Comments (11)
您可以试试epoch为1,train_batch_size为64看结果是否正常
from deepke.
您可以试试epoch为1,train_batch_size为64看结果是否正常
由于计算资源限制,我只能设置epoch为1,train_batch_size为32的结果,如下:
[2024-01-19 23:06:14,854][main][INFO] - ***** Eval results *****
[2024-01-19 23:06:14,854][main][INFO] -
precision recall f1-score support
好转 0.00 0.00 0.00 69
无法判断 0.00 0.00 0.00 17
未愈 0.00 0.00 0.00 22
痊愈 0.00 0.00 0.00 1
micro avg 0.00 0.00 0.00 109
macro avg 0.00 0.00 0.00 109
weighted avg 0.00 0.00 0.00 109
from deepke.
您好,看结果可能数据量只有100多条,太少了,epoch调大多训练几个epoch(GPU内存限制只会影响batch_size大小)。推荐使用fewshot NER
from deepke.
您好,看结果可能数据量只有100多条,太少了,epoch调大多训练几个epoch(GPU内存限制只会影响batch_size大小)。推荐使用fewshot NER
这个是验证集上的,训练集大概300,然后我epoch设置的是200,我觉得应该是够的,我用另外一个run_lstmcrf.py可以正常跑出结果。
from deepke.
对于bert来说可能样本有点少了,建议您多收集一些数据样本再试试。
from deepke.
请问您的问题是否解决?
from deepke.
请问您的问题是否解决?
暂时未解决,令我不解的是在训练集上的f1也都是0,预测出来的都是标签‘O’
from deepke.
您重新pull最新的代码,数据量扩大一些,多跑几个epoch再测下训练集效果,如果仍未全0请联系我们。
from deepke.
请问您的问题解决了吗
from deepke.
请问您的问题解决了吗
结果还是全0,我再仔细检查下是否是我数据方面的问题,之后有问题的话再进行咨询,感谢解答!
from deepke.
好的,您有问题可以随时问
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