F2023888
commented
4 months ago 如果是的话，请问大家有训练好的re_robert.pth可以提供嘛
Closed F2023888 closed 4 months ago
F2023888
commented
4 months ago 如果是的话，请问大家有训练好的re_robert.pth可以提供嘛
BeasterYong
commented
4 months ago 您好，下载模型后直接运行predict.py即可，请检查一下当前环境中的python包版本是否与requirements.txt中的一致，建议重新执行pip install -r requirements.txt
F2023888
commented
4 months ago 我的predict出了点错误，现在已经解决上述问题啦，但运行范例之后显示 [2024-06-14 16:41:45,756][main][INFO] - "男人的爱" 和 "人生长路" 在句中关系为："目"，置信度为0.04。
F2023888
commented
4 months ago 同时依然存在错误： RuntimeError: Error(s) in loading state_dict for LM: Missing key(s) in state_dict: "bert.encoder.layer.1.attention.self.query.weight", "bert.encoder.layer.1.attention.self.query.bias", "bert.encoder.layer.1.attention.self.key.weight", "bert.encoder.layer.1.attention.self.key.bias", "bert.encoder.layer.1.attention.self.value.weight", "bert.encoder.layer.1.attention.self.value.bias", "bert.encoder.layer.1.attention.output.dense.weight", "bert.encoder.layer.1.attention.output.dense.bias", "bert.encoder.layer.1.attention.output.LayerNorm.weight", "bert.encoder.layer.1.attention.output.LayerNorm.bias", "bert.encoder.layer.1.intermediate.dense.weight", "bert.encoder.layer.1.intermediate.dense.bias", "bert.encoder.layer.1.output.dense.weight", "bert.encoder.layer.1.output.dense.bias", "bert.encoder.layer.1.output.LayerNorm.weight", "bert.encoder.layer.1.output.LayerNorm.bias", "bert.encoder.layer.2.attention.self.query.weight", "bert.encoder.layer.2.attention.self.query.bias", "bert.encoder.layer.2.attention.self.key.weight", "bert.encoder.layer.2.attention.self.key.bias", "bert.encoder.layer.2.attention.self.value.weight", "bert.encoder.layer.2.attention.self.value.bias", "bert.encoder.layer.2.attention.output.dense.weight", "bert.encoder.layer.2.attention.output.dense.bias", "bert.encoder.layer.2.attention.output.LayerNorm.weight", "bert.encoder.layer.2.attention.output.LayerNorm.bias", "bert.encoder.layer.2.intermediate.dense.weight", "bert.encoder.layer.2.intermediate.dense.bias", "bert.encoder.layer.2.output.dense.weight", "bert.encoder.layer.2.output.dense.bias", "bert.encoder.layer.2.output.LayerNorm.weight", "bert.encoder.layer.2.output.LayerNorm.bias", "bert.encoder.layer.3.attention.self.query.weight", "bert.encoder.layer.3.attention.self.query.bias", "bert.encoder.layer.3.attention.self.key.weight", "bert.encoder.layer.3.attention.self.key.bias", "bert.encoder.layer.3.attention.self.value.weight", "bert.encoder.layer.3.attention.self.value.bias", "bert.encoder.layer.3.attention.output.dense.weight", "bert.encoder.layer.3.attention.output.dense.bias", "bert.encoder.layer.3.attention.output.LayerNorm.weight", "bert.encoder.layer.3.attention.output.LayerNorm.bias", "bert.encoder.layer.3.intermediate.dense.weight", "bert.encoder.layer.3.intermediate.dense.bias", "bert.encoder.layer.3.output.dense.weight", "bert.encoder.layer.3.output.dense.bias", "bert.encoder.layer.3.output.LayerNorm.weight", "bert.encoder.layer.3.output.LayerNorm.bias", "bert.encoder.layer.4.attention.self.query.weight", "bert.encoder.layer.4.attention.self.query.bias", "bert.encoder.layer.4.attention.self.key.weight", "bert.encoder.layer.4.attention.self.key.bias", "bert.encoder.layer.4.attention.self.value.weight", "bert.encoder.layer.4.attention.self.value.bias", "bert.encoder.layer.4.attention.output