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python

Extract content from Wikipedia article

def pre_process(api_input): url = api_input['wikipedia_url'] content = get_content(url) return {'model_input': content}

Run named-entity recognition model

* Model running.....

Translate output to enriched text

def post_process(out): predictions = out['predictions'] store_predictions(predictions) return {'display': enrich(predictions)}

https://en.wikipedia.org/wiki/Barack_Obama
{ "model_input": [ "Barack", "Hussein", "Obama", "II", "is", "an", "American", "politician", ...] }
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{ "predictions": [ { term: "Barack", type: "name" }, { term: "Hussein", type: "name" }, ... ] }
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...
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Barack Hussein Obama II American 44th United States
Heading
Text display
Text area
Button
NER output
Button
NER output
NER model
Input
Barack Hussein Obama II is an American politician and attorney who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.
Analyze text Analyzing... Analyzed!
Output
Barack Hussein Obama II American 44th United States 2009 2017 Democratic Party Obama African-American United States Illinois
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— Made with BaseTen —
content moderation

Content moderation

A language model driven content moderation workflow.

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data labeling

Data labeling

A an app for labeling restaurant photos to train a model.

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active learning

Active learning

A human-in-the-loop workflow to improve model performance on the fly.

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style transfer

Model evaluation

An app for other stakeholders to test and give feedback on model performance.

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Bring data in from other databases and systems of record
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