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
Add training results
Browse files- train_results.json +8 -0
train_results.json
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{
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"epoch": 5.0,
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"total_flos": 1754902906982400.0,
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"train_loss": 0.13912125594856203,
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"train_runtime": 186.6365,
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"train_samples_per_second": 55.187,
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"train_steps_per_second": 6.912
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}
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