Performance on Ontonotes v5.0

[hary00078]

Hi

First of all, Thanks for your last reply.
As your command I execute model with Ontonotes v5.0.
Although your official f1-score is 88.16%, I always get 85%.
When I execute your model with UD, I got very good performance. So I think I have something mistake.

It is my command.
python main.py --learning_rate 0.01 --lr_decay 0.035 --dropout 0.5 --hidden_dim 400 --lstm_layer 4 --momentum 0.9 --whether_clip_grad True --clip_grad 5.0 --train_dir 'data/onto.train.txt' --dev_dir 'data/onto.development.txt' --test_dir 'data/onto.test.txt' --model_dir 'model/' --word_emb_dir 'glove.6B.100d.txt'

It is summary.
DATA SUMMARY START:
I/O:
Tag scheme: BIO
MAX SENTENCE LENGTH: 250
MAX WORD LENGTH: -1
Number normalized: False
Word alphabet size: 69812
Char alphabet size: 119
Label alphabet size: 38
Word embedding dir: glove.6B.100d.txt
Char embedding dir: None
Word embedding size: 100
Char embedding size: 30
Norm word emb: False
Norm char emb: False
Train file directory: data/onto.train.txt
Dev file directory: data/onto.development.txt
Test file directory: data/onto.test.txt
Raw file directory: None
Dset file directory: None
Model file directory: model/
Loadmodel directory: None
Decode file directory: None
Train instance number: 115812
Dev instance number: 15679
Test instance number: 12217
Raw instance number: 0
FEATURE num: 0
++++++++++++++++++++++++++++++++++++++++
Model Network:
Model use_crf: False
Model word extractor: LSTM
Model use_char: True
Model char extractor: LSTM
Model char_hidden_dim: 50
++++++++++++++++++++++++++++++++++++++++
Training:
Optimizer: SGD
Iteration: 100
BatchSize: 10
Average batch loss: False
++++++++++++++++++++++++++++++++++++++++
Hyperparameters:
Hyper lr: 0.01
Hyper lr_decay: 0.035
Hyper HP_clip: 5.0
Hyper momentum: 0.9
Hyper l2: 1e-08
Hyper hidden_dim: 400
Hyper dropout: 0.5
Hyper lstm_layer: 4
Hyper bilstm: True
Hyper GPU: True
DATA SUMMARY END.

I think I follow the hyperparameters well written in your paper.
Is there any mistake?

Thanks for reading.