Model: shalindasilva1/SOAPGemma
Author: shalindasilva1
Pipeline: text-generation
Library: transformers
Tags: transformers, safetensors, text-generation, summarization, medical, soap-notes, txgemma, gemma, lora, peft, en, dataset:omi-health/medical-dialogue-to-soap-summary, base_model:google/txgemma-2b-predict, base_model:adapter:google/txgemma-2b-predict, license:mit, model-index, co2_eq_emissions, endpoints_compatible, region:us
Base models (adapter): id: google/txgemma-2b-predict
Card metadata: language: en
license: mit
library_name: transformers
tags: text-generation, summarization, medical, soap-notes, txgemma, gemma, lora, peft
datasets: omi-health/medical-dialogue-to-soap-summary
pipeline_tag: text-generation
co2_eq_emissions: training_type: fine-tuning
geographical_location: europe-north1
hardware_used: RTX A4000
training_time: 5
emissions: 0.39
model-index: name: SOAPgemma_v1
results: task: type: text-generation
name: Medical SOAP Note Generation
dataset: name: OMI-Health (Test Split)
type: junxian-tang/OMI-Health-Dataset
args: test
metrics: name: ROUGE-1 F1
value: 0.5826
type: rouge
verified: no, name: ROUGE-2 F1
value: 0.3261
type: rouge
verified: no, name: ROUGE-L F1
value: 0.4033
type: rouge
verified: no, name: BERTScore F1
value: 0.7467
type: bertscore
verified: no
base_model: google/txgemma-2b-predict
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Created: 2025-05-29 12:50:48