Update README.md

README.md

@@ -23,17 +23,20 @@ license: mit
 ---
 
 # T5 One Line Summary
-A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers
+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)
-
-Trained with [**simpleT5**](https://https://github.com/Shivanandroy/simpleT5)⚡️in just 3 lines of code 
-- [**simpleT5**](https://https://github.com/Shivanandroy/simpleT5)⚡️ is a python package built on top of **pytorch lightning** and **transformers**🤗, to quickly train T5 models.
 
 ## Usage:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing)
 ```python
-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.
+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.
 """
 ```
-Transformers🤗
+### Using Transformers🤗
 ```python
 model_name = "snrspeaks/t5-one-line-summary"
 
@@ -50,7 +53,7 @@ print(preds)
  "Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
  "Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
  ```
-simpleT5⚡️
+### Using simpleT5⚡️
 ```python
 # pip install --upgrade simplet5
 from simplet5 import SimpleT5