender of occupation in "Occupation" field |
20
+ Male Sentence | Occupation in base sentence replaced by male pronouns |
21
+ Female Sentence | Occupation in base sentence replaced by female pronouns |
22
+
23
+ **Dataset Size**
24
+
25
+ * The dataset consists of 1585 examples.
huggingface_dataset/Dataset_Card/fuliucansheng_mininlp.md ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # Dataset Card for "MiniNLP"
3
+
4
+
5
+ ## Dataset Description
6
+
7
+ ### Dataset Summary
8
+
9
+ This is a mini-nlp dataset for unitorch package.
10
+
11
+ ### Data Instances
12
+
13
+ #### plain_text
14
+
15
+ An example of 'train' looks as follows.
16
+ ```
17
+ {
18
+ "id": 1,
19
+ "num": 3,
20
+ "query": "Is this a test?",
21
+ "doc": "train test",
22
+ "label": "Good",
23
+ "score": 0.882
24
+ }
25
+ ```
26
+
27
+ ### Data Fields
28
+
29
+ The data fields are the same among all splits.
30
+
31
+ #### plain_text
32
+ - `id`: a `int32` feature.
33
+ - `num`: a `int32` feature.
34
+ - `query`: a `string` feature.
35
+ - `doc`: a `string` feature.
36
+ - `label`: a `string` feature.
37
+ - `score`: a `float32` feature.
38
+
39
+
40
+ ### Data Splits Sample Size
41
+
42
+ | name |train|validation|test|
43
+ |----------|----:|---------:|---:|
44
+ |plain_text|15000| 1000 |1000|
huggingface_dataset/Dataset_Card/huggingnft_mini-mutants.md ADDED
@@ -0,0 +1,175 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - huggingnft
4
+ - nft
5
+ - huggan
6
+ - gan
7
+ - image
8
+ - images
9
+ task:
10
+ - unconditional-image-generation
11
+ datasets:
12
+ - huggingnft/mini-mutants
13
+ license: mit
14
+ ---
15
+
16
+ # Dataset Card
17
+
18
+ ## Disclaimer
19
+
20
+ All rights belong to their owners.
21
+ Models and datasets can be removed from the site at the request of the copyright holder.
22
+
23
+ ## Table of Contents
24
+ - [Dataset Description](#dataset-description)
25
+ - [Dataset Summary](#dataset-summary)
26
+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
27
+ - [Languages](#languages)
28
+ - [How to use](#how-to-use)
29
+ - [Dataset Structure](#dataset-structure)
30
+ - [Data Fields](#data-fields)
31
+ - [Data Splits](#data-splits)
32
+ - [Dataset Creation](#dataset-creation)
33
+ - [Curation Rationale](#curation-rationale)
34
+ - [Source Data](#source-data)
35
+ - [Annotations](#annotations)
36
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
37
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
38
+ - [Social Impact of Dataset](#social-impact-of-dataset)
39
+ - [Discussion of Biases](#discussion-of-biases)
40
+ - [Other Known Limitations](#other-known-limitations)
41
+ - [Additional Information](#additional-information)
42
+ - [Dataset Curators](#dataset-curators)
43
+ - [Licensing Information](#licensing-information)
44
+ - [Citation Information](#citation-information)
45
+ - [About](#about)
46
+
47
+ ## Dataset Description
48
+
49
+ - **Homepage:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
50
+ - **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
51
+ - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
52
+ - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
53
+
54
+
55
+ ### Dataset Summary
56
+
57
+ NFT images dataset for unconditional generation.
58
+
59
+ NFT collection available [here](https://opensea.io/collection/mini-mutants).
60
+
61
+ Model is available [here](https://huggingface.co/huggingnft/mini-mutants).
62
+
63
+ Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
64
+
65
+ ### Supported Tasks and Leaderboards
66
+
67
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
68
+
69
+
70
+ ## How to use
71
+
72
+ How to load this dataset directly with the datasets library:
73
+
74
+ ```python
75
+ from datasets import load_dataset
76
+
77
+ dataset = load_dataset("huggingnft/mini-mutants")
78
+ ```
79
+
80
+ ## Dataset Structure
81
+
82
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
83
+
84
+
85
+ ### Data Fields
86
+
87
+ The data fields are the same among all splits.
