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rpunct - Restore Punctuation
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: -
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Usage
Below is a quick way to get up and running with the model.
- First, install the package.
pip install rpunct
- 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 |
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🎯
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.
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Contact
Contact Daulet Nurmanbetov for questions, feedback and/or requests for similar models.
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