Description Classify texts/sentences in two categories: True : The sentence is talking about a possible ADE. False : The sentence doesn’t have any information about an ADE. This model is a BioBERT-based classifier. Predicted Entities Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU document_assembler = DocumentAssembler() \ .setInputCol("text") \ .setOutputCol("document") tokenizer = Tokenizer() \ .setInputCols(["document"]) \ .setOutput...| Spark NLP
Description This model is a BioBERT-based Age Group Text Classifier and it is trained for analyzing the age group of a person mentioned in health documents. Age of the person may or may not be mentioned explicitly in the training dataset. The Text Classifier model has been trained using in-house annotated health-related text that have been labeled with three different classes: Adult: A person who is fully grown or developed. Typically refers to someone who is 18 years or older, Child: Require...| Spark NLP
Introduction Fin’s north start metric is resolution rate; it’s how we measure how well Fin, our customer support AI agent, is performing. Each resolution is priced at US$0.99, so accurately detecting when Fin resolves a conversation… The post “Was that helpful?” Understanding User Feedback in Customer Support AI Agents appeared first on /research.| /research
One of Fin AI Agent’s most critical tasks is deciding when to escalate customer interactions to human support. This challenge has only grown as Fin has become more conversational, and now most escalations happen through natural… The post To escalate, or not to escalate, that is the question appeared first on /research.| /research