Instructions to use rwillh11/mDeBERTa-EAD-sentiment-3class-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwillh11/mDeBERTa-EAD-sentiment-3class-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rwillh11/mDeBERTa-EAD-sentiment-3class-final")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rwillh11/mDeBERTa-EAD-sentiment-3class-final") model = AutoModelForSequenceClassification.from_pretrained("rwillh11/mDeBERTa-EAD-sentiment-3class-final") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Trained on 3500 paragraphs from english and spanish newspapers, with balanced classes (Negative, Neutral, Positive).
Accuracy 0.785311 F1 Macrco 0.782946 Balanced Accuracy 0.785611
Best Hyperparameters found using Optuna: {'learning_rate': 2.1934387205051642e-05, 'weight_decay': 0.08994276977837379, 'warmup_ratio': 0.07452391812825375}
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