content: name =          "spladev3_no_knn_mem_budget_2.0"
  title =         "Seismic with no knn on Spladev3 with memory budget 2.0 on MS MARCO"
  description =   "Seismic with no knn on Spladev3 with memory budget 2.0 on MS MARCO for the Table 1 (row 7) on CIKM 2024 paper"
  dataset =       "Spladev3 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!
  build =         true   # Need to build the index or the index is already built?
  metric =        "RR@10"
  
  [folder] 
  data =          "~/sparse_datasets/msmarco_v1_passage/spladev3/data"
  index =         "~/sparse_indexes/msmarco_v1_passage/spladev3/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 =         "cikm_2024_spladev3_no_knn_mem_budget_2.0"
  
  [indexing_parameters]
  n-postings =            5000
  centroid-fraction =     0.2
  summary-energy =        0.5
  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"
  kmeans-pruning-factor = 0.005 # Only used by random-kmeans-inverted-index
  
  [query]
      [query.recall_90]
      query-cut =         2
      heap-factor =       1.0
  
      [query.recall_91]
      query-cut =         3
      heap-factor =       1.0
      
      [query.recall_92]
      query-cut =         3
      heap-factor =       1.0
  
      [query.recall_93]
      query-cut =         3
      heap-factor =       1.0
  
      [query.recall_94]
      query-cut =         3
      heap-factor =       1.0
  
      [query.recall_95]
      query-cut =         4
      heap-factor =       1.0
  
      [query.recall_96]
      query-cut =         4
      heap-factor =       1.0
  
      [query.recall_97]
      query-cut =         6
      heap-factor =       1.0
  
      [query.recall_98]
      query-cut =         5
      heap-factor =       0.9
  
      [query.recall_99]
      query-cut =         10
      heap-factor =       0.9