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TeslaXLM
te-sla/TeslaXLM
TeslaXLM is a machine learning model from te-sla. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-sa-4.0.
<table style="width:100%;height:100%" <tr <td colspan=2 <h4<i class="highlight-container"<b class="highlight"TeslaXLM</b</i</h4 </td </tr <tr style="width:100%;height:100%" <td width=50% <pВишејезични модел, 561 милио…
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At a glance
- License
- cc-by-sa-4.0
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 26, 2024
- Updated
- Aug 9, 2026
- SHA
- 07d38e85
Base models
Model card
From the Hugging Face model README
<table style="width:100%;height:100%">
<tr>
<td colspan=2>
<h4><i class="highlight-container"><b class="highlight">TeslaXLM</b></i></h4>
</td>
</tr>
<tr style="width:100%;height:100%">
<td width=50%>
<p>Вишејезични модел, 561 милион параметара</p>
<p>Обучаван над корпусима српског и српскохрватског језика - 20 милијарди речи</p>
<p>Једнака подршка уноса на ћирилици и латиници!</p>
</td>
<td>
<p>Multilingual model, 561 million parameters</p>
<p>Trained on Serbian and Serbo-Croatian corpora - 20 billion words</p>
<p>Equal support for Cyrillic and Latin input!</p>
</td>
</tr>
</table>
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='te-sla/teslaXLM')
>>> unmasker("Kada bi čovek znao gde će pasti on bi<mask>.")
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM
>>> from torch import LongTensor, no_grad
>>> from scipy import spatial
>>> tokenizer = AutoTokenizer.from_pretrained('te-sla/teslaXLM')
>>> model = AutoModelForMaskedLM.from_pretrained('te-sla/teslaXLM', 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))
Cit.
@incollection{skoric2025:juznoslovenskijezici,
author = {Škorić, Mihailo and Petalinkar, Saša},
orcid = {0000-0003-4811-8692 and 0009-0007-9664-3594},
title = {Quality Textual Corpora and New South Slavic Language Models},
license = {https://creativecommons.org/licenses/by/4.0/},
booktitle = {Proceedings of the International Conference South Slavic Languages in the Digital Environment JuDig : Thematic Collection of Papers},
editor = {Moskovljević Popović, Jasmina and Stanković, Ranka},
isbn = {978-86-6153-791-2},
series = {South Slavic Languages in the Digital Environment JuDig},
publisher = {University of Belgrade — Faculty of Philology},
address = {Belgrade},
year = {2025},
volume = {1},
pages = {337--348},
note = {19},
doi = {10.18485/judig.2025.1.ch19},
doiurl = {http://doi.fil.bg.ac.rs/volume.php?pt=eb_ser&issue=judig-2025-1&i=19},
url = {http://doi.fil.bg.ac.rs/pdf/eb_ser/judig/2025-1/judig-2025-1-ch19.pdf}
}
- Type
- xlm-roberta
- License
- cc-by-sa-4.0
- Languages
- sr, hr
- Created
- Nov 26, 2024
- Updated
- Aug 9, 2026