请问使用的Dynet版本是多少？

Traceback (most recent call last):
File "train.py", line 28, in
word_proportion= 0.5
File "/home/lyt/workspace/CWS/greedyCWS-master/src/model.py", line 187, in dy_train_model
cws = CWS(Cemb,character_idx_map,options)
File "/home/lyt/workspace/CWS/greedyCWS-master/src/model.py", line 18, in init
self.trainer = dy.MomentumSGDTrainer(model,options['lr'],options['momentum'],options['edecay']) # we use Momentum SGD
File "_dynet.pyx", line 5878, in _dynet.MomentumSGDTrainer.cinit
TypeError: cinit() takes at most 3 positional arguments (4 given)

请问这是什么错误？

在重现PKU测试文本实验结果时，遇到下列问题：
论文源码（未修改）

cws.trainer.update_epoch(1.)
end_time = time.time()
print 'Trained %s epoch(s) (%d samples) took %.lfs per epoch'%(eidx+1,nsamples,(end_time-start_time)/(eidx+1))
test(cws,dev_file,'../result/dev_result%d'%(eidx+1))
os.system('python score.py %s %d %d'%(dev_file,eidx+1,eidx+1))
cws.save('epoch%d'%(eidx+1))
print 'Current model saved'
按照README.md 运行代码，测试PKU，F1值95.1

[@gpu01 src]$ ./score ../data/pku_train ../data/pku_test ../result/dev/dev_result28 > ../../result/score/greedy_pku_test_seg.utf8
[@gpu01 src]$ tail -n 14 ../../result/score/greedy_pku_test_seg.utf8
=== SUMMARY:
=== TOTAL INSERTIONS: 1766
=== TOTAL DELETIONS: 1496
=== TOTAL SUBSTITUTIONS: 3477
=== TOTAL NCHANGE: 6739
=== TOTAL TRUE WORD COUNT: 104371
=== TOTAL TEST WORD COUNT: 104641
=== TOTAL TRUE WORDS RECALL: 0.952
=== TOTAL TEST WORDS PRECISION: 0.950
=== F MEASURE: 0.951
=== OOV Rate: 0.927
=== OOV Recall Rate: 0.950
=== IV Recall Rate: 0.988
../result/dev/dev_result28 1766 1496 3477 6739 104371 104641 0.952 0.950 0.951 0.927 0.950 0.988

1、但是，test(cws,dev_file,'../result/dev_result%d'%(eidx+1))，测试文件dev_file是../data/pku_test文件，这个pku_test是已经分词的，test函数输出分词结果保存为../result/dev_result%d'%(eidx+1)，然后运行score评分脚本，=== F MEASURE: 0.951 是分词结果和pku_test对比得出。

2、分词结果和标准分割icwb2-data/gold/pku_test_gold.utf8 对比=== F MEASURE: 0.843

3、运行test模式，load最好的模型，测试文件为未分词的icwb2-data/testing/pku_test.utf8，得出的分词结果和标准分割icwb2-data/gold/pku_test_gold.utf8对比是F值是79。

这是我疑惑的地方，是不是1中得出的评分结果有失偏颇，因为一是使用已分词的文本pku_test做测试，二是使用这个已分词文本作为标准分割。

小白一枚，如有问题，多多包涵