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, hyphens and question marks.
Training
Dataset
The model was trained with BERT base multilingual cased checkpoint on a subset of data from the following sources:
- 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
Suggested Reading
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License
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