Model Overview

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

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 12 layers in the encoder and 2 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:

Europarl de-en set from: http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz
De-En version of parallel common crawl from: http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
De-En version of paracrawl from: https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-de.txt.gz
De-En News commentary version from: http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz
De-En Wikipedia Parallel Titles from http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.de-en.tmx.zip
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.de-en.tmx.zip
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.de-en.tmx.zip
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.de-en.tmx.zip
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

Tokenizer Construction

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

Performance

The accuracy of translation models are often measured using BLEU scores [3].

WMT14 - 30.2\nWMT18 - 46.4\nWMT19 - 41.1\nWMT20 - 31.5\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.

Usage

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_de_transformer12x2\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_de_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang de --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

[3] https://en.wikipedia.org/wiki/BLEU

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

[5] NVIDIA NeMo Toolkit

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.

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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* 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\n### Tokenizer Construction\nWe used the YouTokenToMe tokenizer [2] with joint encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\n\n```\nWMT14 - 30.2\nWMT18 - 46.4\nWMT19 - 41.1\nWMT20 - 31.5\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## Usage\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_de_transformer12x2\")\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_de_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang de --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[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",["D",1680635753454],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_en_de_transformer12x2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[282,283,284,285,286,287,288,289,290],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_252":314,"_113":315,"_292":316,"_294":295},{"_252":312,"_113":312,"_292":313,"_294":295},{"_252":308,"_113":309,"_292":310,"_294":295},{"_252":306,"_113":306,"_292":307,"_294":295},{"_252":304,"_113":304,"_292":305,"_294":295},{"_252":302,"_113":302,"_292":303,"_294":295},{"_252":299,"_113":299,"_292":300,"_294":295},{"_252":297,"_113":297,"_292":298,"_294":295},{"_252":291,"_113":291,"_292":293,"_294":295},"Transformer","filter",{"_296":295,"_113":291},"category","label","field","PytorchLightning",{"_296":295,"_113":297},"PyTorch",{"_296":301,"_113":299},"framework","NMT",{"_296":295,"_113":302},"NeMo",{"_296":301,"_113":304},"En",{"_296":295,"_113":306},"DL","soln_dl",{"_296":311,"_113":309},"technology","De",{"_296":295,"_113":312},"AI","soln_ai",{"_296":311,"_113":315},{"_163":1292,"_165":1293},"ngcApps",[1245],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1226":1227,"_1228":1229,"_1230":1231,"_1232":1233,"_1234":328,"_194":354,"_1235":1236,"_1237":59,"_1238":142,"_1239":142,"_1240":1241,"_1242":142,"_1243":1244},"amplitude",{"_1223":1224,"_866":-7,"_1225":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1217":1218,"_1219":1220,"_1221":1222},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787646776898,"1.12.0","defaultSearchSuggestions",{"_1106":225,"_1107":1108,"_1109":1110,"_1111":1112},"announcements",[],"consentState",{"_391":1096,"_1097":142,"_1098":34,"_1099":34,"_1100":1101},"consentConfig",{"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392},"showContactAdmin",1787646776899,[],[],[368,369,370,371,372],{"_48":63,"_50":74,"_52":158,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":158,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":158,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":158,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":158,"_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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647],[594,595,596,597,598],{"_432":507,"_252":507,"_224":225,"_405":618},{"_432":481,"_252":481,"_224":225,"_405":613},{"_432":608,"_252":608,"_224":225,"_405":609},{"_432":603,"_252":603,"_224":225,"_405":604},{"_432":599,"_252":599,"_224":225,"_405":600},"nvidia.partners",[601],{"_78":602,"_438":599,"_439":34,"_224":475},"edge","app.hushly.com",[605],{"_78":606,"_438":603,"_439":34,"_224":607},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[610],{"_78":611,"_438":608,"_439":34,"_224":612},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734],[686,687,688,689],{"_432":701,"_252":701,"_224":225,"_405":702},{"_432":696,"_252":696,"_224":225,"_405":697},{"_432":494,"_252":494,"_224":225,"_405":693},{"_432":608,"_252":608,"_224":225,"_405":690},[691],{"_78":692,"_438":608,"_439":142,"_224":612},"_gh_sess",[694],{"_78":695,"_438":494,"_439":142,"_224":501},"YSC","bizible.com",[698],{"_78":699,"_438":696,"_439":34,"_224":700},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[703],{"_78":704,"_438":701,"_439":34,"_224":705},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":776,"_438":553,"_439":34,"_224":777},{"_78":761,"_438":775,"_439":142,"_224":762},{"_78":744,"_438":556,"_439":34,"_224":669},{"_78":744,"_438":657,"_439":34,"_224":669},{"_78":773,"_438":553,"_439":34,"_224":774},{"_78":761,"_438":559,"_439":142,"_224":762},{"_78":771,"_438":553,"_439":34,"_224":772},{"_78":761,"_438":556,"_439":142,"_224":762},{"_78":769,"_438":656,"_439":34,"_224":770},{"_78":761,"_438":678,"_439":142,"_224":762},{"_78":741,"_438":556,"_439":34,"_224":743},{"_78":767,"_438":553,"_439":142,"_224":768},{"_78":765,"_438":553,"_439":34,"_224":766},{"_78":763,"_438":553,"_439":142,"_224":764},{"_78":761,"_438":652,"_439":142,"_224":762},{"_78":759,"_438":553,"_439":34,"_224":760},{"_78":757,"_438":553,"_439":34,"_224":758},{"_78":755,"_438":553,"_439":142,"_224":756},{"_78":753,"_438":661,"_439":34,"_224":754},{"_78":741,"_438":652,"_439":34,"_224":743},{"_78":751,"_438":553,"_439":34,"_224":752},{"_78":749,"_438":553,"_439":142,"_224":750},{"_78":747,"_438":553,"_439":34,"_224":748},{"_78":745,"_438":553,"_439":34,"_224":746},{"_78":744,"_438":553,"_439":34,"_224":669},{"_78":741,"_438":742,"_439":34,"_224":743},{"_78":739,"_438":553,"_439":34,"_224":740},{"_78":737,"_438":553,"_439":142,"_224":738},{"_78":735,"_438":553,"_439":34,"_224":736},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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Key	Value
architecture	NMT En De Transformer12x2
feedforward dimension	4096
hidden dimension	1024
input	text in english
number of decoder layers	2
number of encoder layers	12
output	text in german

Key	Value
WMT'20	31.5
wmt'14	30.2
wmt'18	46.4
wmt'19	41.1