88
+
89
+ - `image`: an `image` feature.
90
+ - `id`: an `int` feature.
91
+ - `token_metadata`: a `str` feature.
92
+ - `image_original_url`: a `str` feature.
93
+
94
+ ### Data Splits
95
+
96
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
97
+
98
+
99
+ ## Dataset Creation
100
+
101
+ ### Curation Rationale
102
+
103
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
104
+
105
+ ### Source Data
106
+
107
+ #### Initial Data Collection and Normalization
108
+
109
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
110
+
111
+ #### Who are the source language producers?
112
+
113
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
114
+
115
+ ### Annotations
116
+
117
+ #### Annotation process
118
+
119
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
120
+
121
+ #### Who are the annotators?
122
+
123
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
124
+
125
+ ### Personal and Sensitive Information
126
+
127
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
128
+
129
+ ## Considerations for Using the Data
130
+
131
+ ### Social Impact of Dataset
132
+
133
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
134
+
135
+ ### Discussion of Biases
136
+
137
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
138
+
139
+ ### Other Known Limitations
140
+
141
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
142
+
143
+ ## Additional Information
144
+
145
+ ### Dataset Curators
146
+
147
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
148
+
149
+ ### Licensing Information
150
+
151
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
152
+
153
+ ### Citation Information
154
+
155
+ ```
156
+ @InProceedings{huggingnft,
157
+ author={Aleksey Korshuk}
158
+ year=2022
159
+ }
160
+ ```
161
+
162
+
163
+ ## About
164
+
165
+ *Built by Aleksey Korshuk*
166
+
167
+ [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
168
+
169
+ [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
170
+
171
+ [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
172
+
173
+ For more details, visit the project repository.
174
+
175
+ [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft)
huggingface_dataset/Dataset_Card/irds_istella22_test_fold2.md ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: '`istella22/test/fold2`'
3
+ viewer: false
4
+ source_datasets: ['irds/istella22']
5
+ task_categories:
6
+ - text-retrieval
7
+ ---
8
+
9
+ # Dataset Card for `istella22/test/fold2`
10
+
11
+ The `istella22/test/fold2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
12
+ For more information about the dataset, see the [documentation](https://ir-datasets.com/istella22#istella22/test/fold2).
13
+
14
+ # Data
15
+
16
+ This dataset provides:
17
+ - `queries` (i.e., topics); count=440
18
+ - `qrels`: (relevance assessments); count=2,140
19
+
20
+ - For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
21
+
22
+ ## Usage
23
+
24
+ ```python
25
+ from datasets import load_dataset
26
+
27
+ queries = load_dataset('irds/istella22_test_fold2', 'queries')
28
+ for record in queries:
29
+ record # {'query_id': ..., 'text': ...}
30
+
31
+ qrels = load_dataset('irds/istella22_test_fold2', 'qrels')
32
+ for record in qrels:
33
+ record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
34
+
35
+ ```
36
+
37
+ Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
38
+ data in ๐Ÿค— Dataset format.
huggingface_dataset/Dataset_Card/irds_mmarco_v2_dt.md ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: '`mmarco/v2/dt`'
3
+ viewer: false
4
+ source_datasets: []
5
+ task_categories:
6
+ - text-retrieval
7
+ ---
8
+
9
+ # Dataset Card for `mmarco/v2/dt`
10
+
11
+ The `mmarco/v2/dt` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
12
+ For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/dt).
