content: name =          "test_grid_search"
  title =         "Script to configure a grid search"
  description =   "Script to configure a grid search"
  dataset =       "Splade cocondenser on MS-MARCO"
  # compile-command = "cargo build" # Compile in debug mode? default is release mode
  # build-command = "./target/release/build_inverted_index"
  # query-command = "./target/debug/perf_inverted_index"
  
  [settings]
  k =             10
  n-runs =        1       # Number of runs. PLEASE think carefully how increasing 
                          # this may influence your query time
  # NUMA =        ""      # "numactl --physcpubind='0-15' --localalloc"    # Is the machine NUMA and need to prepend "numactl --physcpubind='0-15' --localalloc" to query command? Otherwise comment this!
  metric =        "RR@10"
  build =         true    # Do we need to build the index?
  delete =        false   # If true, it removes index files after use to save disk space
  
  [folder] 
  data =          "~/sparse_datasets/msmarco_v1_passage/cocondenser/data"
  index =         "~/sparse_indexes/msmarco_v1_passage/cocondenser/indexes"
  qrels_path =    "~/sparse_datasets/msmarco_v1_passage/qrels.dev.small.tsv"
  experiment =    "."     # stdout and stderr here of running the experiment is saved here. in a specific subfolder for the current execution
  
  [filename]
  dataset =       "documents.bin"
  queries =       "queries.bin"
  groundtruth =   "groundtruth.tsv"
  doc_ids =       "doc_ids.npy"
  query_ids =     "queries_ids.npy"
  index =         "test_grid_search"
  
  [indexing_parameters]
  n-postings =            [500, 1000]
  centroid-fraction =     [0.1]
  summary-energy =        [0.4, 0.5]
  value-type =            ["f16"] # Possible options are "f32", "f16", "bf16", "fixedu16", and "fixedu8". Default is "f16"
  knn =                   [0]
  clustering-algorithm =  ["random-kmeans"] # Possible options are "random-kmeans", "random-kmeans-inverted-index", "random-kmeans-inverted-index-approx"
  kmeans-doc-cut =        [15]    # needed by "random-kmeans-inverted-index" and "random-kmeans-inverted-index-approx"
  min-cluster-size =      [2]    # minimum size of clusters during k-means clustering (clusters smaller than this are removed and documents reassigned)
  kmeans-pruning-factor = [0.005] # Only used by random-kmeans-inverted-index
  
  [querying_parameters]
  query-cut =             [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  heap-factor =           [0.7, 0.8, 0.9, 1.0]
  knn =                   [0]
  first_sorted =          [true, false]
  