Model Overview

BART is a denoising autoencoder for pretraining sequence-to-sequence models.

Model Architecture

BART uses a standard sequence-to-sequence Transformer architecture with GeLU activations. The base model consists of 6 layers in encoder and decoder, whereas large consists of 12. The architecture has roughly 10% more parameters than BERT.

BART is trained by corrupting documents and then optimizing the reconstruction loss. The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.

Training

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

Dataset

The following datasets were used to train this model:

CNN/DailyMail - Dataset consisting of news articles (39 sentences on average) paired with multi-sentence summaries.

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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The base model consists of 6 layers in encoder and decoder, whereas large consists of 12. The architecture has roughly 10% more parameters than BERT.\n\nBART is trained by corrupting documents and then optimizing the reconstruction loss. The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/bart_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BART).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [CNN/DailyMail](https://github.com/abisee/cnn-dailymail) - Dataset consisting of news articles (39 sentences on average) paired with multi-sentence summaries.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/bart_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1910.13461)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/bart_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BART)\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/LanguageModeling/BART) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","BART PyT checkpoint (Summarization, CNN-DM)",["D",1680637324423],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/bart__pyt_ckpt_ds-cnndm","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[237,238],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":246,"_240":246,"_241":247,"_243":244},{"_205":239,"_240":239,"_241":242,"_243":244},"NLU","value","filter",{"_245":244,"_240":239},"category","label","field","NLP",{"_245":244,"_240":246},{"_106":1423,"_108":1424},"ngcApps",[1376],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1357":1358,"_1359":1360,"_1361":1362,"_1363":1364,"_1365":259,"_137":283,"_1366":1367,"_1368":56,"_1369":85,"_1370":85,"_1371":1372,"_1373":85,"_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":167,"_1231":1232,"_1233":1234,"_1235":1236},"announcements",[],"consentState",{"_320":1220,"_1221":85,"_1222":31,"_1223":31,"_1224":1225},"consentConfig",{"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321},"showContactAdmin",1784203353281,[],[],[297,298,299,300,301],{"_45":59,"_47":70,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":101,"_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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