HenriqueLz
/

albertina-100m-portuguese-ptbr-fakerecogna2-extrativa-elections

@@ -6,202 +6,134 @@ language:
 - pt
 metrics:
 - f1
 base_model:
-- PORTULAN/albertina-100m-portuguese-ptbr-encoder
 pipeline_tag: text-classification
 ---
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 - pt
 metrics:
 - f1
+- accuracy
 base_model:
+- PORTULAN/albertina-100m-portuguese-ptbr
 pipeline_tag: text-classification
+license: mit
+tags:
+- fake-news
+- portuguese
+- brazil
+- elections
+- deberta
+- albertina
 ---
+# Albertina 100M PT-BR — Detecção de Fake News (Eleições BR)
+Fine-tune do [Albertina 100M PT-BR](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptbr) (DeBERTa) para classificação binária de notícias falsas em português brasileiro, treinado no corpus eleitoral [fakerecogna2-extrativa-elections](https://huggingface.co/datasets/HenriqueLz/fakerecogna2-extrativa-elections).
+## Uso rápido
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "text-classification",
+    model="HenriqueLz/albertina-100m-portuguese-ptbr-fakerecogna2-extrativa-elections",
+)
+result = classifier("A OMS confirmou que a vacina causa autismo em crianças.")
+# [{'label': 'FALSA', 'score': 0.9999}]
+```
+Ou carregando manualmente:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_id = "HenriqueLz/albertina-100m-portuguese-ptbr-fakerecogna2-extrativa-elections"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+inputs = tokenizer("Texto a classificar", return_tensors="pt", truncation=True)
+with torch.no_grad():
+    logits = model(**inputs).logits
+pred = logits.argmax(-1).item()
+print(model.config.id2label[pred])  # "VERDADEIRA" ou "FALSA"
+```
+## Labels
+| ID | Label | Descrição |
+|----|-------|-----------|
+| 0 | `VERDADEIRA` | Notícia verdadeira / conteúdo factual |
+| 1 | `FALSA` | Notícia falsa / desinformação |
+## Detalhes de Treinamento
+### Dados
+- **Dataset base:** [recogna-nlp/fakerecogna2-extrativa](https://huggingface.co/datasets/recogna-nlp/fakerecogna2-extrativa) / [HenriqueLz/fakerecogna2-extrativa-elections](https://huggingface.co/datasets/HenriqueLz/fakerecogna2-extrativa-elections)
+- **Train:** 42.031 exemplos
+- **Test:** 10.504 exemplos (2.326 FALSA · 8.178 VERDADEIRA)
+- **Domínio:** Notícias sobre eleições brasileiras
+### Hiperparâmetros
+| Parâmetro | Valor |
+|-----------|-------|
+| Learning rate | 1e-5 |
+| Batch size | 16 |
+| Épocas | 5 |
+| Weight decay | 0.01 |
+| Precisão | fp16 |
+| Otimizador | AdamW |
+| Scheduler | Linear com warmup |
+| Hardware | NVIDIA Tesla P100 (Kaggle) |
+### Modelo base
+[Albertina 100M PT-BR](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptbr) — Modelo baseado na arquitetura DeBERTa (100M parâmetros) pré-treinado em português brasileiro pela PORTULAN CLARIN.
+## Limitações
+- **Domínio restrito:** Treinado exclusivamente em notícias do contexto eleitoral brasileiro.
+- **Corte temporal:** O corpus reflete padrões linguísticos de um período eleitoral específico.
+- **Viés de dataset:** A distribuição de classes reflete o corpus coletado.
+## Citações
+```bibtex
+@inproceedings{garcia-etal-2024-text,
+    title = "Text Summarization and Temporal Learning Models Applied to {P}ortuguese Fake News Detection in a Novel {B}razilian Corpus Dataset",
+    author = "Garcia, Gabriel Lino  and
+      Paiola, Pedro Henrique  and
+      Jodas, Danilo Samuel  and
+      Sugi, Luis Afonso  and
+      Papa, Jo{\~a}o Paulo",
+    editor = "Gamallo, Pablo  and
+      Claro, Daniela  and
+      Teixeira, Ant{'o}nio  and
+      Real, Livy  and
+      Garcia, Marcos  and
+      Oliveira, Hugo Gon{\c{c}}alo  and
+      Amaro, Raquel",
+    booktitle = "Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1",
+    month = mar,
+    year = "2024",
+    address = "Santiago de Compostela, Galicia/Spain",
+    publisher = "Association for Computational Lingustics",
+    url = "https://aclanthology.org/2024.propor-1.9/",
+    pages = "86--96"
+}
+@inproceedings{rodrigues-etal-2023-advancing,
+    title = "Advancing Neural Encoding of {P}ortuguese with {T}ransformer {A}lbertina {PT}-*",
+    author = "Rodrigues, Jo{\~a}o  and
+      Gomes, Lu{\'\i}s  and
+      Silva, Jo{\~a}o  and
+      Branco, Ant{'o}nio  and
+      Santos, Rodrigo  and
+      Cardoso, Henrique Lopes  and
+      Os{'o}rio, Tom{'a}s",
+    booktitle = "Progress in Artificial Intelligence: 22nd EPIA Conference on Artificial Intelligence (EPIA 2023)",
+    month = sep,
+    year = "2023",
+    address = "Faial Island, Portugal",
+    publisher = "Springer Nature Switzerland",
+    pages = "441--453"
+}
+```