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XLM-R-BERTić dataset

Composition and usage

This dataset contains 11.5 billion words of texts written in Croatian, Bosnian, Montenegrin and Serbian.

It is an extension of the BERTić-data dataset, a 8.4 billion-words collection used to pre-train the BERTić model (paper). In this dataset there are two major additions: the MaCoCu HBS crawling collection, a collection of crawled news items, and the mC4 HBS dataset. The order of deduplication is as stated in the list of parts/splits:

  • macocu_hbs
  • hr_news
  • mC4
  • BERTić-data
    • hrwac
    • classla_hr
    • cc100_hr
    • riznica
    • srwac
    • classla_sr
    • cc100_sr
    • bswac
    • classla_bs
    • cnrwac

The dataset was deduplicated with onion on the basis of 5-tuples of words with duplicate threshold set to 90%.

The entire dataset can be downloaded and used as follows:

import datasets
dict_of_datasets = datasets.load_dataset("classla/xlm-r-bertic-data")
full_dataset = datasets.concatenate_datasets([d for d in dict_of_datasets.values()])

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:

import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica")

To circumvent this one option is using streaming:

import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica", streaming=True)
for i in riznica.take(2):
# Output:
# {'text': 'Ivica Župan'}

Read more on streaming here.

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