🤗 bert-restore-punctuation-ptbr
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This is a bert-base-portuguese-cased model finetuned for punctuation restoration on WikiLingua.
This model is intended for direct use as a punctuation restoration model for the general Portuguese 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
🇧🇷 easy-to-use package to restore punctuation of portuguese texts.
Below is a quick way to use the template.
- First, install the package.
pip install respunct
- Sample python code.
from respunct import RestorePuncts
model = RestorePuncts()
model.restore_puncts("""
henrique foi no lago pescar com o pedro mais tarde foram para a casa do pedro fritar os peixes""")
# output:
# Henrique foi no lago pescar com o Pedro. Mais tarde, foram para a casa do Pedro fritar os peixes.
🎯 Accuracy
label | precision | recall | f1-score | support |
---|---|---|---|---|
Upper - OU | 0.89 | 0.91 | 0.90 | 69376 |
None - OO | 0.99 | 0.98 | 0.98 | 857659 |
Full stop/period - .O | 0.86 | 0.93 | 0.89 | 60410 |
Comma - ,O | 0.85 | 0.83 | 0.84 | 48608 |
Upper + Comma - ,U | 0.73 | 0.76 | 0.75 | 3521 |
Question - ?O | 0.68 | 0.78 | 0.73 | 1168 |
Upper + period - .U | 0.66 | 0.72 | 0.69 | 1884 |
Upper + colon - :U | 0.59 | 0.63 | 0.61 | 352 |
Colon - :O | 0.70 | 0.53 | 0.60 | 2420 |
Question Mark - ?U | 0.50 | 0.56 | 0.53 | 36 |
Upper + Exclam. - !U | 0.38 | 0.32 | 0.34 | 38 |
Exclamation Mark - !O | 0.30 | 0.05 | 0.08 | 783 |
Semicolon - ;O | 0.35 | 0.04 | 0.08 | 1557 |
Apostrophe - 'O | 0.00 | 0.00 | 0.00 | 3 |
Hyphen - -O | 0.00 | 0.00 | 0.00 | 3 |
accuracy | 0.96 | 1047818 | ||
macro avg | 0.57 | 0.54 | 0.54 | 1047818 |
weighted avg | 0.96 | 0.96 | 0.96 | 1047818 |
🤙 Contact
Maicon Domingues for questions, feedback and/or requests for similar models.
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Dataset used to train dominguesm/bert-restore-punctuation-ptbr
Evaluation results
- F1 Score on wiki_linguaself-reported55.700
- Precision on wiki_linguaself-reported57.720
- Recall on wiki_linguaself-reported53.830