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--- |
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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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## Usage: |
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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. |
|
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 = "Suva/uptag-url-model" |
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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=100,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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``` |
