Model Overview

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

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 shared encoder and decoder BPE tokenizers.

Performance

The accuracy of translation models are often measured using BLEU scores [3].\nOn WMT20 Test set this model achieves 36.4 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt20+tok.13a+version.1.5.0 = 36.4 71.7/46.7/32.6/23.3 (BP = 0.912 ratio = 0.915 hyp_len = 34980 ref_len = 38220)

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_de_en_transformer12x2\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_de_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang en --source_lang de\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 shared encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\nOn WMT20 Test set this model achieves 36.4 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt20+tok.13a+version.1.5.0 = 36.4 71.7/46.7/32.6/23.3 (BP = 0.912 ratio = 0.915 hyp_len = 34980 ref_len = 38220)\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_de_en_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_de_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang en --source_lang de\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","NMT De En Transformer12x2",["D",1680636596479],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_de_en_transformer12x2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[279,280,281,282,283,284,285,286,287],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_248":311,"_113":312,"_289":313,"_291":292},{"_248":309,"_113":309,"_289":310,"_291":292},{"_248":305,"_113":306,"_289":307,"_291":292},{"_248":303,"_113":303,"_289":304,"_291":292},{"_248":301,"_113":301,"_289":302,"_291":292},{"_248":299,"_113":299,"_289":300,"_291":292},{"_248":296,"_113":296,"_289":297,"_291":292},{"_248":294,"_113":294,"_289":295,"_291":292},{"_248":288,"_113":288,"_289":290,"_291":292},"Transformer","filter",{"_293":292,"_113":288},"category","label","field","PytorchLightning",{"_293":292,"_113":294},"PyTorch",{"_293":298,"_113":296},"framework","NMT",{"_293":292,"_113":299},"NeMo",{"_293":298,"_113":301},"En",{"_293":292,"_113":303},"DL","soln_dl",{"_293":308,"_113":306},"technology","De",{"_293":292,"_113":309},"AI","soln_ai",{"_293":308,"_113":312},{"_159":1294,"_161":1295},"ngcApps",[1247],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1228":1229,"_1230":1231,"_1232":1233,"_1234":1235,"_1236":325,"_190":353,"_1237":1238,"_1239":59,"_1240":138,"_1241":138,"_1242":1243,"_1244":138,"_1245":1246},"amplitude",{"_1225":1226,"_864":-7,"_1227":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1219":1220,"_1221":1222,"_1223":1224},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786100190393,"1.11.0","defaultSearchSuggestions",{"_1104":221,"_1105":1106,"_1107":1108,"_1109":1110},"announcements",[],"consentState",{"_390":1094,"_1095":138,"_1096":34,"_1097":34,"_1098":1099},"consentConfig",{"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391},"showContactAdmin",1786100190394,[],[],[367,368,369,370,371],{"_48":63,"_50":73,"_52":154,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":154,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":154,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":154,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":154,"_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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Key	Value
Architecture	NMT De en transformer 12x2
attention heads	16
feedforward dimension	4096
hidden dimension	1024
inputs	text in german
number of decoder layers	2
number of encoder layers	12
outputs	text in english

Key	Value
WMT'19	40.8
WMT'20	36.4