bert-restore-punctuation
Maintainer: felflare
56
✅
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The bert-restore-punctuation model is a BERT-based model that has been fine-tuned on the Yelp Reviews dataset for the task of punctuation restoration. This model can predict the punctuation and upper-casing of plain, lower-cased text, making it useful for tasks like automatic speech recognition output or other cases where text has lost its original punctuation.
The model was fine-tuned by felflare, who describes it as intended for direct use as a punctuation restoration model for general English language. However, it can also be used as a starting point for further fine-tuning on domain-specific texts for punctuation restoration.
Model inputs and outputs
Inputs
- Plain, lower-cased text without punctuation
Outputs
- The input text with restored punctuation and capitalization
Capabilities
The bert-restore-punctuation model is capable of restoring the following punctuation marks: [! ? . , - : ; ' ]. It also restores the upper-casing of words in the input text.
What can I use it for?
This model can be used for a variety of applications that involve processing text with missing punctuation, such as:
- Automatic speech recognition (ASR) output processing
- Cleaning up text data that has lost its original formatting
- Preprocessing text for downstream natural language processing tasks
Things to try
One interesting aspect of this model is its ability to restore not just punctuation, but also capitalization. This could be useful in scenarios where the case information has been lost, such as when working with text that has been converted to all lower-case. You could experiment with using the bert-restore-punctuation model as a preprocessing step for other NLP tasks to see if the restored formatting improves the overall performance.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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