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@@ -107,34 +107,34 @@ We applied extensive data processing using our CURATE pipeline.
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  We first filter documents by their language content using [FastText](https://fasttext.cc/docs/en/language-identification.html). Only documents with at least 50% of characters in Catalan are kept. We then perform exact document deduplication. After this stage, we score each document with a tested set of 8 heuristic evaluators, inspired from other web filterings and from our own creation.
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  The following pre-existing datasets were used:
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- - `[OSCAR-2301](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301)`
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- - `[OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201)`
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- - `[CaText](https://zenodo.org/records/5483031)`
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- - `[MaCoCu-ca 1.0](http://hdl.handle.net/11356/1837)`
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- - `[caWaC](https://huggingface.co/datasets/cawac)`
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- - `[Colossal OSCAR 1.0](https://huggingface.co/datasets/oscar-corpus/colossal-oscar-1.0)`
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- - `[mC4]({https://huggingface.co/datasets/mc4)`
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  #### Who are the source language producers?
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  Apart from the pre-existing datasets, all of them coming from [CommonCrawl](https://commoncrawl.org/) dumps, the following
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  sources provided their data on Open Data Agreements:
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- - Media Groups
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- - `[IB3](https://ib3.org/)`
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- - `[Grup El Món](https://grupmon.cat/)`
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- - `[Vilaweb](https://www.vilaweb.cat/)`
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- - `[Nació Digita](https://www.naciodigital.cat/)`
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- - `[ACN](https://www.acn.cat/)`
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- - `[Racó Català](https://www.racocatala.cat/)`
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- - `[Aquí Berguedà](https://www.aquibergueda.cat/)`
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- - Academic & Book Repositories
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- - `[Tesis Doctorals en Xarxa](https://www.tesisenred.net/)`
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- - `[Wikipedia](https://ca.wikipedia.org/)`
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- - `[Project Gutenberg](https://www.gutenberg.org/)`
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- - Government Institutions
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  - [`Valencian Parliament`](https://www.cortsvalencianes.es/)
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- - `[Diari Oficial de la Generalitat Valenciana](https://dogv.gva.es/)`
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- - `[Butlletí Oficial de la Universitat d'Alacant](https://www.boua.ua.es/)`
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  ### Annotations
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  We first filter documents by their language content using [FastText](https://fasttext.cc/docs/en/language-identification.html). Only documents with at least 50% of characters in Catalan are kept. We then perform exact document deduplication. After this stage, we score each document with a tested set of 8 heuristic evaluators, inspired from other web filterings and from our own creation.
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  The following pre-existing datasets were used:
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+ - [`OSCAR-2301`](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301)
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+ - [`OSCAR-2201`](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201)
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+ - [`CaText`](https://zenodo.org/records/5483031)
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+ - [`MaCoCu-ca 1.0`](http://hdl.handle.net/11356/1837)
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+ - [`caWaC`](https://huggingface.co/datasets/cawac)
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+ - [`Colossal OSCAR 1.0`](https://huggingface.co/datasets/oscar-corpus/colossal-oscar-1.0)
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+ - [`mC4`]({https://huggingface.co/datasets/mc4)
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  #### Who are the source language producers?
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  Apart from the pre-existing datasets, all of them coming from [CommonCrawl](https://commoncrawl.org/) dumps, the following
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  sources provided their data on Open Data Agreements:
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+ - **Media Groups**
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+ - [`IB3`](https://ib3.org/)
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+ - [`Grup El Món`](https://grupmon.cat/)
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+ - [`Vilaweb`](https://www.vilaweb.cat/)
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+ - [`Nació Digita`](https://www.naciodigital.cat/)
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+ - [`ACN`](https://www.acn.cat/)
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+ - [`Racó Català`](https://www.racocatala.cat/)
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+ - [`Aquí Berguedà`](https://www.aquibergueda.cat/)
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+ - **Academic & Book Repositories**
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+ - [`Tesis Doctorals en Xarxa`](https://www.tesisenred.net/)
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+ - [`Wikipedia`](https://ca.wikipedia.org/)
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+ - [`Project Gutenberg`](https://www.gutenberg.org/)
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+ - **Government Institutions**
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  - [`Valencian Parliament`](https://www.cortsvalencianes.es/)
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+ - [`Diari Oficial de la Generalitat Valenciana`](https://dogv.gva.es/)
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+ - [`Butlletí Oficial de la Universitat d'Alacant`](https://www.boua.ua.es/)
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  ### Annotations
140