README.md

metadata

license: cc-by-sa-4.0
task_categories:
  - text-generation
language:
  - sr
  - hr
  - bs
tags:
  - webdataset
pretty_name: Umbrella corp.
size_categories:
  - 10B<n<100B

Kišobran - krovni veb korpus srpskog i srpskohrvatskog jezika Najveća agregacija veb korpusa do sada, neophodna za obučavanje velikih jezičkih modela za srpski jezik. Ukupno x dokumenata, ukupno sa preko 20 milijardi reči. Svaka linija predstavlja novi dokument Rečenice unutar dokumenata su obeležene. Sadrži obrađene i deduplikovane verzije sledećih korpusa: Deduplikacija je izvršena pomoću alata onion korišćenjem pretrage 6-torki i pragom dedumplikacije 75%.

Umbrella corp. - umbrella web corpus of Serbian and Serbo-Croatian The largest aggregation of web corpora so far, necessary for training Serbian large language models. A total of x documents containing over 20 billion words. Each line represents a document. Each Sentence in a document is delimited. Contains processed and deduplicated versions of the following corpora: The dataset was deduplicated using onion using 6-tuples search and a duplicate threshold of 75%.

Load complete dataset / Učitavanje kopletnog dataseta

from datasets import load_dataset
dataset = load_dataset("procesaur/umbrella")

Load a specific language / Učitavanje pojedinačnih jezika

from datasets import load_dataset
dataset_sr = load_dataset("procesaur/umbrella", "sr")
dataset_cnr = load_dataset("procesaur/umbrella", "cnr")
dataset_hr = load_dataset("procesaur/umbrella", "hr")
dataset_bs = load_dataset("procesaur/umbrella", "bs")

Editor

Mihailo Škorić

@procesaur

Citation:

@article{skoric24korpusi,
  author    = {\vSkori\'c, Mihailo and Jankovi\'c, Nikola},
  title     = {New Textual Corpora for Serbian Language Modeling},
  journal   = {Infotheca},
  volume    = {24},
  issue     = {1},
  year      = {2024},
  publisher = {Zajednica biblioteka univerziteta u Srbiji, Beograd}
}

Истраживање jе спроведено уз подршку Фонда за науку Републике Србиjе, #7276, Text Embeddings – Serbian Language Applications – TESLA.

This research was supported by the Science Fund of the Republic of Serbia, #7276, Text Embeddings - Serbian Language Applications - TESLA.