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• TimeAssign

  1 resources

  TimeAssign is a program which recognizes temporal expressions and assigns TimeML labels to words in Polish text using a Bi-LSTM based neural net and wordform embeddings.

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• The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian 1.1

  2 resources

  The model for lemmatisation of standard Serbian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~97.9. The difference to the previous version of the model is that it is trained with the lemmatiser padding bug removed, cf. https://github.com/stanfordnlp/stanfordnlp/issues/143.

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• The CLASSLA-Stanza model for lemmatisation of standard Serbian 2.1

  2 resources

  The model for lemmatisation of standard Serbian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) combined with the Serbian non-standard training corpus ReLDI-NormTagNER-sr (http://hdl.handle.net/11356/1794) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~98.02. The difference to the previous version is that this version was trained on a combination of the standard (SETimes.SR) and non-standard (ReLDI-NormTagNER-sr) Serbian training corpora.

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• Universal Dependencies 2.5 Models for UDPipe (2019-12-06)

  97 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 94 treebanks of 61 languages of Universal Depenencies 2.5 Treebanks, created solely using UD 2.5 data (http://hdl.handle.net/11234/1-3105). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_25_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

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• Slovenian RoBERTa contextual embeddings model: SloBERTa 2.0

  3 resources

  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)

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• Djview for Shapes - a demonstration (Open Virtual Appliance)

  3 resources

  A shape browser for DjVu documents

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• The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.1

  2 resources

  The model for lemmatisation of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and using the Sloleks inflectional lexicon (http://hdl.handle.net/11356/1230). The estimated F1 of the lemma annotations is ~99.0. The difference to the previous version of the model is that it is trained with the lemmatiser padding bug removed, cf. https://github.com/stanfordnlp/stanfordnlp/issues/143.

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• The CLASSLA-Stanza model for semantic role labeling of standard Slovenian

  2 resources

  The model for semantic role labeling of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1434). The estimated F1 of the semantic role annotations is ~77.2.

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• VIADAT-ANNOTATE

  2 resources

  A VIADAT module; VIADAT-ANNOTATE is an interactive annotation environment. Developed in cooperation with ÚSD AV ČR and NFA.

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• Liner2.5 model NER

  2 resources

  Przygotował: Michał Marcińczuk <marcinczuk@gmail.com> Data: 25.05.2016 Projekt: Clarin-PL (http://clarin-pl.eu) Autorzy: Michał Marcińczuk, Jan Kocoń, Michał Krautforst Modele do narzędzia Liner2.5 do rozpoznawania jednostek identyfikacyjnych. Narzędzie Liner2.5 dostępne jest pod linkiem http://hdl.handle.net/11321/231. Paczka zawiera trzy modele: 1. config-nam.ini -- granice jednostek identyfikacyjnych, 2. config-top9.ini -- granice i ogólna kategoryzacja jednostek (9 kategorii), 3. config-n82.ini -- granice i szczegółowa kategoryzacja jednostek (82 kategorie).

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