Summarization
Transformers
Safetensors
English
t5
text2text-generation
multi-document
long-context
Long Context
text-generation-inference
Instructions to use yale-nlp/MDCure-FlanT5-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yale-nlp/MDCure-FlanT5-Base 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="yale-nlp/MDCure-FlanT5-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-FlanT5-Base") model = AutoModelForSeq2SeqLM.from_pretrained("yale-nlp/MDCure-FlanT5-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag and library name
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- adding a relevant pipeline tag, so the model can be found at https://huggingface.co/models?pipeline_tag=summarization
- adding the
library_name, which istransformers(indicated by config and tokenizer files)
pybeebee changed pull request status to merged