-
Notifications
You must be signed in to change notification settings - Fork 475
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
How to use tensorflow ranking for prediction ? #48
Comments
This package uses the Tensorflow Estimators API, so accordingly your mode has a
|
I'm new to learning to rank, and I need some help on understanding the model's output. Does it indicate the relevance score of the first 100 "document" (order by document_id)? If so, how can I find the corresponding document in the test dataset? (it seems we don't specify an document_id in the dataset) Thanks! |
Vertika - Apologies for the delayed response. Alex is correct; you will need to invoke the learned model in predict mode to obtain per-document score. You may then sort documents by their score to obtain a ranked list. Tom - generally speaking, the per-query output is a Tensor of relevance scores where the i^{th} score corresponds to the i^{th} document in the input Tensor. Note that during training, the dimension of the input Tensor that corresponds to the number of documents per query is fixed to "list-size" (in your case, it appears to be 100). If there are fewer than list-size documents available for your a query, its Tensor is padded. Please let us know if you have additional questions. |
As per my understanding from sample dataset, libsvm generator is assuming that relevance level will be present.
I am using sample
Can you suggest what changes to make so that I can use my trained model later for prediction of relevance levels/ranking based on features and document list for a query.
The text was updated successfully, but these errors were encountered: