Comments (1)
3%|▎ | 20/725 [00:03<02:07, 5.53it/s]training batch: 20, loss: 21.37336, precision: 0.000 recall: 0.000 f1: 0.000 accuracy: 0.890 6%|▌ | 40/725 [00:07<01:55, 5.94it/s]training batch: 40, loss: 1131.92053, precision: 0.000 recall: 0.000 f1: 0.000 accuracy: 0.856 8%|▊ | 60/725 [00:11<02:00, 5.52it/s]training batch: 60, loss: 208.67366, precision: 0.000 recall: 0.000 f1: 0.000 accuracy: 0.889 11%|█ | 80/725 [00:15<01:57, 5.47it/s]training batch: 80, loss: 19.80162, precision: 0.000 recall: 0.000 f1: 0.000 accuracy: 0.880 14%|█▍ | 100/725 [00:18<01:43, 6.02it/s]training batch: 100, loss: 15.38830, precision: 0.000 recall: 0.000 f1: 0.000 accuracy: 0.899 17%|█▋ | 120/725 [00:22<01:45, 5.73it/s]training batch: 120, loss: 17.61979, precision: 0.000 recall: 0.000 f1: 0.000 accuracy: 0.875 各项数据都为0 ,数据是本项目提供的,仅仅是改成训练模式。
增大学习率,或者前面加一个Bert.
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Related Issues (20)
- 自己重新训练报错 HOT 5
- 替换为自己的数据后,出现错误 HOT 1
- python版本 HOT 2
- 代码问题 HOT 1
- 更换预训练模型,比如albert HOT 3
- 为什么每次predict的结果会不同呢 HOT 1
- 当我训练完bilstm-crf和idcnn-crf模型的时候,开始训练bert-bilstm-crf模型,老是报错 HOT 2
- 使用微调的时候,训练老是0,为什么 HOT 1
- 将自己的数据集分为四类替换进去并且修改了suffix里面的标签为自己的标签 ,出现如下错误 HOT 2
- tensorflow.python.framework.errors_impl.InvalidArgumentError: indices[24,95] = -7 is not in [0, 100)出现这个问题应该怎么办 HOT 1
- idcnn训练时出现准确率和召回率全0 HOT 2
- 将bert微调后的模型进行迁移,迁移后预测就结果就会错误,而在原来的机器上是正常的 HOT 1
- Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on. HOT 1
- ValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on. HOT 1
- TypeError: 'NoneType' object is not callable
- 如何训练自己的数据集 HOT 1
- equests.exceptions.SSLError:
- 代码是否可以用在英文数据集上
- 无法生成lab2id和token2id两个文件 HOT 1
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