Update README.md

README.md

@@ -93,7 +93,7 @@ CATalog is mainly built on filtered, non-overlapping versions of [CommonCrawl](h
 
 In the design of CATalog, we adhere to the following values:
 
-- (1) **Scale & Flexibility**. We intend to produce datasets that have a significant impact on the training of multilingual models in the range of 7B-180B parameters. Since Catalan is a medium-resource language and data acquisition is already a challenge, binary filtering will limit us in terms of the amount of data. By providing a score, we are able to easily filter the corpus according to our corpus according to our needs.
+- (1) **Scale & Flexibility**. We intend to produce datasets that have a significant impact on the training of multilingual models in the range of 7B-180B parameters. Since Catalan is a medium-resource language and data acquisition is already a challenge, binary filtering will limit us in terms of the amount of data. By providing a score, we are able to easily filter the corpus according to any requirements.
 - (2) **Neutral scoring**. As opposed to ML-based filtering, we can use simple rules and heuristics to avoid introducing further bias into the model ([Dodge et al., 2021](https://arxiv.org/abs/2104.08758); [Welbl et al., 2021](https://arxiv.org/abs/2109.07445)). We only use [FastText](https://fasttext.cc/docs/en/language-identification.html) to reject documents in other languages.
 
 During development, we performed comparative judgment experiments to evaluate the usefulness of the scoring from the CURATE pipeline, which appears in most documents in CATalog and is intended for further filtering and analysis. We found a moderate correlation between the score and the perceived quality of the text. Our main goal was to maximize the usability of the corpus without getting into a trade-off between quantity and quality.