mirror of https://github.com/kubeflow/examples.git
38 lines
1.2 KiB
Markdown
38 lines
1.2 KiB
Markdown
# Prediction
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Open AI Platform and navigate to your [model](https://console.cloud.google.com/ai-platform/models), there is one model listed:
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Open the model and choose your version then click on the Tab `TEST & USE` and enter the following input data:
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```
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{"instances": ["London on Monday evening"]}
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```
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After a couple of seconds, you get the prediction response. Where `London` got pedicted as geolocation (B-geo), and `Monday evening` as time where Monday is the beginning (B-tim) and evening is inisde (I-tim).
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```json
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{
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"predictions": [
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[
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"B-geo",
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"O",
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"B-tim",
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"I-tim",
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]
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]
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}
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```
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Congratulations you trained and deployed a Named Entity Recognition model where you can extract entities. There are many use cases where such models can be used.
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Examples:
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* Optimize search results by extract specific entities out of search queries.
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* Classify large document archives by making entities filterable.
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* Enhance access for digital research of large document archives.
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* Route customer support message by extracting the department or product.
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*Previous*: [Monitor the training](step-6-monitor-training.md) |