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
| emcoder/model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| outputs/epistemic_unc_scatter.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/aleatoric_unc_scatter.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/ridge_aleatoric.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/ridge_epistemic.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/admiration_scatters.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/fear_scatters.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/neutral_scatters.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/architecture.png filter=lfs diff=lfs merge=lfs -text | |
| outputs/workflow.png filter=lfs diff=lfs merge=lfs -text | |