/rpunct

πŸ“An easy-to-use package to restore punctuation of the text.

Primary LanguagePython

✏️ rpunct - Restore Punctuation

forthebadge

This repo contains code for Punctuation restoration.

This package is intended for direct use as a punctuation restoration model for the general English language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks. It uses HuggingFace's bert-base-uncased model weights that have been fine-tuned for Punctuation restoration.

Punctuation restoration works on arbitrarily large text. And uses GPU if it's available otherwise will default to CPU.

List of punctuations we restore:

  • Upper-casing
  • Period: .
  • Exclamation: !
  • Question Mark: ?
  • Comma: ,
  • Colon: :
  • Semi-colon: ;
  • Apostrophe: '
  • Dash: -

πŸš€ Usage

Below is a quick way to get up and running with the model.

  1. First, install the package.
pip install rpunct
  1. Sample python code.
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.

🎯 Accuracy

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

Language Number of text samples
English 560,000

We found the best convergence around 3 epochs, which is what presented here and available via a download.


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

Accuracy Overall F1 Eval Support
91% 90% 45,990

πŸ’»πŸŽ― Further Fine-Tuning

To start fine-tuning or training please look into training/train.py file. Running python training/train.py will replicate the results of this model.


β˜• Contact

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