63 lines
3.9 KiB
Markdown
63 lines
3.9 KiB
Markdown
---
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datasets:
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- arxiv
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widget:
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- text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
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machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
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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.
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In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
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Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing.
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In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors
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1.7-2.9 times versus production systems."
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license: mit
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---
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# T5 One Line Summary
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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
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## Usage:[](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing)
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```python
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abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
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machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and
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handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a
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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.
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In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,
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Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time,
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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.
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"""
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```
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### Using Transformers🤗
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```python
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model_name = "snrspeaks/t5-one-line-summary"
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True)
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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)
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preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
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print(preds)
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# output
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["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers",
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"Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
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"Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
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```
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### Using simpleT5⚡️
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```python
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# pip install --upgrade simplet5
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from simplet5 import SimpleT5
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model = SimpleT5()
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model.load_model("t5","snrspeaks/t5-one-line-summary")
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model.predict(abstract)
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# output
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"Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers"
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``` |