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license: creativeml-openrail-m |
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metadata |
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tags: |
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- T5 |
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- transformers |
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- Question Answering |
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- multilingual |
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- en |
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- ru |
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- uk |
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- pl |
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In [this article](https://medium.com/@uaritm/why-is-there-so-much-noise-around-one-of-the-neural-network-models-5d58cac8d706), you will find information about applying this model in the [clinic search application](https://aihealth.site/) |
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uaritm/T5_ukruen_qa_all_clean_10 |
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The model is trained on a question-answer dataset (250000 questions to doctors of various specialties and short answers from doctors). |
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Texts in four languages (English, Russian, Ukrainian and Polish). |
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Model trained 10 epochs based on the model 'uaritm/lik_neuro_202' |
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You can talk about your health problems and this neural network will give you advice. |
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You can see how the model works and test it at the link: https://aihealth.site |
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More detailed information about this neural network can be found here: https://www.esemi.org/vgp-the-new-online-resource-for-medical-assistance/ |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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import torch |
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Citing & Authors |
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@misc{Uaritm, |
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title={SetFit: Question Answering with medical context}, |
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author={Vitaliy Ostashko}, |
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year={2023}, |
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url={https://aihealth.site}, |
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} |