Result filters

Metadata provider

Language

...more

Resource type

Availability

703 record(s) found

Search results

  • The Trankit model for linguistic process of standard written Slovenian 1.1

    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 reference SSJ UD treebank featuring fiction, non-fiction, periodical and Wikipedia texts in standard modern Slovenian. 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/). The model was trained using a dataset published by Universal Dependencies in release 2.14 (https://github.com/UniversalDependencies/UD_Slovenian-SSJ/tree/r2.14). 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. This version was trained on a newer, slightly improved version of the SSJ UD treebank (UD v2.14) than the previous version of the model and produces similar results.

  • GreynirSeq - A Natural Language Processing Toolkit for Icelandic (v0.2.0)

    GreynirSeq is a natural language parsing toolkit for Icelandic focused on sequence modeling with neural networks. The modeling part (nicenlp) of GreynirSeq is built on top of the excellent Fairseq from Meta (which is built on top of PyTorch). Interfaces for POS-tagging, NER-tagging and machine translation are included in this version v.0.2.0. For updated versions of the software please refer to https://github.com/mideind/GreynirSeq -- GreynirSeq er málvinnsluhugbúnaður fyrir íslensku með áherslu á notkun runulíkana sem byggja á tauganetum. Sá hluti sem snýr að tauganetum er byggður á Fairseq frá Meta og byggir á PyTorch. Í þessari útgáfu (v0.2.0) er stuðningur við orðflokkagreiningu, nafnamörkun og þýðingu í gegnum viðmót á skipanalínu. Nýjustu útgáfu af hugbúnaðinum má ávallt finna á https://github.com/mideind/GreynirSeq

  • Upload2S3 (22.06)

    [ENGLISH] This project is a simple and straight forward way to upload podcast data like text files via a form to an AWS S3 bucket. This web application codebase is minimally derived from an existing ReactJS web application, samromur-chat. However, using this codebase does not require knowledge of samromur-chat. [ÍSLENSKA] Þetta verkefni býður upp á einfalda leið til þess að hlaða upp hlaðvarpsgögnum, t.d. textagögnum, á AWS S3. Kóðinn er lítillega byggður á ReactJS-forritinu samromur-chat. Ekki þarf þó þekkingu á samromur-chat til þess að nota þetta tól.

  • GreynirT2T - En--Is NMT with Tensor2Tensor (1.0)

    A program library for training English-Icelandic neural machine translation systems, built on top of Tensor2Tensor and Tensorflow. Supports training with or without back-translated data. Forritasafn til að þjálfa þýðingarlíkön sem þýða milli íslensku og ensku. Uppsetningin er byggð á Tensor2Tensor og Tensorflow. Safnið styður þjálfun með og án bakþýðingargagna.

  • MSTperl parser (2015-05-19)

    MSTperl is a Perl reimplementation of the MST parser of Ryan McDonald (http://www.seas.upenn.edu/~strctlrn/MSTParser/MSTParser.html). MST parser (Maximum Spanning Tree parser) is a state-of-the-art natural language dependency parser -- a tool that takes a sentence and returns its dependency tree. In MSTperl, only some functionality was implemented; the limitations include the following: the parser is a non-projective one, curently with no possibility of enforcing the requirement of projectivity of the parse trees; only first-order features are supported, i.e. no second-order or third-order features are possible; the implementation of MIRA is that of a single-best MIRA, with a closed-form update instead of using quadratic programming. On the other hand, the parser supports several advanced features: parallel features, i.e. enriching the parser input with word-aligned sentence in other language; adding large-scale information, i.e. the feature set enriched with features corresponding to pointwise mutual information of word pairs in a large corpus (CzEng); weighted/unweighted parser model interpolation; combination of several instances of the MSTperl parser (through MST algorithm); combination of several existing parses from any parsers (through MST algorithm). The MSTperl parser is tuned for parsing Czech. Trained models are available for Czech, English and German. We can train the parser for other languages on demand, or you can train it yourself -- the guidelines are part of the documentation. The parser, together with detailed documentation, is avalable on CPAN (http://search.cpan.org/~rur/Treex-Parser-MSTperl/).

  • Neural Machine Translation model for Slovene-English language pair RSDO-DS4-NMT 1.2.6

    This Neural Machine Translation model for Slovene-English language pair was trained following the NVIDIA NeMo NMT AAYN recipe (for details see the official NVIDIA NeMo NMT documentation, https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/machine_translation/machine_translation.html, and NVIDIA NeMo GitHub repository https://github.com/NVIDIA/NeMo). It provides functionality for translating text written in Slovene language to English and vice versa. The training corpus was built from publicly available datasets, including Parallel corpus EN-SL RSDO4 1.0 (https://www.clarin.si/repository/xmlui/handle/11356/1457), as well as a small portion of proprietary data. In total the training corpus consisted of 32.638.758 translation pairs and the validation corpus consisted of 8.163 translation pairs. The model was trained on 64GPUs and on the validation corpus reached a SacreBleu score of 48.3191 (at epoch 37) for translation from Slovene to English and a SacreBleu score of 53.8191 (at epoch 47) for translation from English to Slovene.

  • Webrice extension (22.09)

    The Webrice plugin is a software add-on that gives access to people to listen to web pages instead of reading them. This chrome browser extension changes Icelandic text to speech. Webrice-viðbótin er hugbúnaðarforrit sem hjálpar notendum að velja texta og hlusta á hann í staðinn fyrir að lesa. Þessi Chrome-viðbót breytir íslenskum textan í tal.

  • Trankit model for SST 2.15 1.1

    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 SST treebank of spoken Slovenian (UD v2.15, https://github.com/UniversalDependencies/UD_Slovenian-SST/tree/r2.15) featuring transcriptions of spontaneous speech in various everyday 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 feature prediction, and dependency parses in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). Please note this model has been published for archiving purposes only. For production use, we recommend using the state-of-the art Trankit model available here: http://hdl.handle.net/11356/1965 (v1.2 or newest). The latter was trained on both spoken (SST) and written (SSJ) data, and demonstrates a significantly higher performance to the model featured in this submission. In comparison with version 1.0, this model was trained on a new train-dev-test split of the SST treebank introduced in release UD v2.15.

  • Tokenizer for Icelandic text (2.3.1)

    Tokenizer is a compact pure-Python (2.7 and 3) executable program and module for tokenizing Icelandic text. It converts input text to streams of tokens, where each token is a separate word, punctuation sign, number/amount, date, e-mail, URL/URI, etc. It also segments the token stream into sentences, considering corner cases such as abbreviations and dates in the middle of sentences. More information at: https://github.com/mideind/Tokenizer Tokenizer er pakki fyrir Python 2.7 og 3, ásamt skipanalínutóli, sem sér um tilreiðslu íslensks texta. Pakkinn umbreytir inntakstexta í tókastraum. Hver tóki er stakt orð, greinarmerki, tala/upphæð, dags-/tímasetning, netfang, vefslóð o.s.frv. Tólið skiptir tókastraumnum einnig í setningar og tekur tillit til jaðartilvika eins og skammstafana og dagsetninga í miðjum setningum. Frekari upplýsingar á: https://github.com/mideind/Tokenizer