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") - Notebooks
- Google Colab
- Kaggle
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# Project Description
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As a result, many people may prefer to trust short stories on social media rather than attempting to read a scientific paper.
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This is understandable, as we humans often prefer stories to dry, technical information.
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So, why not "translate" these complex scientific abstracts into simpler, more accessible stories? Some prestigious journals are already taking steps towards greater accessibility.
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# Project Description
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Open science has greatly reduced the barriers to accessing scientific papers. However, reachable research does not mean accessible knowledge.
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As a result, many people may prefer to trust short stories on social media rather than attempting to read a scientific paper.
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This is understandable, as we humans often prefer stories to dry, technical information.
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So, why not "translate" these complex scientific abstracts into simpler, more accessible stories? Some prestigious journals are already taking steps towards greater accessibility.
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