13
+
14
+ # Data
15
+
16
+ This dataset provides:
17
+ - `docs` (documents, i.e., the corpus); count=8,841,823
18
+
19
+
20
+ This dataset is used by: [`mmarco_v2_dt_dev`](https://huggingface.co/datasets/irds/mmarco_v2_dt_dev), [`mmarco_v2_dt_train`](https://huggingface.co/datasets/irds/mmarco_v2_dt_train)
21
+
22
+
23
+ ## Usage
24
+
25
+ ```python
26
+ from datasets import load_dataset
27
+
28
+ docs = load_dataset('irds/mmarco_v2_dt', 'docs')
29
+ for record in docs:
30
+ record # {'doc_id': ..., 'text': ...}
31
+
32
+ ```
33
+
34
+ Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
35
+ data in ๐Ÿค— Dataset format.
36
+
37
+ ## Citation Information
38
+
39
+ ```
40
+ @article{Bonifacio2021MMarco,
41
+ title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
42
+ author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
43
+ year={2021},
44
+ journal={arXiv:2108.13897}
45
+ }
46
+ ```
huggingface_dataset/Dataset_Card/mideind_icelandic-error-corpus-IceEC.md ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - expert-generated
4
+ language:
5
+ - is
6
+ license:
7
+ - cc-by-4.0
8
+ multilinguality:
9
+ - monolingual
10
+ size_categories:
11
+ - 10K<n<100K
12
+ source_datasets:
13
+ - original
14
+ pretty_name: Icelandic Error Corpus
15
+ ---
16
+
17
+
18
+ # Icelandic Error Corpus
19
+
20
+ Refer to [https://github.com/antonkarl/iceErrorCorpus](https://github.com/antonkarl/iceErrorCorpus) for a description of the dataset.
21
+
22
+ Please cite the dataset as follows if you use it.
23
+
24
+ ```
25
+ Anton Karl Ingason, Lilja Bjรถrk Stefรกnsdรณttir, รžรณrunn Arnardรณttir, and Xindan Xu. 2021. The Icelandic Error Corpus (IceEC). Version 1.1. (https://github.com/antonkarl/iceErrorCorpus)
26
+ ```
huggingface_dataset/Dataset_Card/squad_it.md ADDED
@@ -0,0 +1,215 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - machine-generated
4
+ language_creators:
5
+ - machine-generated
6
+ language:
7
+ - it
8
+ language_bcp47:
9
+ - it-IT
10
+ license:
11
+ - unknown
12
+ multilinguality:
13
+ - monolingual
14
+ size_categories:
15
+ - unknown
16
+ source_datasets:
17
+ - extended|squad
18
+ task_categories:
19
+ - question-answering
20
+ task_ids:
21
+ - open-domain-qa
22
+ - extractive-qa
23
+ paperswithcode_id: squad-it
24
+ pretty_name: SQuAD-it
25
+ dataset_info:
26
+ features:
27
+ - name: id
28
+ dtype: string
29
+ - name: context
30
+ dtype: string
31
+ - name: question
32
+ dtype: string
33
+ - name: answers
34
+ sequence:
35
+ - name: text
36
+ dtype: string
37
+ - name: answer_start
38
+ dtype: int32
39
+ splits:
40
+ - name: train
41
+ num_bytes: 50864824
42
+ num_examples: 54159
43
+ - name: test
44
+ num_bytes: 7858336
45
+ num_examples: 7609
46
+ download_size: 8776531
47
+ dataset_size: 58723160
48
+ ---
49
+
50
+ # Dataset Card for "squad_it"
51
+
52
+ ## Table of Contents
53
+ - [Dataset Description](#dataset-description)
54
+ - [Dataset Summary](#dataset-summary)
55
+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
56
+ - [Languages](#languages)
57
+ - [Dataset Structure](#dataset-structure)
58
+ - [Data Instances](#data-instances)
59
+ - [Data Fields](#data-fields)
60
+ - [Data Splits](#data-splits)
61
+ - [Dataset Creation](#dataset-creation)
62
+ - [Curation Rationale](#curation-rationale)
63
+ - [Source Data](#source-data)
64
+ - [Annotations](#annotations)
65
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
66
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
67
+ - [Social Impact of Dataset](#social-impact-of-dataset)
68
+ - [Discussion of Biases](#discussion-of-biases)
69
+ - [Other Known Limitations](#other-known-limitations)
70
+ - [Additional Information](#additional-information)
71
