Summarization
Transformers
PyTorch
ONNX
Safetensors
English
longt5
text2text-generation
lay summaries
paper summaries
biology
medical
Instructions to use pszemraj/long-t5-tglobal-base-sci-simplify-elife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/long-t5-tglobal-base-sci-simplify-elife 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="pszemraj/long-t5-tglobal-base-sci-simplify-elife")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/long-t5-tglobal-base-sci-simplify-elife") model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/long-t5-tglobal-base-sci-simplify-elife", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a8893aa6bc78cf3caafe08011794985ceb2f32f9e3d3d2f912ed4ba0594d51c
- Size of remote file:
- 993 MB
- SHA256:
- 38a98540510aa957aa3d05f449ae1cd5f8e48a077c291ecd0d8c2630db039a01
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