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--- |
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license: cc-by-sa-4.0 |
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size_categories: |
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- 10B<n<100B |
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--- |
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# XLM-R-BERTić dataset |
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## Composition and usage |
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This dataset contains 11.5 billion words of texts written in Croatian, Bosnian, Montenegrin and Serbian. |
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It is an extension of the [BERTić-data dataset](http://hdl.handle.net/11356/1426), a 8.4 billion-words collection used to pre-train the [BERTić model](https://huggingface.co/classla/bcms-bertic) ([paper](https://aclanthology.org/2021.bsnlp-1.5.pdf)). In this dataset there are two major additions: the MaCoCu HBS crawling collection, a collection of crawled news items, and the [mC4](https://huggingface.co/datasets/mc4) HBS dataset. The order of deduplication is as stated in the list of parts/splits: |
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* macocu_hbs |
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* hr_news |
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* mC4 |
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* BERTić-data |
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* hrwac |
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* classla_hr |
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* cc100_hr |
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* riznica |
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* srwac |
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* classla_sr |
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* cc100_sr |
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* bswac |
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* classla_bs |
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* cnrwac |
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The dataset was deduplicated with `onion` on the basis of 5-tuples of words with duplicate threshold set to 90%. |
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The entire dataset can be downloaded and used as follows: |
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```python |
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import datasets |
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dict_of_datasets = datasets.load_dataset("classla/xlm-r-bertic-data") |
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full_dataset = datasets.concatenate_datasets([d for d in dict_of_datasets.values()]) |
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``` |
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A single split can be taken as well, but note that this means all the splits will be downloaded and generated, which can take a long time: |
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```python |
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import datasets |
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riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica") |
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``` |
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To circumvent this one option is using streaming: |
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```python |
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import datasets |
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riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica", streaming=True) |
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for i in riznica.take(2): |
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print(i) |
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# Output: |
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# {'text': 'PRAGMATIČARI DOGMATI SANJARI'} |
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# {'text': 'Ivica Župan'} |
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``` |
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Read more on streaming [here](https://huggingface.co/docs/datasets/stream). |
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If you use this dataset, please cite |
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``` |
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@inproceedings{ljubesic-etal-2024-language, |
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title = "Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining", |
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author = "Ljube{\v{s}}i{\'c}, Nikola and |
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Suchomel, V{\'\i}t and |
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Rupnik, Peter and |
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Kuzman, Taja and |
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van Noord, Rik", |
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editor = "Melero, Maite and |
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Sakti, Sakriani and |
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Soria, Claudia", |
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booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024", |
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month = may, |
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year = "2024", |
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address = "Torino, Italia", |
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publisher = "ELRA and ICCL", |
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url = "https://aclanthology.org/2024.sigul-1.23", |
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pages = "189--203", |
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} |
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``` |
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