ServerRun 8241
Creatorinternal
Programmiralium_bigram
DatasetNikhilDatasetTokenWithPTBT
Task typeSequenceTagging
Created6y349d ago
Done! Flag_green
2s
1172M
SequenceTagging
0.990
0
0.959
0

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
read 22 templates from "featureTemplates"

counting: 0
counting: 100
counting: 200
counting: 268
unigrams: 24867, bigrams: 641
unigrams: 6532, bigrams: 440, cutoff: 2
labels: 4
model: 33168 weights
iteration 0

  train: 100 examples, terr=0.0963 fscore=0.9037
  train: 200 examples, terr=0.0757 fscore=0.9243
  train: 268 examples, terr=0.0715 fscore=0.9285
iteration 1

  train: 100 examples, terr=0.0640 fscore=0.9360
  train: 200 examples, terr=0.0588 fscore=0.9412
  train: 268 examples, terr=0.0550 fscore=0.9450
iteration 2

  train: 100 examples, terr=0.0419 fscore=0.9581
  train: 200 examples, terr=0.0430 fscore=0.9570
  train: 268 examples, terr=0.0439 fscore=0.9561
iteration 3

  train: 100 examples, terr=0.0350 fscore=0.9650
  train: 200 examples, terr=0.0330 fscore=0.9670
  train: 268 examples, terr=0.0317 fscore=0.9683
iteration 4

  train: 100 examples, terr=0.0355 fscore=0.9645
  train: 200 examples, terr=0.0345 fscore=0.9655
  train: 268 examples, terr=0.0309 fscore=0.9691
iteration 5

  train: 100 examples, terr=0.0322 fscore=0.9678
  train: 200 examples, terr=0.0258 fscore=0.9742
  train: 268 examples, terr=0.0243 fscore=0.9757
iteration 6

  train: 100 examples, terr=0.0267 fscore=0.9733
  train: 200 examples, terr=0.0216 fscore=0.9784
  train: 268 examples, terr=0.0243 fscore=0.9757
iteration 7

  train: 100 examples, terr=0.0180 fscore=0.9820
  train: 200 examples, terr=0.0163 fscore=0.9837
  train: 268 examples, terr=0.0161 fscore=0.9839
iteration 8

  train: 100 examples, terr=0.0235 fscore=0.9765
  train: 200 examples, terr=0.0182 fscore=0.9818
  train: 268 examples, terr=0.0175 fscore=0.9825
iteration 9

  train: 100 examples, terr=0.0180 fscore=0.9820
  train: 200 examples, terr=0.0127 fscore=0.9873
  train: 268 examples, terr=0.0126 fscore=0.9874
writing model: model
=== END program1: ./run learn ../dataset2/train --- OK [3s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
reading model: model
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
reading model: model
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	0m7.197s
user	0m2.356s
sys	0m0.280s

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