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
Download .gitattributes from yezdata/EmCoder: direct link, hf CLI and curl.
- Browser
- Download file 186 Bytes
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https://huggingface.co/yezdata/EmCoder/resolve/ff42f173248d29abfc4f58edbe4e6ea96ffc9692/.gitattributes
- Command line
-
hf download hf://yezdata/EmCoder@ff42f173248d29abfc4f58edbe4e6ea96ffc9692/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/yezdata/EmCoder/resolve/ff42f173248d29abfc4f58edbe4e6ea96ffc9692/.gitattributes
186 Bytes
| 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 | |