RIVA Punctuation for Hindi

RIVA Punctuation for Hindi

Logo for RIVA Punctuation for Hindi
For each word in the input text, the model predicts a punctuation mark that should follow the word (if any), the model supports commas, poornvirams, exclaimation marks and question marks.
Latest Version
March 26, 2024
841.63 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.


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



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

  • Wiki Corpus
  • MCV Corpus
  • MKB Corpus



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.


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

Suggested Reading

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