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RIVA Punctuation and Capitalization for English

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Description

For each word in the input text, the model: 1) predicts a punctuation mark that should follow the word (if any), the model supports commas, periods and question marks) and 2) predicts if the word should be capitalized or not.

Publisher

NVIDIA

Use Case

Punctuation And Capitalization

Framework

NeMo

Latest Version

trainable_v2.0

Modified

February 2, 2023

Size

386.99 MB

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. predicts 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

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

  1. Tatoeba sentences
  2. MCV Corpus
  3. Proprietary datasets.

Performance

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

To use this model , we can use Riva Skills Quick start guide , it is a starting point to try out Riva models . Information regarding Quick start guide can be found : here. To use Riva Speech ASR service using this model, document has all the necessary information.

References

Citations

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, you would be accepting 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.