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| # Model Card for Model ID |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| - **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] |
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| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** [More Information Needed] |
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| ## Uses |
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| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| ### Direct Use |
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| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| [More Information Needed] |
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| ### Downstream Use [optional] |
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| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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| ### Out-of-Scope Use |
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| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| ## Training Details |
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| ### Training Data |
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| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| ### Training Procedure |
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| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| #### Preprocessing [optional] |
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| [More Information Needed] |
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| #### Training Hyperparameters |
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| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| #### Speeds, Sizes, Times [optional] |
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| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| ## Evaluation |
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| <!-- This section describes the evaluation protocols and provides the results. --> |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| #### Metrics |
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| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| ### Results |
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| #### Summary |
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| ## Model Examination [optional] |
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| <!-- Relevant interpretability work for the model goes here --> |
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| ## Environmental Impact |
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| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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| ## Technical Specifications [optional] |
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| ### Model Architecture and Objective |
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| #### Hardware |
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| #### Software |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| ## Glossary [optional] |
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| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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| ## More Information [optional] |
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| ## Model Card Authors [optional] |
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| ## Model Card Contact |
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| # BERTimbau for Fake News Detection (Portuguese) |
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| ## Model Overview |
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| 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. |
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| - **Architecture**: BERTimbau (base, cased) |
| - **Task**: Binary text classification (Fake vs. True news) |
| - **Language**: Portuguese (`pt`) |
| - **Framework**: 🤗 Transformers |
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| --- |
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| ## Available Variants |
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| - **bertimbau-combined** |
| Fine-tuned on the aligned corpus (`data/corpus_train_df.parquet`, etc.). |
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| - **bertimbau-fake-br** |
| Fine-tuned on the **Fake.br** dataset. |
| Corpus is available in [`corpus/`](./corpus) with preprocessed and size-normalized versions. |
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| - **bertimbau-faketrue-br** |
| Fine-tuned on the **FakeTrue.Br** dataset. |
| Includes both raw CSV and aligned corpus partitions. |
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| Each variant has its own confusion matrix, classification report, and predictions stored as artifacts. |
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| --- |
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| ## Training Details |
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| - **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 |
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| > ⚠️ Adjust hyperparameters above if you changed them during training. |
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| --- |
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| ## Evaluation Results |
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| Evaluation metrics are stored in the repo as: |
| - `confusion_matrix.png` |
| - `final_classification_report.parquet` |
| - `final_predictions.parquet` |
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| These files provide per-class performance and prediction logs for reproducibility. |
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| --- |
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| ## Corpus |
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| The corpora used for training and evaluation are provided in the `corpus/` folder. |
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| - **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`. |
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| Each corpus variant may also include a dedicated `README.md` with further details. |
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| --- |
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| ## How to Use |
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| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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| 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) |
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| clf = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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| text = "Presidente anuncia novas medidas econômicas em Brasília." |
| print(clf(text)) |
| ``` |
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| Expected output: |
| ``` |
| [{'label': False, 'score': 0.92}] |
| ``` |
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| ## License |
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| - 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. |
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| ## Citation |
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| Coming soon. |
