Bert Restore Punctuation

felflare

bert-restore-punctuation

The bert-restore-punctuation model is a token classification model that has been trained to add punctuation marks to sentences. It takes a sentence as input and predicts the punctuation marks that should be placed at the appropriate positions in the sentence. This can be useful for tasks such as restoring punctuation to transcribed speech or correcting punctuation errors in text. The model is based on the BERT architecture and has been fine-tuned on a specific punctuation restoration task.

Use cases

The bert-restore-punctuation model has several potential use cases for a technical audience. One use case is in speech-to-text systems, where it can be used to restore punctuation to transcribed speech. This can improve the readability and accuracy of the transcriptions. Another use case is in natural language processing tasks, where the model can be used to correct punctuation errors in text. This can be particularly useful in tasks such as sentiment analysis or named entity recognition, where punctuation can affect the semantics of a sentence. Additionally, the model can be integrated into writing assistants or grammar checkers to provide suggestions for punctuation placement. Overall, the bert-restore-punctuation model has the potential to be a valuable tool for improving the quality and clarity of text in various applications.

token-classification

Pricing

Cost per run
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Avg run time
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Creator Models

ModelCostRuns
EasyOCR Weights$?0
Lilt Only Base$?0
TextFuseNet_weights$?0

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Overview

Summary of this model and related resources.

PropertyValue
Creatorfelflare
Model NameBert Restore Punctuation
Description
Platform did not provide a description for this model.
Tagstoken-classification
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Popularity

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PropertyValue
Runs19,854
Model Rank
Creator Rank

Cost

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PropertyValue
Cost per Run$-
Prediction Hardware-
Average Completion Time-