Instructions to use xtie/BARTScore-PET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xtie/BARTScore-PET with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="xtie/BARTScore-PET")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xtie/BARTScore-PET") model = AutoModelForSeq2SeqLM.from_pretrained("xtie/BARTScore-PET", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
tags:
- summarization
- medical
library_name: transformers
pipeline_tag: summarization
Automatic Personalized Impression Generation for PET Reports Using Large Language Models πβ
Authored by: Xin Tie, Muheon Shin, Ali Pirasteh, Nevein Ibrahim, Zachary Huemann, Sharon M. Castellino, Kara Kelly, John Garrett, Junjie Hu, Steve Y. Cho, Tyler J. Bradshaw
π Model Description
This is the domain-adapted BARTScore for evaluating the quality of PET impressions.
To check our domain-adapted text-generation-based evaluation metrics:
π Usage
Clone this GitHub repository in a local folder
git clone https://github.com/xtie97/PET-Report-Summarization.git
Go the the folder containing codes for computing BARTScore and create a new folder called "checkpoints"
cd ./PET-Report-Summarization/evaluation_metrics/metrics/BARTScore
mkdir checkpoints
mkdir checkpoints/bart-large
Download the model weights and put them in the folder "checkpoints/bart-large". Run the code for computing text-generation-based metrics
python compute_metrics_text_generation.py
π Additional Resources
- Codebase for evaluation metrics: GitHub