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How to Build a Pipeline for Natural Language Processing Using Python & Redis

How to Build a Pipeline for Natural Language Processing Using Python & Redis

in the world of ever increasing complexity we need new tools to help find relevant knowledge  specifically for the task at health let me demonstrate the pattern project which  can be used to navigate through medical articles the project have a free machine learning  natural language processing pipelines and novel user experience elements and all  of it powered by radius working as a data fabric first popular a pipeline is building a  knowledge graph from medical literature using medical dictionary processing information in  radius gears and storing it in a radius graph second pipeline is using newest radius gears  feature to capture key miss events and trigger calculation of dirt question answering model  and store output and radius third pipeline is using radius gears to precalculate language  tokens and create a queue in radius to run google tip 5 model for text  summarization on dedicated in the gpu but there is a challenge and the challenge of  relevance of the information to the medical professional simple word bleeding means 11  000 different things in medical dictionary so we had to narrow down to  different semantic layers assuming they are of the interest  of general practitioner or nurse and then we took it further and we built  separate interfaces for each of the medical roles lets start with a simple role medical  student medical student can be interested in various things and is comfortable of using  model technology such as 3d visualization or virtual reality in this 3d graph the node is  the concept is a thing but the edge is a list of articles where those two concepts were  found together and so this maps to medical dictionary the summary is built by googles t5  model and we also expanded that because we are not sure about the relevance you can mark the note as  not important and it will be deleted from the role and the user will be able to create  his own or his own filters and then walk the information via the lenses relevant  for them rather than the lens more generic for the world but there are other types of  medical professionals who may prefer to avoid touching the surfaces to avoid changing  gloves or washing hands or may prefer text to speech interaction so as you  can see here im demonstrating that we can use a finger without touching the  screen to trigger a new role for the nurse and the nurse have a text-to-speech  interface which is powered by a birth question answering model  please wait as i retrieve an answer and limited data from the published literature was  the valid answer as per model it was presented as a text in conclusion you have seen we can build  a complex natural language processing machine learning pipelines deploying most advanced  machine learning inside regis using radius as a data fabric and building novel user  interfaces where ai complements to the human please join us and contribute to our  project and vote for us thank you

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