+ - [Dataset Curators](#dataset-curators)
72
+ - [Licensing Information](#licensing-information)
73
+ - [Citation Information](#citation-information)
74
+ - [Contributions](#contributions)
75
+
76
+ ## Dataset Description
77
+
78
+ - **Homepage:** [https://github.com/crux82/squad-it](https://github.com/crux82/squad-it)
79
+ - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
80
+ - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
81
+ - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
82
+ - **Size of downloaded dataset files:** 8.37 MB
83
+ - **Size of the generated dataset:** 56.07 MB
84
+ - **Total amount of disk used:** 64.44 MB
85
+
86
+ ### Dataset Summary
87
+
88
+ SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
89
+ into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
90
+ The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
91
+ split into training and test sets to support the replicability of the benchmarking of QA systems:
92
+
93
+ ### Supported Tasks and Leaderboards
94
+
95
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
96
+
97
+ ### Languages
98
+
99
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
100
+
101
+ ## Dataset Structure
102
+
103
+ ### Data Instances
104
+
105
+ #### default
106
+
107
+ - **Size of downloaded dataset files:** 8.37 MB
108
+ - **Size of the generated dataset:** 56.07 MB
109
+ - **Total amount of disk used:** 64.44 MB
110
+
111
+ An example of 'train' looks as follows.
112
+ ```
113
+ This example was too long and was cropped:
114
+
115
+ {
116
+ "answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...",
117
+ "context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...",
118
+ "id": "5725b5a689a1e219009abd28",
119
+ "question": "Perchรจ le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?"
120
+ }
121
+ ```
122
+
123
+ ### Data Fields
124
+
125
+ The data fields are the same among all splits.
126
+
127
+ #### default
128
+ - `id`: a `string` feature.
129
+ - `context`: a `string` feature.
130
+ - `question`: a `string` feature.
131
+ - `answers`: a dictionary feature containing:
132
+ - `text`: a `string` feature.
133
+ - `answer_start`: a `int32` feature.
134
+
135
+ ### Data Splits
136
+
137
+ | name | train | test |
138
+ | ------- | ----: | ---: |
139
+ | default | 54159 | 7609 |
140
+
141
+ ## Dataset Creation
142
+
143
+ ### Curation Rationale
144
+
145
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
146
+
147
+ ### Source Data
148
+
149
+ #### Initial Data Collection and Normalization
150
+
151
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
152
+
153
+ #### Who are the source language producers?
154
+
155
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
156
+
157
+ ### Annotations
158
+
159
+ #### Annotation process
160
+
161
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
162
+
163
+ #### Who are the annotators?
164
+
165
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
166
+
167
+ ### Personal and Sensitive Information
168
+
169
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
170
+
171
+ ## Considerations for Using the Data
172
+
173
+ ### Social Impact of Dataset
174
+
175
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
176
+
177
+ ### Discussion of Biases
178
+
179
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
180
+
181
+ ### Other Known Limitations
182
+
183
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
184
+
185
+ ## Additional Information
186
+
187
+ ### Dataset Curators
188
+
189
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
190
+
191
+ ### Licensing Information
192
+
193
+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
194
+
195
+ ### Citation Information
196
+
197
+ ```
198
