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  • The CLASSLA-StanfordNLP model for named entity recognition of standard Croatian 1.0

    This model for named entity recognition of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205).

  • Database of the Western South Slavic Verb HyperVerb -- Derivation

    The verbal Western South Slavic database (WeSoSlaV) contains 3000 most frequent Slovenian and 5300 most frequent BCS verbs which are all coded for a number of properties related to verb derivation. The database is a table where each verb is given a row of its own. The coded properties are organized in columns. Verbs in the database are coded for the following properties: root information, whether or not the verb has prefixes and the identity of the included prefix(es), whether or not the verb has suffixes and the identity of the included suffix(es) etc. All coded properties are explained in the accompanying pdf file.

  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Croatian

    The model for morphosyntactic annotation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~94.1.

  • CroSloEngual BERT 1.1

    Trilingual BERT (Bidirectional Encoder Representations from Transformers) model, trained on Croatian, Slovenian, and English data. State of the art tool representing words/tokens as contextually dependent word embeddings, used for various NLP classification tasks by finetuning the model end-to-end. CroSloEngual BERT are neural network weights and configuration files in pytorch format (i.e. to be used with pytorch library). Changes in version 1.1: fixed vocab.txt file, as previous verson had an error causing very bad results during fine-tuning and/or evaluation.

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

    The model for lemmatisation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). The estimated F1 of the lemma annotations is ~97.6. 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.

  • Slavic Forest, Norwegian Wood (models)

    Trained models for UDPipe used to produce our final submission to the Vardial 2017 CLP shared task (https://bitbucket.org/hy-crossNLP/vardial2017). The SK model was trained on CS data, the HR model on SL data, and the SV model on a concatenation of DA and NO data. The scripts and commands used to create the models are part of separate submission (http://hdl.handle.net/11234/1-1970). The models were trained with UDPipe version 3e65d69 from 3rd Jan 2017, obtained from https://github.com/ufal/udpipe -- their functionality with newer or older versions of UDPipe is not guaranteed. We list here the Bash command sequences that can be used to reproduce our results submitted to VarDial 2017. The input files must be in CoNLLU format. The models only use the form, UPOS, and Universal Features fields (SK only uses the form). You must have UDPipe installed. The feats2FEAT.py script, which prunes the universal features, is bundled with this submission. SK -- tag and parse with the model: udpipe --tag --parse sk-translex.v2.norm.feats07.w2v.trainonpred.udpipe sk-ud-predPoS-test.conllu A slightly better after-deadline model (sk-translex.v2.norm.Case-feats07.w2v.trainonpred.udpipe), which we mention in the accompanying paper, is also included. It is applied in the same way (udpipe --tag --parse sk-translex.v2.norm.Case-feats07.w2v.trainonpred.udpipe sk-ud-predPoS-test.conllu). HR -- prune the Features to keep only Case and parse with the model: python3 feats2FEAT.py Case < hr-ud-predPoS-test.conllu | udpipe --parse hr-translex.v2.norm.Case.w2v.trainonpred.udpipe NO -- put the UPOS annotation aside, tag Features with the model, merge with the left-aside UPOS annotation, and parse with the model (this hassle is because UDPipe cannot be told to keep UPOS and only change Features): cut -f1-4 no-ud-predPoS-test.conllu > tmp udpipe --tag no-translex.v2.norm.tgttagupos.srctagfeats.Case.w2v.udpipe no-ud-predPoS-test.conllu | cut -f5- | paste tmp - | sed 's/^\t$//' | udpipe --parse no-translex.v2.norm.tgttagupos.srctagfeats.Case.w2v.udpipe

  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Croatian 1.1

    The model for morphosyntactic annotation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~94.1. The difference to the previous version of the model is that now the whole XPOS tag is predicted and not specific characters, as was the case in stanfordnlp, which resulted in illegal XPOS tags (and slightly decreased performance).

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

    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.

  • Universal Dependencies 2.4 Models for UDPipe (2019-05-31)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 90 treebanks of 60 languages of Universal Depenencies 2.4 Treebanks, created solely using UD 2.4 data (http://hdl.handle.net/11234/1-2988). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_24_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.

  • The CLASSLA-StanfordNLP model for lemmatisation of standard Croatian

    The model for lemmatisation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). The estimated F1 of the lemma annotations is ~97.6.