Bert Restore Punctuation

🚀 bert-restore-punctuation

This is a bert-base-uncased model finetuned for punctuation restoration on Yelp Reviews. It predicts the punctuation and upper - casing of plain, lower - cased text, which can be useful for ASR output or other scenarios where text has lost punctuation. This model can be directly used for general English punctuation restoration or further fine - tuned for domain - specific punctuation restoration tasks. It restores punctuations like [! ? . , - : ; ' ] and also the upper - casing of words.

🚀 Quick Start

Installation

First, install the package:

pip install rpunct

Usage Examples

Basic Usage

from rpunct import RestorePuncts
# The default language is 'english'
rpunct = RestorePuncts()
rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record
by tripling the resolution of a state-of-the-art electron microscope as successful as it was that approach had a weakness it only worked with ultrathin samples that were
a few atoms thick anything thicker would cause the electrons to scatter in ways that could not be disentangled now a team again led by david muller the samuel b eckert
professor of engineering has bested its own record by a factor of two with an electron microscope pixel array detector empad that incorporates even more sophisticated
3d reconstruction algorithms the resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves""")
# Outputs the following:
# In 2018, Cornell researchers built a high-powered detector that, in combination with an algorithm-driven process called Ptychography, set a world record by tripling the
# resolution of a state-of-the-art electron microscope. As successful as it was, that approach had a weakness. It only worked with ultrathin samples that were a few atoms
# thick. Anything thicker would cause the electrons to scatter in ways that could not be disentangled. Now, a team again led by David Muller, the Samuel B. 
# Eckert Professor of Engineering, has bested its own record by a factor of two with an Electron microscope pixel array detector empad that incorporates even more
# sophisticated 3d reconstruction algorithms. The resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves.

This model works on arbitrarily large text in English language and uses GPU if available.

📦 Training Data

Here is the number of product reviews used for finetuning the model:

📚 Accuracy

The fine - tuned model obtained the following accuracy on 45,990 held - out text samples:

Below is a breakdown of the performance of the model by each label:

📄 License

This project is licensed under the MIT license.

☕ Contact

Contact Daulet Nurmanbetov for questions, feedback and/or requests for similar models.