Model Overview

This model is a single-pass tagger-based model for inverse text normalization based on bert-base-uncased, trained on 2 mln sentences from Google Text Normalization Dataset.

It converts text from spoken domain into its written form:\nInput: \"on may third we paid one hundred and twenty three dollars\"\nOutput: \"on may 3 we paid $123\"

Model Architecture

Thutmose Tagger is a single-pass tagging model. It utilizes a backbone BERT encoder (bert-base-uncased) followed by two classification heads: one is trained to predict written fragments as replacement tags, the other is trained to predict tags representing semiotic classes, like DATE, CARDINAL etc. The final tags have one-to-one correspondence with input words.

Training

The NeMo toolkit [1] was used for training the models.\nThe training corpus for the model consists of 2 mln sentences.

This model is trained with this example script and this base config.

python [NEMO_GIT_FOLDER]/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_train.py \\  \n  lang=en \\\n  data.validation_ds.data_path=${DATA_PATH}/valid.tsv \\\n  data.train_ds.data_path=${DATA_PATH}/train.tsv \\\n  data.train_ds.batch_size=128 \\\n  model.language_model.pretrained_model_name=bert-base-uncased \\\n  model.label_map=${DATA_PATH}/label_map.txt \\\n  model.semiotic_classes=${DATA_PATH}/semiotic_classes.txt \\\n  trainer.max_epochs=5\n

Datasets

The initial dataset is Google Text Normalization Dataset.\nThe dataset preparation is described example script and includes running GIZA++ automatic alignment tool to find granular alignments between spoken words and written fragments.

Performance

The performance of ITN models can be measured using Word Error Rate(WER), and Sentence Accuracy. We measure Sentence Accuracy w.r.t. multi-variant reference and subdivide the errors into \"digit\" and \"other\".

The model obtains the following scores on the following evaluation datasets

Default test set\n    WER:  3.75%\n    Sentence accuracy:  97.47%\n        digit errors:    0.35%\n        other errors:    2.18%\n\n\nHard test set\n    WER:  8.98%\n    Sentence accuracy:  87.88%\n        digit errors:    3.12%\n        other errors:    9.00%\n

Note that reference files were taken from the test part of Google TN Dataset, which is not considered 100% correct because of its synthetic nature. So these scores are not particularly indicative of the quality of final inverse text normalization, but they are a useful proxy.

How to Use this Model

The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference.

Automatically load the model from NGC

import nemo.collections.nlp as nemo_nlp\nnlp_model = nemo_nlp.models.ThutmoseTaggerModel.from_pretrained(model_name=\"itn_en_thutmose_bert\")\n

Running inference with this model

Example of inference and evaluation is provided in this [script] (https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/run_infer.sh)

" \\ \n inference.out_file="" \\ \n model.max_sequence_len=1024 \\ \n inference.batch_size=128\n\">

python [NEMO_GIT_FOLDER]/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \\  \n  pretrained_model=\"itn_en_thutmose_bert\" \\  \n  inference.from_file=\"<INPUT_FILE>\" \\  \n  inference.out_file=\"<OUTPUT_FILE>\" \\  \n  model.max_sequence_len=1024 \\  \n  inference.batch_size=128\n

Input

This model expects the input file to be plain text without punctuation, similar to the ASR text output.

Output

This model provides an output file with exactly the same number of lines as in input. Each line consists of 5 columns:

Final output text.
Input text.
Sequence of predicted tags.
Sequence of tags after post-processing (some swaps may be applied).
Sequence of predicted semiotic classes - one class for each input word.

Limitations

Since this model was trained on syntetic data its performance might degrade on some constructions that were systematically biased in the initial corpus.

