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)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
remove redundant model registration
Browse files- modeling_emcoder.py +1 -7
modeling_emcoder.py
CHANGED
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@@ -295,10 +295,4 @@ class EmCoder(PreTrainedModel):
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logits=logits,
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hidden_states=None,
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attentions=None,
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try:
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AutoConfig.register("emcoder", EmCoderConfig)
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AutoModelForSequenceClassification.register(EmCoderConfig, EmCoder)
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except ValueError:
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pass
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logits=logits,
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hidden_states=None,
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attentions=None,
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