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
- Xet hash:
- 36da4cbadde560126aed3e8f2a587735bf6f2cf85c3aca0a51beb11741200e0a
- Size of remote file:
- 5.2 kB
- SHA256:
- 0a2ef8a24c72c1c03caacb548c0d0dee1ecc0c3a56895fc35a67bfde3088bddc
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