Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C3BETO4060 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C3BETO4060 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C3BETO4060")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C3BETO4060") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C3BETO4060", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.0537630319595337
f1_macro: 0.7038979188659462
f1_micro: 0.7408906882591093
f1_weighted: 0.7358061238652147
precision_macro: 0.7382339159513447
precision_micro: 0.7408906882591093
precision_weighted: 0.7841447742113813
recall_macro: 0.7168580218207663
recall_micro: 0.7408906882591093
recall_weighted: 0.7408906882591093
accuracy: 0.7408906882591093
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Model tree for Defensa2025/C3BETO4060
Base model
dccuchile/bert-base-spanish-wwm-cased