Retail and Ecommerce
CRM stands for Customer Relationship Management. It's a technology used to manage interactions with customers and potential customers. A CRM system helps organisations build customer relationships and streamline processes so they can increase sales, improve customer service, and increase profitability.
Natural Language Processing
Generates text summarizing the key ideas in the input text.
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.
Natural Language Processing
Generates text summarizing the key ideas in the input text.
This is a newer version of our content generation model. It uses and intermediate step of extracting product specific features and specifications from the description. The model then 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.
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.
This product classification model performs hierarchical product classification using discriminative techniques.The evaluation metrics are available at: Evaluation ResultsData set analysis details are as follows:Training DetailsEvaluation DetailsError DetailsSample Hierarchy VisualizationInterpretability
This is a newer version of our content generation model. It uses and intermediate step of extracting product specific features and specifications from the description. The model then 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.