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+---
+language:
+- en
+license: mit
+---
+# ✨ bert-restore-punctuation
+[![forthebadge](https://forthebadge.com/images/badges/gluten-free.svg)]()
+This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews). 
+The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
+This model 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.
+
+Model restores the following punctuations -- [` ! ? . , - : ; '`]
+Model also restores upper-casing of words.
+
+-----------------------------------------------
+## 🚋 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.
+```
+
+`This model works on arbitrarily large text in English language and uses GPU if available.`
+
+-----------------------------------------------
+## 📡 Training data
+Here is the number of product reviews we used for finetuning the model:
+| Language | Number of reviews |
+| -------- | ----------------- |
+| English  | 560,000           |
+
+We found the best convergence around `3 epochs`, which is what presented here and available via a download.
+
+-----------------------------------------------
+## 🎯 Accuracy
+The fine-tuned model obtained the following accuracy on 45,990 held-out text samples:
+
+| Accuracy | Overall F1 | Eval Support |
+| -------- | ---------------------- | ------------------- |
+| 91%  | 90%                 | 45,990
+
+Below is a breakdown of the performance of the model by each label:
+
+
+|  label    |   precision  |  recall | f1-score  | support|
+| --------- | -------------|-------- | ----------|--------|
+|     **!**    |   0.45       | 0.17    |  0.24     |  424   
+|     **!+Upper**    |   0.43       | 0.34    |  0.38     |   98   
+|     **'**    |   0.60       | 0.27    |  0.37     |   11   
+|    **,**    |   0.59       | 0.51    |  0.55     | 1522   
+|     **,+Upper**    |   0.52       | 0.50    |  0.51     |  239   
+|     **-**    |   0.00       | 0.00    |  0.00     |   18   
+|     **.**    |   0.69       | 0.84    |  0.75     | 2488   
+|     **.+Upper**    |   0.65       | 0.52    |  0.57     |  274   
+|     **:**    |   0.52       | 0.31    |  0.39     |   39   
+|     **:+Upper**    |   0.36       | 0.62    |  0.45     |   16   
+|     **;**    |   0.00       | 0.00    |  0.00     |   17   
+|     **?**    |   0.54       | 0.48    |  0.51     |   46   
+|     **?+Upper**    |   0.40       | 0.50    |  0.44     |    4   
+|     **none**    |   0.96       | 0.96    |  0.96     |35352   
+|     **Upper**    |   0.84       | 0.82    |  0.83     | 5442   
+-----------------------------------------------
+
+## ☕ Contact 
+Contact [Daulet Nurmanbetov]([email protected]) for questions, feedback and/or requests for similar models.
+
+-----------------------------------------------