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:
- 049c794801dc9446918d9287c3ff4415ca5f7766a9354723ca04a6590319b3ce
- Size of remote file:
- 5.48 MB
- SHA256:
- be59672fc1de44b5c2b9cfc0c323c61e1b36ebc0a9f2d27dc85d8f459d7a5d03
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