Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C3MDEBERTA4060 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C3MDEBERTA4060 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C3MDEBERTA4060")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C3MDEBERTA4060") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C3MDEBERTA4060", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 2.1142737865448
f1_macro: 0.3041746174507269
f1_micro: 0.3724696356275304
f1_weighted: 0.30727194281408704
precision_macro: 0.34855130333805473
precision_micro: 0.3724696356275304
precision_weighted: 0.35832513072551436
recall_macro: 0.37891057303529496
recall_micro: 0.3724696356275304
recall_weighted: 0.3724696356275304
accuracy: 0.3724696356275304
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Model tree for Defensa2025/C3MDEBERTA4060
Base model
microsoft/mdeberta-v3-base