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• Universal Dependencies 2.10 models for UDPipe 2 (2022-07-11)

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  Tokenizer, POS Tagger, Lemmatizer and Parser models for 123 treebanks of 69 languages of Universal Depenencies 2.10 Treebanks, created solely using UD 2.10 data (https://hdl.handle.net/11234/1-4758). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_210_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• Universal Dependencies 2.15 models for UDPipe 2 (2024-11-21)

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  Tokenizer, POS Tagger, Lemmatizer and Parser models for 147 treebanks of 78 languages of Universal Depenencies 2.15 Treebanks, created solely using UD 2.15 data (https://hdl.handle.net/11234/1-5787). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_215_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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

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  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 .

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• UDPipe

  1 resources

  UDPipe is an trainable pipeline for tokenization, tagging, lemmatization and dependency parsing of CoNLL-U files. UDPipe is language-agnostic and can be trained given only annotated data in CoNLL-U format. Trained models are provided for nearly all UD treebanks.

• IceEval - Icelandic Natural Language Processing Benchmark 22.09

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  IceEval is a benchmark for evaluating and comparing the quality of pre-trained language models. The models are evaluated on a selection of four NLP tasks for Icelandic: part-of-speech tagging (using the MIM-GOLD corpus), named entity recognition (using the MIM-GOLD-NER corpus), dependency parsing (using the IcePaHC-UD corpus) and automatic text summarization (using the IceSum corpus). IceEval includes scripts for downloading the datasets, splitting them into training, validation and test splits and training and evaluating models for each task. The benchmark uses the Transformers, DiaParser and TransformerSum libraries for fine-tuning and evaluation. IceEval er tól til að meta og bera saman forþjálfuð mállíkön. Líkönin eru metin á fjórum máltækniverkefnum fyrir íslensku: mörkun (með MIM-GOLD málheildinni), nafnakennslum (með MIM-GOLD-NER málheildinni), þáttun (með IcePaHC-UD málheildinni) og sjálfvirkri samantekt (með IceSum málheildinni). IceEval inniheldur skriftur til að sækja gagnasöfnin, skipta þeim í þjálfunar- og prófunargögn og að fínstilla og meta líkön fyrir hvert verkefni. Transformers, DiaParser og TransformerSum forritasöfnin eru notuð til að fínstilla líkönin.

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