Text Classification
Scikit-learn
Joblib
English
intent-classification
input-guard
medical-triage
pre-triage
scikit-learn
tf-idf
logistic-regression
healthcare
symptom-checker
academic-project
Eval Results (legacy)
Instructions to use cristian-untaru/sortmed-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use cristian-untaru/sortmed-intent-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("cristian-untaru/sortmed-intent-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "tfidf+logreg", | |
| "classes": [ | |
| "diagnosis_request", | |
| "general_medical_question", | |
| "medication_request", | |
| "non_medical", | |
| "symptom_description" | |
| ], | |
| "valid_intent": "symptom_description", | |
| "confidence_threshold": 0.5, | |
| "n_features": 1936, | |
| "n_train": 382, | |
| "n_test": 96, | |
| "sklearn_version": "1.6.1" | |
| } |