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
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metrics:
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- name: Macro F1
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---
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# EmCoder
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## Performance
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**Using `thresholds.json` optimization for binarizing and filtering (uncertainty) predictions**
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| | precision | recall | f1-score | support |
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|:---------------|------------:|---------:|-----------:|----------:|
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| micro avg | 0.476 | 0.611 | 0.535 | 6329 |
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metrics:
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- name: Macro F1
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type: f1
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value: 0.44
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- name: Macro Precision
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type: precision
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value: 0.408
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- name: Macro Recall
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type: recall
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value: 0.495
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---
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# EmCoder
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## Performance
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**Using `thresholds.json` optimization on val set for binarizing and filtering (uncertainty) predictions**
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| | precision | recall | f1-score | support |
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|:---------------|------------:|---------:|-----------:|----------:|
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| micro avg | 0.476 | 0.611 | 0.535 | 6329 |
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