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Datasets:
eduagarcia
/
CrawlPT_dedup

Tasks:
Text Generation
Modalities:
Tabular
Text
Formats:
parquet
Languages:
Portuguese
Size:
100M - 1B
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CrawlPT_dedup
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---
language:
- pt
license: cc-by-4.0
size_categories:
- 10M<n<100M
task_categories:
- text-generation
pretty_name: CrawlPT (deduplicated)
dataset_info:
- config_name: OSCAR-2301
  features:
  - name: id
    dtype: int64
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: categories
      sequence: string
    - name: dedup
      struct:
      - name: exact_norm
        struct:
        - name: cluster_main_idx
          dtype: int64
        - name: cluster_size
          dtype: int64
        - name: exact_hash_idx
          dtype: int64
        - name: is_duplicate
          dtype: bool
      - name: minhash
        struct:
        - name: cluster_main_idx
          dtype: int64
        - name: cluster_size
          dtype: int64
        - name: is_duplicate
          dtype: bool
        - name: minhash_idx
          dtype: int64
    - name: harmful_pp
      dtype: float64
    - name: identification
      struct:
      - name: label
        dtype: string
      - name: prob
        dtype: float64
    - name: quality_warnings
      sequence: string
    - name: sentence_identifications
      list:
      - name: label
        dtype: string
      - name: prob
        dtype: float64
    - name: tlsh
      dtype: string
    - name: warc_headers
      struct:
      - name: content-length
        dtype: int64
      - name: content-type
        dtype: string
      - name: warc-block-digest
        dtype: string
      - name: warc-date
        dtype: string
      - name: warc-identified-content-language
        dtype: string
      - name: warc-record-id
        dtype: string
      - name: warc-refers-to
        dtype: string
      - name: warc-target-uri
        dtype: string
      - name: warc-type
        dtype: string
  splits:
  - name: train
    num_bytes: 77259995670.30853
    num_examples: 10888966
  download_size: 42589347661
  dataset_size: 77259995670.30853
- config_name: all
  features:
  - name: id
    dtype: int64
  - name: source
    dtype: string
  - name: orig_id
    dtype: int64
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 133074727589
    num_examples: 52462533
  download_size: 81483949567
  dataset_size: 133074727589
- config_name: brwac
  features:
  - name: id
    dtype: int64
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: dedup
      struct:
      - name: exact_norm
        struct:
        - name: cluster_main_idx
          dtype: int64
        - name: cluster_size
          dtype: int64
        - name: exact_hash_idx
          dtype: int64
        - name: is_duplicate
          dtype: bool
      - name: minhash
        struct:
        - name: cluster_main_idx
          dtype: int64
        - name: cluster_size
          dtype: int64
        - name: is_duplicate
          dtype: bool
        - name: minhash_idx
          dtype: int64
    - name: doc_id
      dtype: string
    - name: title
      dtype: string
    - name: uri
      dtype: string
  splits:
  - name: train
    num_bytes: 18218935459.169613
    num_examples: 3513588
  download_size: 11210909325
  dataset_size: 18218935459.169613
- config_name: cc100
  features:
  - name: id
    dtype: int64
  - name: text
    dtype: string
  - name: meta
    struct:
    - name: dedup
      struct:
      - name: exact_norm
        struct:
        - name: cluster_main_idx
          dtype: int64
        - name: cluster_size
          dtype: int64
        - name: exact_hash_idx
          dtype: int64
        - name: is_duplicate
          dtype: bool
      - name: minhash
        struct:
        - name: cluster_main_idx
          dtype: int64
        - name: cluster_size
          dtype: int64
        - name: is_duplicate
          dtype: bool
        - name: minhash_idx
          dtype: int64
  splits:
  - name: train
    num_bytes: 53707749127.11777
    num_examples: 38059979
  download_size: 34844109320
  dataset_size: 53707749127.11777
configs:
- config_name: OSCAR-2301
  data_files:
  - split: train
    path: OSCAR-2301/train-*
- config_name: all
  data_files:
  - split: train
    path: all/train-*
- config_name: brwac
  data_files:
  - split: train
    path: brwac/train-*
- config_name: cc100
  data_files:
  - split: train
    path: cc100/train-*
---

# CrawlPT (deduplicated)

CrawlPT is a generic Portuguese corpus extracted from various web pages.

This version is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).
The raw version is also available [here](https://huggingface.co/datasets/eduagarcia/CrawlPT).

## Dataset Details

Dataset is composed by three corpora:
[brWaC](https://aclanthology.org/L18-1686/), [C100-PT](https://arxiv.org/abs/1911.02116), [OSCAR-2301](http://arxiv.org/abs/2201.06642).

- **brWaC**: a web corpus for Brazilian Portuguese from 120,000 different websites.
- **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.
- **OSCAR-2301-PT**: curation from OSCAR-2301 in the Portuguese language.

### Dataset Description

- **Language(s) (NLP):** Brazilian Portuguese (pt-BR)
- **License:** [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/deed.en)
- **Repository:** https://github.com/eduagarcia/roberta-legal-portuguese
- **Paper:** [More Information Needed]

## Data Collection and Processing

Raw corpora sizes in terms of billions of tokens and file size in GiB:

| Corpus          |  Domain | Tokens (B) | Size (GiB) |
|-----------------|:-------:|:----------:|:----------:|
| brWaC           | General |     2.7    |    16.3    |
| CC100 (PT)      | General |     8.4    |    49.1    |
| OSCAR-2301 (PT) | General |    18.1    |    97.8    |


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).

We used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.
Deduplicate rate found by the Minhash-LSH algorithm  for the CrawlPT corpus:

| Corpus                 |  Documents | Docs. after deduplicatio} | Duplicates (%) |
|------------------------|:----------:|:-------------------------:|:--------------:|
| brWaC                  |  3,530,796 |         3,513,588         |      0.49      |
| OSCAR-2301 (PT Subset) | 18,031,400 |         10,888,966        |      39.61     |
| CC100 (PT Subset)      | 38,999,388 |         38,059,979        |      2.41      |
| Total (CrawlPT)        | 60,561,584 |         52,462,533        |      13.37     |


## Citation

```bibtex
@InProceedings{garcia2024_roberlexpt,
    author="Garcia, Eduardo A. S.
    and Silva, N{\'a}dia F. F.
    and Siqueira, Felipe
    and Gomes, Juliana R. S.
    and Albuqueruqe, Hidelberg O.
    and Souza, Ellen
    and Lima, Eliomar
    and De Carvalho, André",
    title="RoBERTaLexPT: A Legal RoBERTa Model pretrained with deduplication for Portuguese",
    booktitle="Computational Processing of the Portuguese Language",
    year="2024",
    publisher="Association for Computational Linguistics"
}
```

## Acknowledgment

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).