N-Gram Language Model Notebook

LM, or Language model, Language Models estimate the probability distriubtion of sequences of words. In general, this is a large task, with arbitrary sequence lengths, so it is often assumed that the probability of a word is only dependent on the N words preceding it. This is known as an N-Gram Language Model. An N-Gram model of order N saves the counts of all observed sequences of words in the training data of lengths one (known as unigrams) to lengths N. During inference, if an N-gram sequence not seen during training is queried, the sequence is then simplified to the probability of the N-1 last words, weighted by a calculated backoff probability.

The best place to get started with TAO Toolkit - LM would be the TAO - N-Gram LM jupyter notebooks sample enclosed in this sample.

This resource has 1 notebook included.

Training: Sample workflow for training an ASR model and export the model to a .riva file

If you are a seasoned Conversation AI developer we recommend installing TAO and referring to the TAO documentation for detailed information.

Pre-Requisites

Please make sure to install the following before proceeding further:

python 3.6.9
docker-ce > 19.03.5
docker-API 1.40
nvidia-container-toolkit > 1.3.0-1
nvidia-container-runtime > 3.4.0-1
nvidia-docker2 > 2.5.0-1
nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows

virtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n

TAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex\npip install nvidia-tao\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tao/ngram_lm_notebook:v1.0\"\n

Instantiate the jupyter notebook server

jupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n

License

By downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva license

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In general, this is a large task, with arbitrary sequence lengths, so it is often assumed that the probability of a word is only dependent on the N words preceding it. This is known as an N-Gram Language Model. An N-Gram model of order N saves the counts of all observed sequences of words in the training data of lengths one (known as unigrams) to lengths N. During inference, if an N-gram sequence not seen during training is queried, the sequence is then simplified to the probability of the N-1 last words, weighted by a calculated backoff probability.\n\nThe best place to get started with TAO Toolkit - LM would be the TAO - N-Gram LM jupyter notebooks sample enclosed in this sample.\n\nThis resource has 1 notebook included.\n\n1. Training: Sample workflow for training an ASR model and export the model to a `.riva` file\n\nIf you are a seasoned Conversation AI developer we recommend installing TAO and referring to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html) for detailed information.\n\n## Pre-Requisites\n\nPlease make sure to install the following before proceeding further:\n\n* python 3.6.9\n* docker-ce > 19.03.5\n* docker-API 1.40\n* nvidia-container-toolkit > 1.3.0-1\n* nvidia-container-runtime > 3.4.0-1\n* nvidia-docker2 > 2.5.0-1\n* nvidia-driver >= 455.23\n\n*Note: A compatible NVIDIA GPU would be required.*\n\n## Installation\n\nWe recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows \n\n```sh\nvirtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n```\n\nTAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.\n\n```sh\npip install nvidia-pyindex\npip install nvidia-tao\n```\n\nTo download the jupyter notebook please:\n\n1. Download the samples using the ngc cli with the following command\n\n```sh\nngc registry resource download-version \"nvidia/tao/ngram_lm_notebook:v1.0\"\n```\n\n2. Instantiate the jupyter notebook server\n\n```sh\njupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n```\n\n## License\n\nBy downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","NGram Language Model Notebook",["D",1680632661280],"/api/image/nvidia/tao/resources/ngram_lm_notebook","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","bias","explainability","privacy","safetyAndSecurity",[],[259,260,261,262,263,264],[],[],[],{"_90":277,"_123":277,"_124":278,"_126":127},{"_90":129,"_123":129,"_124":276,"_126":127},{"_90":272,"_123":273,"_124":274,"_126":127},{"_90":268,"_123":269,"_124":270,"_126":127},{"_90":266,"_123":266,"_124":267,"_126":127},{"_90":122,"_123":122,"_124":265,"_126":127},{"_128":127,"_123":122},"Text Classification",{"_128":127,"_123":266},"Natural Language Understanding","uscs_natural_language_understanding",{"_128":271,"_123":269},"application","Inference","soln_inference",{"_128":275,"_123":273},"technology",{"_128":127,"_123":129},"BERT",{"_128":127,"_123":277},{"_156":1258,"_158":1259},"ngcApps",[1211],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1192":1193,"_1194":1195,"_1196":1197,"_1198":1199,"_1200":290,"_187":318,"_1201":1202,"_1203":51,"_1204":136,"_1205":136,"_1206":1207,"_1208":136,"_1209":1210},"amplitude",{"_1189":1190,"_831":-7,"_1191":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1183":1184,"_1185":1186,"_1187":1188},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786103035279,"1.11.0","defaultSearchSuggestions",{"_1071":399,"_1072":1073,"_1074":1075,"_1076":1077},"announcements",[],"consentState",{"_356":1061,"_1062":136,"_1063":26,"_1064":26,"_1065":1066},"consentConfig",{"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357},"showContactAdmin",[],[],[331,332,333,334,335,336],{"_40":54,"_42":65,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":337,"_50":51},4,"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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