Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C3BETO8020")
model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C3BETO8020", device_map="auto")Quick Links
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.11664070188999176
f1_macro: 0.8794906659276022
f1_micro: 0.9696048632218845
f1_weighted: 0.9699750049518782
precision_macro: 0.885611464558833
precision_micro: 0.9696048632218845
precision_weighted: 0.9715389811134494
recall_macro: 0.8780378977747398
recall_micro: 0.9696048632218845
recall_weighted: 0.9696048632218845
accuracy: 0.9696048632218845
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Model tree for Defensa2025/C3BETO8020
Base model
dccuchile/bert-base-spanish-wwm-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C3BETO8020")