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:
- acd80f3a466959b8b3b9e9f9c5fad267528de6afd35d359e58dd8b5b1e743cc4
- Size of remote file:
- 990 MB
- SHA256:
- 7a57adbe9257694fbc97efd3ac1c08d55dba441a58e1eff9f3e81df593376e5d
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