Deep Learning NLP: Build and Deploy a BERT COVID Q&A System

Read and assemble the dataset by extracting files, reading PDFs, and combining video and PMC content to create per-paper data across five locations.

Access and prepare the sample dataset for the BERT covid q&a system by downloading the subset, reading pickle, pdf, and pmc samples, and merging into a unified data frame.

Learn how to clean and merge metadata with pdf data, select relevant abstracts by length, handle missing values, unify column naming, and prepare a unified text dataset for downstream processing.

Pre-process a cross-source dataset by aligning body text with politics content, remove unused columns, and prepare a 22-sample data subset for elasticsearch-based q&a.

Download and run an Elasticsearch docker container, configure a single-node cluster, and prepare a script to load sample data into Elasticsearch, enabling a knowledge base for a Q&A system.

Read and format a dataset, convert it to dictionaries, and write documents with metadata to an Elasticsearch index using Haystack for a BERT NLP Q&A system.

build streamlit ui - part 3 demonstrates sending user input to a backend handler, passing three data points (question, no answers, none of) and receiving a three-element answer list with scores.

Build a docker image for the knowledge base (kb) handler by configuring Elasticsearch, setting up a data cluster, enabling migration across environments, and accessing it through port 9200.

Build a docker image for the UI app, containerize three independent components, and deploy a scalable, host-agnostic UI across environments.