Overview

NVIDIA NeMo toolkit supports multiple Natural Language Processing(NLP) tasks from text classification and language modelling all the way to glue benchmarking.\nNatural Language Processing (NLP) field experienced a huge leap in recent years due to the concept of transfer learning enabled through pretrained language models.\nBERT, RoBERTa, Megatron-LM, and many other proposed language models achieve state-of-the-art results on many NLP tasks, such as: question answering, sentiment analysis, named entity recognition and many others.\nIn NeMo, most of the NLP models represent a pretrained language model followed by a Token Classification layer or a Sequence Classification layer or a combination of both. By changing the language model, you can improve the performance of your final model on the specific downstream task you are solving.\nWith NeMo you can use either pretrain a BERT model from your data or use a pretrained language model from HuggingFace transformers or Megatron-LM libraries.\nAll NLP models require text tokenization as data preprocessing steps. The list of tokenizers can be found in nemo.collections.common.tokenizers, and include WordPiece tokenizer, SentencePiece tokenizer or simple tokenizers like Word tokenizer.

Language Modelling - Assigns a probability distribution over a sequence of words. Can be either generative e.g. left-right-transformer or BERT with a masked language model loss.\nText Classification - Classifies an entire text based on its content into predefined categories, e.g. news, finance, science etc. These models are BERT-based and can be used for applications such as sentiment analysis, relationship extraction\nToken Classification - Classifies each input token separately. Models are based on BERT. Applications include named entity recognition, punctuation and capitalization, etc.\nIntent Slot Classification - used for joint recognition of Intents and Slots (Entities) for building conversational assistants.\nQuestion Answering - Currently only SQuAD is supported. This takes in a question and a passage as input and predicts a span in the passage, from which the answer is extracted.\nGlue Benchmarks - A benchmark of nine sentence- or sentence-pair language understanding tasks.

Use the Jupyter notebooks to quickly get started with using the pre-trained checkpoints or pretraining BERT.

Usage

You can instantiate all these models automatically directly from NGC. To do so, start your script with:

import nemo\nimport nemo.collections.nlp as nemo_nlp\n

Then choose what type of model you would like to instantiate. See table below for the list of model base classes. Then use base_class.from_pretrained(...) method.\nFor example:

pretrained_ner_model = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"NERModel\")\n

Note that you can also list all available models using API by calling base_class.list_available_models(...) method.

You can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with base_class.restore_from(PATH_TO_DOTNEMO_FILE) method. In this case, make sure you are matching NeMo and models' versions.

Here is a list of currently available models together with their base classes and short descriptions.

\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\">

Model name	Model Base Class	Description
NERModel	`TokenClassificationModel`	The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition and achieves 74.61 F1 Macro score.
Punctuation_Capitalization_with_BERT	`TokenClassificationModel`	The model was trained with NeMo BERT base uncased checkpoint on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.
Punctuation_Capitalization_with_DistilBERT	`TokenClassificationModel`	The model was trained with DiltilBERT base uncased checkpoint from HuggingFace on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.
BERTBaseUncasedSQuADv1.1	`QAModel`	Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 82.43% and an F1 score of 89.59%.
BERTBaseUncasedSQuADv2.0	`QAModel`	Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 73.35% and an F1 score of 76.44%.
BERTLargeUncasedSQuADv1.1	`QAModel`	Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 85.47% and an F1 score of 92.10%.
BERTLargeUncasedSQuADv2.0	`QAModel`	Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 78.8% and an F1 score of 81.85%.
Joint_Intent_Slot_Assistant	`IntentSlotClassificationModel`	This models is trained on this https://github.com/xliuhw/NLU-Evaluation-Data dataset which includes 64 various intents and 55 slots. Final Intent accuracy is about 87%, Slot accuracy is about 89%.

