dc.contributor.author | Ulčar, Matej |
dc.contributor.author | Robnik-Šikonja, Marko |
dc.date.accessioned | 2021-02-17T16:58:47Z |
dc.date.available | 2021-02-17T16:58:47Z |
dc.date.issued | 2021-01-17 |
dc.identifier.uri | http://hdl.handle.net/11356/1397 |
dc.description | The monolingual Slovene RoBERTa (A Robustly Optimized Bidirectional Encoder Representations from Transformers) model is a state-of-the-art model representing words/tokens as contextually dependent word embeddings, used for various NLP tasks. Word embeddings can be extracted for every word occurrence and then used in training a model for an end task, but typically the whole RoBERTa model is fine-tuned end-to-end. SloBERTa model is closely related to French Camembert model https://camembert-model.fr/. The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used for data preparation and training the model are available on https://github.com/clarinsi/Slovene-BERT-Tool Compared with the previous version (1.0), this version was trained for further 61 epochs (v1.0 37 epochs, v2.0 98 epochs), for a total of 200,000 iterations/updates. The released model here is a pytorch neural network model, intended for usage with the transformers library https://github.com/huggingface/transformers (sloberta.2.0.transformers.tar.gz) or fairseq library https://github.com/pytorch/fairseq (sloberta.2.0.fairseq.tar.gz) |
dc.language.iso | slv |
dc.publisher | Faculty of Computer and Information Science, University of Ljubljana |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/825153 |
dc.relation.replaces | http://hdl.handle.net/11356/1387 |
dc.rights | Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) |
dc.rights.uri | https://creativecommons.org/licenses/by-sa/4.0/ |
dc.rights.label | PUB |
dc.source.uri | https://rsdo.slovenscina.eu/en/semantic-resources-and-technologies |
dc.subject | BERT |
dc.subject | RoBERTa |
dc.subject | word embeddings |
dc.subject | language model |
dc.subject | contextual embeddings |
dc.title | Slovenian RoBERTa contextual embeddings model: SloBERTa 2.0 |
dc.type | toolService |
metashare.ResourceInfo#ContentInfo.detailedType | other |
metashare.ResourceInfo#ResourceComponentType#ToolServiceInfo.languageDependent | true |
has.files | yes |
branding | CLARIN.SI data & tools |
contact.person | Matej Ulčar matej.ulcar@fri.uni-lj.si Faculty of Computer and Information Science, University of Ljubljana |
sponsor | European Union EC/H2020/825153 EMBEDDIA - Cross-Lingual Embeddings for Less-Represented Languages in European News Media euFunds info:eu-repo/grantAgreement/EC/H2020/825153 |
sponsor | Ministry of Culture C3340-20-278001 Development of Slovene in a Digital Environment Other |
files.count | 2 |
files.size | 1387435323 |
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- Ime
- sloberta.2.0.transformers.tar.gz
- Velikost
- 249.44 MB
- Format
- application/gzip
- Opis
- SloBERTa 2.0 model for use with transformers toolset.
- MD5
- 0afe61f4cdd7f2977db2a077bc3d4091

- Ime
- sloberta.2.0.fairseq.tar.gz
- Velikost
- 1.05 GB
- Format
- application/gzip
- Opis
- SloBERTa 2.0 model for use with fairseq toolset/library.
- MD5
- e0f9d421e2fd33a524fbed193c0f1dae