Description
This LLM model is trained to perform Summarization and Q&A based on a given context.
Predicted Entities
How to use
from sparknlp_jsl.llm import LLMLoader
llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_meds_q8_v1", "en", "clinical/models")
prompt = """
Based on the following text, what age group is most susceptible to breast cancer?
## Text:
The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
- A personal or family history of breast cancer
- A genetic mutation, such as BRCA1 or BRCA2
- Exposure to radiation
- Age (most commonly occurring in women over 50)
- Early onset of menstruation or late menopause
- Obesity
- Hormonal factors, such as taking hormone replacement therapy
"""
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_meds_q8_v1", "en", "clinical/models")
val prompt = """Based on the following text, what age group is most susceptible to breast cancer?
|## Text:
|The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
|- A personal or family history of breast cancer
|- A genetic mutation, such as BRCA1 or BRCA2
|- Exposure to radiation
|- Age (most commonly occurring in women over 50)
|- Early onset of menstruation or late menopause
|- Obesity
|- Hormonal factors, such as taking hormone replacement therapy""".stripMargin
val response = llmLoader.generate(prompt)
Results
"The age group most susceptible to breast cancer, as mentioned in the text, is women over the age of 50."
Model Information
| Model Name: | jsl_meds_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 |
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