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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 21 1 Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages ... Ulčar, Matej; Robnik-Šikonja. - : Zenodo, 2022 BASE Show details 2 Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages ... Ulčar, Matej; Robnik-Šikonja. - : Zenodo, 2022 BASE Show details 3 Word-embedding based bilingual terminology alignment ... Repar, Andraž; Martinc, Matej; Ulčar, Matej. - : Zenodo, 2021 BASE Show details 4 Word-embedding based bilingual terminology alignment ... Repar, Andraž; Martinc, Matej; Ulčar, Matej. - : Zenodo, 2021 BASE Show details 5 List of single-word male and female occupations in Slovenian Supej, Anka; Ulčar, Matej; Robnik-Šikonja, Marko. - : Jožef Stefan Institute, 2021. : Faculty of Computer and Information Science, University of Ljubljana, 2021 BASE Show details 6 SloBERTa: Slovene monolingual large pretrained masked language model ... Ulčar, Matej; Robnik-Šikonja, Marko. - : Zenodo, 2021 BASE Show details 7 SloBERTa: Slovene monolingual large pretrained masked language model ... Ulčar, Matej; Robnik-Šikonja, Marko. - : Zenodo, 2021 BASE Show details 8 Evaluation of contextual embeddings on less-resourced languages ... Ulčar, Matej; Žagar, Aleš; Armendariz, Carlos S.. - : arXiv, 2021 BASE Show details 9 Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages ... Ulčar, Matej; Robnik-Šikonja, Marko. - : arXiv, 2021 BASE Show details 10 Slovenian RoBERTa contextual embeddings model: SloBERTa 1.0 Ulčar, Matej; Robnik-Šikonja, Marko. - : Faculty of Computer and Information Science, University of Ljubljana, 2020 BASE Show details 11 CroSloEngual BERT Ulčar, Matej; Robnik-Šikonja, Marko. - : Faculty of Computer and Information Science, University of Ljubljana, 2020 BASE Show details 12 A Resource for Evaluating Graded Word Similarity in Context: CoSimLex Armendariz, Carlos; Matthew, Purver; Ulčar, Matej. - : Queen Mary University, 2020 BASE Show details 13 CroSloEngual BERT 1.1 Ulčar, Matej; Robnik-Šikonja, Marko. - : Faculty of Computer and Information Science, University of Ljubljana, 2020 BASE Show details 14 FinEst BERT and CroSloEngual BERT: less is more in multilingual models ... Ulčar, Matej; Robnik-Šikonja, Marko. - : arXiv, 2020 BASE Show details 15 Multilingual Culture-Independent Word Analogy Datasets ... Ulčar, Matej; Vaik, Kristiina; Lindström, Jessica. - : Zenodo, 2020 BASE Show details 16 Multilingual Culture-Independent Word Analogy Datasets ... Ulčar, Matej; Vaik, Kristiina; Lindström, Jessica. - : Zenodo, 2020 BASE Show details 17 SemEval-2020 Task 3: Graded Word Similarity in Context Santos Armendariz, Carlos; Purver, Matthew; Pollak, Senja. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.3, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020 BASE Show details 18 ELMo embeddings models for seven languages Ulčar, Matej. - : Faculty of Computer and Information Science, University of Ljubljana, 2019 Abstract: ELMo language model (https://github.com/allenai/bilm-tf) used to produce contextual word embeddings, trained on large monolingual corpora for 7 languages: Slovenian, Croatian, Finnish, Estonian, Latvian, Lithuanian and Swedish. Each language's model was trained for approximately 10 epochs. Corpora sizes used in training range from over 270 M tokens in Latvian to almost 2 B tokens in Croatian. About 1 million most common tokens were provided as vocabulary during the training for each language model. The model can also infer OOV words, since the neural network input is on the character level. Each model is in its own .tar.gz archive, consisting of two files: pytorch weights (.hdf5) and options (.json). Both are needed for model inference, using allennlp (https://github.com/allenai/allennlp/blob/master/tutorials/how_to/elmo.md) python library. Keyword: contextual embeddings; Croatian language; ELMo; Estonian language; Finnish language; Latvian language; Lithuanian language; Slovenian language; Swedish language; word embeddings URL: http://hdl.handle.net/11356/1277 BASE Hide details 19 Multilingual Culture-Independent Word Analogy Datasets Ulčar, Matej; Vaik, Kristiina; Lindström, Jessica. - : Faculty of Computer and Information Science, University of Ljubljana, 2019 BASE Show details 20 ELMo embeddings model, Slovenian Ulčar, Matej. - : Faculty of Computer and Information Science, University of Ljubljana, 2019 BASE Show details Page: 1 2

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