Punctuation and Capitalization Model Card =========================================

Model Overview --------------

Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words.\nBesides 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,\nthis 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.\nSuch 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 length of the input text is currently constrained by the maximum sequence length of the BERT base uncased model, which is 512 tokens after tokenization.\nThe punctuation model supports commas, periods and question marks.

Training

Dataset

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

Tatoeba sentences
Books from the Project Gutenberg that were used as part of the LibriSpeech corpus
Transcripts from Fisher English Training Speech

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:\n(1) marking the whole entity as a single label\n(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.\nDue 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 77%.

How to Use this Model

This pre-trained model needs to be used with NVIDIA Hardware and Software.\nThis model can be used with Train Adapt Optimize (TAO) Toolkit with model load key: tlt_encode (Please make sure to use this as the key for all TAO commands that require a model load key).

For more details on how to prepare your data for this model and how to fine-tune the model with TAO, see TAO user guide for Punctuation and Capitalization model.

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 TAO Conversational AI, 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.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat 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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If the input text has punctuation and words are capitalized correctly,\nthis could potentially boost the performance of such models.\n\n#### Intended Use\n\nFor each word in the input text, the model:\n\n1. predicts a punctuation mark that should follow the word (if any). The model supports commas, periods and question marks.\n2. predicts 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)](https://arxiv.org/pdf/1810.04805.pdf) 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.\nSuch 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 length of the input text is currently constrained by the maximum sequence length of the BERT base uncased model, which is 512 tokens after tokenization.\nThe punctuation model supports commas, periods and question marks.\n\n### Training\n\n#### Dataset\n\nThe model was trained with [NeMo BERT base uncased checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:bertbaseuncasedfornemo) on a subset of data from the following sources:\n\n- [Tatoeba sentences](https://tatoeba.org/eng/downloads)\n- Books from the Project Gutenberg that were used as part of [the LibriSpeech corpus](http://www.openslr.org/12)\n- Transcripts from [Fisher English Training Speech](https://catalog.ldc.upenn.edu/LDC2004S13)\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:\n(1) marking the whole entity as a single label\n(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.\nDue 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 77%.\n\n### How to Use this Model\n\nThis pre-trained model needs to be used with NVIDIA Hardware and Software.\nThis model can be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit) with model load key: `tlt_encode` (Please make sure to use this as the key for all TAO commands that require a model load key).\n\nFor more details on how to prepare your data for this model and how to fine-tune the model with TAO, see [TAO user guide for Punctuation and Capitalization model](https://docs.nvidia.com/tao/tao-toolkit/text/nlp/punctuation_and_capitalization.html).\n\n### References\n\n#### Citations\n\n- [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\n- More information about the TAO Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/tao-toolkit.\n- Read the [TAO getting Started guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- More information about the experiment spec files can be found in the [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html).\n- More information about deploying the model with Riva Services see [this](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/model-overview.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n\n# License -------\n\nBy downloading and using the models and resources packaged with TAO Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/license)\n\n# Ethical AI ----------\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; 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