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 24 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 roughly a hundred million parallel sentences. The NeMo toolkit [5] was used for training this model over roughly 700k 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
CC-Aligned - http://www.statmt.org/cc-aligned/sentence-aligned/en_XX-ru_RU.tsv.xz
CC-Matrix - https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix

Tokenizer Construction

We used the YouTokenToMe tokenizer [2] with separate 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 - 30.5\nWMT'14 - 44.4\nWMT'18 - 35.1\nWMT'19 - 35.8\nWMT'20 - 25.3\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_transformer24x6\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_ru_transformer24x6.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 700k 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* CC-Aligned - http://www.statmt.org/cc-aligned/sentence-aligned/en_XX-ru_RU.tsv.xz\n* CC-Matrix - https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix\n\n### Tokenizer Construction\nWe used the YouTokenToMe tokenizer [2] with separate 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 - 30.5\nWMT'14 - 44.4\nWMT'18 - 35.1\nWMT'19 - 35.8\nWMT'20 - 25.3\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_transformer24x6\")\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_transformer24x6.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",["D",1680635678956],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_en_ru_transformer24x6","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[282,283,284,285,286,287,288],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_252":310,"_112":311,"_290":312,"_292":293},{"_252":307,"_112":308,"_290":309,"_292":293},{"_252":303,"_112":304,"_290":305,"_292":293},{"_252":299,"_112":300,"_290":301,"_292":293},{"_252":297,"_112":297,"_290":298,"_292":293},{"_252":295,"_112":295,"_290":296,"_292":293},{"_252":289,"_112":289,"_290":291,"_292":293},"Transformer","filter",{"_294":293,"_112":289},"category","label","field","PytorchLightning",{"_294":293,"_112":295},"NMT",{"_294":293,"_112":297},"Natural Language Processing","uscs_natural_language_processing",{"_294":302,"_112":300},"application","DL","soln_dl",{"_294":306,"_112":304},"technology","Conversational AI","soln_conversational_ai",{"_294":306,"_112":308},"AI","soln_ai",{"_294":306,"_112":311},{"_167":1497,"_169":1498},"ngcApps",[1450],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1432":1433,"_1434":1435,"_1436":1437,"_1438":1439,"_1440":324,"_194":348,"_1441":1442,"_1430":59,"_1443":146,"_1444":146,"_1445":1446,"_1447":146,"_1448":1449},"amplitude",{"_1428":1429,"_1430":59,"_1020":-7,"_1431":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1422":1423,"_1424":1425,"_1426":1427},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1295":225,"_1296":1297,"_1298":1299,"_1300":1301},"announcements",[],"consentState",{"_385":1285,"_1286":34,"_1287":34,"_1288":146,"_1289":1290},"consentConfig",{"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386},"showContactAdmin",1782555676030,[],[],[362,363,364,365,366],{"_48":62,"_50":72,"_52":162,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":162,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":162,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":162,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":162,"_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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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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. 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The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Key	Value
Architecture	NMT En Ru Transformer24x6
Attention Heads	16
Feedforward Dimension	4096
Hidden Dimension	1024
Inputs	Text in English
Number of Decoder Layers	6
Number of Encoder Layers	24
Outputs	Text in Russian

Key	Value
WMT'13	30.5
WMT'14	44.4
WMT'18	35.1
WMT'19	35.8
WMT'20	25.3