Model Overview

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

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 200k steps.

Datasets

While training this model, we used the following datasets:

CC-Aligned - www.statmt.org/cc-aligned/

Tokenizer Construction

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

Performance

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

On WMT14 Test set this model achieves 20.9 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.en-hi+numrefs.1+smooth.exp+test.wmt14+tok.13a+version.1.5.1 = 20.9 55.1/28.5/15.7/8.9 (BP = 0.966 ratio = 0.967 hyp_len = 58865 ref_len = 60872)

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_hi_transformer12x2\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_en_hi_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang hi --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.

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

License

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 200k steps.\n\n### Datasets\nWhile training this model, we used the following datasets:\n\n* CC-Aligned - www.statmt.org/cc-aligned/\n\n### Tokenizer Construction\nWe used the YouTokenToMe tokenizer [2] with separate encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\n\nOn WMT14 Test set this model achieves 20.9 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.en-hi+numrefs.1+smooth.exp+test.wmt14+tok.13a+version.1.5.1 = 20.9 55.1/28.5/15.7/8.9 (BP = 0.966 ratio = 0.967 hyp_len = 58865 ref_len = 60872)\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_hi_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_hi_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang hi --source_lang en\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## 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## License\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 Hi Transformer12x2",["D",1680635215607],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_en_hi_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],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":257,"_245":257,"_246":258,"_248":249},{"_207":255,"_245":255,"_246":256,"_248":249},{"_207":253,"_245":253,"_246":254,"_248":249},{"_207":251,"_245":251,"_246":252,"_248":249},{"_207":244,"_245":244,"_246":247,"_248":249},"Transformer","value","filter",{"_250":249,"_245":244},"category","label","field","PytorchLightning",{"_250":249,"_245":251},"NMT",{"_250":249,"_245":253},"Hi",{"_250":249,"_245":255},"En",{"_250":249,"_245":257},{"_108":1432,"_110":1433},"ngcApps",[1385],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1366":1367,"_1368":1369,"_1370":1371,"_1372":1373,"_1374":270,"_139":294,"_1375":1376,"_1377":56,"_1378":87,"_1379":87,"_1380":1381,"_1382":87,"_1383":1384},"amplitude",{"_1363":1364,"_965":-7,"_1365":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1357":1358,"_1359":1360,"_1361":1362},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1240":169,"_1241":1242,"_1243":1244,"_1245":1246},"announcements",[],"consentState",{"_331":1230,"_1231":87,"_1232":31,"_1233":31,"_1234":1235},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1783975739147,[],[],[308,309,310,311,312],{"_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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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.",[603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623],[568,569,570,571,572],{"_371":463,"_207":463,"_168":169,"_345":594},{"_371":429,"_207":429,"_168":169,"_345":589},{"_371":582,"_207":582,"_168":169,"_345":583},{"_371":578,"_207":578,"_168":169,"_345":579},{"_371":573,"_207":573,"_168":169,"_345":574},"nvcrm.my.site.com",[575],{"_79":576,"_377":573,"_378":31,"_168":577},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[580],{"_79":581,"_377":578,"_378":31,"_168":412},"edge","github.com",[584,585],{"_79":588,"_377":582,"_378":31,"_168":587},{"_79":586,"_377":582,"_378":31,"_168":587},"logged_in","This domain is owned by Github. 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