Text Classification
Transformers
Safetensors
PyTorch
Portuguese
modernbert
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use tcepi/mbp_pas_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcepi/mbp_pas_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tcepi/mbp_pas_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tcepi/mbp_pas_model") model = AutoModelForSequenceClassification.from_pretrained("tcepi/mbp_pas_model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "test_accuracy": 0.9860627177700348, | |
| "test_f1": 0.9863013698630136, | |
| "test_f1_macro": 0.9860584863499465, | |
| "test_false_negatives": 1, | |
| "test_false_positives": 3, | |
| "test_loss": 0.05948644131422043, | |
| "test_precision": 0.9795918367346939, | |
| "test_precision_macro": 0.9862244897959184, | |
| "test_recall": 0.993103448275862, | |
| "test_recall_macro": 0.9859883438562409, | |
| "test_roc_auc": 0.9987858183584265, | |
| "test_runtime": 2.872, | |
| "test_samples_per_second": 99.932, | |
| "test_specificity": 0.9788732394366197, | |
| "test_steps_per_second": 3.134, | |
| "test_true_negatives": 139, | |
| "test_true_positives": 144 | |
| } |