Transformers
PyTorch
Safetensors
English
t5
text2text-generation
text2text generation
text-generation-inference
Instructions to use haining/scientific_abstract_simplification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haining/scientific_abstract_simplification with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("haining/scientific_abstract_simplification") model = AutoModelForSeq2SeqLM.from_pretrained("haining/scientific_abstract_simplification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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You can choose an existing example or paste in any (perhaps full-of-jargon) abstract. Remember to prepend the instruction to the abstract ("summarize, simplify, and contextualize: "; notice, there is a whitespace after the colon). Local use refers to Section [Usage](#Usage).
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## Model Description
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Open science has significantly lowered the barriers to scientific papers.
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You can choose an existing example or paste in any (perhaps full-of-jargon) abstract. Remember to prepend the instruction to the abstract ("summarize, simplify, and contextualize: "; notice, there is a whitespace after the colon). Local use refers to Section [Usage](#Usage).
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# Project Description
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Open science has significantly lowered the barriers to scientific papers.
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