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# BERTimbau for Fake News Detection (Portuguese)

## Model Overview

This repository contains fine-tuned versions of **[BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)** for the task of **fake news detection in Portuguese**.
The models are trained and evaluated on corpora derived from Brazilian Portuguese datasets such as **Fake.br** and **FakeTrue.Br**, in addition to a base aligned corpus.

- **Architecture**: BERTimbau (base, cased)
- **Task**: Binary text classification (Fake vs. True news)
- **Language**: Portuguese (`pt`)
- **Framework**: 🤗 Transformers

---

## Available Variants

- **bertimbau-combined**
  Fine-tuned on the aligned corpus (`data/corpus_train_df.parquet`, etc.).

- **bertimbau-fake-br**
  Fine-tuned on the **Fake.br** dataset.
  Corpus is available in [`corpus/`](./corpus) with preprocessed and size-normalized versions.

- **bertimbau-faketrue-br**
  Fine-tuned on the **FakeTrue.Br** dataset.
  Includes both raw CSV and aligned corpus partitions.

Each variant has its own confusion matrix, classification report, and predictions stored as artifacts.

---

## Training Details

- **Base model**: `neuralmind/bert-base-portuguese-cased`
- **Fine-tuning**: 3–5 epochs, batch size 16, AdamW optimizer
- **Sequence length**: 512
- **Loss function**: Cross-entropy
- **Evaluation metrics**: Accuracy, Precision, Recall, F1-score

> ⚠️ Adjust hyperparameters above if you changed them during training.

---

## Evaluation Results

Evaluation metrics are stored in the repo as:
- `confusion_matrix.png`
- `final_classification_report.parquet`
- `final_predictions.parquet`

These files provide per-class performance and prediction logs for reproducibility.

---

## Corpus

The corpora used for training and evaluation are provided in the `corpus/` folder.

- **Base**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`
- **Fake.br**: Contains raw full texts, preprocessed CSV, size-normalized texts, and metadata.
- **FakeTrue.Br**: Contains aligned corpus splits and `FakeTrueBr_corpus.csv`.

Each corpus variant may also include a dedicated `README.md` with further details.

---

## How to Use

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_name = "vzani/portuguese-fake-news-classifier-bertimbau-fake-br"  # or combined / faketrue-br
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

clf = pipeline("text-classification", model=model, tokenizer=tokenizer)

text = "Presidente anuncia novas medidas econômicas em Brasília."
print(clf(text))
```

Expected output:
```
[{'label': False, 'score': 0.92}]
```

## License

- Base model BERTimbau: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- Fine-tuned models and corpora: Released under the same license for academic and research use.

## Citation

Coming soon.