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By changing the language model, you can improve the performance of your final model on the specific downstream task you are solving.\nWith NeMo you can use either pretrain a BERT model from your data or use a pretrained language model from HuggingFace transformers or Megatron-LM libraries.\nAll NLP models require text tokenization as data preprocessing steps. The list of tokenizers can be found in nemo.collections.common.tokenizers, and include WordPiece tokenizer, SentencePiece tokenizer or simple tokenizers like Word tokenizer.\n\nLanguage Modelling - Assigns a probability distribution over a sequence of words. Can be either generative e.g. left-right-transformer or BERT with a masked language model loss.\nText Classification - Classifies an entire text based on its content into predefined categories, e.g. news, finance, science etc. These models are BERT-based and can be used for applications such as sentiment analysis, relationship extraction\nToken Classification - Classifies each input token separately. Models are based on BERT. Applications include named entity recognition, punctuation and capitalization, etc.\nIntent Slot Classification - used for joint recognition of Intents and Slots (Entities) for building conversational assistants.\nQuestion Answering - Currently only SQuAD is supported. This takes in a question and a passage as input and predicts a span in the passage, from which the answer is extracted.\nGlue Benchmarks - A benchmark of nine sentence- or sentence-pair language understanding tasks.\n\nUse the [Jupyter notebooks](https://github.com/NVIDIA/NeMo/tree/main/tutorials/nlp) to quickly get started with using the pre-trained checkpoints or pretraining BERT.\n\n## Usage\n\nYou can instantiate all these models automatically directly from NGC. To do so, start your script with:\n```\nimport nemo\nimport nemo.collections.nlp as nemo_nlp\n```\nThen choose what type of model you would like to instantiate. See table below for the list of model base classes. Then use ```base_class.from_pretrained(...)``` method.\nFor example:\n```\npretrained_ner_model = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"NERModel\")\n```\n\nNote that you can also list all available models using API by calling ```base_class.list_available_models(...)``` method.\n\nYou can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with ```base_class.restore_from(PATH_TO_DOTNEMO_FILE)``` method. In this case, make sure you are matching NeMo and models' versions.\n\n\nHere is a list of currently available models together with their base classes and short descriptions.\n\n| Model name | Model Base Class | Description |\n|-----------------------------------|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| NERModel | ```TokenClassificationModel``` |The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition and achieves 74.61 F1 Macro score. |\n| Punctuation_Capitalization_with_BERT | ```TokenClassificationModel``` | The model was trained with NeMo BERT base uncased checkpoint on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.|\n| Punctuation_Capitalization_with_DistilBERT | ```TokenClassificationModel``` | The model was trained with DiltilBERT base uncased checkpoint from HuggingFace on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts. |\n| BERTBaseUncasedSQuADv1.1 | ```QAModel``` | Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 82.43% and an F1 score of 89.59%.|\n| BERTBaseUncasedSQuADv2.0 | ```QAModel``` | Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 73.35% and an F1 score of 76.44%. |\n| BERTLargeUncasedSQuADv1.1 | ```QAModel``` | Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 85.47% and an F1 score of 92.10%. |\n| BERTLargeUncasedSQuADv2.0 | ```QAModel``` | Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 78.8% and an F1 score of 81.85%. |\n| Joint_Intent_Slot_Assistant | ```IntentSlotClassificationModel``` | This models is trained on this https://github.com/xliuhw/NLU-Evaluation-Data dataset which includes 64 various intents and 55 slots. Final Intent accuracy is about 87%, Slot accuracy is about 89%. |","displayName","NeMo Natural Language Processing models",["D",1680636896070],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/nemonlpmodels","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[234,235],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_203":243,"_237":244,"_238":245,"_240":241},{"_203":236,"_237":236,"_238":239,"_240":241},"NLP","value","filter",{"_242":241,"_237":236},"category","label","field","Conversational AI","soln_conversational_ai",{"_242":246,"_237":244},"technology",{"_120":1421,"_122":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1356":1357,"_1358":1359,"_1360":1361,"_1362":1363,"_1364":258,"_147":282,"_1365":1366,"_1354":59,"_1367":99,"_1368":99,"_1369":1370,"_1371":99,"_1372":1373},"amplitude",{"_1352":1353,"_1354":59,"_955":-7,"_1355":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1346":1347,"_1348":1349,"_1350":1351},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1230":174,"_1231":1232,"_1233":1234,"_1235":1236},"announcements",[],"consentState",{"_321":1220,"_1221":34,"_1222":34,"_1223":99,"_1224":1225},"consentConfig",{"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322},"showContactAdmin",1783243695717,[],[],[296,297,298,299,300,301],{"_48":62,"_50":73,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":302,"_58":59},{"_48":49,"_50":51,"_52":115,"_54":-5,"_56":-5,"_58":59},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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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