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
| 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 | |