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
File size: 504 Bytes
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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 |