References

[1] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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It utilizes a backbone BERT encoder (bert-base-uncased) followed by two classification heads: one is trained to predict written fragments as replacement tags, the other is trained to predict tags representing semiotic classes, like `DATE`, `CARDINAL` etc. The final tags have one-to-one correspondence with input words.\n\n## Training\n\nThe NeMo toolkit [1] was used for training the models.\nThe training corpus for the model consists of 2 mln sentences.\n\nThis model is trained with this [example script](normalization_as_tagging_train.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/conf/thutmose_tagger_itn_config.yaml).\n\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_train.py \\ \n lang=en \\\n data.validation_ds.data_path=${DATA_PATH}/valid.tsv \\\n data.train_ds.data_path=${DATA_PATH}/train.tsv \\\n data.train_ds.batch_size=128 \\\n model.language_model.pretrained_model_name=bert-base-uncased \\\n model.label_map=${DATA_PATH}/label_map.txt \\\n model.semiotic_classes=${DATA_PATH}/semiotic_classes.txt \\\n trainer.max_epochs=5\n```\n\n### Datasets\n\nThe initial dataset is Google Text Normalization Dataset.\nThe dataset preparation is described [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/prepare_dataset_en.sh) and includes running GIZA++ automatic alignment tool to find granular alignments between spoken words and written fragments.\n\n## Performance\n\nThe performance of ITN models can be measured using Word Error Rate(WER), and Sentence Accuracy. We measure Sentence Accuracy w.r.t. multi-variant reference and subdivide the errors into \"digit\" and \"other\".\n\nThe model obtains the following scores on the following evaluation datasets\n\n```\nDefault test set\n WER: 3.75%\n Sentence accuracy: 97.47%\n digit errors: 0.35%\n other errors: 2.18%\n\n\nHard test set\n WER: 8.98%\n Sentence accuracy: 87.88%\n digit errors: 3.12%\n other errors: 9.00%\n```\n\nNote that reference files were taken from the test part of Google TN Dataset, which is not considered 100% correct because of its synthetic nature. So these scores are not particularly indicative of the quality of final inverse text normalization, but they are a useful proxy.\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference.\n\n### Automatically load the model from NGC\n\n```python\nimport nemo.collections.nlp as nemo_nlp\nnlp_model = nemo_nlp.models.ThutmoseTaggerModel.from_pretrained(model_name=\"itn_en_thutmose_bert\")\n```\n\n### Running inference with this model\n\nExample of inference and evaluation is provided in this [script] (https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/run_infer.sh)\n\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/text_normalization_as_tagging/normalization_as_tagging_infer.py \\ \n pretrained_model=\"itn_en_thutmose_bert\" \\ \n inference.from_file=\"\" \\ \n inference.out_file=\"\" \\ \n model.max_sequence_len=1024 \\ \n inference.batch_size=128\n```\n\n### Input\n\nThis model expects the input file to be plain text without punctuation, similar to the ASR text output.\n\n### Output\n\nThis model provides an output file with exactly the same number of lines as in input. Each line consists of 5 columns:\n1. Final output text.\n2. Input text.\n3. Sequence of predicted tags.\n4. Sequence of tags after post-processing (some swaps may be applied).\n5. Sequence of predicted semiotic classes - one class for each input word.\n\n## Limitations\n\nSince this model was trained on syntetic data its performance might degrade on some constructions that were systematically biased in the initial corpus.\n\n## References\n\n[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n### Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","English Tagger-based Inverse Text Normalization",["D",1680637333491],"/api/image/nvidia/nemo/models/itn_en_thutmose_bert","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[275,276,277,278,279],[],[],[],{"_91":290,"_128":291,"_129":292,"_131":132},{"_91":288,"_128":288,"_129":289,"_131":132},{"_91":284,"_128":285,"_129":286,"_131":132},{"_91":282,"_128":282,"_129":283,"_131":132},{"_91":280,"_128":280,"_129":281,"_131":132},"PytorchLightning",{"_133":132,"_128":280},"NLP",{"_133":132,"_128":282},"Natural Language Processing","uscs_natural_language_processing",{"_133":287,"_128":285},"application","En",{"_133":132,"_128":288},"Conversational AI","soln_conversational_ai",{"_133":293,"_128":291},"technology",{"_170":1464,"_172":1465},"ngcApps",[1417],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1398":1399,"_1400":1401,"_1402":1403,"_1404":1405,"_1406":305,"_201":329,"_1407":1408,"_1409":51,"_1410":149,"_1411":149,"_1412":1413,"_1414":149,"_1415":1416},"amplitude",{"_1395":1396,"_1001":-7,"_1397":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1389":1390,"_1391":1392,"_1393":1394},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1276":409,"_1277":1278,"_1279":1280,"_1281":1282},"announcements",[],"consentState",{"_366":1266,"_1267":149,"_1268":26,"_1269":26,"_1270":1271},"consentConfig",{"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367},"showContactAdmin",1784211612476,[],[],[343,344,345,346,347],{"_40":55,"_42":66,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":165,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":165,"_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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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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