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Add means for evaluation/calibration of embedding models

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python

Research direction

Start by reviewing the linked StellarGraph calibration example and Neo4j node-classification documentation, then locate BlueGraph's embedding-model and link-prediction entry points. Define how validation links or k-fold splits should be reserved and evaluated; done means the project has a documented, usable evaluation path for embedding models.

Written by the indexing model from the issue text.

Description

enhancement

We need to have means for reserving a validation set (or even make a k-fold cross validation) for the embedding models (usually based on link prediction, i.e. reserve sets of links for validation).

Useful references:

Dominant language
Python
Stars
34
Forks
5
PR merge metrics
No merged PRs in 30d

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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