Utilized ASR to transcribe 20+ years of cataloged audio records from a multimedia company, then created a semantic search engine via natural language embeddings to retrieve relevant recordings and pinpoint the most relevant audio segments down to the second level.
Trained a hyperspecific Named Entity Recognition (NER) and Entity Linking model on hundreds of thousands of industry specific documents which resulted in near full automation of multiple job types in the food regulatory industry, where inaccurate predictions have severe consequences. Coupled this with a powerful graphical interface for verifying predictions downstream to produce a production application on Azure Cloud.
Trained a model to reliably extract event details such as title, date, time, participants, and location from blocks of unstructured text and automatically create standardized event entries. This enabled event extraction from both the internet and from conversations, as well as instant event creation via voice.