Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C1BERT4060 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C1BERT4060 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C1BERT4060")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C1BERT4060") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C1BERT4060", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.131284236907959
f1_macro: 0.28301779516622877
f1_micro: 0.5403556771545828
f1_weighted: 0.41365052612867165
precision_macro: 0.2382665305606896
precision_micro: 0.5403556771545828
precision_weighted: 0.34578668059661094
recall_macro: 0.3653484722249133
recall_micro: 0.5403556771545828
recall_weighted: 0.5403556771545828
accuracy: 0.5403556771545828
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Model tree for Defensa2025/C1BERT4060
Base model
google-bert/bert-base-uncased