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 V1.5 README
Browse files
README.md
CHANGED
|
@@ -179,6 +179,7 @@ $$
|
|
| 179 |
### Entropy-based uncertainty quantification
|
| 180 |
|
| 181 |
**Model uncertainty quantification on GoEmotions test set**
|
|
|
|
| 182 |
| Mean probability vs Epistemic | Mean probability vs Aleatoric |
|
| 183 |
| :---: | :---: |
|
| 184 |
|  |  |
|
|
|
|
| 179 |
### Entropy-based uncertainty quantification
|
| 180 |
|
| 181 |
**Model uncertainty quantification on GoEmotions test set**
|
| 182 |
+
Flattened emotion predictions
|
| 183 |
| Mean probability vs Epistemic | Mean probability vs Aleatoric |
|
| 184 |
| :---: | :---: |
|
| 185 |
|  |  |
|