 | Run 7403 |
| Creator | njakob |
| Program | miralium_bigram |
| Dataset | jdpa-bnp-pos BIO |
| Task type | SequenceTagging |
| Created | 2y227d ago |
Done! 
| 1m33s | |
| 1176M | |
| SequenceTagging | |
| 0.926 | |
| 0.490 | |
| 0.910 | |
| 0.355 | |
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: 300 counting: 361 unigrams: 359639, bigrams: 1836 unigrams: 132995, bigrams: 1566, cutoff: 2 labels: 3 model: 413079 weights iteration 0 train: 100 examples, terr=0.2137 fscore=0.7863 train: 200 examples, terr=0.1904 fscore=0.8096 train: 300 examples, terr=0.1809 fscore=0.8191 train: 361 examples, terr=0.1815 fscore=0.8185 iteration 1 train: 100 examples, terr=0.1551 fscore=0.8449 train: 200 examples, terr=0.1546 fscore=0.8454 train: 300 examples, terr=0.1512 fscore=0.8488 train: 361 examples, terr=0.1530 fscore=0.8470 iteration 2 train: 100 examples, terr=0.1387 fscore=0.8613 train: 200 examples, terr=0.1439 fscore=0.8561 train: 300 examples, terr=0.1426 fscore=0.8574 train: 361 examples, terr=0.1443 fscore=0.8557 iteration 3 train: 100 examples, terr=0.1386 fscore=0.8614 train: 200 examples, terr=0.1447 fscore=0.8553 train: 300 examples, terr=0.1439 fscore=0.8561 train: 361 examples, terr=0.1446 fscore=0.8554 iteration 4 train: 100 examples, terr=0.1530 fscore=0.8470 train: 200 examples, terr=0.1501 fscore=0.8499 train: 300 examples, terr=0.1488 fscore=0.8512 train: 361 examples, terr=0.1483 fscore=0.8517 iteration 5 train: 100 examples, terr=0.1423 fscore=0.8577 train: 200 examples, terr=0.1405 fscore=0.8595 train: 300 examples, terr=0.1368 fscore=0.8632 train: 361 examples, terr=0.1378 fscore=0.8622 iteration 6 train: 100 examples, terr=0.1239 fscore=0.8761 train: 200 examples, terr=0.1321 fscore=0.8679 train: 300 examples, terr=0.1349 fscore=0.8651 train: 361 examples, terr=0.1361 fscore=0.8639 iteration 7 train: 100 examples, terr=0.1198 fscore=0.8802 train: 200 examples, terr=0.1326 fscore=0.8674 train: 300 examples, terr=0.1354 fscore=0.8646 train: 361 examples, terr=0.1362 fscore=0.8638 iteration 8 train: 100 examples, terr=0.1214 fscore=0.8786 train: 200 examples, terr=0.1274 fscore=0.8726 train: 300 examples, terr=0.1292 fscore=0.8708 train: 361 examples, terr=0.1311 fscore=0.8689 iteration 9 train: 100 examples, terr=0.1338 fscore=0.8662 train: 200 examples, terr=0.1333 fscore=0.8667 train: 300 examples, terr=0.1313 fscore=0.8687 train: 361 examples, terr=0.1295 fscore=0.8705 writing model: model === END program1: ./run learn ../dataset2/train --- OK [68s] ===== 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 [1s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out reading model: model === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [12s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [8s] ===== 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 [6s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [3s] real 1m38.114s user 1m23.377s sys 0m3.536s
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