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  • Pretrained models for recognising sex education concepts SemSEX 1.0

    Pretrained language models for detecting and classifying the presence of sex education concepts in Slovene curriculum documents. The models are PyTorch neural network models, intended for usage with the HuggingFace transformers library (https://github.com/huggingface/transformers). The models are based on the Slovenian RoBERTa contextual embeddings model SloBERTa 2.0 (http://hdl.handle.net/11356/1397) and on the CroSloEngual BERT model (http://hdl.handle.net/11356/1330). The source code of the model and example usage is available in GitHub repository https://github.com/TimotejK/SemSex. The models and tokenizers can be loaded using the AutoModelForSequenceClassification.from_pretrained() and the AutoTokenizer.from_pretrained() functions from the transformers library. An example of such usage is available at https://github.com/TimotejK/SemSex/blob/main/Concept%20detection/Classifiers/full_pipeline.py. The corpus on which these models have been trained is available at http://hdl.handle.net/11356/1895.

  • Smashcima

    Smashcima is a library and framework for synthesizing images containing handwritten music for creating synthetic training data for OMR models. It is primarily intended to be used as part of optical music recognition workflows, esp. with domain adaptation in mind. The target user is therefore a machine-learning, document processing, library sciences, or computational musicology researcher with minimal skills in python programming. Smashcima is the only tool that simultaneously: - synthesizes handwritten music notation, - produces not only raster images but also segmentation masks, classification labels, bounding boxes, and more, - synthesizes entire pages as well as individual symbols, - synthesizes background paper textures, - synthesizes also polyphonic and pianoform music images, - accepts just MusicXML as input, - is written in Python, which simplifies its adoption and extensibility. Therefore, Smashcima brings a unique new capability for optical music recognition (OMR): synthesizing a near-realistic image of handwritten sheet music from just a MusicXML file. As opposed to notation editors, which work with a fixed set of fonts and a set of layout rules, it can adapt handwriting styles from existing OMR datasets to arbitrary music (beyond the music encoded in existing OMR datasets), and randomize layout to simulate the imprecisions of handwriting, while guaranteeing the semantic correctness of the output rendering. Crucially, the rendered image is provided also with the positions of all the visual elements of music notation, so that both object detection-based and sequence-to-sequence OMR pipelines can utilize Smashcima as a synthesizer of training data. (In combination with the LMX canonical linearization of MusicXML, one can imagine the endless possibilities of running Smashcima on inputs from a MusicXML generator.)

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

    This model for JOS 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 ~93.89. 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.

  • VIADAT

    This component integrates other VIADAT modules; together with VIADAT-REPO this composes the Virtual Assistant for accessing historical audiovisual data. The zip archive contains sources for the following modules: VIADAT, VIADAT-DEPOSIT, VIADAT-TEXT, VIADAT-ANNOTATE, VIADAT-ANALYZE, VIADAT-STAT, VIADAT-GIS and VIADAT-SEARCH. Developed in cooperation with ÚSD AV ČR and NFA.

  • CorPipe 23 multilingual CorefUD 1.2 model (corpipe23-corefud1.2-240906)

    The `corpipe23-corefud1.2-240906` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 <https://github.com/ufal/crac2023-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. However, the model expects empty nodes to be already present on input, predicted by the https://www.kaggle.com/models/ufal-mff/crac2024_zero_nodes_baseline/. This model was present in the CorPipe 24 paper as an alternative to a single-stage approach, where the empty nodes are predicted joinly with coreference resolution (via http://hdl.handle.net/11234/1-5672), an approach circa twice as fast but of slightly worse quality.

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

    This model for named entity recognition of standard Bulgarian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on 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).

  • Slovenian text summarization models

    A text summarisation task aims to convert a longer text into a shorter text while preserving the essential information of the source text. In general, there are two approaches to text summarization. The extractive approach simply rewrites the most important sentences or parts of the text, whereas the abstractive approach is more similar to human-made summaries. We release 5 models that cover extractive, abstractive, and hybrid types: Metamodel: a neural model based on the Doc2Vec document representation that suggests the best summariser. Graph-based model: unsupervised graph-based extractive approach that returns the N most relevant sentences. Headline model: a supervised abstractive approach (T5 architecture) that returns returns headline-like abstracts. Article model: a supervised abstract approach (T5 architecture) that returns short summaries. Hybrid-long model: unsupervised hybrid (graph-based and transformer model-based) approach that returns short summaries of long texts. Details and instructions to run and train the models are available at https://github.com/clarinsi/SloSummarizer. The web service with a demo is available at https://slovenscina.eu/povzemanje.

  • Piper TTS (VITS) models for Talrómur1

    Trained models for four voices from the Talrómur [1] corpus trained with VITS [2] and exported to the onnxruntime [3] for Piper TTS [4]. The four voices are Búi, Salka, Steinn and Ugla. Módel fyrir fjórar raddir úr Talrómi [1]. Raddirnar eru þjálfaðar með VITS [2] og varpað í onnxruntime [3] skrá fyrir Piper TTS [4] verkefnið. Raddirnar fjórar eru Búi, Salka, Steinn og Ugla. [1] http://hdl.handle.net/20.500.12537/104 [2] https://github.com/jaywalnut310/vits/ [3] https://onnxruntime.ai/ [4] https://github.com/rhasspy/piper