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--- |
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language: |
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- en |
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tags: |
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- punctuation |
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license: mit |
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datasets: |
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- yelp_polarity |
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metrics: |
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- f1 |
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--- |
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# ✨ bert-restore-punctuation |
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[]() |
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This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews). |
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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. |
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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. |
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Model restores the following punctuations -- **[! ? . , - : ; ' ]** |
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The model also restores the upper-casing of words. |
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## 🚋 Usage |
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**Below is a quick way to get up and running with the model.** |
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1. First, install the package. |
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```bash |
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pip install rpunct |
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``` |
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2. Sample python code. |
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```python |
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from rpunct import RestorePuncts |
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# The default language is 'english' |
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rpunct = RestorePuncts() |
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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 |
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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 |
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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 |
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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 |
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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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# Outputs the following: |
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# 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 |
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# 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 |
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# 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. |
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# 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 |
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# 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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``` |
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**This model works on arbitrarily large text in English language and uses GPU if available.** |
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## 📡 Training data |
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Here is the number of product reviews we used for finetuning the model: |
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| Language | Number of text samples| |
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| -------- | ----------------- | |
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| English | 560,000 | |
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We found the best convergence around _**3 epochs**_, which is what presented here and available via a download. |
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## 🎯 Accuracy |
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The fine-tuned model obtained the following accuracy on 45,990 held-out text samples: |
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| Accuracy | Overall F1 | Eval Support | |
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| -------- | ---------------------- | ------------------- | |
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| 91% | 90% | 45,990 |
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Below is a breakdown of the performance of the model by each label: |
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| label | precision | recall | f1-score | support| |
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| --------- | -------------|-------- | ----------|--------| |
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| **!** | 0.45 | 0.17 | 0.24 | 424 |
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| **!+Upper** | 0.43 | 0.34 | 0.38 | 98 |
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| **'** | 0.60 | 0.27 | 0.37 | 11 |
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| **,** | 0.59 | 0.51 | 0.55 | 1522 |
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| **,+Upper** | 0.52 | 0.50 | 0.51 | 239 |
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| **-** | 0.00 | 0.00 | 0.00 | 18 |
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| **.** | 0.69 | 0.84 | 0.75 | 2488 |
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| **.+Upper** | 0.65 | 0.52 | 0.57 | 274 |
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| **:** | 0.52 | 0.31 | 0.39 | 39 |
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| **:+Upper** | 0.36 | 0.62 | 0.45 | 16 |
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| **;** | 0.00 | 0.00 | 0.00 | 17 |
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| **?** | 0.54 | 0.48 | 0.51 | 46 |
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| **?+Upper** | 0.40 | 0.50 | 0.44 | 4 |
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| **none** | 0.96 | 0.96 | 0.96 |35352 |
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| **Upper** | 0.84 | 0.82 | 0.83 | 5442 |
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## ☕ Contact |
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Contact [Daulet Nurmanbetov](daulet.nurmanbetov@gmail.com) for questions, feedback and/or requests for similar models. |
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