Can anybody explain result below but I have.. I could not understand any of it. I delete repeated output with ……… to make the question a bit smaller. Hope I will get answer sooner
| Task presenting examples to AnnotatorLearner: 1 document(s) in 1.83 sec
| Task presenting examples to AnnotatorLearner: 2 document(s) in 3.56 sec
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| Task presenting examples to AnnotatorLearner: 69 document(s) in 166.73 sec
| Task presenting examples to AnnotatorLearner: 70 document(s) in 167.91 sec
Task training semi-markov voted-perceptron: 0.29% (1/350 sequences) in 1.56 sec
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Task training semi-markov voted-perceptron: 19.71% (69/350 sequences) in 137.30 sec
Task training semi-markov voted-perceptron: 20.00% (70/350 sequences) in 138.30 sec
Epoch 0: sequenceErr=60 transitionErrors=3335/90433
| Task training semi-markov voted-perceptron: 20.29% (71/350 sequences) in 139.50 sec
| Task training semi-markov voted-perceptron: 20.57% (72/350 sequences) in 140.65 sec
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| Task training semi-markov voted-perceptron: 39.71% (139/350 sequences) in 273.91 sec
| Task training semi-markov voted-perceptron: 40.00% (140/350 sequences) in 274.92 sec
Everything before "Test partition 1" are logs/debug outputs by minorthird and the specific learning algorithm - in this case VotedPerceptron. You can learn more about voted perceptron by using google scholar.
Test partition shows you the evaluation of the performance of the extractor on the particular test partition. You can read about the different between Token- and Span- Precision and Recall on a previous thread:
Hi,
Can anybody explain result below but I have.. I could not understand any of it. I delete repeated output with ……… to make the question a bit smaller. Hope I will get answer sooner
| Task presenting examples to AnnotatorLearner: 1 document(s) in 1.83 sec
| Task presenting examples to AnnotatorLearner: 2 document(s) in 3.56 sec
………………………………………………………………………………………………………..
………………………………………………………………………………………………………..
| Task presenting examples to AnnotatorLearner: 69 document(s) in 166.73 sec
| Task presenting examples to AnnotatorLearner: 70 document(s) in 167.91 sec
Epoch 0: sequenceErr=60 transitionErrors=3335/90433
| Task training semi-markov voted-perceptron: 20.29% (71/350 sequences) in 139.50 sec
| Task training semi-markov voted-perceptron: 20.57% (72/350 sequences) in 140.65 sec
…………………………………………………………………………………………………………………………
………………………………………………………………………………………………………………………..
| Task training semi-markov voted-perceptron: 39.71% (139/350 sequences) in 273.91 sec
| Task training semi-markov voted-perceptron: 40.00% (140/350 sequences) in 274.92 sec
Epoch 1: sequenceErr=52 transitionErrors=1161/90433
Epoch 3: sequenceErr=35 transitionErrors=654/90433
Epoch 4: sequenceErr=35 transitionErrors=608/90433
| Task tagging with segmenter: 1 document(s) in 7.82 sec
| Task tagging with segmenter: 3 document(s) in 10.88 sec
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| Task tagging with segmenter: 30 document(s) in 116.77 sec
| Task tagging with segmenter: 31 document(s) in 117.84 sec
Test partition 1:
TokenPrecision: 1.0000 TokenRecall: 0.0960 F: 0.1752
SpanPrecision: 1.0000 SpanRecall: 0.0845 F: 0.1558
Task train/test experiment: 100.00% (1/1 folds) in 982.71 sec
Overall performance:
TokenPrecision: 1.0000 TokenRecall: 0.0960 F: 0.1752
SpanPrecision: 1.0000 SpanRecall: 0.0845 F: 0.1558
Total time for task: 982.735 sec
Prakash
Everything before "Test partition 1" are logs/debug outputs by minorthird and the specific learning algorithm - in this case VotedPerceptron. You can learn more about voted perceptron by using google scholar.
Test partition shows you the evaluation of the performance of the extractor on the particular test partition. You can read about the different between Token- and Span- Precision and Recall on a previous thread:
https://sourceforge.net/projects/minorthird/forums/forum/358215/topic/2826472
The overall performance is the same as the test partition 1 because you have only 1 test partition.