Text Classification
Transformers
Safetensors
English
emcoder
emotion-recognition
bayesian-deep-learning
mc-dropout
uncertainty-quantification
multi-label-classification
custom_code
Eval Results (legacy)
Instructions to use yezdata/EmCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yezdata/EmCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yezdata/EmCoder", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
UPDATE EmCoder TO V2
Browse files
README.md
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@@ -106,7 +106,6 @@ mean_probs = all_probs.mean(dim=0) # Mean Predicted Probability
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# base std estimation of Epistemic Uncertainty
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uncertainty = all_probs.std(dim=0)
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# Formatted Output
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m_probs = mean_probs.squeeze(0)
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u_vals = uncertainty.squeeze(0)
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# base std estimation of Epistemic Uncertainty
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uncertainty = all_probs.std(dim=0)
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# Formatted Output
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m_probs = mean_probs.squeeze(0)
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u_vals = uncertainty.squeeze(0)
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