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pretty_name: CrawlPT (deduplicated)
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size_categories:
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- 10M<n<100M
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---
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pretty_name: CrawlPT (deduplicated)
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size_categories:
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- 10M<n<100M
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---
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# CrawlPT (deduplicated)
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CrawlPT is a generic Portuguese corpus extracted from various web pages.
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## Dataset Details
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Dataset is composed by three corpora:
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[brWaC](https://aclanthology.org/L18-1686/), [C100-PT](https://arxiv.org/abs/1911.02116), [OSCAR-2301](http://arxiv.org/abs/2201.06642).
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- **brWaC**: a web corpus for Brazilian Portuguese from 120,000 different websites.
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- **C100-PT**: Portuguese subset from CC-100. C100 was created for training the multilingual Transformer XLM-R, containing two terabytes of cleaned data from 2018 snapshots of the [Common Crawl project](\url{https://commoncrawl.org/about/) in 100 languages. We use the , which contains 49.1 GiB of text.
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- **OSCAR-2301-PT**: curation from OSCAR-2301 in the Portuguese language.
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### Dataset Description
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- **Curated by:** [More Information Needed]
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- **Funded by:** [More Information Needed]
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- **Language(s) (NLP):** Brazilian Portuguese (pt-BR)
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- **License:** [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/deed.en)
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### Dataset Sources
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- **Repository:** https://github.com/eduagarcia/roberta-legal-portuguese
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- **Paper:** [More Information Needed]
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## Dataset Structure
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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[More Information Needed]
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## Data Collection and Processing
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Raw corpora sizes in terms of billions of tokens and file size in GiB:
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| Corpus | Domain | Tokens (B) | Size (GiB) |
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|-----------------|:-------:|:----------:|:----------:|
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| brWaC | General | 2.7 | 16.3 |
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| CC100 (PT) | General | 8.4 | 49.1 |
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| OSCAR-2301 (PT) | General | 18.1 | 97.8 |
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CrawlPT is deduplicated using [MinHash algorithm](https://dl.acm.org/doi/abs/10.5555/647819.736184) and [Locality Sensitive Hashing](https://dspace.mit.edu/bitstream/handle/1721.1/134231/v008a014.pdf?sequence=2&isAllowed=y), following the approach of [Lee et al. (2022)](http://arxiv.org/abs/2107.06499).
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We used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.
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Deduplicate rate found by the Minhash-LSH algorithm for the CrawlPT corpus:
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| Corpus | Documents | Docs. after deduplicatio} | Duplicates (%) |
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|------------------------|:----------:|:-------------------------:|:--------------:|
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| brWaC | 3,530,796 | 3,513,588 | 0.49 |
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| OSCAR-2301 (PT Subset) | 18,031,400 | 10,888,966 | 39.61 |
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| CC100 (PT Subset) | 38,999,388 | 38,059,979 | 2.41 |
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| Total (CrawlPT) | 60,561,584 | 52,462,533 | 13.37 |
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## Citation
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```bibtex
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@InProceedings{garcia2024_roberlexpt,
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author="Garcia, Eduardo A. S.
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and Silva, N{\'a}dia F. F.
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and Siqueira, Felipe
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and Gomes, Juliana R. S.
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and Albuqueruqe, Hidelberg O.
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and Souza, Ellen
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and Lima, Eliomar
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and De Carvalho, André",
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title="RoBERTaLexPT: A Legal RoBERTa Model pretrained with deduplication for Portuguese",
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booktitle="Computational Processing of the Portuguese Language",
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year="2024",
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publisher="Association for Computational Linguistics"
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}
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```
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## Acknowledgment
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This work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG).
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