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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| precision recall f1-score support | |
| Negativo 0.9981 0.9905 0.9943 527 | |
| Positivo 0.9908 0.9982 0.9945 542 | |
| accuracy 0.9944 1069 | |
| macro avg 0.9945 0.9943 0.9944 1069 | |
| weighted avg 0.9944 0.9944 0.9944 1069 | |