.dense.weight", "bert.encoder.layer.4.attention.output.dense.bias", "bert.encoder.layer.4.attention.output.LayerNorm.weight", "bert.encoder.layer.4.attention.output.LayerNorm.bias", "bert.encoder.layer.4.intermediate.dense.weight", "bert.encoder.layer.4.intermediate.dense.bias", "bert.encoder.layer.4.output.dense.weight", "bert.encoder.layer.4.output.dense.bias", "bert.encoder.layer.4.output.LayerNorm.weight", "bert.encoder.layer.4.output.LayerNorm.bias", "bert.encoder.layer.5.attention.self.query.weight", "bert.encoder.layer.5.attention.self.query.bias", "bert.encoder.layer.5.attention.self.key.weight", "bert.encoder.layer.5.attention.self.key.bias", "bert.encoder.layer.5.attention.self.value.weight", "bert.encoder.layer.5.attention.self.value.bias", "bert.encoder.layer.5.attention.output.dense.weight", "bert.encoder.layer.5.attention.output.dense.bias", "bert.encoder.layer.5.attention.output.LayerNorm.weight", "bert.encoder.layer.5.attention.output.LayerNorm.bias", "bert.encoder.layer.5.intermediate.dense.weight", "bert.encoder.layer.5.intermediate.dense.bias", "bert.encoder.layer.5.output.dense.weight", "bert.encoder.layer.5.output.dense.bias", "bert.encoder.layer.5.output.LayerNorm.weight", "bert.encoder.layer.5.output.LayerNorm.bias", "bert.encoder.layer.6.attention.self.query.weight", "bert.encoder.layer.6.attention.self.query.bias", "bert.encoder.layer.6.attention.self.key.weight", "bert.encoder.layer.6.attention.self.key.bias", "bert.encoder.layer.6.attention.self.value.weight", "bert.encoder.layer.6.attention.self.value.bias", "bert.encoder.layer.6.attention.output.dense.weight", "bert.encoder.layer.6.attention.output.dense.bias", "bert.encoder.layer.6.attention.output.LayerNorm.weight", "bert.encoder.layer.6.attention.output.LayerNorm.bias", "bert.encoder.layer.6.intermediate.dense.weight", "bert.encoder.layer.6.intermediate.dense.bias", "bert.encoder.layer.6.output.dense.weight", "bert.encoder.layer.6.output.dense.bias", "bert.encoder.layer.6.output.LayerNorm.weight", "bert.encoder.layer.6.output.LayerNorm.bias", "bert.encoder.layer.7.attention.self.query.weight", "bert.encoder.layer.7.attention.self.query.bias", "bert.encoder.layer.7.attention.self.key.weight", "bert.encoder.layer.7.attention.self.key.bias", "bert.encoder.layer.7.attention.self.value.weight", "bert.encoder.layer.7.attention.self.value.bias", "bert.encoder.layer.7.attention.output.dense.weight", "bert.encoder.layer.7.attention.output.dense.bias", "bert.encoder.layer.7.attention.output.LayerNorm.weight", "bert.encoder.layer.7.attention.output.LayerNorm.bias", "bert.encoder.layer.7.intermediate.dense.weight", "bert.encoder.layer.7.intermediate.dense.bias", "bert.encoder.layer.7.output.dense.weight", "bert.encoder.layer.7.output.dense.bias", "bert.encoder.layer.7.output.LayerNorm.weight", "bert.encoder.layer.7.output.LayerNorm.bias", "bert.encoder.layer.8.attention.self.query.weight", "bert.encoder.layer.8.attention.self.query.bias", "bert.encoder.layer.8.attention.self.key.weight", "bert.encoder.layer.8.attention.self.key.bias", "bert.encoder.layer.8.attention.self.value.weight", "bert.encoder.layer.8.attention.self.value.bias", "bert.encoder.layer.8.attention.output.dense.weight", "bert.encoder.layer.8.attention.output.dense.bias", "bert.encoder.layer.8.attention.output.LayerNorm.weight", "bert.encoder.layer.8.attention.output.LayerNorm.bias", "bert.encoder.layer.8.intermediate.dense.weight", "bert.encoder.layer.8.intermediate.dense.bias", "bert.encoder.layer.8.output.dense.weight", "bert.encoder.layer.8.output.dense.bias", "bert.encoder.layer.8.output.LayerNorm.weight", "bert.encoder.layer.8.output.LayerNorm.bias", "bert.encoder.layer.9.attention.self.query.weight", "bert.encoder.layer.9.attention.self.query.bias", "bert.encoder.layer.9.attention.self.key.weight", "bert.encoder.layer.9.attention.self.key.bias", "bert.encoder.layer.9.attention.self.value.weight", "bert.encoder.layer.9.attention.self.value.bias", "bert.encoder.layer.9.attention.output.dense.weight", "bert.encoder.layer.9.attention.output.dense.bias", "bert.encoder.layer.9.attention.output.LayerNorm.weight", "bert.encoder.layer.9.attention.output.LayerNorm.bias", "bert.encoder.layer.9.intermediate.dense.weight", "bert.encoder.layer.9.intermediate.dense.bias", "bert.encoder.layer.9.output.dense.weight", "bert.encoder.layer.9.output.dense.bias", "bert.encoder.layer.9.output.LayerNorm.weight", "bert.encoder.layer.9.output.LayerNorm.bias", "bert.encoder.layer.10.attention.self.query.weight", "bert.encoder.layer.10.attention.self.query.bias", "bert.encoder.layer.10.attention.self.key.weight", "bert.encoder.layer.10.attention.self.key.bias", "bert.encoder.layer.10.attention.self.value.weight", "bert.encoder.layer.10.attention.self.value.bias", "bert.encoder.layer.10.attention.output.dense.weight", "bert.encoder.layer.10.attention.output.dense.bias", "bert.encoder.layer.10.attention.output.LayerNorm.weight", "bert.encoder.layer.10.attention.output.LayerNorm.bias", "bert.encoder.layer.10.intermediate.dense.weight", "bert.encoder.layer.10.intermediate.dense.bias", "bert.encoder.layer.10.output.dense.weight", "bert.encoder.layer.10.output.dense.bias", "bert.encoder.layer.10.output.LayerNorm.weight", "bert.encoder.layer.10.output.LayerNorm.bias", "bert.encoder.layer.11.attention.self.query.weight", "bert.encoder.layer.11.attention.self.query.bias", "bert.encoder.layer.11.attention.self.key.weight", "bert.encoder.layer.11.attention.self.key.bias", "bert.encoder.layer.11.attention.self.value.weight", "bert.encoder.layer.11.attention.self.value.bias", "bert.encoder.layer.11.attention.output.dense.weight", "bert.encoder.layer.11.attention.output.dense.bias", "bert.encoder.layer.11.attention.output.LayerNorm.weight", "bert.encoder.layer.11.attention.output.LayerNorm.bias", "bert.encoder.layer.11.intermediate.dense.weight", "bert.encoder.layer.11.intermediate.dense.bias", "bert.encoder.layer.11.output.dense.weight", "bert.encoder.layer.11.output.dense.bias", "bert.encoder.layer.11.output.LayerNorm.weight", "bert.encoder.layer.11.output.LayerNorm.bias". 就是说，能运行但结果不对，请问这个问题该怎么解决呢？
zxlzr
commented
4 months ago 我的predict出了点错误，现在已经解决上述问题啦，但运行范例之后显示 [2024-06-14 16:41:45,756][main][INFO] - "男人的爱" 和 "人生长路" 在句中关系为："目"，置信度为0.04。
置信度0.04 您可以忽略低置信度预测
zxlzr
commented
