Instructions to use toolevalxm/MedicalExpertModel-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedicalExpertModel-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="toolevalxm/MedicalExpertModel-TestRepo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("toolevalxm/MedicalExpertModel-TestRepo") model = AutoModelForMaskedLM.from_pretrained("toolevalxm/MedicalExpertModel-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from toolevalxm/MedicalExpertModel-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
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https://huggingface.co/toolevalxm/MedicalExpertModel-TestRepo/resolve/main/README.md
- Command line
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hf download hf://toolevalxm/MedicalExpertModel-TestRepo/README.md
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curl -L -o README.md https://huggingface.co/toolevalxm/MedicalExpertModel-TestRepo/resolve/main/README.md
3.5 kB
| license: apache-2.0 | |
| library_name: transformers | |
| # MedicalExpertModel | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="figures/banner.png" width="60%" alt="MedicalExpertModel" /> | |
| </div> | |
| <hr> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="LICENSE" style="margin: 2px;"> | |
| <img alt="License" src="figures/license_badge.png" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| ## 1. Introduction | |
| The MedicalExpertModel represents a breakthrough in medical AI assistance. This model has been specifically fine-tuned for clinical and medical applications, demonstrating exceptional performance across medical benchmarks including diagnosis support, treatment recommendations, and medical literature comprehension. | |
| <p align="center"> | |
| <img width="80%" src="figures/performance_chart.png"> | |
| </p> | |
| The model excels at understanding complex medical terminology, interpreting clinical notes, and providing evidence-based medical information. It has been validated against multiple medical knowledge benchmarks and shows strong performance in clinical reasoning tasks. | |
| ## 2. Evaluation Results | |
| ### Comprehensive Medical Benchmark Results | |
| <div align="center"> | |
| | | Benchmark | GPT-Medical | MedPaLM | ClinicalBERT | MedicalExpertModel | | |
| |---|---|---|---|---|---| | |
| | **Clinical Reasoning** | Diagnosis Accuracy | 0.721 | 0.745 | 0.689 | 0.750 | | |
| | | Treatment Planning | 0.658 | 0.672 | 0.645 | 0.657 | | |
| | | Clinical Note Understanding | 0.812 | 0.834 | 0.798 | 0.888 | | |
| | **Medical Knowledge** | Drug Interactions | 0.789 | 0.801 | 0.756 | 0.769 | | |
| | | Anatomy Knowledge | 0.845 | 0.856 | 0.823 | 0.820 | | |
| | | Pathology Understanding | 0.712 | 0.728 | 0.695 | 0.760 | | |
| | | Medical Terminology | 0.901 | 0.912 | 0.878 | 0.927 | | |
| | **Patient Care** | Symptom Analysis | 0.734 | 0.751 | 0.712 | 0.721 | | |
| | | Patient Communication | 0.678 | 0.692 | 0.654 | 0.670 | | |
| | | Care Plan Generation | 0.645 | 0.659 | 0.623 | 0.627 | | |
| | | Medical History Analysis | 0.756 | 0.771 | 0.734 | 0.713 | | |
| | **Specialized Tasks** | Radiology Report Interpretation | 0.689 | 0.705 | 0.667 | 0.731 | | |
| | | Lab Result Analysis | 0.723 | 0.738 | 0.701 | 0.731 | | |
| | | Medical Coding Accuracy | 0.767 | 0.782 | 0.745 | 0.771 | | |
| | | Safety Compliance | 0.834 | 0.848 | 0.812 | 0.871 | | |
| </div> | |
| ### Overall Performance Summary | |
| The MedicalExpertModel demonstrates strong performance across all evaluated medical benchmark categories, with particularly notable results in clinical reasoning and medical knowledge domains. | |
| ## 3. API and Integration | |
| We offer a secure API for integration with hospital systems and clinical workflows. Please contact our enterprise team for access. | |
| ## 4. How to Run Locally | |
| Please refer to our documentation for detailed instructions on running MedicalExpertModel in your environment. | |
| ### System Prompt | |
| We recommend using the following system prompt: | |
| ``` | |
| You are MedicalExpertModel, a medical AI assistant trained to provide accurate medical information. | |
| Always recommend consulting healthcare professionals for medical decisions. | |
| Today is {current date}. | |
| ``` | |
| ### Temperature | |
| We recommend setting the temperature parameter to 0.3 for medical applications. | |
| ## 5. License | |
| This model is licensed under the Apache 2.0 License. Medical use requires appropriate validation. | |
| ## 6. Contact | |
| For enterprise inquiries, please contact us at enterprise@medicalexpertmodel.ai. | |