Hugging Face's logo

vzani
/
portuguese-fake-news-classifier-bertimbau-combined

Text Classification
Transformers
Safetensors
Portuguese
bert
bertimbau
portuguese
fake-news
binary-classification
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
portuguese-fake-news-classifier-bertimbau-combined / README.md
vzani's picture
vzani
Add autogenerated Model Card
3ef3db7 verified
preview code
|
raw
history blame
7.94 kB

Model Card for Model ID

Model Details

Model Description

  • Developed by: [More Information Needed]
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: [More Information Needed]
  • Language(s) (NLP): pt
  • License: apache-2.0
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

BERTimbau for Fake News Detection (Portuguese)

Model Overview

This repository contains fine-tuned versions of BERTimbau 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/ 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 

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
  • Fine-tuned models and corpora: Released under the same license for academic and research use.

Citation

Coming soon.