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README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- toxicity-detection
+- NLP
+- classification
+- fine-tuning
+license: mit
+language:
+- pt
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for BERT Uncased Fine-Tuned on Toxicity Detection
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Este modelo é um BERT base uncased fine-tuned para a detecção de toxicidade em tweets. Ele foi treinado em um dataset anotado com classificação binária: 0 para tweets não tóxicos e 1 para tweets tóxicos.
 
-- **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):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Desenvolvido por:** Carlos André Dos Santos Lima
+- **Tipo de modelo:** BERT (base-uncased)
+- **Idiomas:** Portugês
+- **Licença:** MIT
+- **Fine-tuned a partir de:** bert-base-uncased
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Uso Direto
 
-[More Information Needed]
+Este modelo pode ser usado para identificar tweets tóxicos em Português. Pode ser aplicado diretamente em moderação de conteúdo, análise de sentimentos e detecção de discurso de ódio.
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Uso Fora do Escopo
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+O modelo pode apresentar viés ao classificar tweets fora do contexto do dataset de treinamento. Ele não é adequado para tomada de decisões críticas sem revisão humana.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- O modelo pode apresentar viés em suas predições devido à distribuição do dataset de treinamento.
+- Pode não generalizar bem para contextos diferentes daqueles presentes no dataset.
+- Recomenda-se revisão humana para evitar classificações injustas.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import pipeline
+
+toxicity_classifier = pipeline("text-classification", model="[modelo no Hugging Face]")
 
-[More Information Needed]
+text = "This is an example tweet."
+result = toxicity_classifier(text)
+print(result)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
+O modelo foi treinado em um dataset contendo tweets anotados manualmente como tóxicos ou não tóxicos.
 
-[More Information Needed]
+Arquivos do dataset:
+- `train.csv` - Dados de treino
+- `test.csv` - Dados de teste
+- `sample_submission.csv` - Exemplo de submissão
 
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+Colunas:
+- `id` - Identificador do tweet
+- `text` - Conteúdo do tweet
+- `label` - 0: não tóxico, 1: tóxico
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Training Procedure
 
-[More Information Needed]
+- **Hardware:** GPU T4
+- **Hiperparâmetros:**
+  - Batch size: 64
+  - Learning rate: 2e-5
+  - Epochs: 5
+  - Otimizador: AdamW
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **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]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Dados e Métricas
 
-[More Information Needed]
+- Dataset de teste utilizado para avaliação.
+- Principais métricas:
+  - Acurácia
+  - Precisão
+  - Recall
+  - F1-score
 
-## More Information [optional]
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+Se usar este modelo, cite da seguinte forma:
 
-[More Information Needed]
+```bibtex
+@article{Carlos2025,
+  title={Fine-Tuning BERT for Toxicity Detection},
+  author={Carlos André Dos Santos Lima},
+  journal={Hugging Face Model Hub},
+  year={2025}
+}
+```
 
-## Model Card Contact
+## Contato
 
-[More Information Needed]
+Caso tenha dúvidas ou sugestões, entre em contato pelo e-mail: [email protected] ou abra uma issue no repositório do modelo no Hugging Face.