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• UDify Pretrained Model

  3 resources

  Pretrained model weights for the UDify model, and extracted BERT weights in pytorch-transformers format. Note that these weights slightly differ from those used in the paper.

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

  3 resources

  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).

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• CorPipe 24 Multilingual CorefUD 1.2 Model (corpipe24-corefud1.2-240906)

  2 resources

  The `corpipe24-corefud1.2-240906` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 24 (https://github.com/ufal/crac2024-corpipe). It is released under the CC BY-NC-SA 4.0 license. The model is language agnostic (no corpus id on input), so it can be in theory used to predict coreference in any `mT5` language. This model jointly predicts also the empty nodes needed for zero coreference. The paper introducing this model also presents an alternative two-stage approach first predicting empty nodes (via https://www.kaggle.com/models/ufal-mff/crac2024_zero_nodes_baseline/) and then performing coreference resolution (via http://hdl.handle.net/11234/1-5673), which is circa twice as slow but slightly better.

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• The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian

  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.

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• MCSQ Translation Models (en-ru) (v1.0)

  2 resources

  En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip). Their main use should be in-domain translation of social surveys. Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on MCSQ test set (BLEU): en->ru: 64.3 (train: genuine in-domain MCSQ data) ru->en: 74.7 (train: additional backtranslated in-domain MCSQ data) (Evaluated using multeval: https://github.com/jhclark/multeval)

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• Image Annotation Tool

  1 resources

  Image annotation tool is a web application that allows users to mark zones of interest in an image. These zones are then converted to TEI P5 code snippet that can be used in your document to connect the image and the text. This tool was developed to help students and teachers at the Faculty of Arts, Charles University to mark and annotate images of manuscripts.

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• The CLASSLA-Stanza model for morphosyntactic annotation of standard Macedonian 2.1

  3 resources

  This model for morphosyntactic annotation of standard Macedonian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the 1984 training corpus expanded with the Macedonian SETimes corpus (to be published) and using the Macedonian CLARIN.SI word embeddings (http://hdl.handle.net/11356/1788). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~97.14. The difference from the previous version is that this version was trained using a larger training dataset and the new version of the Macedonian word embeddings.

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• Universal Dependencies 1.2 Models for Parsito

  2 resources

  Parsing models for all Universal Depenencies 1.2 Treebanks, created solely using UD 1.2 data (http://hdl.handle.net/11234/1-1548). To use these models, you need Parsito binary, which you can download from http://hdl.handle.net/11234/1-1584.

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• Information extraction from EIA documents

  2 resources

  Environmental impact assessment (EIA) is the formal process used to predict the environmental consequences of a plan. We present a rule-based extraction system to mine Czech EIA documents. The extraction rules work with a set of documents enriched with morphological information and manually created vocabularies of terms supposed to be extracted from the documents, e.g. basic information about the project (address, ID company, ...), data on the impacts and outcomes (waste substances, endangered species, ...), a final opinion. The documents Notice of Intent contains the section BI2 with the information on the scope (capacity) of the plan.

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• The CLASSLA-Stanza model for UD dependency parsing of standard Slovenian 2.0

  3 resources

  This model for UD dependency parsing of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated LAS of the parser is ~91.11. The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses the updated embeddings.

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