| --- |
| datasets: |
| - legal_documents |
| language: pt |
| license: apache-2.0 |
| tags: |
| - text-classification |
| - legal |
| - bert |
| - portuguese |
| - brazilian-legal-documents |
| model-index: |
| - name: testmodel |
| results: |
| - task: |
| type: text-classification |
| dataset: |
| name: Legal Documents Classification Dataset |
| type: legal_documents |
| metrics: |
| - type: accuracy |
| value: 0.8881829733163914 |
| - type: f1 |
| value: 0.8777856821629471 |
| - type: precision |
| value: 0.8772538874253621 |
| - type: recall |
| value: 0.8881829733163914 |
| - type: f1_macro |
| value: 0.3692552572091707 |
| --- |
| |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
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|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
|
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|
|
| # Modelo de Classificação de Documentos Jurídicos |
|
|
| Este modelo foi treinado para classificar documentos jurídicos brasileiros usando BERT multilíngue. |
|
|
| ## Detalhes do Modelo |
|
|
| - **Base Model:** google-bert/bert-base-multilingual-cased |
| - **Tipo:** Classificação Multiclasse |
| - **Número de Classes:** 23 |
| - **Idioma:** Português (Brasil) |
| - **Domínio:** Documentos Jurídicos |
|
|
| ## Classes |
|
|
| 0. Alvará |
| 1. Sentença |
| 2. Penhora/Arresto/Sequestro |
| 3. Citação |
| 4. Art.40 |
| 5. Não |
| 6. Pelo prazo de 10 dias |
| 7. Sentença de mérito ou definitiva |
| 8. Citação de embargos |
| 9. Forma tácita |
| 10. Citação de penhora |
| 11. Art. 40 |
| 12. sentença em apensado. |
| 13. Unificado |
| 14. Citação Negativa |
| 15. Arquivo provisório do art. 28 lef |
| 16. leilão |
| 17. intimação |
| 18. Citação de arresto |
| 19. Citação Positiva |
| 20. Penhora |
| 21. Desentramento |
| 22. Remessa para o arquivo |
|
|
| ## Métricas de Avaliação |
|
|
| As métricas abaixo foram calculadas no conjunto de validação: |
|
|
| - **Accuracy:** 0.8882 |
| - **F1-Score (Weighted):** 0.8778 |
| - **Precision (Weighted):** 0.8773 |
| - **Recall (Weighted):** 0.8882 |
| - **F1-Score (Macro):** 0.3693 |
|
|
| ## Classification Report Completo |
|
|
| ``` |
| precision recall f1-score support |
| |
| 0 0.00 0.00 0.00 7 |
| 1 0.96 0.94 0.95 51 |
| 2 0.93 0.88 0.91 49 |
| 3 0.82 0.88 0.85 42 |
| 4 0.94 0.95 0.94 138 |
| 5 0.96 0.95 0.96 414 |
| 6 0.00 0.00 0.00 7 |
| 7 1.00 1.00 1.00 16 |
| 8 0.00 0.00 0.00 4 |
| 9 0.40 1.00 0.57 6 |
| 10 0.08 1.00 0.15 2 |
| 11 0.67 1.00 0.80 10 |
| 12 0.00 0.00 0.00 8 |
| 13 0.53 0.91 0.67 11 |
| 14 0.00 0.00 0.00 3 |
| 15 0.00 0.00 0.00 2 |
| 16 0.00 0.00 0.00 1 |
| 17 0.00 0.00 0.00 6 |
| 19 0.00 0.00 0.00 3 |
| 20 1.00 0.20 0.33 5 |
| 21 0.00 0.00 0.00 1 |
| 22 0.00 0.00 0.00 1 |
| |
| accuracy 0.89 787 |
| macro avg 0.38 0.44 0.37 787 |
| weighted avg 0.88 0.89 0.88 787 |
| |
| ``` |
|
|
| ## Uso |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| # Carregar modelo e tokenizer |
| model = AutoModelForSequenceClassification.from_pretrained("rkenji/testmodel") |
| tokenizer = AutoTokenizer.from_pretrained("rkenji/testmodel") |
| |
| # Fazer predição |
| texto = "Seu texto jurídico aqui" |
| inputs = tokenizer(texto, return_tensors="pt", truncation=True, max_length=512) |
| outputs = model(**inputs) |
| prediction = torch.argmax(outputs.logits, dim=1).item() |
| |
| print(f"Classe predita: {prediction}") |
| ``` |
|
|
| ## Treinamento |
|
|
| O modelo foi treinado com early stopping e validação cruzada em um dataset de documentos jurídicos brasileiros. |
|
|
|
|
| - **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] |
|
|
| <!-- 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. --> |
|
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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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| [More Information Needed] |
|
|
| ### Out-of-Scope Use |
|
|
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
|
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| [More Information Needed] |
|
|
| ## 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. --> |
|
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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. |
|
|
| ## How to Get Started with the Model |
|
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| Use the code below to get started with the model. |
|
|
| [More Information Needed] |
|
|
| ## 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. --> |
|
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| [More Information Needed] |
|
|
| ### Training Procedure |
|
|
| <!-- 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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|
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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 --> |
|
|
| #### Speeds, Sizes, Times [optional] |
|
|
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
|
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| [More Information Needed] |
|
|
| ## 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. --> |
|
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| [More Information Needed] |
|
|
| #### Metrics |
|
|
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
| [More Information Needed] |
|
|
| ### Results |
|
|
| [More Information Needed] |
|
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| #### Summary |
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|
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| ## 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] |
|
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| ### Model Architecture and Objective |
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| [More Information Needed] |
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| ### Compute Infrastructure |
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| [More Information Needed] |
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| #### Hardware |
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| [More Information Needed] |
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| #### Software |
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| [More Information Needed] |
|
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| ## 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:** |
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| [More Information Needed] |
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| **APA:** |
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| [More Information Needed] |
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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 Needed] |
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| ## More Information [optional] |
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| ## Model Card Authors [optional] |
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| [More Information Needed] |
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| ## Model Card Contact |
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| [More Information Needed] |
