---
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- sr
- hr
- bs
tags:
- webdataset
pretty_name: Kišobran (Umbrella corp.)
size_categories:
- 10B<n<100B
configs:
- config_name: default
  data_files:
  - split: train
    path: '*.txt'
  - split: sr
    path: '*_sr.txt'
  - split: cnr
    path: '*_cnr.txt'
  - split: hr
    path: '*_hr.txt'
  - split: bs
    path: '*_bs.txt'
---

<img src="cover.png" class="cover">


<table style="width:100%;height:100%">
  <!--tr style="width:100%;height:30px">
  <td colspan=2 align=center>
    <h1>Kišobran (Umbrella corp.)</h1>
  </td>
  <tr-->
  <tr style="width:100%;height:100%">
    <td width=50%>
      <h2><span class="highlight-container"><b class="highlight">Kišobran korpus</b></span> - krovni veb korpus srpskog i srpskohrvatskog jezika</h2>
      <p>Najveća agregacija veb korpusa do sada, pogodna za obučavanje velikih jezičkih modela za srpski jezik.</p>
      <p>Ukupno x dokumenata, ukupno sa <span class="highlight-container"><span class="highlight">preko 18.5 milijardi reči</span></span>.</p>
      <p></p>
      <p>Svaka linija predstavlja novi dokument</p>
      <p>Rečenice unutar dokumenata su obeležene.</p>    
      <h4>Sadrži obrađene i deduplikovane verzije sledećih korpusa:</h4>
    </td>
    <td>
      <h2><span class="highlight-container"><b class="highlight">Umbrella corp.</b></span> - umbrella web corpus of Serbian and Serbo-Croatian</h2>
      <p>The largest aggregation of web corpora so far, suitable for training Serbian large language models.</p>
      <p>A total of x documents containing <span class="highlight-container"><span class="highlight">over 18.5 billion words</span></span>.</p>
      <p></p>
      <p>Each line represents a document.</p>
      <p>Each Sentence in a document is delimited.</p>
      <h4>Contains processed and deduplicated versions of the following corpora:</h4>     
    </td>
  </tr>
</table>


<table class="lista"> 
  <tr>
    <td>Korpus<br/>Corpus</td>
    <td>Jezik<br/>Language</td>
    <td>Broj reči<br/>Word count</td>
    <td>Broj dokumenata<br/>Doc. count</td>
    <td>Udeo<br/>Share</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/HPLT/hplt_monolingual_v1_2">HPLT_sr</a></td>
    <td>🇷🇸</td>
    <td>2.9 M</td>
    <td>2.5 B</td>
    <td>13.74%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1807">MaCoCu_sr</a></td>
    <td>🇷🇸</td>
    <td>6.7 M</td>
    <td>2.1 B</td>
    <td>11.54%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/allenai/c4">MC4_sr</a></td>
    <td>🇷🇸</td>
    <td>2.3 M</td>
    <td>782 M</td>
    <td>4.19%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/cc100">cc100_sr</a></td>
    <td>🇷🇸</td>
    <td>2.3 M</td>
    <td>659 M</td>
    <td>3.53%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1752">PDRS1.0</a></td>
    <td>🇷🇸</td>
    <td>400 K</td>
    <td>506 M</td>
    <td>2.71%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/jerteh/SrpKorNews">SrpKorNews</a></td>
    <td>🇷🇸</td>
    <td>35 K</td>
    <td>469 M</td>
    <td>2.51%</td>
  </tr>
   <tr>
    <td><a href="https://huggingface.co/datasets/oscar-corpus/OSCAR-2301">OSCAR_sr</a></td>
    <td>🇷🇸</td>
    <td>500 K</td>
    <td>410 M</td>
    <td>2.2%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1063">srWaC</a></td>
    <td>🇷🇸</td>
    <td>1.2 M</td>
    <td>307 M</td>
    <td>1.65%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1426">CLASSLA_sr</a></td>
    <td>🇷🇸</td>
    <td>1.3 M</td>
    <td>240 M</td>
    <td>1.29%</td>
  </tr> 
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1809">MaCoCu_cnr</a></td>
    <td>🇷🇸/🇲🇪</td>
    <td>500 K</td>
    <td>152 M</td>
    <td>0.82%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1429">meWaC</a></td>
    <td>🇷🇸/🇲🇪</td>
    <td>200 K</td>
    <td>41 M</td>
    <td>0.22%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/cc100">cc100_hr</a></td>
    <td>🇭🇷</td>
    <td>13.3 M</td>
    <td>2.5 B</td>
    <td>13.73%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1806">MaCoCu_hr</a></td>
    <td>🇭🇷</td>
    <td>8 M</td>
    <td>2.3 B</td>
    <td>12.63%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/HPLT/hplt_monolingual_v1_2">HPLT_hr</a></td>
    <td>🇭🇷</td>
    <td>2.3 M</td>
    <td>1.8 B</td>
    <td>9.95%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/classla/xlm-r-bertic-data">hr_news</a></td>
    <td>🇭🇷</td>
    <td>4.1 M</td>
    <td>1.4 B</td>
    <td>7.65%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1064">hrWaC</a></td>
    <td>🇭🇷</td>
    <td>3.1 M</td>
    <td>935 M</td>
    <td>5.01%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1426">CLASSLA_hr</a></td>
    <td>🇭🇷</td>
    <td>1.2 M</td>
    <td>160 M</td>
    <td>0.86%</td>
  </tr>
   <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1180">riznica</a></td>
    <td>🇭🇷</td>
    <td>20 K</td>
    <td>69 M</td>
    <td>0.37%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1808">MaCoCu_bs</a></td>
    <td>🇧🇦</td>
    <td>2.6 M</td>
    <td>700 M</td>
    <td>3.75%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1062">bsWaC</a></td>
    <td>🇧🇦</td>
    <td>800 K</td>
    <td>194 M</td>
    <td>1.04%</td>
  </tr>
  <tr>
    <td><a href="https://www.clarin.si/repository/xmlui/handle/11356/1426">CLASSLA_bs</a></td>
    <td>🇧🇦</td>
    <td>800 K</td>
    <td>105 M</td>
    <td>0.56%</td>
  </tr>
  <tr>
    <td><a href="https://huggingface.co/datasets/cc100">cc100_bs</a></td>
    <td>🇧🇦</td>
    <td>300 K</td>
    <td>9 M</td>
    <td>0.05%</td>
  </tr>
   <tr>
    <td><b>TOTAL</b></td>
    <td></td>
    <td><b>54.75 M</b></td>
    <td><b>18.65 B</b></td>
    <td>100%</td>
  </tr>
</table>