4 months ago 同时依然存在错误： RuntimeError: Error(s) in loading state_dict for LM: Missing key(s) in state_dict: "bert.encoder.layer.1.attention.self.query.weight", "bert.encoder.layer.1.attention.self.query.bias", "bert.encoder.layer.1.attention.self.key.weight", "bert.encoder.layer.1.attention.self.key.bias", "bert.encoder.layer.1.attention.self.value.weight", "bert.encoder.layer.1.attention.self.value.bias", "bert.encoder.layer.1.attention.output.dense.weight", "bert.encoder.layer.1.attention.output.dense.bias", "bert.encoder.layer.1.attention.output.LayerNorm.weight", "bert.encoder.layer.1.attention.output.LayerNorm.bias", "bert.encoder.layer.1.intermediate.dense.weight", "bert.encoder.layer.1.intermediate.dense.bias", "bert.encoder.layer.1.output.dense.weight", "bert.encoder.layer.1.output.dense.bias", "bert.encoder.layer.1.output.LayerNorm.weight", "bert.encoder.layer.1.output.LayerNorm.bias", "bert.encoder.layer.2.attention.self.query.weight", "bert.encoder.layer.2.attention.self.query.bias", "bert.encoder.layer.2.attention.self.key.weight", "bert.encoder.layer.2.attention.self.key.bias", "bert.encoder.layer.2.attention.self.value.weight", "bert.encoder.layer.2.attention.self.value.bias", "bert.encoder.layer.2.attention.output.dense.weight", "bert.encoder.layer.2.attention.output.dense.bias", "bert.encoder.layer.2.attention.output.LayerNorm.weight", "bert.encoder.layer.2.attention.output.LayerNorm.bias", "bert.encoder.layer.2.intermediate.dense.weight", "bert.encoder.layer.2.intermediate.dense.bias", "bert.encoder.layer.2.output.dense.weight", "bert.encoder.layer.2.output.dense.bias", "bert.encoder.layer.2.output.LayerNorm.weight", "bert.encoder.layer.2.output.LayerNorm.bias", "bert.encoder.layer.3.attention.self.query.weight", "bert.encoder.layer.3.attention.self.query.bias", "bert.encoder.layer.3.attention.self.key.weight", "bert.encoder.layer.3.attention.self.key.bias", "bert.encoder.layer.3.attention.self.value.weight", "bert.encoder.layer.3.attention.self.value.bias", "bert.encoder.layer.3.attention.output.dense.weight", "bert.encoder.layer.3.attention.output.dense.bias", "bert.encoder.layer.3.attention.output.LayerNorm.weight", "bert.encoder.layer.3.attention.output.LayerNorm.bias", "bert.encoder.layer.3.intermediate.dense.weight", "bert.encoder.layer.3.intermediate.dense.bias", "bert.encoder.layer.3.output.dense.weight", "bert.encoder.layer.3.output.dense.bias", "bert.encoder.layer.3.output.LayerNorm.weight", "bert.encoder.layer.3.output.LayerNorm.bias", "bert.encoder.layer.4.attention.self.query.weight", "bert.encoder.layer.4.attention.self.query.bias", "bert.encoder.layer.4.attention.self.key.weight", "bert.encoder.layer.4.attention.self.key.bias", "bert.encoder.layer.4.attention.self.value.weight", "bert.encoder.layer.4.attention.self.value.bias", "bert.encoder.layer.4.attention.output.dense.weight", "bert.encoder.layer.4.attention.output.dense.bias", "bert.encoder.layer.4.attention.output.LayerNorm.weight", "bert.encoder.layer.4.attention.output.LayerNorm.bias", "bert.encoder.layer.4.intermediate.dense.weight", "bert.encoder.layer.4.intermediate.dense.bias", "bert.encoder.layer.4.output.dense.weight", "bert.encoder.layer.4.output.dense.bias", "bert.encoder.layer.4.output.LayerNorm.weight", "bert.encoder.layer.4.output.LayerNorm.bias", "bert.encoder.layer.5.attention.self.query.weight", "bert.encoder.layer.5.attention.self.query.bias", "bert.encoder.layer.5.attention.self.key.weight", "bert.encoder.layer.5.attention.self.key.bias", "bert.encoder.layer.5.attention.self.value.weight", "bert.encoder.layer.5.attention.self.value.bias", "bert.encoder.layer.5.attention.output