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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# TL;DR
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Scientific Abstract Simplification rewrites
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If you already used our baseline model [`sas_baseline`](https://huggingface.co/haining/sas_baseline), the current model just performs better on **all** evaluation metrics.
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Try it now with the Hosted inference API on the right.
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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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# TL;DR
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Scientific Abstract Simplification (SAS) is a tool that rewrites difficult-to-understand scientific abstracts into simpler, easier-to-read versions. Our goal is to make scientific knowledge more accessible to everyone. If you've already tried our baseline model (sas_baseline), the current model is even better on all evaluation metrics. You can try it now with the Hosted Inference API on the right. Simply choose one of the provided examples or enter your own scientific abstract. Just remember to include the instruction "summarize, simplify, and contextualize: " at the beginning (with a space after the colon). For local use, see the [Usage] section.
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# Project Description
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