content: name = "splade-cocondenser_recall_95"
  title = "Seismic Splade-cocondenser - Recall 95%"
  description = "Optimized configuration for splade-cocondenser targeting recall 95% from grid search"
  dataset = "Splade-cocondenser on MS-MARCO"
  
  [settings]
  k = 10
  n-runs = 1
  build = true
  metric = "RR@10"
  
  [folder]
  data = "~/knn_datasets/sparse_datasets/msmarco_v1_passage/cocondenser/data"
  index = "~/knn_indexes/sparse_datasets/msmarco_v1_passage/cocondenser/indexes"
  qrels_path = "~/knn_datasets/sparse_datasets/msmarco_v1_passage/qrels.dev.small.tsv"
  experiment = "."
  
  [filename]
  dataset = "documents.bin"
  queries = "queries.bin"
  groundtruth = "groundtruth.tsv"
  doc_ids = "doc_ids.npy"
  query_ids = "queries_ids.npy"
  index = "splade-cocondenser_recall_95"
  
  [indexing_parameters]
  n-postings = 4000
  centroid-fraction = 0.2
  summary-energy = 0.5
  knn = 0
  clustering-algorithm = "random-kmeans-inverted-index-approx"
  kmeans-doc-cut = 15
  kmeans-pruning-factor = 0.0
  max-fraction = 2.0
  min-cluster-size = 2
  pruning-strategy = "global-threshold"
  value-type = "f16"
  
  [query]
  
  [query.recall_95]
  query-cut = 4
  heap-factor = 0.9
  first_sorted = true
  