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",[],[260,261,262,263,264,265],[],[],[],{"_92":278,"_125":278,"_126":279,"_128":129},{"_92":131,"_125":131,"_126":277,"_128":129},{"_92":273,"_125":274,"_126":275,"_128":129},{"_92":269,"_125":270,"_126":271,"_128":129},{"_92":267,"_125":267,"_126":268,"_128":129},{"_92":124,"_125":124,"_126":266,"_128":129},{"_130":129,"_125":124},"Text Classification",{"_130":129,"_125":267},"Natural Language Understanding","uscs_natural_language_understanding",{"_130":272,"_125":270},"application","Inference","soln_inference",{"_130":276,"_125":274},"technology",{"_130":129,"_125":131},"BERT",{"_130":129,"_125":278},{"_159":1450,"_161":1451},"ngcApps",[1403],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1384":1385,"_1386":1387,"_1388":1389,"_1390":1391,"_1392":291,"_190":315,"_1393":1394,"_1395":51,"_1396":138,"_1397":138,"_1398":1399,"_1400":138,"_1401":1402},"amplitude",{"_1381":1382,"_988":-7,"_1383":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1375":1376,"_1377":1378,"_1379":1380},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1263":396,"_1264":1265,"_1266":1267,"_1268":1269},"announcements",[],"consentState",{"_353":1253,"_1254":138,"_1255":26,"_1256":26,"_1257":1258},"consentConfig",{"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354},"showContactAdmin",1784272466424,[],[],[329,330,331,332,333,334],{"_40":55,"_42":66,"_44":154,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":154,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":154,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":154,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":154,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":154,"_46":-5,"_48":-5,"_50":51},"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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