README.md

---
license: cc-by-sa-4.0
datasets:
- procesaur/kisobran
- procesaur/STARS
- procesaur/Vikipedija
- procesaur/Vikizvornik
- jerteh/SrpELTeC
language:
- sr
library_name: fasttext
---

<table style="width:100%;height:100%">
  <tr>
<td colspan=2>
    <h4><i class="highlight-container"><b class="highlight">FastText Sr</b></i></h4>
</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 модели у Gensim, али и оригиналном формату</p>
    </td>
    <td>
      <p>Trained on the Serbian language corpus - 9.5 billion words</p>
      <p>The files include models in both Gensim and the original format.</p>
    </td>
  </tr>
  </table>


```python
from gensim.models import FastText
model = Word2Vec.load("TeslaFT")
examples = [
    ("dim", "zavesa"),
    ("staklo", "zavesa"),
    ("ormar", "zavesa"),
    ("prozor", "zavesa"),
    ("draperija", "zavesa")
]
for e in examples:
    model.wv.cosine_similarities(ft.wv[e[0]], ft.wv[[e[1]]])[0]
```
```
0.5305264
0.7095266
0.6041575
0.5771946
0.8870213
```
```python
from gensim.models.fasttext import load_facebook_model
model = load_facebook_model("TeslaFT.bin")
examples = [
    ("dim", "zavesa"),
    ("staklo", "zavesa"),
    ("ormar", "zavesa"),
    ("prozor", "zavesa"),
    ("draperija", "zavesa")
]
for e in examples:
    model.wv.cosine_similarities(ft.wv[e[0]], ft.wv[[e[1]]])[0]
```
```
0.5305264
0.7095266
0.6041575
0.5771946
0.8870213
```


<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/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>

<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/>

```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}
  publisher = {SASA, 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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