Model Overview

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

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 - http://www.statmt.org/europarl/v10/training/europarl-v10.fr-en.tsv.gz
Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-fr.txt.gz
Giga French - http://statmt.org/wmt10/training-giga-fren.tar
News Commentary - http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-fr.tsv.gz
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-fr.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-fr.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-fr.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-fr.tmx.zip
WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.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].\nThe model achieves the following sacreBLEU [4] scores on the WMT'13 and WMT'14 test sets

WMT13 - 35.3\nWMT14 - 41.3\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_fr_transformer12x2\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_fr_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang fr --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 - http://www.statmt.org/europarl/v10/training/europarl-v10.fr-en.tsv.gz\n* Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-fr.txt.gz\n* Giga French - http://statmt.org/wmt10/training-giga-fren.tar\n* News Commentary - http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-fr.tsv.gz\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-fr.tmx.zip\n* WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.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].\nThe model achieves the following sacreBLEU [4] scores on the WMT'13 and WMT'14 test sets\n\n```\nWMT13 - 35.3\nWMT14 - 41.3\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_fr_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_fr_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang fr --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","NMT En Fr Transformer12x2",["D",1680636726725],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_en_fr_transformer12x2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241,242,243,244,245,246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":272,"_249":273,"_250":274,"_252":253},{"_207":268,"_249":269,"_250":270,"_252":253},{"_207":266,"_249":266,"_250":267,"_252":253},{"_207":264,"_249":264,"_250":265,"_252":253},{"_207":262,"_249":262,"_250":263,"_252":253},{"_207":260,"_249":260,"_250":261,"_252":253},{"_207":257,"_249":257,"_250":258,"_252":253},{"_207":255,"_249":255,"_250":256,"_252":253},{"_207":248,"_249":248,"_250":251,"_252":253},"Transformer","value","filter",{"_254":253,"_249":248},"category","label","field","PyTorchLightning",{"_254":253,"_249":255},"PyTorch",{"_254":259,"_249":257},"framework","NMT",{"_254":253,"_249":260},"NeMo",{"_254":259,"_249":262},"Fr",{"_254":253,"_249":264},"En",{"_254":253,"_249":266},"DL","soln_dl",{"_254":271,"_249":269},"technology","AI","soln_ai",{"_254":271,"_249":273},{"_108":1255,"_110":1256},"ngcApps",[1208],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1189":1190,"_1191":1192,"_1193":1194,"_1195":1196,"_1197":286,"_139":312,"_1198":1199,"_1200":56,"_1201":87,"_1202":87,"_1203":1204,"_1205":87,"_1206":1207},"amplitude",{"_1186":1187,"_824":-7,"_1188":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1180":1181,"_1182":1183,"_1184":1185},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789421264715,"1.15.0","defaultSearchSuggestions",{"_1064":169,"_1065":1066,"_1067":1068,"_1069":1070},"announcements",[],"consentState",{"_349":1054,"_1055":87,"_1056":31,"_1057":31,"_1058":1059},"consentConfig",{"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350},"showContactAdmin",1789421264716,[],[],[326,327,328,329,330],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":103,"_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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