Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C1MDEBERTA8020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C1MDEBERTA8020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C1MDEBERTA8020")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C1MDEBERTA8020") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C1MDEBERTA8020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.2561890482902527
f1_macro: 0.8807113572349892
f1_micro: 0.9262295081967213
f1_weighted: 0.9248450040704191
precision_macro: 0.9282800518581417
precision_micro: 0.9262295081967213
precision_weighted: 0.9257478147863171
recall_macro: 0.8579645928783861
recall_micro: 0.9262295081967213
recall_weighted: 0.9262295081967213
accuracy: 0.9262295081967213
- Downloads last month
- 10
Model tree for Defensa2025/C1MDEBERTA8020
Base model
microsoft/mdeberta-v3-base