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Model Overview

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

It converts text from spoken domain into its written form:\nInput: \"в тысяча девятьсот тринадцатом году в кузнецке было пятьдесят две версты мощеных дорог\"\nOutput: \"в 1913 году в кузнецке было 52 версты мощеных дорог\"

Model Architecture

Thutmose Tagger is a single-pass tagging model. It utilizes a backbone BERT encoder (DeepPavlov/rubert-base-cased) 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=DeepPavlov/rubert-base-cased \\\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 for Russian.\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:  3.55%\n    Sentence accuracy:  92.96%\n        digit errors:    0.40%\n        other errors:    6.63%\n\n\nHard test set\n    WER:  7.80%\n    Sentence accuracy:  83.83%\n        digit errors:    3.41%\n        other errors:   12.76%\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_ru_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_ru_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 (DeepPavlov/rubert-base-cased) 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](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/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=DeepPavlov/rubert-base-cased \\\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 for Russian.\nThe dataset preparation is described [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/prepare_dataset_ru.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: 3.55%\n Sentence accuracy: 92.96%\n digit errors: 0.40%\n other errors: 6.63%\n\n\nHard test set\n WER: 7.80%\n Sentence accuracy: 83.83%\n digit errors: 3.41%\n other errors: 12.76%\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_ru_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_ru_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).","displayName","Russian Tagger-based Inverse Text Normalization",["D",1680638538141],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/itn_ru_thutmose_bert","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":255,"_243":256,"_244":257,"_246":247},{"_206":251,"_243":252,"_244":253,"_246":247},{"_206":249,"_243":249,"_244":250,"_246":247},{"_206":242,"_243":242,"_244":245,"_246":247},"Ru","value","filter",{"_248":247,"_243":242},"category","label","field","PytorchLightning",{"_248":247,"_243":249},"Natural Language Processing","uscs_natural_language_processing",{"_248":254,"_243":252},"application","Conversational AI","soln_conversational_ai",{"_248":258,"_243":256},"technology",{"_107":1235,"_109":1236},"ngcApps",[1188],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1169":1170,"_1171":1172,"_1173":1174,"_1175":1176,"_1177":270,"_138":296,"_1178":1179,"_1180":56,"_1181":86,"_1182":86,"_1183":1184,"_1185":86,"_1186":1187},"amplitude",{"_1166":1167,"_807":-7,"_1168":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1160":1161,"_1162":1163,"_1164":1165},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787538709464,"1.12.0","defaultSearchSuggestions",{"_1047":168,"_1048":1049,"_1050":1051,"_1052":1053},"announcements",[],"consentState",{"_333":1037,"_1038":86,"_1039":31,"_1040":31,"_1041":1042},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1787538709468,[],[],[310,311,312,313,314],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_51":-5,"_53":-5,"_55":56},"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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588],[535,536,537,538,539],{"_374":449,"_206":449,"_167":168,"_347":559},{"_374":423,"_206":423,"_167":168,"_347":554},{"_374":549,"_206":549,"_167":168,"_347":550},{"_374":544,"_206":544,"_167":168,"_347":545},{"_374":540,"_206":540,"_167":168,"_347":541},"nvidia.partners",[542],{"_78":543,"_380":540,"_381":31,"_167":417},"edge","app.hushly.com",[546],{"_78":547,"_380":544,"_381":31,"_167":548},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[551],{"_78":552,"_380":549,"_381":31,"_167":553},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675],[627,628,629,630],{"_374":642,"_206":642,"_167":168,"_347":643},{"_374":637,"_206":637,"_167":168,"_347":638},{"_374":436,"_206":436,"_167":168,"_347":634},{"_374":549,"_206":549,"_167":168,"_347":631},[632],{"_78":633,"_380":549,"_381":86,"_167":553},"_gh_sess",[635],{"_78":636,"_380":436,"_381":86,"_167":443},"YSC","bizible.com",[639],{"_78":640,"_380":637,"_381":31,"_167":641},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[644],{"_78":645,"_380":642,"_381":31,"_167":646},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":717,"_380":495,"_381":31,"_167":718},{"_78":702,"_380":716,"_381":86,"_167":703},{"_78":685,"_380":498,"_381":31,"_167":610},{"_78":685,"_380":598,"_381":31,"_167":610},{"_78":714,"_380":495,"_381":31,"_167":715},{"_78":702,"_380":501,"_381":86,"_167":703},{"_78":712,"_380":495,"_381":31,"_167":713},{"_78":702,"_380":498,"_381":86,"_167":703},{"_78":710,"_380":597,"_381":31,"_167":711},{"_78":702,"_380":619,"_381":86,"_167":703},{"_78":682,"_380":498,"_381":31,"_167":684},{"_78":708,"_380":495,"_381":86,"_167":709},{"_78":706,"_380":495,"_381":31,"_167":707},{"_78":704,"_380":495,"_381":86,"_167":705},{"_78":702,"_380":593,"_381":86,"_167":703},{"_78":700,"_380":495,"_381":31,"_167":701},{"_78":698,"_380":495,"_381":31,"_167":699},{"_78":696,"_380":495,"_381":86,"_167":697},{"_78":694,"_380":602,"_381":31,"_167":695},{"_78":682,"_380":593,"_381":31,"_167":684},{"_78":692,"_380":495,"_381":31,"_167":693},{"_78":690,"_380":495,"_381":86,"_167":691},{"_78":688,"_380":495,"_381":31,"_167":689},{"_78":686,"_380":495,"_381":31,"_167":687},{"_78":685,"_380":495,"_381":31,"_167":610},{"_78":682,"_380":683,"_381":31,"_167":684},{"_78":680,"_380":495,"_381":31,"_167":681},{"_78":678,"_380":495,"_381":86,"_167":679},{"_78":676,"_380":495,"_381":31,"_167":677},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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