Text Classification
Transformers
Safetensors
PEFT
English
bert
medical-triage
biomedbert
frozen-encoder
healthcare
symptom-checker
natural-language-processing
academic-project
Eval Results (legacy)
text-embeddings-inference
Instructions to use cristian-untaru/frozen-encoder-biomedbert-medical-triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cristian-untaru/frozen-encoder-biomedbert-medical-triage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cristian-untaru/frozen-encoder-biomedbert-medical-triage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cristian-untaru/frozen-encoder-biomedbert-medical-triage") model = AutoModelForSequenceClassification.from_pretrained("cristian-untaru/frozen-encoder-biomedbert-medical-triage", device_map="auto") - PEFT
How to use cristian-untaru/frozen-encoder-biomedbert-medical-triage with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| { | |
| "label2id": { | |
| "self_monitor": 0, | |
| "consult_gp": 1, | |
| "urgent": 2 | |
| }, | |
| "id2label": { | |
| "0": "self_monitor", | |
| "1": "consult_gp", | |
| "2": "urgent" | |
| } | |
| } |