+ @InProceedings{10.1007/978-3-030-03840-3_29,
199
+ author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto",
200
+ editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
201
+ title="Neural Learning for Question Answering in Italian",
202
+ booktitle="AI*IA 2018 -- Advances in Artificial Intelligence",
203
+ year="2018",
204
+ publisher="Springer International Publishing",
205
+ address="Cham",
206
+ pages="389--402",
207
+ isbn="978-3-030-03840-3"
208
+ }
209
+
210
+ ```
211
+
212
+
213
+ ### Contributions
214
+
215
+ Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
huggingface_dataset/Dataset_Card/squad_kor_v2.md ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - crowdsourced
4
+ language_creators:
5
+ - found
6
+ language:
7
+ - ko
8
+ license:
9
+ - cc-by-nd-4.0
10
+ multilinguality:
11
+ - monolingual
12
+ size_categories:
13
+ - 10K<n<100K
14
+ source_datasets:
15
+ - extended|squad_kor_v1
16
+ - original
17
+ task_categories:
18
+ - question-answering
19
+ task_ids:
20
+ - extractive-qa
21
+ paperswithcode_id: null
22
+ pretty_name: KorQuAD v2.1
23
+ dataset_info:
24
+ features:
25
+ - name: id
26
+ dtype: string
27
+ - name: title
28
+ dtype: string
29
+ - name: context
30
+ dtype: string
31
+ - name: question
32
+ dtype: string
33
+ - name: answer
34
+ struct:
35
+ - name: text
36
+ dtype: string
37
+ - name: answer_start
38
+ dtype: int32
39
+ - name: html_answer_start
40
+ dtype: int32
41
+ - name: url
42
+ dtype: string
43
+ - name: raw_html
44
+ dtype: string
45
+ config_name: squad_kor_v2
46
+ splits:
47
+ - name: train
48
+ num_bytes: 17983434492
49
+ num_examples: 83486
50
+ - name: validation
51
+ num_bytes: 2230543100
52
+ num_examples: 10165
53
+ download_size: 1373763305
54
+ dataset_size: 20213977592
55
+ ---
56
+
57
+ # Dataset Card for KorQuAD v2.1
58
+
59
+ ## Table of Contents
60
+ - [Dataset Description](#dataset-description)
61
+ - [Dataset Summary](#dataset-summary)
62
+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
63
+ - [Languages](#languages)
64
+ - [Dataset Structure](#dataset-structure)
65
+ - [Data Instances](#data-instances)
66
+ - [Data Fields](#data-fields)
67
+ - [Data Splits](#data-splits)
68
+ - [Dataset Creation](#dataset-creation)
69
+ - [Curation Rationale](#curation-rationale)
70
+ - [Source Data](#source-data)
71
+ - [Annotations](#annotations)
72
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
73
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
74
+ - [Social Impact of Dataset](#social-impact-of-dataset)
75
+ - [Discussion of Biases](#discussion-of-biases)
76
+ - [Other Known Limitations](#other-known-limitations)
77
+ - [Additional Information](#additional-information)
78
+ - [Dataset Curators](#dataset-curators)
79
+ - [Licensing Information](#licensing-information)
80
+ - [Citation Information](#citation-information)
81
+ - [Contributions](#contributions)
82
+
83
+ ## Dataset Description
84
+
85
+ - [**Homepage**](https://korquad.github.io/)
86
+ - [**Repository**](https://github.com/korquad/korquad.github.io/tree/master/dataset)
87
+ - [**Paper**](https://korquad.github.io/dataset/KorQuAD_2.0/KorQuAD_2.0_paper.pdf)
88
+
89
+ ### Dataset Summary
90
+
91
+ KorQuAD 2.0 is a Korean question and answering dataset consisting of a total of 100,000+ pairs. There are three major differences from KorQuAD 1.0, which is the standard Korean Q & A data. The first is that a given document is a whole Wikipedia page, not just one or two paragraphs. Second, because the document also contains tables and lists, it is necessary to understand the document structured with HTML tags. Finally, the answer can be a long text covering not only word or phrase units, but paragraphs, tables, and lists.
