Model Overview

This implementation of Transformer model architecture is based on the optimized implementation in Fairseq NLP toolkit.

Model Architecture

The Transformer model uses standard NMT encoder-decoder architecture. This model unlike other NMT models, uses no recurrent connections and operates on fixed size context window.\nThe encoder stack is made up of N identical layers. Each layer is composed of the following sublayers:\n1. Self-attention layer\n2. Feedforward network (which is 2 fully-connected layers)\nLike the encoder stack, the decoder stack is made up of N identical layers. Each layer is composed of the sublayers:\n1. Self-attention layer\n2. Multi-headed attention layer combining encoder outputs with results from\nthe previous self-attention layer.\n3. Feedforward network (2 fully-connected layers)

The encoder uses self-attention to compute a representation of the input sequence. The decoder generates the output sequence one token at a time, taking the encoder output and previous decoder-outputted tokens as inputs.\nThe model also applies embeddings on the input and output tokens, and adds a constant positional encoding. The positional encoding adds information about the position of each token.

\n \n
\n Figure 1. The architecture of a Transformer model.\n

The complete description of the Transformer architecture can be found in Attention Is All You Need paper.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

WMT14 German-English - Dataset for machine translation.

Performance

Performance numbers for this model are available in NGC.

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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\n \n
\n Figure 1. The architecture of a Transformer model.\n

\n\nThe complete description of the Transformer architecture can be found in [Attention Is All You Need](https://arxiv.org/abs/1706.03762) paper.\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/transformer_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/Transformer).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [WMT14 German-English](http://www.statmt.org/wmt16/translation-task.html) - Dataset for machine translation.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/transformer_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1706.03762)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/transformer_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/Transformer)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/Transformer) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","Transformer en-de checkpoint",["D",1680638479730],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/transformer__pyt_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":247,"_241":247,"_242":248,"_244":245},{"_206":240,"_241":240,"_242":243,"_244":245},"NLU","value","filter",{"_246":245,"_241":240},"category","label","field","NLP",{"_246":245,"_241":247},{"_107":1423,"_109":1424},"ngcApps",[1376],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1357":1358,"_1359":1360,"_1361":1362,"_1363":1364,"_1365":260,"_138":284,"_1366":1367,"_1368":56,"_1369":86,"_1370":86,"_1371":1372,"_1373":86,"_1374":1375},"amplitude",{"_1354":1355,"_955":-7,"_1356":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1348":1349,"_1350":1351,"_1352":1353},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1230":168,"_1231":1232,"_1233":1234,"_1235":1236},"announcements",[],"consentState",{"_321":1220,"_1221":86,"_1222":31,"_1223":31,"_1224":1225},"consentConfig",{"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322},"showContactAdmin",1784002516444,[],[],[298,299,300,301,302],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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