content: name =          "grid_search_msmarco_v2_cocondenser" 
  title =         "Seismic without k-NN on Cocondenser MS MARCO v2"
  description =   "Seismic without k-NN on Cocondenser MS MARCO v2"
  dataset =       "Splade cocondenser on MS-MARCO v2"
  # 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 
                          #<C2><A0>this may influence your query time
  NUMA =          "numactl --physcpubind='0' --localalloc"    # Is the machine NUMA and need to prepend "numactl --physcpubind='0-15' --localalloc" to query command? Otherwise comment this!
  build =         true   # Need to build the index or the index is already built?
  metric =        "RR@10"
  delete =        true
  
  [folder] 
  data =          "~/knn_datasets/sparse_datasets/msmarco_v2_passage/cocondenser/data"
  index =         "~/knn_indexes/sparse_datasets/msmarco_v2_passage/cocondenser/indexes"
  qrels_path =    "~/knn_datasets/sparse_datasets/msmarco_v2_passage/qrels.dev1.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 =         "global_threshold_index_f16"
  
  [indexing_parameters]
  n-postings =            [30000, 40000, 50000, 60000]
  centroid-fraction =     [0.1, 0.2]
  summary-energy =        [0.4, 0.5]
  knn =                   [0]
  pruning-strategy =      ["global-threshold"] # Possible options are "coi-threshold", "global-threshold", "fixed-size". Default is "global-threshold"
  #alpha =                 [0.1, 0.15, 0.2] # needed only by coi-threshold
  max-fraction =          [2.0, 3.0, 4.0, 6.0] # needed only by "global-threshold"
  clustering-algorithm =  ["random-kmeans-inverted-index-approx"] # 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"
  # kmeans-pruning-factor = 0.005 # Only used by random-kmeans-inverted-index
  value-type =            ["f16", "fixedu8"]
  min-cluster-size =      [2, 10, 100] 
  
  
  
  [querying_parameters]
  query-cut =             [2, 4, 6, 7, 8, 10, 12]
  heap-factor =           [0.7, 0.8, 0.9, 1.0]
  knn =                   [0]
  first_sorted =          [true, false]
  