Text Classification
Transformers
Safetensors
PyTorch
Portuguese
modernbert
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use tcepi/prog_integridade_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcepi/prog_integridade_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tcepi/prog_integridade_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tcepi/prog_integridade_model") model = AutoModelForSequenceClassification.from_pretrained("tcepi/prog_integridade_model") - Notebooks
- Google Colab
- Kaggle
Add classification report
Browse files
classification_report.txt
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precision recall f1-score support
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Negativo 0.9961 0.9791 0.9876 527
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Positivo 0.9800 0.9963 0.9881 542
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accuracy 0.9878 1069
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macro avg 0.9881 0.9877 0.9878 1069
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weighted avg 0.9880 0.9878 0.9878 1069
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