README.md

---
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
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    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: 3048
    num_examples: 16
  - name: Hungarian
    num_bytes: 2251
    num_examples: 14
  - name: Croatian
    num_bytes: 2454
    num_examples: 19
  - name: Danish
    num_bytes: 11392
    num_examples: 87
  - name: Slovenian
    num_bytes: 3418
    num_examples: 27
  - name: Polish
    num_bytes: 4472
    num_examples: 29
  - name: Dutch
    num_bytes: 12067
    num_examples: 81
  - name: Catalan
    num_bytes: 11514
    num_examples: 77
  download_size: 18324280
  dataset_size: 35404642
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:** daniel_furman@berkeley.edu

### Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

### 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]