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dc.contributor.author Ulčar, Matej
dc.date.accessioned 2019-10-15T09:10:40Z
dc.date.available 2019-10-15T09:10:40Z
dc.date.issued 2019-10-15
dc.identifier.uri http://hdl.handle.net/11356/1257
dc.description ELMo language model (https://github.com/allenai/bilm-tf) used to produce contextual word embeddings, trained on entire Gigafida 2.0 corpus (https://viri.cjvt.si/gigafida/System/Impressum) for 10 epochs. 1,364,064 most common tokens were provided as vocabulary during the training. The model can also infer OOV words, since the neural network input is on the character level.
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.isreplacedby http://hdl.handle.net/11356/1277
dc.rights Apache License 2.0
dc.rights.uri https://opensource.org/licenses/Apache-2.0
dc.rights.label PUB
dc.source.uri http://embeddia.eu
dc.subject ELMo
dc.subject contextual embeddings
dc.subject word embeddings
dc.title ELMo embeddings model, Slovenian
dc.type languageDescription
metashare.ResourceInfo#ContentInfo.detailedType other
metashare.ResourceInfo#ContentInfo.mediaType text
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
size.info 213 mb
size.info 1364064 tokens
files.count 2
files.size 223309306


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