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)","displayName","NGram Language Model Notebook",["D",1680632661280],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/ngram_lm_notebook","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[271,272,273,274,275,276],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_240":295,"_278":295,"_279":296,"_281":88},{"_240":293,"_278":293,"_279":294,"_281":88},{"_240":289,"_278":290,"_279":291,"_281":88},{"_240":285,"_278":286,"_279":287,"_281":88},{"_240":283,"_278":283,"_279":284,"_281":88},{"_240":277,"_278":277,"_279":280,"_281":88},"Transfer Learning","value","filter",{"_282":88,"_278":277},"category","field","Text Classification",{"_282":88,"_278":283},"Natural Language Understanding","uscs_natural_language_understanding",{"_282":288,"_278":286},"application","Inference","soln_inference",{"_282":292,"_278":290},"technology","Finetuning",{"_282":88,"_278":293},"BERT",{"_282":88,"_278":295},{"_152":1278,"_154":1279},"ngcApps",[1231],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1212":1213,"_1214":1215,"_1216":1217,"_1218":1219,"_1220":308,"_110":336,"_1221":1222,"_1223":59,"_1224":91,"_1225":91,"_1226":1227,"_1228":91,"_1229":1230},"amplitude",{"_1209":1210,"_848":-7,"_1211":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1203":1204,"_1205":1206,"_1207":1208},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786103055278,"1.11.0","defaultSearchSuggestions",{"_1088":416,"_1089":1090,"_1091":1092,"_1093":1094},"announcements",[],"consentState",{"_373":1078,"_1079":91,"_1080":34,"_1081":34,"_1082":1083},"consentConfig",{"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374},"showContactAdmin",1786103055281,[],[],[350,351,352,353,354],{"_48":63,"_50":74,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":69,"_54":-5,"_56":-5,"_58":59},"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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