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
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### Entropy-based Uncertainty Decomposition
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EmCoder computes probabilistic uncertainty using Information Theory metrics over
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**Demonstration of model uncertainty utilization**
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To validate uncertainty quantification, reject the top **X%** most uncertain (epistemic) classifications. The model's Macro F1 jumps from 0.488 to above 0.70, proving that the model's self-reported uncertainty is highly correlated with its actual error rate
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### Entropy-based Uncertainty Decomposition
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EmCoder computes probabilistic uncertainty using Information Theory metrics over N stochastic forward passes
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**Demonstration of model uncertainty utilization**
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To validate uncertainty quantification, reject the top **X%** most uncertain (epistemic) classifications. The model's Macro F1 jumps from 0.488 to above 0.70, proving that the model's self-reported uncertainty is highly correlated with its actual error rate
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