Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C1MDEBERTA4060 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C1MDEBERTA4060 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C1MDEBERTA4060")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C1MDEBERTA4060") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C1MDEBERTA4060", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6089382171630859
f1_macro: 0.6699753389439982
f1_micro: 0.8084815321477428
f1_weighted: 0.7912426491865815
precision_macro: 0.7011969455390926
precision_micro: 0.8084815321477428
precision_weighted: 0.8067341044326841
recall_macro: 0.673737570252346
recall_micro: 0.8084815321477428
recall_weighted: 0.8084815321477428
accuracy: 0.8084815321477428
- Downloads last month
- 9
Model tree for Defensa2025/C1MDEBERTA4060
Base model
microsoft/mdeberta-v3-base