tensorflow / tensorflow/recommenders
Retrain retrieval model
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- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
Hi, I am very excited about tensorflow-recommenders. I've been testing the examples, and everything has worked quite well. However I would like to know how can I retrain a retrieval model? How should I load the saved model to train with new data?
Contributor 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.
Research direction
Review the retrieval model examples first, then trace how a saved model is loaded and how new data enters training. Determine whether the existing examples support retraining and document a reproducible workflow, including what successful retraining should produce.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100