Model: snrspeaks/t5-one-line-summary
Author: snrspeaks
Library: transformers
Tags: transformers, pytorch, t5, text2text-generation, dataset:arxiv, license:mit, text-generation-inference, endpoints_compatible, deploy:azure, region:us
Card metadata: datasets: arxiv
widget: summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production  machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and  handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks.  In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,  Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing.  In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors  1.7-2.9 times versus production systems.
license: mit
Likes: 91 | All-time downloads: 9150809
Created: 2022-03-02 23:29:05