Load complete dataset / Učitavanje kopletnog dataseta
```python
from datasets import load_dataset
dataset = load_dataset("procesaur/umbrella")
```


Load a specific language / Učitavanje pojedinačnih jezika
```python
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")
```


<div class="inline-flex flex-col" style="line-height: 1.5;padding-right:50px">
  <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">Editor</div>
    <a href="https://huggingface.co/procesaur">  
      <div class="flex">
          <div
  			style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; 
            background-size: cover; background-image: url(&#39;https://cdn-uploads.huggingface.co/production/uploads/1673534533167-63bc254fb8c61b8aa496a39b.jpeg?w=200&h=200&f=face&#39;)">
          </div>
      </div>
    </a>
    <div style="text-align: center; font-size: 16px; font-weight: 800">Mihailo Škorić</div>
    <div>  
      <a href="https://huggingface.co/procesaur">
      	<div style="text-align: center; font-size: 14px;">@procesaur</div>
      </a>
    </div>
  </div>
</div>



Citation:
```bibtex
@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}
}
```


<table style="width:100%;height:100%">
  <tr style="width:100%;height:100%">
    <td width=50%>
       <p>Istraživanje je sprovedeno uz podršku Fonda za nauku Republike Srbije, #7276, Text Embeddings – Serbian Language Applications – TESLA.</p>
        <p>Svaki korpus u tabeli vezan je za URL sa kojeg je preuzet. Prikazani brojevi dokumenata i reči, odnose se na stanje nakon čićenja i deduplikacije.</p>
       <p>Deduplikacija je izvršena pomoću alata <a href="http://corpus.tools/wiki/Onion">onion</a> korišćenjem pretrage 6-torki i pragom dedumplikacije 75%.</p>
       <p>Računarske resursre neophodne za deduplikaciju korpusa obezbedila je Nacionalna platforma za veštačku inteligenciju Srbije.</p>
    </td>
    <td>
      <p>This research was supported by the Science Fund of the Republic of Serbia, #7276, Text Embeddings - Serbian Language Applications - TESLA.</p>
      <p>Each corpus in the table is linked to the URL from which it was downloaded. The displayed numbers of documents and words refer to after cleaning and deduplication.</p>
      <p>The dataset was deduplicated using <a href="http://corpus.tools/wiki/Onion">onion</a> using 6-tuples search and a duplicate threshold of 75%.</p>
      <p>Computer resources necessary for the deduplication of the corpus were provided by the National Platform for Artificial Intelligence of Serbia.</p>
    </td>
  </tr>
</table>



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