Description
This LLM model is trained to extract and link entities in a document. Users needs to define an input schema as explained in the example section. Drug is defined as a list which tells the model that there could be multiple drugs in the document and it has to extract all of them. Each drug has properties like name and reaction. Since “name” is only one, it is a string, but there could be multiple reactions, hence it is a list. Similarly, users can define any schema for any type of entity.
Predicted Entities
How to use
from sparknlp_jsl.llm import LLMLoader
llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medsner_zs_q8_v1", "en", "clinical/models")
prompt = """
### Template:
{
"drugs": [
{
"name": "",
"reactions": []
}
]
}
### Text:
I feel a bit drowsy & have a little blurred vision , and some gastric problems .
I 've been on Arthrotec 50 for over 10 years on and off , only taking it when I needed it .
Due to my arthritis getting progressively worse , to the point where I am in tears with the agony.
Gp 's started me on 75 twice a day and I have to take it every day for the next month to see how I get on , here goes .
So far its been very good , pains almost gone , but I feel a bit weird , did n't have that when on 50.
"""
response = llm_loader_pretrained.generate(prompt)
import com.johnsnowlabs.ml.gguf.LLMLoader
import com.johnsnowlabs.nlp.SparkAccessor.spark
val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medsner_zs_q8_v1", "en", "clinical/models")
val prompt = """
### Template:
{
"drugs": [
{
"name": "",
"reactions": []
}
]
}
### Text:
I feel a bit drowsy & have a little blurred vision , and some gastric problems .
I 've been on Arthrotec 50 for over 10 years on and off , only taking it when I needed it .
Due to my arthritis getting progressively worse , to the point where I am in tears with the agony.
Gp 's started me on 75 twice a day and I have to take it every day for the next month to see how I get on , here goes .
So far its been very good , pains almost gone , but I feel a bit weird , did n't have that when on 50.
"""
val response = llmLoader.generate(prompt)
Results
'''
{
"drugs": [
{
"name": "Arthrotec",
"reactions": [
"drowsy",
"blurred vision",
"gastric problems"
]
}
]
}
'''
Model Information
| Model Name: | jsl_medsner_zs_q8_v1 |
| Compatibility: | Healthcare NLP 5.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.7 GB |
Benchmarking
We have generated a total of 400 questions, 100 from each category. These questions were labeled and reviewed by 3 physician annotators. % indicates the preference rate
## Overall
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.24 | 0.25 | 0.38 |
| GPT4o | 0.19 | 0.26 | 0.27 |
| Neutral | 0.43 | 0.36 | 0.18 |
| None | 0.14 | 0.13 | 0.17 |
| Total | 1.00 | 1.00 | 1.00 |
## Summary
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.47 | 0.48 | 0.42 |
| GPT4o | 0.25 | 0.25 | 0.25 |
| Neutral | 0.22 | 0.22 | 0.25 |
| None | 0.07 | 0.05 | 0.08 |
| Total | 1.00 | 1.00 | 1.00 |
## QA
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.35 | 0.36 | 0.42 |
| GPT4o | 0.24 | 0.24 | 0.29 |
| Neutral | 0.33 | 0.33 | 0.18 |
| None | 0.09 | 0.07 | 0.11 |
| Total | 1.00 | 1.00 | 1.00 |
## BioMedical
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.33 | 0.24 | 0.57 |
| GPT4o | 0.12 | 0.08 | 0.16 |
| Neutral | 0.45 | 0.57 | 0.16 |
| None | 0.10 | 0.10 | 0.10 |
| Total | 1.00 | 1.00 | 1.00 |
## OpenEnded
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.35 | 0.30 | 0.39 |
| GPT4o | 0.30 | 0.33 | 0.41 |
| Neutral | 0.19 | 0.20 | 0.02 |
| None | 0.17 | 0.17 | 0.19 |
| Total | 1.00 | 1.00 | 1.00 |