Text Classification
Transformers
Safetensors
English
emotion-classification
healthcare
distilbert
patient-doctor-conversations
clinical-AI
mental-health
Instructions to use StringJammer/patient-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StringJammer/patient-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StringJammer/patient-emotion-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("StringJammer/patient-emotion-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
README.md
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| Accuracy |
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| Macro F1 | 0.
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| Weighted F1 | 0.
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### Per-Class Performance
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| Accuracy | 71.3% |
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| Macro F1 | 0.72 |
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| Weighted F1 | 0.72 |
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### Per-Class Performance
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