metadata
tags:
  - T5
  - transformers
  - Question Answering
  - multilingual
  - en
  - ru
  - uk
  - pl

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/) 


uaritm/T5_ukruen_qa_all_clean_10

The model is trained on a question-answer dataset (250000 questions to doctors of various specialties and short answers from doctors). 
Texts in four languages (English, Russian, Ukrainian and Polish).

Model trained 10 epochs based on the model 'uaritm/lik_neuro_202'

You can talk about your health problems and this neural network will give you advice.
You can see how the model works and test it at the link: https://aihealth.site
More detailed information about this neural network can be found here: https://www.esemi.org/vgp-the-new-online-resource-for-medical-assistance/



from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch


Citing & Authors
@misc{Uaritm,
      title={SetFit: Question Answering with medical context}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={https://aihealth.site},
}
{"license": "creativeml-openrail-m"}
transformers pytorch t5 text2text-generation license:creativeml-openrail-m autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #license-creativeml-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

metadata
tags:
  - T5
  - transformers
  - Question Answering
  - multilingual
  - en
  - ru
  - uk
  - pl

In this article, you will find information about applying this model in the clinic search application 


uaritm/T5_ukruen_qa_all_clean_10

The model is trained on a question-answer dataset (250000 questions to doctors of various specialties and short answers from doctors). 
Texts in four languages (English, Russian, Ukrainian and Polish).

Model trained 10 epochs based on the model 'uaritm/lik_neuro_202'

You can talk about your health problems and this neural network will give you advice.
You can see how the model works and test it at the link: URL
More detailed information about this neural network can be found here: URL



from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch


Citing & Authors
@misc{Uaritm,
      title={SetFit: Question Answering with medical context}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={URL},
}
TAGS
#transformers #pytorch #t5 #text2text-generation #license-creativeml-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #t5 #text2text-generation #license-creativeml-openrail-m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
