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Datasets:
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CATalog

Tasks:
Fill-Mask
Text Generation
Modalities:
Text
Formats:
parquet
Sub-tasks:
masked-language-modeling
slot-filling
language-modeling
Languages:
Catalan
Size:
10M - 100M
ArXiv:
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metadata
annotations_creators:
  - machine-generated
language_creators:
  - found
language:
  - ca
license:
  - cc-by-nc-nd-4.0
multilinguality:
  - monolingual
size_categories:
  - 10B<n<100B
source_datasets:
  - extended|mc4
  - extended|oscar
  - extended|cawac
task_categories:
  - fill-mask
  - text-generation
task_ids:
  - masked-language-modeling
  - slot-filling
  - language-modeling
pretty_name: CATalog
tags: []
dataset_info:
  features:
    - name: id
      dtype: string
    - name: text
      dtype: string
    - name: score
      dtype: float64
    - name: strategy
      dtype: string
    - name: languages
      dtype: string
    - name: url
      dtype: string
  splits:
    - name: train
      num_bytes: 411464092
      num_examples: 123113
  download_size: 250266941
  dataset_size: 411464092
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Description

Dataset Summary

CATalog is a diverse, open-source Catalan corpus for language modelling. It consists of text documents from 26 different sources, including web crawling, news, forums, digital libraries and public institutions, totaling in 17.45 billion words.

Supported Tasks and Leaderboards

  • Fill-Mask
  • Text Generation
  • other:Language-Modelling: The dataset is suitable for training a model in Language Modelling, predicting the next word in a given context. Success is measured by achieving a low Perplexity score, indicating the model's proficiency in accurately predicting subsequent words.
  • other:Masked-Language-Modelling: The dataset is designed for training models in Masked Language Modelling. This task involves predicting masked or hidden words within a sentence. Success is typically measured by achieving a high performance score, such as accuracy or F1 score, on correctly predicting the masked tokens.

Languages

This dataset is in Catalan (ca-ES). Coming from the web, some documents may contain other languages.

Dataset Structure

Data Instances

The dataset is provided in a CSV format, where each row corresponds to a single document and contains a document identifier, the text, a quality score, the strategy used to evaluate the document quality, languages, and a URL of the document, if available.

document    text    score   strategy    languages   url

Data Fields

  • document: text string containing the document identifier. Consists of the subdataset code, the part number and a document number.
  • text: text string from the document, with paragraphs separated by two newlines escape sequences. It is meant to be used directly as input for language modelling.
  • score: positive float number representing the document quality, ranging from 0, which represents the worst quality, to 1, the best quality.
  • strategy: text string describing the type of evaluation applied to obtain the document score. generic_hard uses the heuristic evaluation from CURATE and perfect_score means that manual review was done and the highest score (1) is applied.
  • languages: dictionary containing the document languages, with a percentage indicating the character ratio for each one.
  • url: text string with the URL of the document, if available.

Data Splits

We do not provide any canonical splits for CATalog.

Dataset Creation

Curation Rationale

CATalog is mainly built on filtered, non-overlapping versions of CommonCrawl snapshots and a smaller set of manually selected corpora from specific sources. We use the CURATE 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 use simple rules and heuristics to avoid introducing further bias into the model (Dodge et al., 2021; Welbl et al., 2021). We only use FastText to reject documents in other languages.

During development, we performed comparative judgment experiments to evaluate the usefulness of the scoring from the CURATE 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

Initial Data Collection and Normalization

We applied extensive data processing using our CURATE pipeline.

We first filter documents by their language content using FastText. 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.

The following pre-existing datasets were used:

Who are the source language producers?

Apart from the pre-existing datasets, all of them coming from CommonCrawl dumps, the following sources provided their data on Open Data Agreements:

Annotations

The score is an automatic label obtained from the aggregation of different heuristic evaluators based on predefined thresholds. Specific evaluators penalize documents for factors like minimum word count, average word per sentence, punctuation per word rate, unique sentences ratio, stopword ratio, Brunet index, language diversity, and content identified by regular expressions, providing a comprehensive approach to document scoring.

