Summarization
Transformers
PyTorch
English
t5
text2text-generation
medical
text-generation-inference
Instructions to use xtie/T5Score-PET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xtie/T5Score-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/T5Score-PET")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xtie/T5Score-PET") model = AutoModelForSeq2SeqLM.from_pretrained("xtie/T5Score-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 T5Score for evaluating the quality of PET impressions.
To check our domain-adapted text-generation-based evaluation metrics:
π Additional Resources
- Codebase for evaluation metrics: GitHub