| from simplet5 import SimpleT5 |
| model = SimpleT5() |
|
|
| model.load_model("t5","snrspeaks/t5-one-line-summary") |
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|
| abstract = """We describe a system called Overton, whose main design goal is to |
| support engineers in building, monitoring, and improving production machine learning 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. |
| """ |
|
|
| model.predict(abstract) |
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|
