De\nWMT13 - 35.3\nWMT14 - 37.9\n\nEs\nWMT12 - 40.5\nWMT13 - 37.5\n\nFr\nWMT13 - 36.3\nWMT14 - 40.9

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import nemo\nimport nemo.collections.nlp as nemo_nlp\nnmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name=\"mnmt_deesfr_en_transformer24x6\")

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python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=mnmt_deesfr_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang [SOURCE_LANGUAGE]

","nvidia/nemo/mnmt_deesfr_en_transformer24x6","key","nvidia/nemo/models/mnmt_deesfr_en_transformer24x6","resourceId","mnmt_deesfr_en_transformer24x6","shortDescription","Multilingual Neural Machine Translation model to translate from German/Spanish/French to English","## Model Overview\nThis model can be used for translating text in source language (De/Es/Fr) to a text in target language (En).\n\n## Model Architecture\nThe model is based on Transformer \"Big\" architecture originally presented in \"Attention Is All You Need\" paper [1].\nIn this particular instance, the model has 24 layers in the encoder and 6 layers in the decoder.\nIt is using SentencePiece tokenizer [2].\n\n## Training\nThese models were trained on a collection of many publicly available datasets comprising of millions of parallel sentences. The NeMo toolkit [5] was used for training this model over roughly 500k steps.\n\n### Datasets\nWhile training this model, we used the following datasets:\n\nGerman\n* Europarl de-en set from: http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz\n* De-En version of parallel common crawl from: http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* De-En version of paracrawl from: https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-de.txt.gz\n* De-En News commentary version from: http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz\n* De-En Wikipedia Parallel Titles from http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.de-en.tmx.zip\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.de-en.tmx.zip\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.de-en.tmx.zip\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.de-en.tmx.zip\n* De-En portion of WikiMatrix v1 de-en data from: http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz\n\nSpanish\n* Europarl - https://www.statmt.org/europarl/v7/es-en.tgz\n* Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-es.txt.gz\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/worldbank.en-es.tmx.zip\n* WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-es.tsv.gz\n\nFrench\n* Europarl - http://www.statmt.org/europarl/v10/training/europarl-v10.fr-en.tsv.gz\n* Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-fr.txt.gz\n* Giga French - http://statmt.org/wmt10/training-giga-fren.tar\n* News Commentary - http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-fr.tsv.gz\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-fr.tmx.zip\n* WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.tsv.gz\n\n### Tokenizer Construction\nWe used the SentencePiece tokenizer [2] with shared encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\nThe model achieves the following sacreBLEU [4] scores on WMT test sets\n\n```\nDe\nWMT13 - 35.3\nWMT14 - 37.9\n\nEs\nWMT12 - 40.5\nWMT13 - 37.5\n\nFr\nWMT13 - 36.3\nWMT14 - 40.9\n```\n\n## How to Use this Model\nThe model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n```python\nimport nemo\nimport nemo.collections.nlp as nemo_nlp\nnmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name=\"mnmt_deesfr_en_transformer24x6\")\n```\n\n### Translating text with this model\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=mnmt_deesfr_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang [SOURCE_LANGUAGE]\n```\nwhere [SOURCE_LANGUAGE] can be 'de' or 'es' or 'fr'\n\n### Input\n\nThis translate method of the NMT model accepts a list of de-tokenized strings.\n\n### Output\n\nThe translate method outputs a list of de-tokenized strings in the target language.\n\n## Limitations\n\nNo known limitations at this time.\n\n## References\n\n[1] Vaswani, Ashish, et al. \"Attention is all you need.\" arXiv preprint arXiv:1706.03762 (2017).\n\n[2] https://github.com/google/sentencepiece\n\n[3] https://en.wikipedia.org/wiki/BLEU\n\n[4] https://github.com/mjpost/sacreBLEU\n\n[5] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n## Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","NMT Multilingual De/Es/Fr En Transformer24x6",["D",1680635842388],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/mnmt_deesfr_en_transformer24x6","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245,246,247,248,249,250,251,252],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":281,"_254":282,"_255":283,"_257":258},{"_211":279,"_254":279,"_255":280,"_257":258},{"_211":275,"_254":276,"_255":277,"_257":258},{"_211":273,"_254":273,"_255":274,"_257":258},{"_211":271,"_254":271,"_255":272,"_257":258},{"_211":269,"_254":269,"_255":270,"_257":258},{"_211":267,"_254":267,"_255":268,"_257":258},{"_211":265,"_254":265,"_255":266,"_257":258},{"_211":262,"_254":262,"_255":263,"_257":258},{"_211":260,"_254":260,"_255":261,"_257":258},{"_211":253,"_254":253,"_255":256,"_257":258},"Transformer","value","filter",{"_259":258,"_254":253},"category","label","field","PytorchLightning",{"_259":258,"_254":260},"PyTorch",{"_259":264,"_254":262},"framework","NMT",{"_259":258,"_254":265},"Multilingual",{"_259":258,"_254":267},"Fr",{"_259":258,"_254":269},"Es",{"_259":258,"_254":271},"En",{"_259":258,"_254":273},"DL","soln_dl",{"_259":278,"_254":276},"technology","De",{"_259":258,"_254":279},"AI","soln_ai",{"_259":278,"_254":282},{"_119":1466,"_121":1467},"ngcApps",[1419],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1401":1402,"_1403":1404,"_1405":1406,"_1407":1408,"_1409":295,"_146":319,"_1410":1411,"_1399":59,"_1412":98,"_1413":98,"_1414":1415,"_1416":98,"_1417":1418},"amplitude",{"_1397":1398,"_1399":59,"_991":-7,"_1400":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1391":1392,"_1393":1394,"_1395":1396},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.2.3","defaultSearchSuggestions",{"_1266":177,"_1267":1268,"_1269":1270,"_1271":57},"announcements",[],"consentState",{"_356":1256,"_1257":34,"_1258":34,"_1259":98,"_1260":1261},"consentConfig",{"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357},"showContactAdmin",1782185615301,[],[],[333,334,335,336,337],{"_48":63,"_50":73,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":114,"_54":-5,"_56":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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