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)","displayName","NMT En Ru Transformer6x6",["D",1680638495947],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_en_ru_transformer6x6","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241,242,243,244,245],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":268,"_247":269,"_248":270,"_250":251},{"_206":264,"_247":265,"_248":266,"_250":251},{"_206":262,"_247":262,"_248":263,"_250":251},{"_206":259,"_247":259,"_248":260,"_250":251},{"_206":257,"_247":257,"_248":258,"_250":251},{"_206":255,"_247":255,"_248":256,"_250":251},{"_206":253,"_247":253,"_248":254,"_250":251},{"_206":246,"_247":246,"_248":249,"_250":251},"Transformer","value","filter",{"_252":251,"_247":246},"category","label","field","Ru",{"_252":251,"_247":253},"PytorchLightning",{"_252":251,"_247":255},"NMT",{"_252":251,"_247":257},"NeMo",{"_252":261,"_247":259},"framework","En",{"_252":251,"_247":262},"DL","soln_dl",{"_252":267,"_247":265},"technology","AI","soln_ai",{"_252":267,"_247":269},{"_107":1445,"_109":1446},"ngcApps",[1398],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1379":1380,"_1381":1382,"_1383":1384,"_1385":1386,"_1387":282,"_138":306,"_1388":1389,"_1390":56,"_1391":86,"_1392":86,"_1393":1394,"_1395":86,"_1396":1397},"amplitude",{"_1376":1377,"_978":-7,"_1378":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1370":1371,"_1372":1373,"_1374":1375},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1253":168,"_1254":1255,"_1256":1257,"_1258":1259},"announcements",[],"consentState",{"_344":1243,"_1244":86,"_1245":31,"_1246":31,"_1247":1248},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1784063515043,[],[],[320,321,322,323,324],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":325,"_55":56},4,"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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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