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.

This model helps to increase the resolution of an image using ML techniques. Existing algorithm based techniques (such as bicubic) may leave artifacts or make it blurry. ML based approaches produce better resolution.

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.

This model helps to increase the resolution of an image using ML techniques. Existing algorithm based techniques (such as bicubic) may leave artifacts or make it blurry. ML based approaches produce better resolution.