content: stop-words = "../data/lemur-stopwords.txt"
  libsvm-modules = "../deps/libsvm-modules/"
  prefix = "../data/"
  function-words = "../data/function-words.txt"
  punctuation = "../data/sentence-boundaries/sentence-punctuation.txt"
  start-exceptions = "../data/sentence-boundaries/sentence-start-exceptions.txt"
  end-exceptions = "../data/sentence-boundaries/sentence-end-exceptions.txt"
  
  dataset = "ceeaus"
  corpus = "line.toml" # located inside dataset folder
  index = "ceeaus"
  indexer-ram-budget = 1024 # **estimated** RAM budget for indexing in MB
                            # always set this lower than your physical RAM!
  # indexer-num-threads = 8 # default value is system thread concurrency
  
  [[analyzers]]
  method = "ngram-word"
  ngram = 1
  filter = "default-unigram-chain"
  
  [query-runner]
  #query-judgements = "../data/ceeaus-qrels.txt"  # uncomment to run IR eval
  query-path = "../queries.txt"  # create this file!
  trec-format = false            # default: false
  max-results = 10               # default: 10
  query-id-start = 1             # default: 1
  
  [ranker]
  method = "bm25"
  k1 = 1.2
  b = 0.75
  k3 = 500
  
  [classifier]
  method = "one-vs-all"
  [classifier.base]
  method = "sgd"
  loss = "hinge"
  
  [regressor]
  method = "sgd"
  loss = "least-squares"
  
  [lda]
  inference = "gibbs"
  max-iters = 1000
  alpha = 1.0
  beta = 1.0
  topics = 4
  model-prefix = "lda-model"
  
  [crf]
  prefix = "crf"
  treebank = "penn-treebank" # relative to data prefix
  corpus = "wsj"
  section-size = 99
  train-sections = [0, 18]
  dev-sections = [19, 21]
  test-sections = [22, 24]
  
  [language-model]
  arpa-file = "../data/english-sentences.arpa"
  binary-file-prefix = "english-sentences-"
  
  [diff]
  n-value = 3
  max-edits = 3
  # penalty defaults are all zero (no penalty)
  base-penalty = 0.0 # base penalty is for any edit
  insert-penalty = 0.0
  substitute-penalty = 0.0
  remove-penalty = 0.0
  
  [features]
  method = "info-gain"
  prefix = "features"
  features-per-class = 20
  
  [sequence]
  prefix = "perceptron-tagger"
  treebank = "penn-treebank" # relative to data prefix
  corpus = "wsj"
  section-size = 99
  train-sections = [0, 18]
  dev-sections = [19, 21]
  test-sections = [22, 24]
  
  [parser]
  prefix = "parser"
  treebank = "penn-treebank" # relative to data prefix
  corpus = "wsj"
  section-size = 99
  train-sections = [2, 21]
  dev-sections = [22, 22]
  test-sections = [23, 23]
  
  [embeddings]
  prefix = "word-embeddings"
  filter = [{type = "icu-tokenizer", suppress-tags = true}, {type = "lowercase"}]
  vector-size = 50
  [embeddings.vocab]
  min-count = 10
  max-size = 500000
  