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Itau-Unibanco/NorBERTo-base para a tarefa de Detecção de Notícias Falsas (Fake News) em português brasileiro, treinado no dataset HenriqueLz/fakerecogna2-extrativa-elections.0.998| ID | Label | Descrição |
|---|---|---|
0 | VERDADEIRA | Notícia factual / verdadeira |
1 | FALSA | Notícia falsa / desinformação |
pipeline:1from transformers import pipeline
2
3classifier = pipeline(
4 "text-classification",
5 model="HenriqueLz/norberto-base-fakerecogna2-extrativa-elections",
6 tokenizer="HenriqueLz/norberto-base-fakerecogna2-extrativa-elections",
7)
8
9texto = "Ministério da Saúde divulga calendário oficial de vacinação para o próximo ano."
10resultado = classifier(texto)
11print(resultado)
12# Output: [{'label': 'VERDADEIRA', 'score': 0.99...}]1import torch
2from transformers import AutoTokenizer, AutoModelForSequenceClassification
3
4tokenizer = AutoTokenizer.from_pretrained("HenriqueLz/norberto-base-fakerecogna2-extrativa-elections")
5model = AutoModelForSequenceClassification.from_pretrained("HenriqueLz/norberto-base-fakerecogna2-extrativa-elections")
6
7texto = "Texto da notícia para classificação..."
8inputs = tokenizer(texto, return_tensors="pt", truncation=True, max_length=512)
9
10with torch.no_grad():
11 logits = model(**inputs).logits
12
13predicted_class_id = logits.argmax().item()
14label = model.config.id2label[predicted_class_id]
15print(f"Classe predita: {label}")HenriqueLz/fakerecogna2-extrativa-elections (split temporal com data de corte em 30/10/2021).1e-5 com otimizador AdamW e decaimento de peso (weight decay) de 0.01.DataCollatorWithPadding).1@inproceedings{garcia-etal-2024-text,
2 title = "Text Summarization and Temporal Learning Models Applied to {P}ortuguese Fake News Detection in a Novel {B}razilian Corpus Dataset",
3 author = "Garcia, Gabriel Lino and Paiola, Pedro Henrique and Jodas, Danilo Samuel and Sugi, Luis Afonso and Papa, Jo{\~a}o Paulo",
4 booktitle = "Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1",
5 month = mar,
6 year = "2024",
7 address = "Santiago de Compostela, Galicia/Spain",
8 publisher = "Association for Computational Lingustics",
9 url = "https://aclanthology.org/2024.propor-1.9/",
10 pages = "86--96"
11}1@inproceedings{silva-etal-2026-norberto,
2 title = "{N}or{BERT}o: A {M}odern{BERT} Model Trained for {P}ortuguese with 331 Billion Tokens Corpus",
3 author = "Silva, Enzo S. N. and
4 Costa, Pablo B. and
5 Vlasman, Raphael C. and
6 Costa, Rosimeire P. and
7 Silva, Henrique L. P. and
8 Pellicer, Lucas F. A. O. and
9 Rinaldo, Guilherme and
10 Almeida, Renato A. and
11 Rabbani, Darian S. R. and
12 Oestreich, Cinthya O. and
13 Carid{'a}, Vinicius F.",
14 editor = "Souza, Marlo and
15 de-Dios-Flores, Iria and
16 Santos, Diana and
17 Freitas, Larissa and
18 Souza, Jackson Wilke da Cruz and
19 Ribeiro, Eug{'e}nio",
20 booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
21 month = apr,
22 year = "2026",
23 address = "Salvador, Brazil",
24 publisher = "Association for Computational Linguistics",
25 url = "https://aclanthology.org/2026.propor-1.18/",
26 pages = "183--193",
27 ISBN = "979-8-89176-387-6"
28}