Datasets:
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 129624
num_examples: 10000
- name: validation_top1
num_bytes: 10754
num_examples: 1000
- name: test_top1
num_bytes: 10948
num_examples: 1000
- name: validation_1_10
num_bytes: 11618
num_examples: 1000
- name: test_1_10
num_bytes: 11692
num_examples: 1000
- name: validation_10_20
num_bytes: 13401
num_examples: 1000
- name: test_10_20
num_bytes: 13450
num_examples: 1000
- name: validation_20_30
num_bytes: 15112
num_examples: 1000
- name: test_20_30
num_bytes: 15069
num_examples: 1000
- name: validation_bottom50
num_bytes: 15204
num_examples: 1000
- name: test_bottom50
num_bytes: 15076
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 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), 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) 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:
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",
}