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---
license: cc-by-sa-4.0
datasets:
- procesaur/Vikipedija
- procesaur/Vikizvornik
- procesaur/ZNANJE
- jerteh/SrpELTeC
language:
- sr
---
<table style="width:100%;height:100%">
<tr>
<td colspan=2>
<h2>Word2Vec</h2>
</td>
</tr>
<tr style="width:100%;height:100%">
<td width=50%>
<p>обучаван над корпусом српског језика - 9.5 милијарди речи</p>
<p>међу датотекама се налазе два модела (CBOW и SkipGram варијанте)</p>
</td>
<td>
<p>trained on the Serbian language corpus - 9.5 billion words</p>
<p>There are two models among the files (CBOW and SkipGram variants)</p>
</td>
</tr>
</table>
```python
from gensim.models import Word2Vec
model = Word2Vec.load("TeslaW2V")
```
<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">
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</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>
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<table style="width:100%;height:100%">
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<td width=50%>
<p>Истраживање jе спроведено уз подршку Фонда за науку Републике Србиjе, #7276, Text Embeddings – Serbian Language Applications – TESLA</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>
</td>
</tr>
</table>
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