Token Classification
Transformers
PyTorch
Safetensors
Portuguese
bert
named-entity-recognition
Transformer
Eval Results (legacy)
Instructions to use dominguesm/bert-restore-punctuation-ptbr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dominguesm/bert-restore-punctuation-ptbr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dominguesm/bert-restore-punctuation-ptbr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dominguesm/bert-restore-punctuation-ptbr") model = AutoModelForTokenClassification.from_pretrained("dominguesm/bert-restore-punctuation-ptbr") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- pt
license: cc-by-4.0
datasets:
- wiki_lingua
thumbnail: null
tags:
- named-entity-recognition
- Transformer
- pytorch
- bert
metrics:
- f1
- precision
- recall
model-index:
- name: rpunct-ptbr
results:
- task:
type: named-entity-recognition
dataset:
type: wiki_lingua
name: wiki_lingua
metrics:
- type: f1
value: 55.7
name: F1 Score
- type: precision
value: 57.72
name: Precision
- type: recall
value: 53.83
name: Recall
widget:
- text: >-
henrique foi no lago pescar com o pedro mais tarde foram para a casa do
pedro fritar os peixes
- text: >-
cinco trabalhadores da construção civil em capacetes e coletes amarelos
estão ocupados no trabalho
- text: >-
na quinta feira em visita a belo horizonte pedro sobrevoa a cidade
atingida pelas chuvas
- text: >-
coube ao representante de classe contar que na avaliação de língua
portuguesa alguns alunos se mantiveram concentrados e outros dispersos
🤗 bert-restore-punctuation-ptbr
- 🪄 W&B Dashboard
- ⛭ GitHub
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.