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 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 hundreds of millions of parallel sentences. The NeMo toolkit [5] was used for training this model over roughly 800k 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/release8/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
CC-Aligned - http://www.statmt.org/cc-aligned/sentence-aligned/en_XX-fr_XX.tsv.xz
CC-Matrix - https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix

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 - 36.4\nWMT14 - 44.0\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_transformer24x6\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_fr_transformer24x6.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 800k 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/release8/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* CC-Aligned - http://www.statmt.org/cc-aligned/sentence-aligned/en_XX-fr_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 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 - 36.4\nWMT14 - 44.0\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_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_fr_transformer24x6.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",["D",1680637134069],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_en_fr_transformer24x6","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],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_249":307,"_114":308,"_287":309,"_289":290},{"_249":304,"_114":305,"_287":306,"_289":290},{"_249":300,"_114":301,"_287":302,"_289":290},{"_249":296,"_114":297,"_287":298,"_289":290},{"_249":294,"_114":294,"_287":295,"_289":290},{"_249":292,"_114":292,"_287":293,"_289":290},{"_249":286,"_114":286,"_287":288,"_289":290},"Transformer","filter",{"_291":290,"_114":286},"category","label","field","PytorchLightning",{"_291":290,"_114":292},"NMT",{"_291":290,"_114":294},"Natural Language Processing","uscs_natural_language_processing",{"_291":299,"_114":297},"application","DL","soln_dl",{"_291":303,"_114":301},"technology","Conversational AI","soln_conversational_ai",{"_291":303,"_114":305},"AI","soln_ai",{"_291":303,"_114":308},{"_160":1285,"_162":1286},"ngcApps",[1238],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1219":1220,"_1221":1222,"_1223":1224,"_1225":1226,"_1227":321,"_191":347,"_1228":1229,"_1230":59,"_1231":139,"_1232":139,"_1233":1234,"_1235":139,"_1236":1237},"amplitude",{"_1216":1217,"_858":-7,"_1218":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1210":1211,"_1212":1213,"_1214":1215},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787498992427,"1.12.0","defaultSearchSuggestions",{"_1098":222,"_1099":1100,"_1101":1102,"_1103":1104},"announcements",[],"consentState",{"_384":1088,"_1089":139,"_1090":34,"_1091":34,"_1092":1093},"consentConfig",{"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385},"showContactAdmin",1787498992428,[],[],[361,362,363,364,365],{"_48":63,"_50":74,"_52":155,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":155,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":155,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":155,"_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 En Fr 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 French