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---
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# T5 One Line Summary
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A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers
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Trained with [**simpleT5**](https://https://github.com/Shivanandroy/simpleT5)⚡️in just 3 lines of code
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- [**simpleT5**](https://https://github.com/Shivanandroy/simpleT5)⚡️ is a python package built on top of **pytorch lightning** and **transformers**🤗, to quickly train T5 models.
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A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers using [**simpleT5**](https://https://github.com/Shivanandroy/simpleT5) (built on top of pytorch lightning⚡️ & transformers🤗 to train T5 models)
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## Usage:[](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing)
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```python
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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.
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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
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machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
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handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a
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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.
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In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
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Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time,
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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.
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"""
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```
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Transformers🤗
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### Using Transformers🤗
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```python
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model_name = "snrspeaks/t5-one-line-summary"
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"Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
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"Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
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```
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simpleT5⚡️
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### Using simpleT5⚡️
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```python
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# pip install --upgrade simplet5
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from simplet5 import SimpleT5
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