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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 |