Annotation process

The process involves assigning scores between 0 and 1 to sentences, paragraphs, and documents in a hierarchical manner. Individual evaluators at different levels contribute scores that are combined using geometric means, emphasizing a probability-like interpretation to encourage evaluators to assess desirability. The final document score is derived through analogous aggregation of paragraph and document scores, distinct from a linear model.

Who are the annotators?

[N/A]

Personal and Sensitive Information

Being mostly constructed from Common Crawl, personal and sensitive information might be present. This must be considered before training deep learning models with CATalog, specially in the case of text-generation models.

Considerations for Using the Data

Social Impact of Dataset

CATalog promotes the Catalan language in the NLP field, enabling development of advanced applications and chatbots tailored to Catalan speakers, while improving access to information for better community understanding. However, most of the sources in the dataset are web-scraped, which may bring in biases and privacy issues, risking flawed outcomes and potential misuse.

Given that Catalan is a mid-resourced language with low representation in digital sources, this dataset becomes crucial for building inclusive NLP applications. It addresses the language's underrepresentation, empowering the Catalan community with improved access to text resources in their native language. However, careful consideration of potential biases and privacy issues is essential to ensure responsible and equitable technology use.

Discussion of Biases

Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages. Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic groups. Our corpus primarily focuses on Central Catalan, but we actively include Valencian and Balearic Catalan, along with diverse sociolects from platforms like Racó Català Fòrums, aiming for a more representative dataset. Despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures, acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to address privacy concerns and contribute to a more inclusive linguistic dataset.

Other Known Limitations

[N/A]

Additional Information

Dataset Curators

Language Technologies Unit at the Barcelona Supercomputing Center (BSC).

This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

Licensing Information

CATalog is a collection of text documents from sources with various licenses. The whole work is licensed under the most restrictive license in the corpus, which is Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license. Any use of all or part of the text gathered in CATalog must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.

The list of SPDX license identifiers included in the documentation can be found in the following table or in this JSON file.

Source Identifier License
Tesis Doctorales en Xarxa (TDX) tdx_ca_20220518 CC-BY-4.0
Wikipedia wikipedia_ca_20230401 CC-BY-SA-4.0
IB3 crawling-ib3_ca_20230205 Data Sharing Agreement*
Les Corts Valencianes les-corts-valencianes_ca_20230704 Data Sharing Agreement*
Grup El Món grup-elmon_ca_20230726 Data Sharing Agreement*
Vilaweb vilaweb_ca_20220728 Data Sharing Agreement*
Nació Digital naciodigital_ca_20220331 CC-BY-NC-ND-4.0
ACN acn_ca_20201011 Data Sharing Agreement*
Racó Català Articles racoarticles_ca_20221005 Data Sharing Agreement*
Racó Català Fòrums racoforumsanon_ca_20211213 Data Sharing Agreement*
Wikimedia wikimedia_ca_20230829 CC-BY-SA-4.0
Project Gutenberg gutenberg_ca_20220224 Project Gutenberg ToU
DOGC dogc_ca_20230901 Data Sharing Agreement*
DOGV dogv_ca_20231006 Data Sharing Agreement*
BOUA boua_ca_20231006 Data Sharing Agreement*
Aquí Berguedà aquibergueda_ca_20231009 Data Sharing Agreement*
Parlament de Catalunya parlament_ca_20232009 Data Sharing Agreement*
CaWac cawac_ca_20200528 CC-BY-SA-3.0
MaCoCu macocu_ca_20230731 CC-BY-4.0
Crawling populars crawling-populars_ca_20200525 CC0-1.0
Colossal OSCAR 1 (03-04-23) colossal-oscar-03-04-23_ca_20230829 CC0-1.0
Colossal OSCAR 1 (05-06-23) colossal-oscar-05-06-23_ca_20230829 CC0-1.0
Colossal OSCAR 1 (2022-27) colossal-oscar-2022-27_ca_20231005 CC0-1.0
OSCAR-2201 oscar-2201_ca_20230904 CC0-1.0
OSCAR-2301 oscar-2301_ca_20230418 CC0-1.0
mC4 mc4_ca_20230418 CC-BY-4.0

* The data from each entity is governed by a distinct Data Sharing Agreement. All data provided by these entities is open and freely distributable.

Citation Information

[N/A]

Contributions

Thanks to the DFKI group for their collaboration.