Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C1BERT8020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C1BERT8020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C1BERT8020")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C1BERT8020") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C1BERT8020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.3527352511882782
f1_macro: 0.8348723906641119
f1_micro: 0.8770491803278688
f1_weighted: 0.8755938501216848
precision_macro: 0.8881772211372112
precision_micro: 0.8770491803278688
precision_weighted: 0.8788068274369532
recall_macro: 0.8086056185194117
recall_micro: 0.8770491803278688
recall_weighted: 0.8770491803278688
accuracy: 0.8770491803278688
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Model tree for Defensa2025/C1BERT8020
Base model
google-bert/bert-base-uncased