Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C3ROBERTA4060 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C3ROBERTA4060 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C3ROBERTA4060")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C3ROBERTA4060") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C3ROBERTA4060", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 2.034769058227539
f1_macro: 0.2003519158699364
f1_micro: 0.2854251012145749
f1_weighted: 0.19638335320266231
precision_macro: 0.26240728115564854
precision_micro: 0.2854251012145749
precision_weighted: 0.26477929000209
recall_macro: 0.2986111111111111
recall_micro: 0.2854251012145749
recall_weighted: 0.2854251012145749
accuracy: 0.2854251012145749
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Model tree for Defensa2025/C3ROBERTA4060
Base model
FacebookAI/xlm-roberta-base