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 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 of hundreds of millions of 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:

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 v8 from: https://s3.amazonaws.com/web-language-models/paracrawl/release8/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
CC-Aligned: http://www.statmt.org/cc-aligned/sentence-aligned/de_DE-en_XX.tsv.xz
CC-Matrix: https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix

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 - 37.9\nWMT18 - 48.8\nWMT19 - 43.0\nWMT20 - 42.4\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_de_en_transformer24x6\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_de_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --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 700k 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 v8 from: https://s3.amazonaws.com/web-language-models/paracrawl/release8/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* CC-Aligned: http://www.statmt.org/cc-aligned/sentence-aligned/de_DE-en_XX.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 joint encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\n\n```\nWMT14 - 37.9\nWMT18 - 48.8\nWMT19 - 43.0\nWMT20 - 42.4\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_de_en_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_de_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang de\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","NMT De En Transformer24x6",["D",1680637184975],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_de_en_transformer24x6","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],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":262,"_244":263,"_245":264,"_247":248},{"_206":259,"_244":260,"_245":261,"_247":248},{"_206":255,"_244":256,"_245":257,"_247":248},{"_206":251,"_244":252,"_245":253,"_247":248},{"_206":243,"_244":243,"_245":246,"_247":248},"PyTorch","value","filter",{"_249":250,"_244":243},"category","label","field","framework","Natural Language Processing","uscs_natural_language_processing",{"_249":254,"_244":252},"application","DL","soln_dl",{"_249":258,"_244":256},"technology","Conversational AI","soln_conversational_ai",{"_249":258,"_244":260},"AI","soln_ai",{"_249":258,"_244":263},{"_107":1246,"_109":1247},"ngcApps",[1199],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1180":1181,"_1182":1183,"_1184":1185,"_1186":1187,"_1188":276,"_138":304,"_1189":1190,"_1191":56,"_1192":86,"_1193":86,"_1194":1195,"_1196":86,"_1197":1198},"amplitude",{"_1177":1178,"_816":-7,"_1179":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1171":1172,"_1173":1174,"_1175":1176},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786186571383,"1.11.0","defaultSearchSuggestions",{"_1056":168,"_1057":1058,"_1059":1060,"_1061":1062},"announcements",[],"consentState",{"_341":1046,"_1047":86,"_1048":31,"_1049":31,"_1050":1051},"consentConfig",{"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342},"showContactAdmin",1786186571384,[],[],[318,319,320,321,322],{"_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":102,"_51":-5,"_53":-5,"_55":56},"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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