Text Classification
Transformers
Safetensors
English
distilbert
medical-triage
healthcare
symptom-checker
natural-language-processing
academic-project
Eval Results (legacy)
text-embeddings-inference
Instructions to use cristian-untaru/distilbert-medical-triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cristian-untaru/distilbert-medical-triage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cristian-untaru/distilbert-medical-triage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cristian-untaru/distilbert-medical-triage") model = AutoModelForSequenceClassification.from_pretrained("cristian-untaru/distilbert-medical-triage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38b52dea9c9d740baec896d6ee98a1f5d3abeb5fdb0271b91f4328d64b70b624
- Size of remote file:
- 5.27 kB
- SHA256:
- e2f75407db1378c5fa5852f8e609fb752ecfc65e9447d6508c754003ade720a5
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