readme: ---
  
  datasets:
  
  - arxiv
  
  
  widget:
  
  - 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
  
  ---
  
  # 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. It is trained using [simpleT5](https://github.com/Shivanandroy/simpleT5) library - A python package built on top of pytorch lightning⚡️ & 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.
  """
  ```
  ### Using 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)
  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"]
   ```
  ### Using 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"
  ```