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
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Browse files
README.md
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Unlike standard classifiers, EmCoder quantifies what it doesn't know using Monte Carlo Dropout, making it suitable for high-stakes AI pipelines.<br>
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EmCoder is optimized for **MC Dropout inference**.
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## SOTA benchmark
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Unlike standard classifiers, EmCoder quantifies what it doesn't know using Monte Carlo Dropout, making it suitable for high-stakes AI pipelines.<br>
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EmCoder is optimized for **MC Dropout inference** and its architecture has no limit on maximum input length thanks to **RoPE**.
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## SOTA benchmark
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