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
switch automodel for automodelforseqclass
Browse files- config.json +1 -1
config.json
CHANGED
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@@ -2,7 +2,7 @@
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"model_type": "emcoder",
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"auto_map": {
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"AutoConfig": "configuration_emcoder.EmCoderConfig",
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"
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},
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"architectures": ["EmCoder"],
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"vocab_size": 50368,
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"model_type": "emcoder",
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"auto_map": {
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"AutoConfig": "configuration_emcoder.EmCoderConfig",
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+
"AutoModelForSequenceClassification": "modeling_emcoder.EmCoder"
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},
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"architectures": ["EmCoder"],
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"vocab_size": 50368,
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