Extract Decisioning entities from PDF and Image Documents

The model uses OCR to extract information from Acord Certificate of Insurance (COI) document and create a JSON form. It can parse PDFs or image documents.

A newer version of the Content generation solution. This takes a two step process, in the first step it extracts product specific features from the current descriptions and then uses it to generate new content. Generates or recreates a product's description using information from its title, brand, and either a set of product features or an old description. Optionally, choose what audience the description should be marketed to (e.g. "parents" or "professional musicians", and a tone for the new description (e.g. "fun" or "technical"). To specifically avoid terms, add them to the Avoid field (e.g. "Warning", "warranty") or to specifically include them, add them to the Keywords field.

Text summarization reduces larger body of text for quicker consumption. This has variety of

This model provides ability to classify any text. No training is required!

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.

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.

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.