`embedding_size` property for vector models
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 45/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start by locating the vector model implementations for IALS and LightFM and reading how each represents latent factors, feature factors, and biases. Add a property that reports the full learned embedding size, including the factors for IALS and biases for LightFM; done when both models report sizes that include those components and the behavior is covered by tests.
Written by the indexing model from the issue text.
Description
Feature Description
A property that will return full learnt embedding size for vector models. For ials it will count latent + feature factors. For lightfm it will add biases.
Why this feature?
Useful for model complexity comparison
Additional context
No response
- Dominant language
- Python
- Stars
- 457
- Forks
- 55
- PR merge metrics
- No merged PRs in 30d
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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