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",[192,193],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_166":21,"_167":168,"_48":169,"_170":-5,"_171":172,"_173":174,"_175":80,"_176":16,"_177":30,"_178":-5,"_179":30,"_180":-5,"_181":30,"_182":-5,"_183":30,"_184":-5,"_185":30,"_186":-5,"_187":30,"_188":-5,"_189":30,"_190":191},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/models/transformerxl_large_pyt_ckpt/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/dle/transformerxl_large_pyt_ckpt:19.11.0_amp","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/models/transformerxl_large_pyt_ckpt/19.11.0_amp/version-history","accessCheckResult",{"_165":86},"hasRelatedCollections","context",{"_161":162,"_163":164},1784069297014,[],[],[154,155,156,157,158,159,160],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":64,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":58,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","dle","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1663866603735],"updatedDate",["D",1740776873923],"totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_76":195,"_78":196,"_170":-5,"_177":30,"_179":30,"_190":194,"_181":30},{"_76":21,"_78":21,"_170":-5,"_177":30,"_179":30,"_190":194,"_181":30},[],"-","Latest (19.11.0_amp)","nvidia/dle/transformerxl_large_pyt_ckpt","key","nvidia/dle/models/transformerxl_large_pyt_ckpt","resourceType","models","resourceId","transformerxl_large_pyt_ckpt","shortDescription","Tranformer-XL Large 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 (Large, AMP)",["D",1680638368870],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/transformerxl_large_pyt_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":248,"_242":248,"_243":249,"_245":246},{"_207":241,"_242":241,"_243":244,"_245":246},"NLU","value","filter",{"_247":246,"_242":241},"category","label","field","NLP",{"_247":246,"_242":248},{"_107":1425,"_109":1426},"ngcApps",[1378],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1359":1360,"_1361":1362,"_1363":1364,"_1365":1366,"_1367":261,"_138":285,"_1368":1369,"_1370":55,"_1371":86,"_1372":86,"_1373":1374,"_1375":86,"_1376":1377},"amplitude",{"_1356":1357,"_957":-7,"_1358":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1350":1351,"_1352":1353,"_1354":1355},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1232":168,"_1233":1234,"_1235":1236,"_1237":1238},"announcements",[],"consentState",{"_323":1222,"_1223":86,"_1224":30,"_1225":30,"_1226":1227},"consentConfig",{"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324},"showContactAdmin",1784069296879,[],[],[299,300,301,302,303,304],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_50":-5,"_52":-5,"_54":55},"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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It is not used to track users but rather to limit the number of requests that have to be made to doubleclick.net.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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