Model Overview

Transformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding.

Model Architecture

The Transformer-XL \"base\" model for WikiText-103 dataset available in this\nrepository was modified to use the following hyperparameter values:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Hyperparameter	Description	Original setting for the base model	Our modification for the base model
`d_model`	hidden size	410	512
`n_head`	number of attention heads	10	8
`d_head`	size of each attention head	41	64
`d_inner`	hidden size in fully-connected layers	2100	2048
`tgt_len`	number of tokens to predict during training	150	192
`mem_len`	number of tokens cached from previous iterations during training	150	192

Changes described above were made to align certain hyperparameters with powers\nof two, with this modification, the model is able to achieve better hardware\nutilization, and therefore higher training throughput.

The Transformer-XL \"large\" model for WikiText-103 dataset available in this\nrepository uses the original hyperparameters from the reference\nimplementation.

The following table lists the hyperparameters for the large and the base\nTransformer-XL models for WikiText-103 dataset available in this repository.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Hyperparameter	Description	Base model	Large model
`n_layer`	number of layers	16	18
`d_model`	hidden size	512	1024
`n_head`	number of attention heads	8	16
`d_head`	size of each attention head	64	64
`d_inner`	inner hidden size in fully-connected layers	2048	4096
`dropout`	dropout	0.1	0.2
`dropatt`	dropout after softmax in the attention	0.0	0.2
`lr`	base learning rate	0.01	0.01
`eta_min`	minimum learning rate (for cosine decay)	0.001	0.0001
`max_step`	number of training steps	40,000	100,000
`warmup_step`	number of learning rate warmup steps	1,000	16,000
`batch_size`	training batch size	256	128
`tgt_len`	number of tokens to predict during training	192	384
`mem_len`	number of tokens cached from previous iterations during training	192	384

The Transformer-XL model addresses the limitations of vanilla transformer-based\nlanguage models, which are only able to use relatively short context, bounded\nby the segment length. The Transformer-XL introduces a recurrence mechanism,\nwhich is able to use a cached hidden state from previous segments. During\ntraining, the context consists of a concatenation of current segment's hidden\nstate and cached states from previous iterations. Gradients are backpropagated\nonly through the current segment, although the model is able to take advantage\nof the extra information stored in the cache and therefore is able to model\nlong-term dependencies.

An illustration of the recurrence mechanism taken from the Transformer-XL\npaper is shown below.\n $\"model\"$

Training

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

Dataset

The following datasets were used to train this model:

WikiText-103 - A collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia.

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.

","resourceVersions",[196,197],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_169":23,"_170":171,"_49":172,"_173":-5,"_174":175,"_176":177,"_178":80,"_179":16,"_180":31,"_181":-5,"_182":-5,"_183":31,"_184":-5,"_185":31,"_186":-5,"_187":31,"_188":-5,"_189":31,"_190":-5,"_191":31,"_192":-5,"_193":31,"_194":195},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/models/transformerxl_base_pyt_ckpt/-","version","imageReference","registry","targetPath","cliDownloadCommand","ngc registry model download-version nvidia/dle/transformerxl_base_pyt_ckpt:19.11.0_amp","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/models/transformerxl_base_pyt_ckpt/-/version-history","accessCheckResult",{"_168":86},"hasRelatedCollections","showNimMetadataTab","context",{"_164":165,"_166":167},1793175329885,[],[],[157,158,159,160,161,162,163],{"_45":59,"_47":70,"_49":61,"_51":62,"_53":71,"_55":56},{"_45":59,"_47":68,"_49":61,"_51":62,"_53":69,"_55":56},{"_45":59,"_47":66,"_49":61,"_51":62,"_53":67,"_55":56},{"_45":59,"_47":64,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":61,"_51":62,"_53":63,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","dle","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1663866692588],"updatedDate",["D",1740776868276],"totalSizeInBytes","totalFileCount","isSigned","digest","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_76":21,"_78":199,"_173":-5,"_180":31,"_183":31,"_194":198,"_185":31},{"_76":23,"_78":23,"_173":-5,"_180":31,"_183":31,"_194":198,"_185":31},[],"Latest (19.11.0_amp)","nvidia/dle/transformerxl_base_pyt_ckpt","key","nvidia/dle/models/transformerxl_base_pyt_ckpt","resourceType","models","resourceId","transformerxl_base_pyt_ckpt","shortDescription","Tranformer-XL Base PyTorch checkpoint trained with AMP","## Model Overview\n\nTransformer-XL is a transformer-based language model with a segment-level recurrence and a novel relative positional encoding.\n\n## Model Architecture\n\n\nThe Transformer-XL \"base\" model for WikiText-103 dataset available in this\nrepository was modified to use the following hyperparameter values:\n\n|**Hyperparameter**|**Description**|**Original setting for the base model**|**Our modification for the base model**|\n|------------------|---------------|--------------------------------------:|--------------------------------------:|\n| `d_model` | hidden size | 410 | 512 |\n| `n_head` | number of attention heads | 10 | 8 |\n| `d_head` | size of each attention head | 41 | 64 |\n| `d_inner` | hidden size in fully-connected layers | 2100 | 2048 |\n| `tgt_len` | number of tokens to predict during training | 150 | 192 |\n| `mem_len` | number of tokens cached from previous iterations during training | 150 | 192 |\n\nChanges described above were made to align certain hyperparameters with powers\nof two, with this modification, the model is able to achieve better hardware\nutilization, and therefore higher training throughput.\n\nThe Transformer-XL \"large\" model for WikiText-103 dataset available in this\nrepository uses the original hyperparameters from the [reference\nimplementation](https://github.com/kimiyoung/transformer-xl).\n\nThe following table lists the hyperparameters for the large and the base\nTransformer-XL models for WikiText-103 dataset available in this repository.\n\n| **Hyperparameter** | **Description** | **Base model** | **Large model** |\n| ------------------ | ---------------------------------------------------------------- | -------------: | ---------------: |\n| `n_layer` | number of layers | 16 | 18 |\n| `d_model` | hidden size | 512 | 1024 |\n| `n_head` | number of attention heads | 8 | 16 |\n| `d_head` | size of each attention head | 64 | 64 |\n| `d_inner` | inner hidden size in fully-connected layers | 2048 | 4096 |\n| `dropout` | dropout | 0.1 | 0.2 |\n| `dropatt` | dropout after softmax in the attention | 0.0 | 0.2 |\n| `lr` | base learning rate | 0.01 | 0.01 |\n| `eta_min` | minimum learning rate (for cosine decay) | 0.001 | 0.0001 |\n| `max_step` | number of training steps | 40,000 | 100,000 |\n| `warmup_step` | number of learning rate warmup steps | 1,000 | 16,000 |\n| `batch_size` | training batch size | 256 | 128 |\n| `tgt_len` | number of tokens to predict during training | 192 | 384 |\n| `mem_len` | number of tokens cached from previous iterations during training | 192 | 384 |\n\nThe Transformer-XL model addresses the limitations of vanilla transformer-based\nlanguage models, which are only able to use relatively short context, bounded\nby the segment length. The Transformer-XL introduces a recurrence mechanism,\nwhich is able to use a cached hidden state from previous segments. During\ntraining, the context consists of a concatenation of current segment's hidden\nstate and cached states from previous iterations. Gradients are backpropagated\nonly through the current segment, although the model is able to take advantage\nof the extra information stored in the cache and therefore is able to model\nlong-term dependencies.\n\nAn illustration of the recurrence mechanism taken from the [Transformer-XL\npaper](https://arxiv.org/abs/1901.02860) is shown below.\n![model](https://github.com/NVIDIA/DeepLearningExamples/raw/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/img/model.png)\n\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/transformerxl_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/Transformer-XL).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) - A collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/transformerxl_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1901.02860)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/transformerxl_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/Transformer-XL)\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/Transformer-XL) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","Transformer-XL PyTorch checkpoint (Base, AMP)",["D",1680636834314],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/transformerxl_base_pyt_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_210":252,"_246":252,"_247":253,"_249":250},{"_210":245,"_246":245,"_247":248,"_249":250},"NLU","value","filter",{"_251":250,"_246":245},"category","label","field","NLP",{"_251":250,"_246":252},{"_107":1389,"_109":1390},"ngcApps",[1342],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1323":1324,"_1325":1326,"_1327":1328,"_1329":1330,"_1331":265,"_138":291,"_1332":1333,"_1334":56,"_1335":86,"_1336":86,"_1337":1338,"_1339":86,"_1340":1341},"amplitude",{"_1320":1321,"_926":-7,"_1322":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1314":1315,"_1316":1317,"_1318":1319},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793175329781,"1.29.0","defaultSearchSuggestions",{"_1196":171,"_1197":1198,"_1199":1200,"_1201":1202},"announcements",[],"consentState",{"_328":1186,"_1187":31,"_1188":31,"_1189":86,"_1190":1191},"consentConfig",{"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329},"showContactAdmin",1793175329782,[],[],[305,306,307,308,309],{"_45":59,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_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. 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