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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## Dataset
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- **Source**: [Chinese MedDialog Dataset](https://tianchi.aliyun.com/dataset/92110) (阿里云天池)
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- **Total samples**: 28,280
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- **Categories**: 6 emotion labels
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- **Language**: English
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## Dataset
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- **Original Source**: [Chinese MedDialog Dataset](https://tianchi.aliyun.com/dataset/92110) (阿里云天池)
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- **Processing**: Filtered and labeled for 6 emotion categories
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- **Total samples**: 28,280
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- **Categories**: 6 emotion labels
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- **Language**: English
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