Row 89259
Content Data
This page contains data entry 89259 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
In the context of document retrieval, what serves as the ground truth?
In the context of the benchmark datasets that I'm using for document retrieval, the samples are usually comprised of a query and its corresponding positive and negative passages. A positive passage has the label 1 and a negative one has 0.
The setting that I currently have follows the following procedure:
1. Create a FAISS index of all of the passages (positive + negative). 2. Loop through each query and retrieve the top-$k$ documents from the above corpus. 3. Get the labels of each retrieved document (0 or 1). 4. Calculate nDCG.
What I'm confused about is what the ground truth should be. The retrieved arrays of binary labels above would be the predictions.
I'm using scikit-learn's nDCG score function but I'm lost as to what the input for `y_true` should be.
| Field | Value |
|---|---|
| text | In the context of document retrieval, what serves as the ground truth? In the context of the benchmark datasets that I'm using for document retrieval, the samples are usually comprised of a query and its corresponding positive and negative passages. A positive passage has the label 1 and a negative one has 0. The setting that I currently have follows the following procedure: 1. Create a FAISS index of all of the passages (positive + negative). 2. Loop through each query and retrieve the top-$… |
| label | r/machinelearning |
| dataType | post |
| communityName | r/MachineLearning |
| datetime | 2024-05-25 |
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Raw Record
{
"text": "In the context of document retrieval, what serves as the ground truth?\n\nIn the context of the benchmark datasets that I'm using for document retrieval, the samples are usually comprised of a query and its corresponding positive and negative passages. A positive passage has the label 1 and a negative one has 0.\n\nThe setting that I currently have follows the following procedure:\n\n1. Create a FAISS index of all of the passages (positive + negative).\n2. Loop through each query and retrieve the top-$k$ documents from the above corpus.\n3. Get the labels of each retrieved document (0 or 1).\n4. Calculate nDCG.\n\nWhat I'm confused about is what the ground truth should be. The retrieved arrays of binary labels above would be the predictions.\n\nI'm using scikit-learn's nDCG score function but I'm lost as to what the input for `y_true` should be.",
"label": "r/machinelearning",
"dataType": "post",
"communityName": "r/MachineLearning",
"datetime": "2024-05-25",
"username_encoded": "Z0FBQUFBQm5Lak1yY3M0Q21kVUpuUHoyN3gyZjd2M2gzRWc2WFZpY0doaFlGZWU4aDhxTUNydTQwLXNza3BoTG4wTGN2UVQwd3V1Z1hLcmkxcGRiS1Y3dnJoWjc1OVlnN0E9PQ==",
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}
Entry Information
- Entry ID: 89259
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000