Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C3MDEBERTA8020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C3MDEBERTA8020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C3MDEBERTA8020")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C3MDEBERTA8020") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C3MDEBERTA8020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.37181439995765686
f1_macro: 0.5930246222334471
f1_micro: 0.9088145896656535
f1_weighted: 0.9029677741513359
precision_macro: 0.6112859362859363
precision_micro: 0.9088145896656535
precision_weighted: 0.9080249832909408
recall_macro: 0.6222696893749525
recall_micro: 0.9088145896656535
recall_weighted: 0.9088145896656535
accuracy: 0.9088145896656535
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Model tree for Defensa2025/C3MDEBERTA8020
Base model
microsoft/mdeberta-v3-base