
# 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",
}
```
task_categories:text-generation size_categories:1K<n<10K language:en license:mit arxiv:2212.10562 region:us
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "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}, "viewer": true}
TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-mit #arxiv-2212.10562 #region-us 


# 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:



The labels are not included in the dataset files directly.

Please cite the original paper introducing WikiSpell if you're using this dataset:


# 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:



The labels are not included in the dataset files directly.

Please cite the original paper introducing WikiSpell if you're using this dataset:
TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-mit #arxiv-2212.10562 #region-us 
 # 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:



The labels are not included in the dataset files directly.

Please cite the original paper introducing WikiSpell if you're using this dataset:
passage: TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-mit #arxiv-2212.10562 #region-us 
# 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:



The labels are not included in the dataset files directly.

Please cite the original paper introducing WikiSpell if you're using this dataset: