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:
- 50202dd20a455dad670ae0c911bd699b0a06d2fc0ab105c95ce7fb40d89662bb
- Size of remote file:
- 173 kB
- SHA256:
- 15acec12f909d7f6185caea49e0fba73b980bbbe3a049ae6c139155bcf79e4f0
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