Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
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File size: 3,105 Bytes
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---
license: mit
dataset_info:
features:
- name: text
dtype: string
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num_examples: 10000
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num_examples: 1000
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num_examples: 1000
download_size: 241234
dataset_size: 261948
language:
- en
viewer: true
task_categories:
- text-generation
size_categories:
- 1K<n<10K
---
# WikiSpell
## Description
This dataset is a **custom implementation** of the WikiSpell dataset introduced in [Character-Aware Models Improve Visual Text Rendering](https://arxiv.org/pdf/2212.10562.pdf) by Liu et al. (2022).
Similarly to the original WikiSpell dataset, the training set is composed of 5000 words taken uniformly from the 50% least common Wiktionary words (taken from [this Wiktionary extraction](https://kaikki.org/dictionary/rawdata.html)), and 5000 words sampled according to their frequencies taken from the 50% most common Wiktionary words.
The validation and test are splitted in 5 sets, sampled depending on their frequency in the corpus:
- 1% most common words
- 1 - 10% most common words
- 10 - 20% most common words
- 20 - 30% most common words
- 50% least common words
Contrary to the original WikiSpell dataset, we compute the frequency of the words using the first 100k sentences from OpenWebText ([Skylion007/openwebtext](https://huggingface.co/datasets/Skylion007/openwebtext)) instead of mC4.
## Usage
This dataset is used for testing spelling in Large Language Models. To do so, the labels should be computed like in the following snippet:
```python
sample = ds["train"][0]
label = " ".join(sample["text"])
```
**The labels are not included in the dataset files directly.**
## Citation
Please cite the original paper introducing WikiSpell if you're using this dataset:
```
@inproceedings{liu-etal-2023-character,
title = "Character-Aware Models Improve Visual Text Rendering",
author = "Liu, Rosanne and
Garrette, Dan and
Saharia, Chitwan and
Chan, William and
Roberts, Adam and
Narang, Sharan and
Blok, Irina and
Mical, Rj and
Norouzi, Mohammad and
Constant, Noah",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.900",
pages = "16270--16297",
}
``` |