|
--- |
|
license: apache-2.0 |
|
language: |
|
- zh |
|
- en |
|
base_model: |
|
- Qwen/Qwen2.5-7B-Instruct |
|
- meta-llama/Llama-3.1-8B-Instruct |
|
pipeline_tag: feature-extraction |
|
tags: |
|
- structuring |
|
- EHR |
|
- medical |
|
- IE |
|
--- |
|
# Model Card for GENIE |
|
|
|
|
|
## Model Details |
|
|
|
Model Size: 8B (English) / 7B (Chinese) |
|
|
|
Max Tokens: 8192 |
|
|
|
Base model: Llama 3.1 8B (English) / Qwen 2.5 7B (Chinese) |
|
|
|
### Model Description |
|
|
|
GENIE (Generative Note Information Extraction) is an end-to-end model for structuring EHR data. |
|
GENIE can process an entire paragraph of clinical notes in a single pass, outputting structured information on named entities, assertion statuses, locations, other relevant modifiers, clinical values, and intended purposes. |
|
This end-to-end approach simplifies the structuring process, reduces errors, and enables healthcare providers to derive structured data from EHRs more efficiently, without the need for extensive manual adjustments. |
|
And experiments have shown that GENIE achieves high accuracy in each of the task. |
|
|
|
|
|
## Usage |
|
|
|
```python |
|
from vllm import LLM, SamplingParams |
|
|
|
PROMPT_TEMPLATE = "Human:\n{query}\n\n Assistant:" |
|
sampling_params = SamplingParams(temperature=temperature, max_tokens=max_new_token) |
|
EHR = ['xxxxx1','xxxxx2'] |
|
texts = [PROMPT_TEMPLATE.format(query=k) for k in EHR] |
|
output = model.generate(texts, sampling_params) |
|
``` |
|
|
|
|
|
## Citation [optional] |
|
|
|
If you find our paper or models helpful, please consider cite: (to be released) |
|
|
|
**BibTeX:** |
|
|
|
[More Information Needed] |
