Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
multilingual
Size:
1M - 10M
License:
Update README.md
Browse files
README.md
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# CommonLingua-Train
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| **Format** | Apache Parquet (zstd-compressed) |
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| **License** | Per-source (see attribution table) |
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## Files
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| File | Rows | Size | Description |
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| `train.parquet` | 2,482,568 | 1.08 GB | Provenance schema (10 columns) |
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| `val.parquet` | 272,875 | 119 MB | Same schema |
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## Schema
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| `creator` | string | Author / organisation, nullable |
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| `date` | string | Publication / extraction date, nullable |
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##
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Wikipedia provides the bulk (≈ 94 %); the long tail is filled by [Common Corpus](https://huggingface.co/datasets/PleIAs/common_corpus) collections and a handful of dedicated minority-language corpora.
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| Source | Rows | License | Notes |
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| Wikipedia | 2,323,301 | CC-BY-SA 4.0 | Per-article paragraphs across 300+ language editions |
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| OpenAlex | 29,733 | per-journal | Mostly CC-BY academic content (Indonesian/Malaysian, African, English) |
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| Pralekha | 27,076 | CC-BY-SA 4.0 | Indic-language paragraphs |
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| VOA Africa | 24,341 | Public Domain (US Federal) | Voice of America Africa-language broadcasts |
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| Cultural Heritage | 16,381 | per-collection | Digitised heritage archives (Tocharian, Larth Etruscan, ePSD2/CDLI, Thesaurus Linguae Aegyptiae) |
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| Perseus | 11,638 | CC-BY-SA 4.0 | Classical languages (Latin, Ancient Greek, Old Church Slavonic, Gothic) |
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| OpenPecha | 8,317 | Public Domain / CC | Tibetan and Sanskrit (incl. Ambuda) |
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| WaxalNLP | 5,373 | CC-BY-SA 4.0 | West African ASR transcripts |
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| eBible | 4,877 | Public Domain | Bible translations, public-domain editions only |
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| Chinese-Court-Decisions| 4,474 | Public Domain | Chinese court rulings |
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| StackExchange | 3,312 | CC-BY-SA 4.0 | Q&A across StackExchange sites |
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| Project Ben-Yehuda | 2,701 | Public Domain | Hebrew literature |
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| USPTO | 2,029 | Public Domain | US patent filings |
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| US-PD-Newspapers | 1,834 | Public Domain | US public-domain newspapers |
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| HAL | 1,575 | per-license | French academic abstracts |
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| French-PD-Newspapers | 1,539 | Public Domain | |
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| English-PD | 1,353 | Public Domain | |
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| dotgov | 1,130 | Public Domain | US .gov web text |
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| French-PD-diverse | 1,065 | Public Domain | |
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| Sefaria | 947 | Public Domain | Hebrew religious corpus |
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| Court Listener | 931 | Public Domain | US court filings |
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| Krike-Krake | 744 | Public Domain | Tachelhit / Berber documentation |
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| Deutsches Zeitungsportal | 615 | Public Domain | German newspaper archive |
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| govinfo | 572 | Public Domain | US Government publications |
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| Creative Commons Common Crawl (CCC) | 561 | per-license CC | |
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| enevaeldens_nyheder | 496 | Public Domain | Danish historical news |
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| German-PD | 485 | Public Domain | |
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| French-PD-Books | 368 | Public Domain | |
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| French Open Data | 339 | per-license | |
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| Spanish-Science-Pile | 323 | per-journal | |
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The
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```python
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import pyarrow.parquet as pq
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train = pq.read_table("train.parquet", columns=["text", "lang"])
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val = pq.read_table("val.parquet", columns=["text", "lang"])
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# train ByteHybrid using github.com/PleIAs/bytehybrid-lid/train_lid.py
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```
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## License & responsible use
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# CommonLingua-Train
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This is the training dataset for [PleIAs/CommonLingua](https://huggingface.co/PleIAs/CommonLingua) — a byte-level language identification model for 334 languages. It is composed of 2.48 M paragraphs, sourced exclusively from Wikipedia and other open-licensed and public-domain corpora extracted from Common Corpus.
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The training dataset was developed iteratively from the initial Structured Wikipedia data subset. Some of the decisions that account for SOTA-level performance includes:
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* Filtering of Wikipedia sources, including widespread generated content in some versions and multilingual contaminations.
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* Extension to non-encyclopedic sources and formats, especially long documents with OCR errors from Common corpus.
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* Additions of low resource language resources, especially coming from Africa, re-identified in Common Corpus.
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* Targeted sampling of frequent language confusions, especially between Indonesian and Malay thanks so scientific papers.
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## Schema
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| `creator` | string | Author / organisation, nullable |
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| `date` | string | Publication / extraction date, nullable |
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## Composition
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The core dataset is from Wikipedia (2,323,301). Additional major inclusions include OpenAlex (30,000 samples, mostly CC-BY academic content in Indonesian/Malaysian and African languages) and some new multilingual subsets we added for the Global Common Corpus update (VOA Africa, Pralekha)
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Training can be reproduced by keeping only the text and lang columns:
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```python
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import pyarrow.parquet as pq
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train = pq.read_table("train.parquet", columns=["text", "lang"])
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val = pq.read_table("val.parquet", columns=["text", "lang"])
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```
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## License & responsible use
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