Model Overview

BERT is a method of pre-training language representations which obtains state-of-the-art results on a wide array of NLP tasks.

Model Architecture

The BERT model uses the same architecture as the encoder of the Transformer. Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations.

An illustration of the architecture taken from the Transformer paper is shown below.

$\"BERT\"$

Training

This model was trained using script available in GitHub repo.

Dataset

The following datasets were used to train this model:

Wikipedia - Dataset containing a 170GB+ Wikipedia dump.
Bookcorpus - Large-scale text corpus for unsupervised learning of sentence encoders/decoders.
SQuAD 1.1 + 2.0 - Reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

Performance

Performance numbers for this model are available in GitHub readme performance section.

References

Original paper
NVIDIA model implementation on GitHub

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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Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations.\n \nAn illustration of the architecture taken from the [Transformer paper](https://arxiv.org/pdf/1706.03762.pdf) is shown below.\n \n ![BERT](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/LanguageModeling/BERT/images/model.png)\n\n \n\n \n## Training\n\nThis model was trained using script available in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [Wikipedia](https://dumps.wikimedia.org/) - Dataset containing a 170GB+ Wikipedia dump.\n- [Bookcorpus](https://github.com/soskek/bookcorpus) - Large-scale text corpus for unsupervised learning of sentence encoders/decoders.\n- [SQuAD 1.1 + 2.0](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) - Reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\n\n\n## Performance\n\nPerformance numbers for this model are available in [GitHub readme performance section](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT#performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1810.04805)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT)\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/BERT) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","BERT PyTorch checkpoint (Dist-6L-768D, SQuAD1, AMP)",["D",1680636219178],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/bert_6l-768d_pyt_ckpt_mode-distilled-qa_ds-squad11","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",265757602,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[252,253,254],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_219":264,"_256":265,"_257":266,"_259":260},{"_219":262,"_256":262,"_257":263,"_259":260},{"_219":255,"_256":255,"_257":258,"_259":260},"NLU","value","filter",{"_261":260,"_256":255},"category","label","field","NLP",{"_261":260,"_256":262},"Deep Learning Examples","pltfm_deep_learning_examples",{"_261":267,"_256":265},"platform",{"_119":1443,"_121":1444},"ngcApps",[1396],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1377":1378,"_1379":1380,"_1381":1382,"_1383":1384,"_1385":279,"_150":303,"_1386":1387,"_1388":57,"_1389":98,"_1390":98,"_1391":1392,"_1393":98,"_1394":1395},"amplitude",{"_1374":1375,"_975":-7,"_1376":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1368":1369,"_1370":1371,"_1372":1373},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1250":180,"_1251":1252,"_1253":1254,"_1255":1256},"announcements",[],"consentState",{"_340":1240,"_1241":98,"_1242":32,"_1243":32,"_1244":1245},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",1784373872651,[],[],[317,318,319,320,321],{"_46":60,"_48":70,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":68,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":61,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":114,"_52":-5,"_54":-5,"_56":57},"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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Name	Size	Updated	Actions
config.json	849 B	February 28, 2025 UTC	curl wget Download
pytorch_model.bin	253.23 MB	February 28, 2025 UTC	curl wget Download
vocab.txt	226.08 KB	February 28, 2025 UTC	curl wget Download