Comments (5)
me too...
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me,too
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me,too
I found that this problem can be solved by modifying the learning rate,
# learning rate
self.fin_tuning_lr = 1e-4
self.downs_en_lr = 1e-3
self.clip_grad = 2.
self.drop_prob = 0.3 # dropout
self.weight_decay_rate = 0.01
self.warmup_prop = 0.1
self.gradient_accumulation_steps = 2
I change it to
self.fin_tuning_lr = 5e-5
self.downs_en_lr = 5e-4
You can modify it according to your needs.
But the f1 value is very low, only around 0.2. If you have resolved the issue with f1 value, please notify me.
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from prgc.
Related Issues (20)
- 关于实验结果达不到论文所提 HOT 2
- Some issues w.r.t the dataset.
- 参数意义 HOT 1
- bert_config.json HOT 1
- grad can be implicitly created only for scalar outputs
- evaluate时gather_map 收集不同GPU数据时zip报错
- 三元组提取 HOT 1
- x_i HOT 1
- Chinese field HOT 23
- 没有开接口的文件吗?
- 显卡选择 HOT 1
- After training one epoch, got an error HOT 5
- rel HOT 1
- 数据类型错误 'NoneType' *int
- 关于训练一轮后 evaluate.py 测试结果 准确率 f1值全是0 输出pre预测值都没有什么情况 HOT 7
- 关于模型运行速度的问题
- python train.py出现错误,寻求解决 HOT 9
- 求助中文 HOT 6
- 训练时,10个epoch中第七个epoch,loss_mat就为0了,请问如何设置超参数平衡各个loss HOT 1
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