Text Classification
Transformers
Safetensors
English
modernbert
Mixture of Experts
text-embeddings-inference
Instructions to use suayptalha/Medical-Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suayptalha/Medical-Router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="suayptalha/Medical-Router")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("suayptalha/Medical-Router") model = AutoModelForSequenceClassification.from_pretrained("suayptalha/Medical-Router") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- suayptalha/Treatment-Instructions
- suayptalha/Psychological-Support
- suayptalha/Diagnose-Instructions
language:
- en
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
library_name: transformers
tags:
- moe
MoE Router Model
Classify clinical text into:
- 0: Diagnosis
- 1: Treatment
- 2: Psychological Support
Training
- Base model: ModernBERT-base
- Epochs: 3
- Learning rate: 3e-5
- Batch size: 16