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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- **Categories**: 6 emotion labels
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- **Language**: English
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## 🏗️ Technical Specifications
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| Component | Details |
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- **Categories**: 6 emotion labels
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- **Language**: English
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## 📚 References
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1. **MedDialog Dataset**
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Chinese Medical Dialogue Dataset. Alibaba Cloud Tianchi.
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https://tianchi.aliyun.com/dataset/92110
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2. **DistilBERT**
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Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). *DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter*. arXiv preprint arXiv:1910.01108.
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https://arxiv.org/abs/1910.01108
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## 🏗️ Technical Specifications
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| Component | Details |
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