Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C1BETO8020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C1BETO8020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C1BETO8020")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C1BETO8020") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C1BETO8020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.18516024947166443
f1_macro: 0.8904674915802716
f1_micro: 0.9549180327868853
f1_weighted: 0.954151575529671
precision_macro: 0.9013542036532922
precision_micro: 0.9549180327868853
precision_weighted: 0.9564716125425002
recall_macro: 0.8826436494539943
recall_micro: 0.9549180327868853
recall_weighted: 0.9549180327868853
accuracy: 0.9549180327868853
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Model tree for Defensa2025/C1BETO8020
Base model
dccuchile/bert-base-spanish-wwm-cased