Datasets:

projecte-aina
/

CATalog

@@ -75,11 +75,11 @@ We do not provide any canonical splits for CATalog.
 ### Curation Rationale
-CATalog is mainly built on filtered, non-overlapping versions of [CommonCrawl](https://commoncrawl.org/) snapshots and a smaller set of manually scored corpora from specific sources. We use the [CURATE](https://github.com/langtech-bsc/corpus-cleaner-v2) pipeline, which combines exact deduplication, language identification, and scoring heuristics.
 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.
 - (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](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.

 ### Curation Rationale
+CATalog is mainly built on filtered, non-overlapping versions of [CommonCrawl](https://commoncrawl.org/) snapshots and a smaller set of manually selected corpora from specific sources. We use the [CURATE](https://github.com/langtech-bsc/corpus-cleaner-v2) pipeline, which combines exact deduplication, language identification, and scoring heuristics.
 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.
 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.