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Shivanand Roy 👋 2021-06-20 21:18:04 +00:00 committed by huggingface-web
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@ -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"
```