README.md

---
license: cc-by-sa-4.0
datasets:
- procesaur/Vikipedija
language:
- sr
base_model:
- te-sla/Word2VecSr
tags:
- dict2vec
---


<table style="width:100%;height:100%">
  <tr>
<td colspan=2>
    <h4><i class="highlight-container"><b class="highlight">SerbDict2vec</b></i></h4>
</td>
    </tr>
  <tr style="width:100%;height:100%">
    <td width=50%>     
      <p>Обучаван над корпусом српског језика Википедија, СрпКор2013 и део СрпКор2021 - 350 милиона речи</p>
    </td>
    <td>
      <p>Trained on the Serbian language corpus compiled from srWikipedia, SrpKor2013, and part of SrpKor2021 - 350 million words</p>
    </td>
  </tr>
  </table>


```python
from gensim.models import KeyedVectors

# Load the vectors
d2v_vectors = KeyedVectors.load("D:/modeli/dict2vec/SerbDict2vec")

# Check word vector
print(d2v_vectors["klijent"])  

```
```
[-3.1600e-01 -3.4110e+00  1.2158e+01  3.7950e+00  6.1200e-01 -3.1000e-01
 -9.7000e-02 -5.0000e-02 -5.2000e-02 -9.4000e-01  3.5600e-01 -6.0400e-01
 -2.3700e-01  1.1600e-01 -4.5500e-01  1.6100e-01  2.2500e-01 -6.4700e-01
  5.4600e-01 -7.8000e-02  3.5500e-01  5.8000e-02 -3.0000e-02  3.3000e-01
 -1.5700e-01 -5.9700e-01  1.5000e-02  1.9600e-01  1.0000e-03  1.5800e-01
  4.3300e-01 -5.0000e-03 -3.0700e-01 -2.6000e-01 -5.2500e-01  7.4000e-02
 -2.7000e-02  1.8800e-01  5.6000e-02 -2.5200e-01  3.0700e-01 -4.3000e-02
  5.9000e-02 -6.6000e-02 -1.0000e-02  1.3900e-01  7.1000e-02 -4.2000e-02
 -3.2000e-02 -1.3100e-01  1.4000e-02 -8.9000e-02 -3.2200e-01 -6.2000e-02
 -1.0500e-01  1.0800e-01  1.6100e-01 -1.3600e-01 -1.5400e-01  4.0000e-02
 -5.1000e-02  1.1000e-02  2.6600e-01  3.0000e-03 -1.3800e-01  2.3400e-01
 -2.9300e-01  1.5500e-01  2.5600e-01  2.7200e-01  1.2600e-01  1.9000e-01
 -7.2000e-02  7.3000e-02  1.1700e-01 -1.1100e-01  5.9000e-02 -2.1100e-01
 -1.8700e-01 -2.0000e-03 -3.6000e-02 -2.0400e-01  3.1300e-01  1.1600e-01
  1.4800e-01  1.3000e-02  2.5200e-01  1.9700e-01 -6.7000e-02  4.5000e-02
  1.3100e-01 -8.0000e-03  5.9000e-02  3.0800e-01 -3.2200e-01 -5.3000e-02
 -1.5500e-01 -2.2100e-01 -7.6000e-02  1.3600e-01]
```

```python
# Find most similar words
print(d2v_vectors.most_similar("klijent", topn=5))
```
```
[('interfejs', 0.9971136450767517),
('mušterija', 0.996911883354187),
('provajder', 0.9968076348304749),
('sugrađanin', 0.9967014789581299),
('komšija', 0.9965119361877441)]
```

<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">Author</div>
    <a href="https://huggingface.co/rankas">  
      <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-avatars.huggingface.co/v1/production/uploads/63f8fa204ef4aacb65a00043/IlrBetI15qnGsc798R6tO.jpeg?w=200&h=200&f=face&#39;)">
          </div>
      </div>
    </a>
    <div style="text-align: center; font-size: 16px; font-weight: 800">Ranka Stanković</div>
    <div>  
      <a href="https://huggingface.co/rankas">
      	<div style="text-align: center; font-size: 14px;">@rankas</div>
      </a>
    </div>
  </div>
</div>
<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">Author</div>
    <a href="https://huggingface.co/rankas">  
      <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-avatars.huggingface.co/v1/production/uploads/no-auth/gfI5-noC3Si2qlV6vRiwL.png?w=200&h=200&f=face&#39;)">
          </div>
      </div>
    </a>
    <div style="text-align: center; font-size: 16px; font-weight: 800">Jovana Rađenović</div>
    <div>  
      <a href="https://huggingface.co/JovanaR">
      	<div style="text-align: center; font-size: 14px;">@JovanaR</div>
      </a>
    </div>
  </div>
</div>

<div class="inline-flex flex-col" style="line-height: 1.5;"> 
  <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">Computation</div>
    <a href="https://tesla.rgf.bg.ac.rs">  
      <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(https://cdn-avatars.huggingface.co/v1/production/uploads/63bc254fb8c61b8aa496a39b/TfM_-sc8-b34ddfhHBGTA.png?w=200&h=200&f=face)">
          </div>
      </div>
    </a>
    <div style="text-align: center; font-size: 16px; font-weight: 800">TESLA project</div>
    <div>  
      <a href="https://huggingface.co/te-sla">
      	<div style="text-align: center; font-size: 14px;">@te-sla</div>
      </a>
    </div>
  </div>
</div>
<br/><br/>
## Cit.

```bibtex
@inproceedings{stankovic-dict2vec,
  author    = {Ranka Stanković, Jovana Rađenović, Mihailo Škorić, Marko Putniković},
  title     = {Learning Word Embeddings using Lexical Resources and Corpora},
  booktitle   = {15th International Conference on Information Society and Technology, ISIST 2025, Kopaonik},
  year      = {2025},
  address = {Kopaonik, Belgrade} 
  url       = {https://doi.org/10.5281/zenodo.15093900}
}
```
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       <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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