Update README.md

README.md

@@ -40,4 +40,52 @@ dataset_info:
     num_examples: 1000
   download_size: 241234
   dataset_size: 261948
+language:
+- en
 ---
+
+# 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, and 5000 words sampled according to their frequencies taken from the 50% most common Wiktionary words.
+
+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.
+
+## Usage
+This dataset is used for testing spelling in Large Language Models. To do so, the labels should be computed using the following:
+
+```python
+sample = ds["train"][0]
+label = " ".join(sample["text"])
+```
+
+**They are not included in the dataset.**
+
+## 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",
+}
+```