rpunct 1.0.2
An easy-to-use package to  restore punctuation of text.
Requires Python: >=3.6

# ✏️ rpunct - Restore Punctuation
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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.**
1. First, install the package.
```bash
pip install rpunct
```
2. Sample python code.
```python
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.
```

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## 🎯 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](daulet.nurmanbetov@gmail.com) for questions, feedback and/or requests for similar models.

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