README.md

---
license: cc-by-sa-4.0
datasets:
- procesaur/kisobran
- procesaur/ZNANJE
- procesaur/STARS
- procesaur/Vikipedija
- jerteh/SrpELTeC
language:
- sr
- hr
- bs
base_model:
- jerteh/Jerteh-81
pipeline_tag: fill-mask
---


<table style="width:100%;height:100%">
  <tr>
<td colspan=2>
  <h4><i class="highlight-container"><b class="highlight">Tesla 81</b></i></h4>
</td>
    </tr>
  <tr style="width:100%;height:100%">
    <td width=50%>     
      <p>Обучаван над корпусима српског и српскохрватског језика - 20 милијарди речи</p>
      <p>Једнака подршка уноса на ћирилици и латиници!</p>
    </td>
    <td>
<p>Trained on Serbian and Serbo-Croatian corpora - 20 billion words</p>
<p>Equal support for Cyrillic and Latin input!</p>
    </td>
  </tr>
  </table>

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='te-sla/tesla-81')
>>> unmasker("Kada bi čovek znao gde će pasti on bi<mask>.")
```

```python
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM
>>> from torch import LongTensor, no_grad
>>> from scipy import spatial
>>> tokenizer = AutoTokenizer.from_pretrained('te-sla/tesla-81')
>>> model = AutoModelForMaskedLM.from_pretrained('te-sla/tesla-81', output_hidden_states=True)
>>> x = " pas"
>>> y = " mačka"
>>> z = " svemir"
>>> tensor_x = LongTensor(tokenizer.encode(x, add_special_tokens=False)).unsqueeze(0)
>>> tensor_y = LongTensor(tokenizer.encode(y, add_special_tokens=False)).unsqueeze(0)
>>> tensor_z = LongTensor(tokenizer.encode(z, add_special_tokens=False)).unsqueeze(0)
>>> model.eval()
>>> with no_grad():
>>>     vektor_x = model(input_ids=tensor_x).hidden_states[-1].squeeze()
>>>     vektor_y = model(input_ids=tensor_y).hidden_states[-1].squeeze()
>>>     vektor_z = model(input_ids=tensor_z).hidden_states[-1].squeeze()
>>>     print(spatial.distance.cosine(vektor_x, vektor_y))
>>>     print(spatial.distance.cosine(vektor_x, vektor_z))
```

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  <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://www.ai.gov.rs/">  
      <div class="flex">
          <div
  			style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; 
            background-size: contain; background-image: url(https://www.ai.gov.rs/img/logo_60x120-2.png);background-repeat: no-repeat;
  background-position: center;">
          </div>
      </div>
    </a>
    <div style="text-align: center; font-size: 16px; font-weight: 800" title="nVidia DGX-based system">National AI platform</div>
    <div>  
      <a href="https://www.ai.gov.rs/">
      	<div style="text-align: center; font-size: 14px;">ai.gov.rs</div>
      </a>
    </div>
  </div>
</div>
<br/><br/>
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    <img src="https://www.ai.gov.rs/img/logo_60x120-2.png" style="position:relative; left:30px; z-index:10; height:85px">
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  <table width=100% style="border:0px">
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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>
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</table>



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