92
+
93
+ ### Supported Tasks and Leaderboards
94
+
95
+ `question-answering`
96
+
97
+ ### Languages
98
+
99
+ Korean
100
+
101
+ ## Dataset Structure
102
+
103
+ Follows the standart SQuAD format. There is only 1 answer per question
104
+
105
+ ### Data Instances
106
+
107
+ An example from the data set looks as follows:
108
+ ```py
109
+ {'answer': {'answer_start': 3873,
110
+ 'html_answer_start': 16093,
111
+ 'text': '20,890 ํ‘œ'},
112
+ 'context': '<!DOCTYPE html>\n<html>\n<head>\n<meta>\n<title>์‹ฌ๊ทœ์–ธ - ์œ„ํ‚ค๋ฐฑ๊ณผ, ์šฐ๋ฆฌ ๋ชจ๋‘์˜ ๋ฐฑ๊ณผ์‚ฌ์ „</title>\n\n\n<link>\n.....[omitted]',
113
+ 'id': '36615',
114
+ 'question': '์‹ฌ๊ทœ์–ธ์€ 17๋Œ€ ์ง€๋ฐฉ ์„ ๊ฑฐ์—์„œ ๋ช‡ ํ‘œ๋ฅผ ๋“ํ‘œํ•˜์˜€๋Š”๊ฐ€?',
115
+ 'raw_html': '<!DOCTYPE html>\n<html c ...[omitted]',
116
+ 'title': '์‹ฌ๊ทœ์–ธ',
117
+ 'url': 'https://ko.wikipedia.org/wiki/์‹ฌ๊ทœ์–ธ'}
118
+ ```
119
+
120
+ ### Data Fields
121
+ ```py
122
+ {'id': Value(dtype='string', id=None),
123
+ 'title': Value(dtype='string', id=None),
124
+ 'context': Value(dtype='string', id=None),
125
+ 'question': Value(dtype='string', id=None),
126
+ 'answer': {'text': Value(dtype='string', id=None),
127
+ 'answer_start': Value(dtype='int32', id=None),
128
+ 'html_answer_start': Value(dtype='int32', id=None)},
129
+ 'url': Value(dtype='string', id=None),
130
+ 'raw_html': Value(dtype='string', id=None)}
131
+ ```
132
+ ### Data Splits
133
+
134
+ - Train : 83486
135
+ - Validation: 10165
136
+
137
+ ## Dataset Creation
138
+
139
+ ### Curation Rationale
140
+
141
+ [More Information Needed]
142
+
143
+ ### Source Data
144
+
145
+ Wikipedia
146
+
147
+ #### Initial Data Collection and Normalization
148
+
149
+ [More Information Needed]
150
+
151
+ #### Who are the source language producers?
152
+
153
+ [More Information Needed]
154
+
155
+ ### Annotations
156
+
157
+ #### Annotation process
158
+
159
+ [More Information Needed]
160
+
161
+ #### Who are the annotators?
162
+
163
+ [More Information Needed]
164
+
165
+ ### Personal and Sensitive Information
166
+
167
+ [More Information Needed]
168
+
169
+ ## Considerations for Using the Data
170
+
171
+ ### Social Impact of Dataset
172
+
173
+ [More Information Needed]
174
+
175
+ ### Discussion of Biases
176
+
177
+ [More Information Needed]
178
+
179
+ ### Other Known Limitations
180
+
181
+ [More Information Needed]
182
+
183
+ ## Additional Information
184
+
185
+ ### Dataset Curators
186
+
187
+ [More Information Needed]
188
+
189
+ ### Licensing Information
190
+
191
+ [CC BY-ND 2.0 KR](https://creativecommons.org/licenses/by-nd/2.0/kr/deed.en)
192
+
193
+ ### Citation Information
194
+ ```
195
+ @article{NODE09353166,
196
+ author={Youngmin Kim,Seungyoung Lim;Hyunjeong Lee;Soyoon Park;Myungji Kim},
197
+ title={{KorQuAD 2.0: Korean QA Dataset for Web Document Machine Comprehension}},
198
+ booltitle={{Journal of KIISE ์ œ47๊ถŒ ์ œ6ํ˜ธ}},
199
+ journal={{Journal of KIISE}},
200
+ volume={{47}},
201
+ issue={{6}},
202
+ publisher={The Korean Institute of Information Scientists and Engineers},
203
+ year={2020},
204
+ ISSN={{2383-630X}},
205
+ pages={577-586},
206
+ url={http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09353166}}
207
+ ```
208
+
209
+ ### Contributions
210
+
211
+ Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.