Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Update README.md
Browse files
README.md
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num_examples: 1000
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download_size: 241234
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dataset_size: 261948
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---
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num_examples: 1000
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download_size: 241234
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dataset_size: 261948
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language:
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- en
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---
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# WikiSpell
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## Description
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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).
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Similarly to the original WikiSpell dataset, the training set is composed of 5000 words taken uniformly from the 50% least common Wiktionary words, and 5000 words sampled according to their frequencies taken from the 50% most common Wiktionary words.
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Contrary to the original Wiktionary, 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.
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## Usage
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This dataset is used for testing spelling in Large Language Models. To do so, the labels should be computed using the following:
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```python
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sample = ds["train"][0]
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label = " ".join(sample["text"])
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```
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**They are not included in the dataset.**
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## Citation
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Please cite the original paper introducing WikiSpell if you're using this dataset:
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```
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@inproceedings{liu-etal-2023-character,
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title = "Character-Aware Models Improve Visual Text Rendering",
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author = "Liu, Rosanne and
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Garrette, Dan and
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Saharia, Chitwan and
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Chan, William and
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Roberts, Adam and
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Narang, Sharan and
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Blok, Irina and
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Mical, Rj and
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Norouzi, Mohammad and
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Constant, Noah",
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booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
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month = jul,
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year = "2023",
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address = "Toronto, Canada",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2023.acl-long.900",
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pages = "16270--16297",
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}
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```
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