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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# Patient Emotion Analysis
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NLP emotion classification model for patient-doctor conversations.
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- emotion-classification
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- healthcare
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- distilbert
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- patient-doctor-conversations
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- text-classification
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model_index:
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- name: patient-emotion-classifier
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results:
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- task:
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type: text-classification
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metrics:
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- type: accuracy
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value: 0.67
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- type: f1
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value: 0.62
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---
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# Patient Emotion Analysis
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NLP emotion classification model for patient-doctor conversations.
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