Punctuation and Capitalization Model Card

Model Overview

Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. Besides being hard to read, the ASR output could be an input to named entity recognition, machine translation or text-to-speech models. If the input text has punctuation and words are capitalized correctly, this could potentially boost the performance of such models.

Intended Use

For each word in the input text, the model:

predicts a punctuation mark that should follow the word (if any). The model supports commas, periods and question marks.\npredicts if the word should be capitalized or not.

Model Architecture

The Punctuation and Capitalization model consists of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) followed by two token classification heads. One classification head is responsible for the punctuation task, the other one handles the capitalization task. Both token level classification heads take the BERT encoded representation of the [CLS] token as input. Such architecture allows this model to solve two tasks at once with only a single pass through the BERT. Finally, all the parameters are fine-tuned on this joint task.

Limitations

The punctuation model currently supports only commas, periods and question marks.

Training

Dataset

The model was trained with BERT base multilingual cased checkpoint on a subset of data from the following sources:

Tatoeba sentences
MCV Corpus
Proprietary datasets

Performance

Evaluation

Each word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation: (1) marking the whole entity as a single label (2) perform evaluation on the sub token level

During training, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. Each task is evaluated separately. Due to the high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).

This model was evaluated on an internal dataset, and it reached the F1 score of 88%.

How to Use this Model

This pre-trained model needs to be used with NVIDIA Hardware and Software. This model can be loaded with the key: tlt_encode.

References

[1] Devlin, Jacob, et al. \"Bert: Pre-training of deep bidirectional transformers for language understanding.\" arXiv preprint arXiv:1810.04805 (2018).

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva license.

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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Besides being hard to read, the ASR output could be an input to named entity recognition, machine translation or text-to-speech models. If the input text has punctuation and words are capitalized correctly, this could potentially boost the performance of such models.\n\n## Intended Use\n\nFor each word in the input text, the model:\n\npredicts a punctuation mark that should follow the word (if any). The model supports commas, periods and question marks.\npredicts if the word should be capitalized or not.\n\n## Model Architecture\n\nThe Punctuation and Capitalization model consists of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) followed by two token classification heads. One classification head is responsible for the punctuation task, the other one handles the capitalization task. Both token level classification heads take the BERT encoded representation of the [CLS] token as input. Such architecture allows this model to solve two tasks at once with only a single pass through the BERT. Finally, all the parameters are fine-tuned on this joint task.\n\n## Limitations\n\nThe punctuation model currently supports only commas, periods and question marks.\n\n## Training\n\n### Dataset\n\nThe model was trained with BERT base multilingual cased checkpoint on a subset of data from the following sources:\n\n- Tatoeba sentences\n- MCV Corpus\n- Proprietary datasets\n\n## Performance\n\n### Evaluation\n\nEach word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation: (1) marking the whole entity as a single label (2) perform evaluation on the sub token level\n\nDuring training, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. Each task is evaluated separately. Due to the high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).\n\nThis model was evaluated on an internal dataset, and it reached the F1 score of 88%.\n\n## How to Use this Model\n\nThis pre-trained model needs to be used with NVIDIA Hardware and Software. This model can be loaded with the key: `tlt_encode`.\n\n## References\n\n[1] [Devlin, Jacob, et al. \"Bert: Pre-training of deep bidirectional transformers for language understanding.\" arXiv preprint arXiv:1810.04805 (2018).](https://arxiv.org/pdf/1810.04805.pdf)\n\n## Suggested Reading\n\nDeploy your model for Production using Riva. Refer to the [Riva framework](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n## License\n\nBy downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva [license](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical AI\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","RIVA Punctuation for Arabic",["D",1717170879383],"deployable_v1.0_export_v2","/api/image/nvidia/riva/models/punctuationcapitalization_ar_ar_bert_base","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",408492028,"bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications & Domains: | Punctuation\nTypes: | BERT-base\nIntended Users: | Data Scientists in Contact Center Transcription, Video Conferencing Transcription, Virtual Assistants, etc\nOutput: | Text with Punctuations\nDescribe how the model works: | Model adds Punctuations to input text for the input language\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not applicable\nTechnical Limitations: | Outputs may not be 100% accurate. Model can only capitalize first character of words\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Accuracy and F1 scores\nPotential Known Risks: | Not recommended for word-for-word/incomplete sentences as accuracy varies based on the context of input text\nLicensing: | https://developer.nvidia.com/riva/ga/license","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any PII used? | Not Applicable\nProtected class data used to create this model? | Not Applicable\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | PII not collected by NVIDIA for development of model\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | The data is compliant where applicable, but is not applicable for all data.","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Punctuation\nDescribe the life-critical impacts (if present). | Not Applicable for Licensed Use Cases per [NVIDIA RIVA License Agreement](https://developer.nvidia.com/riva/ga/license)\nUse Case Restriction(s): | Abide by https://developer.nvidia.com/riva/ga/license\nModel and Dataset Restriction(s): | Dataset access restrictions\nDescribe access restrictions (if any): | The Principle of Least Privilege (PoLP) is applied limiting access for dataset generation and model development. 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