Text Classification
Transformers
Safetensors
PEFT
English
bottleneck_adapter_model
medical-triage
biobert
bottleneck-adapter
healthcare
symptom-checker
natural-language-processing
academic-project
custom_code
Eval Results (legacy)
Instructions to use cristian-untaru/bottleneck-mlp-biobert-medical-triage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cristian-untaru/bottleneck-mlp-biobert-medical-triage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cristian-untaru/bottleneck-mlp-biobert-medical-triage", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("cristian-untaru/bottleneck-mlp-biobert-medical-triage", trust_remote_code=True, device_map="auto") - PEFT
How to use cristian-untaru/bottleneck-mlp-biobert-medical-triage with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
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