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hello~ 我这边使用train_xnli.sh的默认参数进行了训练,最终得到平均acc为78.63%,和论文中的79.9%的结果有些出入,且看上去应该是每种语言的acc都低了一点。请问下可能是什么原因导致的呢?非常感谢。 我这边是用的单核GPU,采用Tesla M40 24GB卡,Driver Version: 440.64,CUDA Version: 10.2 我从和论文中的fine-tune参数对比上看。 一个是sh代码里面使用的epoch=2,论文中是用的3/5/10选的 另一个是这里TOTAL_BATCH_SIZE=64,BATCH_SIZE=2,论文中是16/32/64选的 会不会是这两个超参导致的出入呢? 非常感谢~
The text was updated successfully, but these errors were encountered:
@zhuchenxi hello! 以下是我们实验的参数设置:
LR=2e-5 EPOCH=2 TOTAL_BATCH_SIZE=64
实验环境配置是:torch Version:1.3.1;transformers Version: 2.3.0;GPU:Tesla V100 16GB;CUDA Version:10.0.130
实验结果在英语的dev集上通过acc选择最优模型(模型会保存到 checkpoint-best 目录下),然后用该模型对测试集进行预测,以下是我们复现的结果:
ar=0.7868263473053893 bg=0.8291417165668663 de=0.8235528942115768 el=0.8191616766467066 en=0.883433133732535 es=0.8471057884231536 fr=0.8297405189620759 hi=0.7606786427145709 ru=0.8011976047904191 sw=0.7351297405189621 th=0.7590818363273453 tr=0.7888223552894211 ur=0.7153692614770459 vi=0.7998003992015968 zh=0.7918163672654691 ===== Avg_lang_acc =0.7980572188955422
有问题欢迎随时交流,十分感谢!
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hello~ 我这边使用train_xnli.sh的默认参数进行了训练,最终得到平均acc为78.63%,和论文中的79.9%的结果有些出入,且看上去应该是每种语言的acc都低了一点。请问下可能是什么原因导致的呢?非常感谢。
我这边是用的单核GPU,采用Tesla M40 24GB卡,Driver Version: 440.64,CUDA Version: 10.2
我从和论文中的fine-tune参数对比上看。
一个是sh代码里面使用的epoch=2,论文中是用的3/5/10选的
另一个是这里TOTAL_BATCH_SIZE=64,BATCH_SIZE=2,论文中是16/32/64选的
会不会是这两个超参导致的出入呢?
非常感谢~
The text was updated successfully, but these errors were encountered: