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 README.md
Verified signaturee1f8f65 -
Upload 2 files
Verified signature8e0810e -
Delete outputs/workflow.svg
Verified signature9b5c41b -
Delete outputs/architecture.svg
Verified signature48ff0b7 -
Upload 2 files
Verified signature7fe1b0d -
Update README.md
Verified signature768b895 -
Upload 4 files
Verified signature17508c1 -
Update README.md
Verified signaturebbcc716 -
Update train_config.json
Verified signaturec682d69 -
Update model_config.json
Verified signature7290115 -
Update README.md
Verified signature588c952
-
Upload 6 files
Verified signaturee587994 -
Delete emcoder
Verified signature80f6bfe
-
Upload 61 files
Verified signature60b66e8 -
initial commit
Verified signature8b284d9