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✨ bert-restore-punctuation

forthebadge

This a bert-base-uncased model finetuned for punctuation restoration on Yelp 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 -- [! ? . , - : ; ' ]

The model also restores the upper-casing of words.


🚋 Usage

Below is a quick way to get up and running with the model.

  1. First, install the package.
    pip install rpunct
    
  2. 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.
    

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 text samples
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 for questions, feedback and/or requests for similar models.


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Dataset used to train st1992/bert-restore-punctuation