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  • CUBBITT Translation Models (en-fr) (v1.0)

    CUBBITT En-Fr translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). 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 newstest2014 (BLEU): en->fr: 38.2 fr->en: 36.7 (Evaluated using multeval: https://github.com/jhclark/multeval)

  • CUBBITT Translation Models (en-cs) (v1.0)

    CUBBITT En-Cs translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). 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 newstest2014 (BLEU): en->cs: 27.6 cs->en: 34.4 (Evaluated using multeval: https://github.com/jhclark/multeval)

  • CUBBITT Translation Models (en-pl) (v1.0)

    CUBBITT En-Pl translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). 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 newstest2020 (BLEU): en->pl: 12.3 pl->en: 20.0 (Evaluated using multeval: https://github.com/jhclark/multeval)

  • GreynirTranslate - mBART25 NMT (with layer drop) models for Translations between Icelandic and English (1.0)

    These are the models in http://hdl.handle.net/20.500.12537/125 trained with 40% layer drop. They are suitable for inference using every other layer for optimized inference speed with lower translation performance. We refer to the prior submission for usage and the documentation on layerdrop at https://github.com/pytorch/fairseq/blob/fcca32258c8e8bcc9f9890bf4714fa2f96b6b3e1/examples/layerdrop/README.md. Þessi líkön eru þjálfuð með 40% laga missi (e. layer drop) á líkönunum í http://hdl.handle.net/20.500.12537/125. Þau henta vel til þýðinga þar sem er búið að henda öðru hverju lagi í netinu og þannig er hægt að hraða á þýðingum á kostnað gæða. Leiðbeiningar um notkun netanna er að finna með upphaflegu líkönunum og í notkunarleiðbeiningum Fairseq í https://github.com/pytorch/fairseq/blob/fcca32258c8e8bcc9f9890bf4714fa2f96b6b3e1/examples/layerdrop/README.md.

  • Translation Models (en-de) (v1.0)

    En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). 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 newstest2020 (BLEU): en->de: 25.9 de->en: 33.4 (Evaluated using multeval: https://github.com/jhclark/multeval)

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

    En-De 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->de: 67.5 (train: genuine in-domain MCSQ data only) de->en: 75.0 (train: additional in-domain backtranslated MCSQ data) (Evaluated using multeval: https://github.com/jhclark/multeval)

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

    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)

  • Translation Models (en-ru) (v1.0)

    En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). 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 newstest2020 (BLEU): en->ru: 18.0 ru->en: 30.4 (Evaluated using multeval: https://github.com/jhclark/multeval)

  • GreynirTranslate - mBART25 NMT models for Translations between Icelandic and English (1.0)

    Provided are a general domain IS-EN and EN-IS translation models developed by Miðeind ehf. They are based on a multilingual BART model (https://arxiv.org/pdf/2001.08210.pdf) and finetuned for translation on parallel and backtranslated data. The model is trained using the Fairseq sequence modeling toolkit by PyTorch. Provided here are a model files, sentencepiece subword-tokenizing model and dictionary files for running the model locally. You can run the scripts infer-enis.sh and infer-isen.sh to test the model by translating sentences command-line. For translating documents and evaluating results you will need to binarize the data using fairseq-preprocess and use fairseq-generate for translating. Please refer to the Fairseq documentation for further information on running a pre-trained model: https://fairseq.readthedocs.io/en/latest/ - Pakkinn inniheldur almenn þýðingarlíkön fyrir áttirnar IS-EN og EN-IS þróuð af Miðeind ehf. Þau eru byggð á margmála BART líkani (https://arxiv.org/pdf/2001.08210.pdf) og fínþjálfuð fyrir þýðingar. Líkönin eru þjálfað með Fairseq og PyTorch. Líkönin sjálf og ásamt sentencepiece tilreiðingarlíkani eru gerð aðgengileg. Skripturnar infer-enis.sh og infer-isen.sh gefa dæmi um hvernig er hægt að keyra líkönin á skipanalínu. Til að þýða stór skjöl og meta niðurstöður þarf að nota fairseq-preprocess skipunina ásamt fairseq-generate. Frekari upplýsingar er að finna í Fairseq leiðbeiningunum: https://fairseq.readthedocs.io/en/latest/

  • 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.