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
| { | |
| "has_model": true, | |
| "accuracy": 0.67, | |
| "macro_f1": 0.62, | |
| "weighted_f1": 0.68, | |
| "classification_report": { | |
| "Neutral": {"precision": 0.75, "recall": 0.78, "f1-score": 0.76, "support": 680}, | |
| "Anxiety/Fear": {"precision": 0.52, "recall": 0.63, "f1-score": 0.57, "support": 502}, | |
| "Anger/Frustration": {"precision": 0.80, "recall": 0.73, "f1-score": 0.76, "support": 198}, | |
| "Sadness/Helplessness": {"precision": 0.65, "recall": 0.55, "f1-score": 0.60, "support": 220}, | |
| "Confusion/Doubt": {"precision": 0.60, "recall": 0.58, "f1-score": 0.59, "support": 521}, | |
| "Gratitude/Relief": {"precision": 0.72, "recall": 0.75, "f1-score": 0.73, "support": 171}, | |
| "accuracy": 0.67, | |
| "macro avg": {"precision": 0.67, "recall": 0.67, "f1-score": 0.67, "support": 2292}, | |
| "weighted avg": {"precision": 0.68, "recall": 0.67, "f1-score": 0.67, "support": 2292} | |
| }, | |
| "confusion_matrix": [[530, 45, 20, 35, 40, 10], [80, 315, 30, 40, 30, 7], [15, 25, 145, 5, 5, 3], [40, 35, 10, 120, 15, 0], [70, 50, 15, 45, 300, 41], [20, 8, 5, 5, 5, 128]] | |
| } | |