Model Overview

This model can be used for translating text in source language (En) to a text in target language (Ru).

Model Architecture

The model is based on Transformer \"Big\" architecture originally presented in \"Attention Is All You Need\" paper [1].\nIn this particular instance, the model has 6 layers in the encoder and 6 layers in the decoder.\nIt is using YouTokenToMe tokenizer [2].

Training

These 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 300k steps.

Datasets

While training this model, we used the following datasets:

Parallel Commoncrawl - \"http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\"
Paracrawl - \"https://s3.amazonaws.com/web-language-models/paracrawl/bonus/en-ru.txt.gz\"
News Commentary - \"http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz\"
WikiTitles - \"http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz\"
WikiMatrix - \"http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz\"
UN Parallel Corpus - https://conferences.unite.un.org/UNCORPUS/en/DownloadOverview

Tokenizer Construction

We used the YouTokenToMe tokenizer [2] with shared encoder and decoder BPE tokenizers.

Performance

The accuracy of translation models are often measured using BLEU scores [3].\nThe model achieves the following sacreBLEU [4] scores on the WMT'13, WMT'14, WMT'18, WMT'19 and WMT'20 test sets

WMT'13 - 27.3\nWMT'14 - 39.9\nWMT'18 - 31.2\nWMT'19 - 30.8\nWMT'20 - 22.3\n

How to Use this Model

The 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.

Automatically load the model from NGC

import nemo\nimport nemo.collections.nlp as nemo_nlp\nnmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name=\"nmt_en_ru_transformer6x6\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_ru_transformer6x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang ru --source_lang en\n

Input

This translate method of the NMT model accepts a list of de-tokenized strings.

Output

The translate method outputs a list of de-tokenized strings in the target language.

Limitations

No known limitations at this time.

References

[1] Vaswani, Ashish, et al. \"Attention is all you need.\" arXiv preprint arXiv:1706.03762 (2017).

[2] https://github.com/VKCOM/YouTokenToMe

Licence

License to use this model is covered by the NGC TERMS OF USE 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.\n[3] https://en.wikipedia.org/wiki/BLEU

[4] https://github.com/mjpost/sacreBLEU

[5] NVIDIA NeMo Toolkit

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The NeMo toolkit [5] was used for training this model over roughly 300k steps.\n\n### Datasets\nWhile training this model, we used the following datasets:\n\n* Parallel Commoncrawl - \"http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\"\n* Paracrawl - \"https://s3.amazonaws.com/web-language-models/paracrawl/bonus/en-ru.txt.gz\"\n* News Commentary - \"http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz\"\n* WikiTitles - \"http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz\"\n* WikiMatrix - \"http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz\"\n* UN Parallel Corpus - https://conferences.unite.un.org/UNCORPUS/en/DownloadOverview\n\n### Tokenizer Construction\nWe used the YouTokenToMe 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 the WMT'13, WMT'14, WMT'18, WMT'19 and WMT'20 test sets\n\n```\nWMT'13 - 27.3\nWMT'14 - 39.9\nWMT'18 - 31.2\nWMT'19 - 30.8\nWMT'20 - 22.3\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=\"nmt_en_ru_transformer6x6\")\n```\n\n### Translating text with this model\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_ru_transformer6x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang ru --source_lang en\n```\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/VKCOM/YouTokenToMe\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).\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)","NMT En Ru Transformer6x6",["D",1680638495947],"/api/image/nvidia/nemo/models/nmt_en_ru_transformer6x6","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[277,278,279,280,281,282,283,284],[],[],[],{"_90":301,"_127":302,"_128":303,"_130":131},{"_90":297,"_127":298,"_128":299,"_130":131},{"_90":295,"_127":295,"_128":296,"_130":131},{"_90":292,"_127":292,"_128":293,"_130":131},{"_90":135,"_127":135,"_128":291,"_130":131},{"_90":289,"_127":289,"_128":290,"_130":131},{"_90":287,"_127":287,"_128":288,"_130":131},{"_90":285,"_127":285,"_128":286,"_130":131},"Transformer",{"_132":131,"_127":285},"Ru",{"_132":131,"_127":287},"PytorchLightning",{"_132":131,"_127":289},{"_132":131,"_127":135},"NeMo",{"_132":294,"_127":292},"framework","En",{"_132":131,"_127":295},"DL","soln_dl",{"_132":300,"_127":298},"technology","AI","soln_ai",{"_132":300,"_127":302},{"_170":1278,"_172":1279},"ngcApps",[1231],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1212":1213,"_1214":1215,"_1216":1217,"_1218":1219,"_1220":315,"_201":341,"_1221":1222,"_1223":51,"_1224":148,"_1225":148,"_1226":1227,"_1228":148,"_1229":1230},"amplitude",{"_1209":1210,"_854":-7,"_1211":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1203":1204,"_1205":1206,"_1207":1208},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788008965130,"1.12.0","defaultSearchSuggestions",{"_1094":229,"_1095":1096,"_1097":1098,"_1099":1100},"announcements",[],"consentState",{"_380":1084,"_1085":148,"_1086":26,"_1087":26,"_1088":1089},"consentConfig",{"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381},"showContactAdmin",1788008965131,[],[],[355,356,357,358,359,360,361],{"_40":55,"_42":66,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":65,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":65,"_46":-5,"_48":-5,"_50":51},"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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