Update README.md

README.md

@@ -82,9 +82,9 @@ 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 any requirement.
-- (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.
+- (2) **Neutral scoring**. As opposed to ML-based filtering, we 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](https://github.com/langtech-bsc/corpus-cleaner-v2) 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.
+During development, we performed comparative judgment experiments to evaluate the usefulness of the scoring from the [CURATE](https://github.com/langtech-bsc/corpus-cleaner-v2) pipeline, which 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.
 
 ### Source Data