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• 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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• The Trankit model for linguistic processing of written and spoken Slovenian 1.2

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  This is a retrained Slovenian model for the Trankit v1.1.1 library for multilingual natural language processing (https://pypi.org/project/trankit/), trained on the concatenation of the SSJ UD treebank of written Slovenian (featuring fiction, non-fiction, periodicals and Wikipedia texts) and the SST UD treebank of spoken Slovenian (featuring transcriptions of spontaneous speech in various settings). It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, morphological features, and dependency parses in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). In comparison to its counterpart models trained on SSJ (http://hdl.handle.net/11356/1963) or SST datasets only, this model yields a significantly better performance on spoken transcripts and an identical state-of-the-art performance on written texts. The model can therefore be recommended as the default, 'universal' Trankit model for processing Slovenian, regardless of the data type. To utilize this model, please follow the instructions provided in our github repository (https://github.com/clarinsi/trankit-train) or refer to the Trankit documentation (https://trankit.readthedocs.io/en/latest/training.html#loading). This ZIP file contains models for both xlm-roberta-large (which delivers better performance but requires more hardware resources) and xlm-roberta-base. In comparison to the previous version, this version was trained on a newer, slightly improved version of the SSJ UD treebank (UD v2.14, https://github.com/UniversalDependencies/UD_Slovenian-SSJ/tree/r2.14) and a substantially extended and improved version of the SST UD treebank (https://github.com/UniversalDependencies/UD_Slovenian-SST/tree/r2.15), thus producing significantly better results for spoken data. In contrast to the previous versions of this model (1.0, 1.1), the model 1.2 was trained on a new SST train-dev-test split introduced in UD v2.15.

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