pytorch t5 text2text-generation dataset:arxiv transformers license:mit autotrain_compatible
---

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"
```