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
-
UPDATE EmCoder TO V2
Verified signaturea10898b -
Delete train_config.json
Verified signaturebb548aa
-
ADD EmCoder V1.5
Verified signature326e148 -
Delete train_config.json
Verified signature156510a
-
Upload 9 files
Verified signature296800d -
Delete train_config.json
Verified signature81b23e9
-
Update train_config.json
Verified signaturec682d69
-
Upload 6 files
Verified signaturee587994