Comments (1)
你好,你解决了嘛。
这个应该是你标注的标签的数量跟原来的B-LOC,B-ORG标签数量不匹配造成的。请问你知道为什么自己标注的数据在训练集里显示数量为0吗。
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Related Issues (20)
- ValueError: Tensor's shape (300, 800) is not compatible with supplied shape (768, 800) HOT 5
- 换分词库 HOT 1
- 用了自己的标注数据集 结果报错 HOT 2
- tensorflow版本与tensorflow-addons版本冲突 HOT 1
- 导入自己标注的文本,显示数据集为0的问题 HOT 7
- 导入自建数据集,出现training set size: 0, validating set size: 0 HOT 2
- 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
- Idcnn+crf 无法训练 HOT 1
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