Comments (7)
hi,您好
可能是因为随机种子的原因,其他超参数应该不用调;事实上,我的超参数大部分是参考Lattice LSTM的,也没有经过微调。
不过,您说效果偏低,我表示很疑惑,因为后面代码是我跑过之后传上去的,效果大部分比论文还要好。但我是分开跑的,不知道是不是整合的时候出现了一些问题。
我看了你发的日志文件,Ontonote4里面差别挺大的,我将Ontonote4的average策略的代码上传到云盘,你可以再试一试!
链接:https://pan.baidu.com/s/1CXnUwvtPbkjMDUIIWNNU3w
提取码:gv2y
希望对比有帮助!
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另外,我还从github上直接clone下来代码,跑了一下weibo的实验,用average策略,我只跑了23大的epoch,效果就已经很明显了,下面是结果的截图:
Epoch: 19 training finished. Time: 139.70s, speed: 9.66st/s, total loss: 2160.899757385254
gold_num = 389 pred_num = 364 right_num = 235
Dev: time: 2.02s, speed: 134.20st/s; acc: 0.9595, p: 0.6456, r: 0.6041, f: 0.6242
Exceed previous best f score: 0.6231884057971016
gold_num = 418 pred_num = 357 right_num = 232
Test: time: 1.84s, speed: 147.24st/s; acc: 0.9558, p: 0.6499, r: 0.5550, f: 0.5987
Epoch: 20/50
Learning rate is setted as: 0.005377288836128128
Instance: 1350; Time: 142.40s; loss: 2099.8514; acc: 72292.0/73778.0=0.9799
Epoch: 20 training finished. Time: 142.40s, speed: 9.48st/s, total loss: 2099.851402282715
gold_num = 389 pred_num = 347 right_num = 221
Dev: time: 2.09s, speed: 129.88st/s; acc: 0.9586, p: 0.6369, r: 0.5681, f: 0.6005
gold_num = 418 pred_num = 330 right_num = 218
Test: time: 2.21s, speed: 122.77st/s; acc: 0.9549, p: 0.6606, r: 0.5215, f: 0.5829
Epoch: 21/50
Learning rate is setted as: 0.005108424394321722
Instance: 1350; Time: 139.88s; loss: 2050.2969; acc: 72306.0/73778.0=0.9800
Epoch: 21 training finished. Time: 139.88s, speed: 9.65st/s, total loss: 2050.2968673706055
gold_num = 389 pred_num = 339 right_num = 228
Dev: time: 2.05s, speed: 132.50st/s; acc: 0.9604, p: 0.6726, r: 0.5861, f: 0.6264
Exceed previous best f score: 0.6241699867197875
gold_num = 418 pred_num = 337 right_num = 224
Test: time: 1.85s, speed: 146.41st/s; acc: 0.9568, p: 0.6647, r: 0.5359, f: 0.5934
Epoch: 22/50
Learning rate is setted as: 0.004853003174605635
Instance: 1350; Time: 141.37s; loss: 2057.7770; acc: 72323.0/73778.0=0.9803
Epoch: 22 training finished. Time: 141.37s, speed: 9.55st/s, total loss: 2057.776954650879
gold_num = 389 pred_num = 347 right_num = 233
Dev: time: 2.14s, speed: 126.53st/s; acc: 0.9603, p: 0.6715, r: 0.5990, f: 0.6332
Exceed previous best f score: 0.6263736263736264
gold_num = 418 pred_num = 329 right_num = 225
Test: time: 2.04s, speed: 132.74st/s; acc: 0.9565, p: 0.6839, r: 0.5383, f: 0.6024
Epoch: 23/50
Learning rate is setted as: 0.004610353015875353
Instance: 1350; Time: 141.84s; loss: 1966.1277; acc: 72389.0/73778.0=0.9812
Epoch: 23 training finished. Time: 141.84s, speed: 9.52st/s, total loss: 1966.1277313232422
gold_num = 389 pred_num = 327 right_num = 218
Dev: time: 2.13s, speed: 127.30st/s; acc: 0.9596, p: 0.6667, r: 0.5604, f: 0.6089
gold_num = 418 pred_num = 314 right_num = 220
Test: time: 1.93s, speed: 140.35st/s; acc: 0.9565, p: 0.7006, r: 0.5263, f: 0.6011
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好的,我再试一下,非常感谢您的耐心回复!
另外,请问您用github上clone下来的代码跑的这个weibo的实验是哪个数据集?all,nm还是ne?
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all的数据集
from ccw-ner.
您好,抱歉再次打扰您,我在weibo.all数据集上还是没能重现出您论文汇报的结果。我对比了我的log日志和您的日志,发现我test数据集和您test数据集的gold_num不同,因此怀疑数据集存在差异。下面是我拿到的weibo.all数据集,请问是否方便分享一下您使用的weibo all数据集,以对比一下是否有哪些不同。非常感谢!
train.all.bmes.txt
dev.all.bmes.txt
test.all.bmes.txt
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我的weibo数据集已经传到github了呀
from ccw-ner.
哦哦好的。抱歉,之前没注意。。。
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Related Issues (13)
- 求助,如何解决警告信息?
- 关于weibo数据集的问题 HOT 1
- 您好,ontonotes4的数据我申请到了,但是不能得到ner数据集
- 关于数据集 HOT 1
- 关于batch化的问题 HOT 1
- 您好,ontonotes4的数据我申请到了,可是拿不出NER的data HOT 6
- 您好,请问这个实验中的msra数据和ontonote4数据的划分情况可以详细说说吗? HOT 3
- 取最长最短词向量那段代码有些疑问
- pre, recall, f1计算结果存在错误 HOT 8
- how to load the .th and do the prediction?
- 您好 我想问一下 关于每个词对应的word的数量 HOT 1
- 有关词id拼接到单词末尾还是开始位置
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