TLT/Riva - Punctuation & Capitalization
Punctuation and Capitalization Collection
This page contains the information about the Punctuation and Capitalization collection with TLT.
NGC Model Collection: Punctuation and Capitalization ====================================================
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.
This collection contains end-to-end neural models that solve Punctuation and Capitalization Tasks using the Transfer Learning Toolkit (TLT).
Available Models ----------------
For instructions on how to use a model, please see its corresponding model card page.
References ----------
- [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)
License -------
License to use these models is covered by the Model EULA. By downloading the model checkpoints, you accept the terms and conditions of these licenses.
Suggested Reading -----------------
- More information about the Transfer Learning Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/transfer-learning-toolkit
- Read the TLT getting Started guide and release notes.
- More information about the experiment spec files can be found in the TLT User Guide
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.