Instructions to use te-sla/FastTextSr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use te-sla/FastTextSr with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("te-sla/FastTextSr", "model.bin")) - Notebooks
- Google Colab
- Kaggle
| license: cc-by-sa-4.0 | |
| datasets: | |
| - procesaur/kisobran | |
| - procesaur/STARS | |
| - procesaur/Vikipedija | |
| - 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('https://cdn-uploads.huggingface.co/production/uploads/1673534533167-63bc254fb8c61b8aa496a39b.jpeg?w=200&h=200&f=face')"> | |
| </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} | |
| } | |
| ``` | |
| <div id="zastava"> | |
| <div class="grb"> | |
| <img src="https://www.ai.gov.rs/img/logo_60x120-2.png" style="position:relative; left:30px; z-index:10; height:85px"> | |
| </div> | |
| <table width=100% style="border:0px"> | |
| <tr style="background-color:#C6363C;width:100%;border:0px;height:30px"><td style="width:100vw"></td></tr> | |
| <tr style="background-color:#0C4076;width:100%;border:0px;height:30px"><td></td></tr> | |
| <tr style="background-color:#ffffff;width:100%;border:0px;height:30px"><td></td></tr> | |
| </table> | |
| </div> | |
| <table style="width:100%;height:100%"> | |
| <tr style="width:100%;height:100%"> | |
| <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> | |
| </tr> | |
| </table> | |
| <style> | |
| .ffeat: { | |
| color:red | |
| } | |
| .cover { | |
| width: 100%; | |
| margin-bottom: 5pt | |
| } | |
| .highlight-container, .highlight { | |
| position: relative; | |
| text-decoration:none | |
| } | |
| .highlight-container { | |
| display: inline-block; | |
| } | |
| .highlight{ | |
| color:white; | |
| text-transform:uppercase; | |
| font-size: 16pt; | |
| } | |
| .highlight-container{ | |
| padding:5px 10px | |
| } | |
| .highlight-container:before { | |
| content: " "; | |
| display: block; | |
| height: 100%; | |
| width: 100%; | |
| margin-left: 0px; | |
| margin-right: 0px; | |
| position: absolute; | |
| background: #e80909; | |
| transform: rotate(2deg); | |
| top: -1px; | |
| left: -1px; | |
| border-radius: 20% 25% 20% 24%; | |
| padding: 10px 18px 18px 10px; | |
| } | |
| div.grb, #zastava>table { | |
| position:absolute; | |
| top:0px; | |
| left: 0px; | |
| margin:0px | |
| } | |
| div.grb>img, #zastava>table{ | |
| margin:0px | |
| } | |
| #zastava { | |
| position: relative; | |
| margin-bottom:120px | |
| } | |
| p { | |
| font-size:14pt | |
| } | |
| </style> |