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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add test results
Browse files- test_results.json +20 -0
test_results.json
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{
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"epoch": 5.0,
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"test_accuracy": 0.9860627177700348,
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"test_f1": 0.9863013698630136,
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"test_f1_macro": 0.9860584863499465,
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"test_false_negatives": 1,
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"test_false_positives": 3,
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"test_loss": 0.05948644131422043,
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"test_precision": 0.9795918367346939,
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"test_precision_macro": 0.9862244897959184,
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"test_recall": 0.993103448275862,
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"test_recall_macro": 0.9859883438562409,
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"test_roc_auc": 0.9987858183584265,
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"test_runtime": 2.872,
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"test_samples_per_second": 99.932,
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"test_specificity": 0.9788732394366197,
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"test_steps_per_second": 3.134,
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"test_true_negatives": 139,
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"test_true_positives": 144
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}
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