Transformers
PyTorch
Safetensors
English
t5
text2text-generation
medical
dialog
text-generation-inference
Instructions to use alimoezzi/ReportQL-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alimoezzi/ReportQL-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("alimoezzi/ReportQL-base") model = AutoModelForSeq2SeqLM.from_pretrained("alimoezzi/ReportQL-base") - Notebooks
- Google Colab
- Kaggle
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- medical
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- dialog
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- arxiv:2209.12177
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license: mit
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---
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# ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique
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- medical
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- dialog
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- arxiv:2209.12177
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- text: "The liver is normal in size and with normal parenchymal echogenicity with no sign of space-occupying lesion or bile ducts dilatation. GB is well distended with no stone or wall thickening. The spleen is normal in size and parenchymal echogenicity with no sign of space-occupying lesion. visualized parts of the pancreas and para-aortic area are unremarkable. Both kidneys are normal in size with normal cortical parenchymal echogenicity with no sign of the stone, stasis, or perinephric collection. ureters are not dilated. The urinary bladder is empty so evaluation of pelvic organs is not possible. no free fluid is seen in the abdominopelvic cavity."
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example_title: "Sample 1"
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license: mit
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---
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# ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique
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