Datasets:
Size:
10B<n<100B
License:
File size: 2,644 Bytes
627c792 d205022 627c792 7b410ee ab40e3b 7b410ee ab40e3b 7b410ee d205022 7b410ee ad72d35 7b410ee 9592098 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
license: cc-by-sa-4.0
size_categories:
- 10B<n<100B
---
# 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](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:
* 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:
```python
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:
```python
import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica")
```
To circumvent this one option is using streaming:
```python
import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica", streaming=True)
for i in riznica.take(2):
print(i)
# Output:
# {'text': 'PRAGMATIČARI DOGMATI SANJARI'}
# {'text': 'Ivica Župan'}
```
Read more on streaming [here](https://huggingface.co/docs/datasets/stream).
If you use this dataset, please cite
```
@inproceedings{ljubesic-etal-2024-language,
title = "Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining",
author = "Ljube{\v{s}}i{\'c}, Nikola and
Suchomel, V{\'\i}t and
Rupnik, Peter and
Kuzman, Taja and
van Noord, Rik",
editor = "Melero, Maite and
Sakti, Sakriani and
Soria, Claudia",
booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.sigul-1.23",
pages = "189--203",
}
```
|