README.md

metadata

license: apache-2.0
tags:
  - natural-language-understanding
language_creators:
  - expert-generated
  - machine-generated
multilinguality:
  - multilingual
pretty_name: Fact Completion Benchmark for Text Models
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
  - fill-mask
  - text2text-generation
dataset_info:
  features:
    - name: dataset_id
      dtype: string
    - name: stem
      dtype: string
    - name: 'true'
      dtype: string
    - name: 'false'
      dtype: string
    - name: relation
      dtype: string
    - name: subject
      dtype: string
    - name: object
      dtype: string
  splits:
    - name: English
      num_bytes: 3474255
      num_examples: 26254
    - name: Spanish
      num_bytes: 3175733
      num_examples: 18786
    - name: French
      num_bytes: 3395566
      num_examples: 18395
    - name: Russian
      num_bytes: 659526
      num_examples: 3289
    - name: Portuguese
      num_bytes: 4158146
      num_examples: 22974
    - name: German
      num_bytes: 2611160
      num_examples: 16287
    - name: Italian
      num_bytes: 3709786
      num_examples: 20448
    - name: Ukrainian
      num_bytes: 1868358
      num_examples: 7918
    - name: Romanian
      num_bytes: 2846002
      num_examples: 17568
    - name: Czech
      num_bytes: 1631582
      num_examples: 9427
    - name: Bulgarian
      num_bytes: 4597410
      num_examples: 20577
    - name: Swedish
      num_bytes: 3226502
      num_examples: 21576
    - name: Serbian
      num_bytes: 1327674
      num_examples: 5426
    - name: Hungarian
      num_bytes: 865409
      num_examples: 4650
    - name: Croatian
      num_bytes: 2454
      num_examples: 19
    - name: Danish
      num_bytes: 3580458
      num_examples: 23365
    - name: Slovenian
      num_bytes: 1299653
      num_examples: 7873
    - name: Polish
      num_bytes: 1683647
      num_examples: 9484
    - name: Dutch
      num_bytes: 3732795
      num_examples: 22590
    - name: Catalan
      num_bytes: 3319466
      num_examples: 18898
  download_size: 26459775
  dataset_size: 51165582
language:
  - en
  - fr
  - es
  - de
  - uk
  - bg
  - ca
  - da
  - hr
  - hu
  - it
  - nl
  - pl
  - pt
  - ro
  - ru
  - sl
  - sr
  - sv
  - cs

Dataset Card for Fact_Completion

Dataset Description

• Homepage: https://bit.ly/ischool-berkeley-capstone
• Repository: https://github.com/daniel-furman/Capstone
• Paper:
• Leaderboard:
• Point of Contact: [email protected]

Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@misc{calibragpt,
  author = {Shreshta Bhat and Daniel Furman and Tim Schott},
  title = {CalibraGPT: The Search for (Mis)Information in Large Language Models},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/daniel-furman/Capstone}},
}

@misc{dong2022calibrating,
      doi = {10.48550/arXiv.2210.03329},
      title={Calibrating Factual Knowledge in Pretrained Language Models}, 
      author={Qingxiu Dong and Damai Dai and Yifan Song and Jingjing Xu and Zhifang Sui and Lei Li},
      year={2022},
      eprint={2210.03329},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{meng2022massediting,
      doi = {10.48550/arXiv.2210.07229},
      title={Mass-Editing Memory in a Transformer}, 
      author={Kevin Meng and Arnab Sen Sharma and Alex Andonian and Yonatan Belinkov and David Bau},
      year={2022},
      eprint={2210.07229},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{elsahar-etal-2018-rex,
    title = "{T}-{RE}x: A Large Scale Alignment of Natural Language with Knowledge Base Triples",
    author = "Elsahar, Hady  and
      Vougiouklis, Pavlos  and
      Remaci, Arslen  and
      Gravier, Christophe  and
      Hare, Jonathon  and
      Laforest, Frederique  and
      Simperl, Elena",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1544",
}

Contributions

[More Information Needed]