Text Classification
Transformers
Safetensors
English
emcoder
feature-extraction
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 AutoModel model = AutoModel.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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- uncertainty-quantification
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- multi-label-classification
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datasets:
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- go_emotions
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- Skylion007/openwebtext
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metrics:
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- precision
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- recall
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- uncertainty-quantification
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- multi-label-classification
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datasets:
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- Skylion007/openwebtext
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- google-research-datasets/go_emotions
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metrics:
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- precision
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- recall
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