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  • Universal Dependencies 2.12 models for UDPipe 2 (2023-07-17)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 131 treebanks of 72 languages of Universal Depenencies 2.12 Treebanks, created solely using UD 2.12 data (https://hdl.handle.net/11234/1-5150). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_212_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of non-standard Serbian 1.0

    This model for morphosyntactic annotation of non-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), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the hr500k training corpus (http://hdl.handle.net/11356/1210) and the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~94.91.

  • GreynirTranslate - mBART25 NMT models for Translations between Icelandic and English (1.0)

    Provided are a general domain IS-EN and EN-IS translation models developed by Miðeind ehf. They are based on a multilingual BART model (https://arxiv.org/pdf/2001.08210.pdf) and finetuned for translation on parallel and backtranslated data. The model is trained using the Fairseq sequence modeling toolkit by PyTorch. Provided here are a model files, sentencepiece subword-tokenizing model and dictionary files for running the model locally. You can run the scripts infer-enis.sh and infer-isen.sh to test the model by translating sentences command-line. For translating documents and evaluating results you will need to binarize the data using fairseq-preprocess and use fairseq-generate for translating. Please refer to the Fairseq documentation for further information on running a pre-trained model: https://fairseq.readthedocs.io/en/latest/ - Pakkinn inniheldur almenn þýðingarlíkön fyrir áttirnar IS-EN og EN-IS þróuð af Miðeind ehf. Þau eru byggð á margmála BART líkani (https://arxiv.org/pdf/2001.08210.pdf) og fínþjálfuð fyrir þýðingar. Líkönin eru þjálfað með Fairseq og PyTorch. Líkönin sjálf og ásamt sentencepiece tilreiðingarlíkani eru gerð aðgengileg. Skripturnar infer-enis.sh og infer-isen.sh gefa dæmi um hvernig er hægt að keyra líkönin á skipanalínu. Til að þýða stór skjöl og meta niðurstöður þarf að nota fairseq-preprocess skipunina ásamt fairseq-generate. Frekari upplýsingar er að finna í Fairseq leiðbeiningunum: https://fairseq.readthedocs.io/en/latest/

  • Byte-Level Neural Error Correction Model for Icelandic - Yfirlestur (24.03)

    This Byte-Level Neural Error Correction Model for Icelandic is a fine-tuned byT5-base Transformer model for error correction in natural language. It acts as a machine translation model in that it “translates” from deficient Icelandic to correct Icelandic. The model is an improved version of a previous model which is accessible here: http://hdl.handle.net/20.500.12537/321. The improved model is trained on contextual and domain-tagged data, with an additional span-masking pre-training, along with a wider variety of text genre. The model is trained on span-masked data, parallel synthetic error data and real error data. The span-masked pre-training data consisted of a wide variety of texts, including forums and texts from the Icelandic Gigaword Corpus (IGC, http://hdl.handle.net/20.500.12537/254). Synthetic error data was taken from different texts, e.g. from IGC (data which was excluded from the span-masked data), MÍM (http://hdl.handle.net/20.500.12537/113), student essays and educational material. This data was scrambled to simulate real grammatical and typographical errors, and some span-masking was included. Fine-tuning data consisted of data from the iceErrorCorpus (IceEC, http://hdl.handle.net/20.500.12537/73) and the three specialised error corpora (L2: http://hdl.handle.net/20.500.12537/131, dyslexia: http://hdl.handle.net/20.500.12537/132, child language: http://hdl.handle.net/20.500.12537/133). The model can correct a variety of textual errors, even in texts containing many errors, such as those written by people with dyslexia. Measured on the Grammatical Error Correction Test Set (http://hdl.handle.net/20.500.12537/320), the model scores 0.898229 on the GLEU metric (modified BLEU for grammatical error correction) and 0.07% in TER (translation error rate). When measured on the Icelandic Error Corpus' test set, the model scores 0.906834 on the GLEU metric and 0.04% in TER. Þetta leiðréttingarlíkan fyrir íslensku er fínþjálfað byT5-base Transformer-líkan. Það er í raun þýðingalíkan sem þýðir úr íslenskum texta með villum yfir í texta án villna. Líkanið er uppfærð útgáfa af fyrra líkani sem má nálgast hér: http://hdl.handle.net/20.500.12537/321. Uppfærða líkanið er þjálfað á samhengi og gögnum sem hafa verið merkt fyrir óðölum ásamt eyðufylllingarþjálfun og þjálfun með fjölbreyttari texta. Líkanið er þjálfað í eyðufyllingu, á samhliða gervivillugögnum og raunverulegum villugögnum. Eyðufyllingargögn voru tekin úr ýmsum texta, m.a. úr spjallborðum og textum úr Risamálheildinni (http://hdl.handle.net/20.500.12537/254). Gervivillugögn voru einnig tekin úr ýmsum texta, m.a. úr Risamálheildinni (þeim hluta sem var ekki í eyðufyllingarverkefninu), MÍM (http://hdl.handle.net/20.500.12537/113), nemendaritgerðum og fræðsluefni. Gögnin voru rugluð til þess að líkja eftir raunverulegum málfræði- og ritunarvillum og voru að hluta til hulin til þess að þjálfa eyðufyllingu. Fínþjálfunargögn voru tekin úr íslensku villumálheildinni (http://hdl.handle.net/20.500.12537/73) og sérhæfðu villumálheildunum þremur (íslenska sem erlent mál: http://hdl.handle.net/20.500.12537/131, lesblinda: http://hdl.handle.net/20.500.12537/132, barnatextar: http://hdl.handle.net/20.500.12537/133). Líkanið getur leiðrétt fjölbreyttar textavillur, jafnvel í texta sem inniheldur mjög margar villur, svo sem frá fólki með lesblindu. Líkanið skorar 0,898229 GLEU-stig (BLEU nema lagað að málrýni) og er með 0,07% villuhlutfall í þýðingu (translation error rate), þegar það er metið á Prófunarmengi fyrir textaleiðréttingar (http://hdl.handle.net/20.500.12537/320). Þegar það er metið á prófunarmengi íslensku villumálheildarinnar skorar líkanið 0,906834 GLEU-stig og er með 0,04% villuhlutfall í þýðingu.

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

    The model for lemmatisation of non-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), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The estimated F1 of the lemma annotations is ~97.54. The difference to the previous version of the lemmatizer is that now it relies solely on XPOS annotations, and not on a combination of UPOS, FEATS (lexicon lookup) and XPOS (lemma prediction) annotations.

  • The CLASSLA-StanfordNLP model for UD dependency parsing of standard Bulgarian 1.0

    The model for UD dependency parsing of standard Bulgarian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the UD-parsed portion of the BulTreeBank training corpus (http://hdl.handle.net/11495/D93F-C6E9-65D9-2) and using the CoNLL2017 word embeddings (http://hdl.handle.net/11234/1-1989). The estimated LAS of the parser is ~91.5.