Models
Aspect and sentiment Identification (medical device provider)
This model does the following:Identifies the aspects in survey feedback responseIdentifies the sentiment based on the aspects
Sentiment Classification
The model detects the sentiment of a given text. This is a binary classification model that classifies the text as either positive or negative. The model also provides a confidence score of the predicted class. The model has been trained on a dataset of Yelp restaurant reviews. The Yelp reviews dataset is constructed by considering stars 1 and 2 as negative, and 3 and 4 as positive. The model showed a 96% accuracy on a validation set of 1000 records.
Question Answering in Text
In Question Answering tasks, the model receives a question regarding text content and is required to mark the beginning and end of the answer in the text.
Sequence Tagging
This model demonstrates sequence tagging of a given text. It recognizes varies entities in a given piece of text. This can be used to tag an incoming email or extract knowledge from a text document.