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apiVersion: image.openshift.io/v1
kind: ImageStream
metadata:
  labels:
    opendatahub.io/notebook-image: "true"
  annotations:
    opendatahub.io/notebook-image-url: "https://github.com/thoth-station/s2i-generic-data-science-notebook"
    opendatahub.io/notebook-image-name: "Standard Data Science"
    opendatahub.io/notebook-image-desc: "Jupyter notebook data science image with TrustyAI library installed"
  name: s2i-generic-data-science-notebook

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Instructions for evaluating ER explanations with CERTEM

1. create a new Google Colab Notebook
2. open the CERTEM-demo Google Colab Notebook at https://colab.research.google.com/drive/1e9L4Edbm3_X4V_xH7EFkxsmT3wE4Qwca?usp=sharing
3. copy each cell from CERTEM-demo.ipynb into your newly created notebook (in the very same order)
4. Run all cells (Runtime -> Run all)
5. Once all cells are loaded you should see something like
6. Select one of the datasets in the dropdown menu, either BA or AB (avoid IA because of visualization issues)
7. Select one of the existing models by checking one of the boxes DeepER, DeepMatcher, Ditto.

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"""

python port of Apache Lucene's OptimizedScalarQuantizer.

source: lucene/core/src/java/org/apache/lucene/util/quantization/OptimizedScalarQuantizer.java

overview
------------------
OSQ is a *per-vector* scalar quantizer based on three main ideas:

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