Question Answering
Transformers
PyTorch
English
t5
text2text-generation
medical
fusion-in-decoder
text-generation-inference
Instructions to use disi-unibo-nlp/MedGENIE-fid-flan-t5-base-medqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disi-unibo-nlp/MedGENIE-fid-flan-t5-base-medqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="disi-unibo-nlp/MedGENIE-fid-flan-t5-base-medqa")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("disi-unibo-nlp/MedGENIE-fid-flan-t5-base-medqa") model = AutoModelForSeq2SeqLM.from_pretrained("disi-unibo-nlp/MedGENIE-fid-flan-t5-base-medqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 099e451a37eb0e8618f10e50a02475c614c67751978a3be46145a1edf45f5ec5
- Size of remote file:
- 990 MB
- SHA256:
- dca6e0c794d6ca59e5db3cc2cc00430a66c03ae9d160bb63813c12ad9d7355d4
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