Update README.md

README.md

@@ -10,7 +10,13 @@ tags:
 
 widget:
 
-- text: "summarize: 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."
+- text: "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
 
@@ -20,57 +26,38 @@ license: mit
 A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers
 
 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.
+- [**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.
+"""
+```
+Transformers🤗
+```python
 model_name = "snrspeaks/t5-one-line-summary"
 
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-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.
-"""
-
-input_ids = tokenizer.encode(
-    "summarize: " + abstract, return_tensors="pt", add_special_tokens=True
-)
-
-generated_ids = model.generate(
-    input_ids=input_ids,
-    num_beams=5,
-    max_length=50,
-    repetition_penalty=2.5,
-    length_penalty=1,
-    early_stopping=True,
-    num_return_sequences=3,
-)
-
-preds = [
-    tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-    for g in generated_ids
-]
-
+input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True)
+generated_ids = model.generate(input_ids=input_ids,num_beams=5,max_length=50,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=3)
+preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
 print(preds)
 
 # output
-
-['Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers',
-
- 'Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems',
-
- 'Overton: Building, Monitoring, and Improving Production Machine Learning Systems']
+["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers",
+ "Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
+ "Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
  ```
+simpleT5⚡️
+```python
+# pip install --upgrade simplet5
+from simplet5 import SimpleT5
+model = SimpleT5()
+model.load_model("t5","snrspeaks/t5-one-line-summary")
+model.predict(abstract)
+
+# output
+"Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers"
+```