Summarization
Transformers
Safetensors
English
bart
text2text-generation
radiology
medical
healthcare
Eval Results (legacy)
Instructions to use Kumud2k16/radiology-expression-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kumud2k16/radiology-expression-summarizer 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="Kumud2k16/radiology-expression-summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Kumud2k16/radiology-expression-summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("Kumud2k16/radiology-expression-summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: mit
library_name: transformers
pipeline_tag: summarization
base_model: facebook/bart-base
base_model_relation: finetune
tags:
- summarization
- radiology
- medical
- bart
- healthcare
datasets:
- openi
metrics:
- rouge
model-index:
- name: radiology-expression-summarizer
results:
- task:
type: summarization
name: Radiology Report Summarization
dataset:
type: openi
name: Open-i NLMCXR (Indiana University Chest X-ray)
metrics:
- type: rouge
value: 0.5174
name: ROUGE-1 F1
- type: rouge
value: 0.3881
name: ROUGE-2 F1
- type: rouge
value: 0.5092
name: ROUGE-L F1