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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This model is designed to make scientific abstracts more accessible. Its outputs should not be relied upon for any purpose outside of this scope. There is no guarantee that the generated text accurately reflects the research it is based on. When making important decisions, it is recommended to seek the advice of human experts or consult the original papers.
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# Acknowledgement
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This research is supported by the Institute of Museum and Library Services (IMLS) RE-246450-OLS-20.
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This model is designed to make scientific abstracts more accessible. Its outputs should not be relied upon for any purpose outside of this scope. There is no guarantee that the generated text accurately reflects the research it is based on. When making important decisions, it is recommended to seek the advice of human experts or consult the original papers.
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# Acknowledgement
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This research is supported by the Institute of Museum and Library Services (IMLS) RE-246450-OLS-20.
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# Citation
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```tex
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@article{Wang2025science,
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author = {Wang, Haining and Clark, Jason and McKelvey, Hannah and Sterman, Leila and Gao, Zheng and Tian, Zuoyu and K{\"u}bler, Sandra and Liu, Xiaozhong},
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title = {{Science out of its Ivory Tower: Improving Accessibility with Reinforcement Learning}},
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journal = {Scientometrics},
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year = {2025},
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volume = {130},
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number = {8},
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pages = {4519--4543},
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month = aug,
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doi = {10.1007/s11192-025-05386-z},
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url = {https://doi.org/10.1007/s11192-025-05386-z},
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issn = {1588-2861}
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}
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```
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