微博数据集
FitlogCallback evaluation on data-test:
SpanFPreRecMetric: f=0.577723, pre=0.591479, rec=0.564593
label_acc: acc=0.957026
Evaluation on dev at Epoch 50/50. Step:6750/6750:
SpanFPreRecMetric: f=0.627097, pre=0.629534, rec=0.624679
label_acc: acc=0.961509
In Epoch:48/Step:6480, got best dev performance:
SpanFPreRecMetric: f=0.640327, pre=0.681159, rec=0.604113
label_acc: acc=0.964209
Reloaded the best model.
resume数据集
FitlogCallback evaluation on data-test:
SpanFPreRecMetric: f=0.947818, pre=0.942927, rec=0.952761
label_acc: acc=0.966689
Evaluation on dev at Epoch 50/50. Step:19150/19150:
SpanFPreRecMetric: f=0.938288, pre=0.932103, rec=0.944556
label_acc: acc=0.969474
In Epoch:22/Step:8426, got best dev performance:
SpanFPreRecMetric: f=0.941294, pre=0.934783, rec=0.947896
label_acc: acc=0.966883
Reloaded the best model.
您好，我跑了一下这两个数据集，可是数据结果并没有达到论文的水平，resume的第22个epoch为f=0.944733, pre=0.940426, rec=0.94908，weibo的第48个epoch为f=0.597132, pre=0.65616, rec=0.547847。请问可能是什么原因呢？是因为我用的不是标准的在线新华字典的原因嘛？
微博数据集
FitlogCallback evaluation on data-test:
SpanFPreRecMetric: f=0.577723, pre=0.591479, rec=0.564593
label_acc: acc=0.957026
Evaluation on dev at Epoch 50/50. Step:6750/6750:
SpanFPreRecMetric: f=0.627097, pre=0.629534, rec=0.624679
label_acc: acc=0.961509
In Epoch:48/Step:6480, got best dev performance:
SpanFPreRecMetric: f=0.640327, pre=0.681159, rec=0.604113
label_acc: acc=0.964209
Reloaded the best model.
resume数据集
FitlogCallback evaluation on data-test:
SpanFPreRecMetric: f=0.947818, pre=0.942927, rec=0.952761
label_acc: acc=0.966689
Evaluation on dev at Epoch 50/50. Step:19150/19150:
SpanFPreRecMetric: f=0.938288, pre=0.932103, rec=0.944556
label_acc: acc=0.969474
In Epoch:22/Step:8426, got best dev performance:
SpanFPreRecMetric: f=0.941294, pre=0.934783, rec=0.947896
label_acc: acc=0.966883
Reloaded the best model.
您好，我跑了一下这两个数据集，可是数据结果并没有达到论文的水平，resume的第22个epoch为f=0.944733, pre=0.940426, rec=0.94908，weibo的第48个epoch为f=0.597132, pre=0.65616, rec=0.547847。请问可能是什么原因呢？是因为我用的不是标准的在线新华字典的原因嘛？