---
license: cc-by-sa-4.0
datasets:
- procesaur/Vikipedija
- procesaur/Vikizvornik
- procesaur/ZNANJE
- jerteh/SrpELTeC
language:
- sr
---

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      <h2>Word2Vec</h2>
      <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>
      <h2>Word2Vec</h2>
      <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>
  


```python
from gensim.models import Word2Vec
model = Word2Vec.load("TeslaW2V")
```





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  <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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    </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>
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       <p>Истраживање jе спроведено уз подршку Фонда за науку Републике Србиjе, #7276, Text Embeddings – Serbian Language Applications – TESLA 
         и Фода за иновациону делатност Републике Србије у оквиру програма GOVTECH, решење #53096, Дигитекс</p>
    </td>
    <td>
      <p>This research was supported by the Science Fund of the Republic of Serbia, #7276, Text Embeddings - Serbian Language Applications - TESLA
        and the Fund for Republic of Serbia Innovation Fund via the GOVTECH program, project #53096, Digiteks</p>
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