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
swap log for log 2 in entropy computation
Browse files
outputs/admiration_scatters.png
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Git LFS Details
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outputs/confusion_matrix.png
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outputs/f1_rejection_epistemic.png
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outputs/fear_scatters.png
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outputs/neutral_scatters.png
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Git LFS Details
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outputs/ridge_aleatoric.png
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Git LFS Details
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outputs/ridge_epistemic.png
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Git LFS Details
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