✏️
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: -
🚀
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 |
💻
🎯
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.
☕
Contact
Contact Daulet Nurmanbetov for questions, feedback and/or requests for similar models.
2 Jun 10, 2022
1 Oct 25, 2021
1k Dec 26, 2022
409 Oct 28, 2022
21 Dec 11, 2022
36 Sep 13, 2022
2 Jan 27, 2022
2 Jun 19, 2022
141 Dec 30, 2022
32 Dec 14, 2022
22 Oct 21, 2022
0 Nov 12, 2022
6k Dec 29, 2022
64 Nov 22, 2022
17 Oct 14, 2022
99 Jan 02, 2023
52 Jun 21, 2022
201 Nov 09, 2022
481 Dec 30, 2022
475 Jan 04, 2023