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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yezdata/EmCoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
fix architecture diagram
Verified signaturec694824 -
Delete outputs/architecture.png
Verified signaturef4efc7a -
fix architecture diagram
Verified signature145a63d -
Delete outputs/architecture.png
Verified signature4b6276b
-
UPDATE EmCoder TO V2
Verified signature9fc62d1 -
Delete outputs/architecture.png
Verified signature4769571
-
Upload 14 files
Verified signaturec47a352 -
Delete outputs
Verified signature865a78d
-
Upload 2 files
Verified signature8e0810e