ServerRun 9802
Creatorinternal
Programmiralium
DatasetNikhilDatasetTokenWithPTBT
Task typeSequenceTagging
Created6y348d ago
Done! Flag_green
5s
1177M
SequenceTagging
0.960
0
0.951
0

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
read 3 templates from "featureTemplates"

counting: 0
counting: 100
counting: 200
counting: 268
unigrams: 1863, bigrams: 1
unigrams: 429, bigrams: 1, cutoff: 2
labels: 4
model: 1732 weights
iteration 0

  train: 100 examples, terr=0.1055 fscore=0.8945
  train: 200 examples, terr=0.0925 fscore=0.9075
  train: 268 examples, terr=0.0876 fscore=0.9124
iteration 1

  train: 100 examples, terr=0.0903 fscore=0.9097
  train: 200 examples, terr=0.0799 fscore=0.9201
  train: 268 examples, terr=0.0796 fscore=0.9204
iteration 2

  train: 100 examples, terr=0.0783 fscore=0.9217
  train: 200 examples, terr=0.0759 fscore=0.9241
  train: 268 examples, terr=0.0734 fscore=0.9266
iteration 3

  train: 100 examples, terr=0.0949 fscore=0.9051
  train: 200 examples, terr=0.0825 fscore=0.9175
  train: 268 examples, terr=0.0787 fscore=0.9213
iteration 4

  train: 100 examples, terr=0.0797 fscore=0.9203
  train: 200 examples, terr=0.0751 fscore=0.9249
  train: 268 examples, terr=0.0717 fscore=0.9283
iteration 5

  train: 100 examples, terr=0.0765 fscore=0.9235
  train: 200 examples, terr=0.0712 fscore=0.9288
  train: 268 examples, terr=0.0701 fscore=0.9299
iteration 6

  train: 100 examples, terr=0.0760 fscore=0.9240
  train: 200 examples, terr=0.0696 fscore=0.9304
  train: 268 examples, terr=0.0672 fscore=0.9328
iteration 7

  train: 100 examples, terr=0.0742 fscore=0.9258
  train: 200 examples, terr=0.0714 fscore=0.9286
  train: 268 examples, terr=0.0682 fscore=0.9318
iteration 8

  train: 100 examples, terr=0.0728 fscore=0.9272
  train: 200 examples, terr=0.0699 fscore=0.9301
  train: 268 examples, terr=0.0682 fscore=0.9318
iteration 9

  train: 100 examples, terr=0.0774 fscore=0.9226
  train: 200 examples, terr=0.0712 fscore=0.9288
  train: 268 examples, terr=0.0678 fscore=0.9322
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 [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [1s]

===== 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 [0s]


real	0m6.328s
user	0m1.384s
sys	0m0.260s

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