Model Overview

This model can be used for translating text in source language (Hi) 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 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].\nOn WMT14 Test set this model achieves 24.2 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.hi-en+numrefs.1+smooth.exp+test.wmt14+tok.13a+version.1.5.1 = 24.2 59.1/30.5/17.8/10.8 (BP = 1.000 ratio = 1.012 hyp_len = 56254 ref_len = 55571)

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_hi_en_transformer12x2\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_hi_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang en --source_lang hi\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].\nOn WMT14 Test set this model achieves 24.2 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.hi-en+numrefs.1+smooth.exp+test.wmt14+tok.13a+version.1.5.1 = 24.2 59.1/30.5/17.8/10.8 (BP = 1.000 ratio = 1.012 hyp_len = 56254 ref_len = 55571)\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_hi_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_hi_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang en --source_lang hi\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 Hi En Transformer12x2",["D",1680637651837],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nmt_hi_en_transformer12x2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[275,276,277,278,279],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_244":292,"_113":292,"_281":293,"_283":284},{"_244":290,"_113":290,"_281":291,"_283":284},{"_244":288,"_113":288,"_281":289,"_283":284},{"_244":286,"_113":286,"_281":287,"_283":284},{"_244":280,"_113":280,"_281":282,"_283":284},"Transformer","filter",{"_285":284,"_113":280},"category","label","field","PytorchLightning",{"_285":284,"_113":286},"NMT",{"_285":284,"_113":288},"Hi",{"_285":284,"_113":290},"En",{"_285":284,"_113":292},{"_155":1467,"_157":1468},"ngcApps",[1420],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1401":1402,"_1403":1404,"_1405":1406,"_1407":1408,"_1409":305,"_186":329,"_1410":1411,"_1412":59,"_1413":135,"_1414":135,"_1415":1416,"_1417":135,"_1418":1419},"amplitude",{"_1398":1399,"_1001":-7,"_1400":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1392":1393,"_1394":1395,"_1396":1397},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1276":217,"_1277":1278,"_1279":1280,"_1281":1282},"announcements",[],"consentState",{"_366":1266,"_1267":135,"_1268":34,"_1269":34,"_1270":1271},"consentConfig",{"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367},"showContactAdmin",1784086602278,[],[],[343,344,345,346,347],{"_48":62,"_50":73,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":68,"_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 Hi En Transformer 12x2
attention heads	16
feedforward dimension	4096
hidden dimension	1024
inputs	Text in Hindi
number of decoder layers	2
number of encoder layers	12
outputs	Text in English