Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C3ROBERTA8020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C3ROBERTA8020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C3ROBERTA8020")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C3ROBERTA8020") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C3ROBERTA8020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.27825045585632324
f1_macro: 0.6572314102522959
f1_micro: 0.9270516717325228
f1_weighted: 0.9219223225714591
precision_macro: 0.75333883952305
precision_micro: 0.9270516717325228
precision_weighted: 0.94946822216959
recall_macro: 0.6780189109136479
recall_micro: 0.9270516717325228
recall_weighted: 0.9270516717325228
accuracy: 0.9270516717325228
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Model tree for Defensa2025/C3ROBERTA8020
Base model
FacebookAI/xlm-roberta-base