.dense.weight", "bert.encoder.layer.5.attention.output.dense.bias", "bert.encoder.layer.5.attention.output.LayerNorm.weight", "bert.encoder.layer.5.attention.output.LayerNorm.bias", "bert.encoder.layer.5.intermediate.dense.weight", "bert.encoder.layer.5.intermediate.dense.bias", "bert.encoder.layer.5.output.dense.weight", "bert.encoder.layer.5.output.dense.bias", "bert.encoder.layer.5.output.LayerNorm.weight", "bert.encoder.layer.5.output.LayerNorm.bias", "bert.encoder.layer.6.attention.self.query.weight", "bert.encoder.layer.6.attention.self.query.bias", "bert.encoder.layer.6.attention.self.key.weight", "bert.encoder.layer.6.attention.self.key.bias", "bert.encoder.layer.6.attention.self.value.weight", "bert.encoder.layer.6.attention.self.value.bias", "bert.encoder.layer.6.attention.output.dense.weight", "bert.encoder.layer.6.attention.output.dense.bias", "bert.encoder.layer.6.attention.output.LayerNorm.weight", "bert.encoder.layer.6.attention.output.LayerNorm.bias", "bert.encoder.layer.6.intermediate.dense.weight", "bert.encoder.layer.6.intermediate.dense.bias", "bert.encoder.layer.6.output.dense.weight", "bert.encoder.layer.6.output.dense.bias", "bert.encoder.layer.6.output.LayerNorm.weight", "bert.encoder.layer.6.output.LayerNorm.bias", "bert.encoder.layer.7.attention.self.query.weight", "bert.encoder.layer.7.attention.self.query.bias", "bert.encoder.layer.7.attention.self.key.weight", "bert.encoder.layer.7.attention.self.key.bias", "bert.encoder.layer.7.attention.self.value.weight", "bert.encoder.layer.7.attention.self.value.bias", "bert.encoder.layer.7.attention.output.dense.weight", "bert.encoder.layer.7.attention.output.dense.bias", "bert.encoder.layer.7.attention.output.LayerNorm.weight", "bert.encoder.layer.7.attention.output.LayerNorm.bias", "bert.encoder.layer.7.intermediate.dense.weight", "bert.encoder.layer.7.intermediate.dense.bias", "bert.encoder.layer.7.output.dense.weight", "bert.encoder.layer.7.output.dense.bias", "bert.encoder.layer.7.output.LayerNorm.weight", "bert.encoder.layer.7.output.LayerNorm.bias", "bert.encoder.layer.8.attention.self.query.weight", "bert.encoder.layer.8.attention.self.query.bias", "bert.encoder.layer.8.attention.self.key.weight", "bert.encoder.layer.8.attention.self.key.bias", "bert.encoder.layer.8.attention.self.value.weight", "bert.encoder.layer.8.attention.self.value.bias", "bert.encoder.layer.8.attention.output.dense.weight", "bert.encoder.layer.8.attention.output.dense.bias", "bert.encoder.layer.8.attention.output.LayerNorm.weight", "bert.encoder.layer.8.attention.output.LayerNorm.bias", "bert.encoder.layer.8.intermediate.dense.weight", "bert.encoder.layer.8.intermediate.dense.bias", "bert.encoder.layer.8.output.dense.weight", "bert.encoder.layer.8.output.dense.bias", "bert.encoder.layer.8.output.LayerNorm.weight", "bert.encoder.layer.8.output.LayerNorm.bias", "bert.encoder.layer.9.attention.self.query.weight", "bert.encoder.layer.9.attention.self.query.bias", "bert.encoder.layer.9.attention.self.key.weight", "bert.encoder.layer.9.attention.self.key.bias", "bert.encoder.layer.9.attention.self.value.weight", "bert.encoder.layer.9.attention.self.value.bias", "bert.encoder.layer.9.attention.output.dense.weight", "bert.encoder.layer.9.attention.output.dense.bias", "bert.encoder.layer.9.attention.output.LayerNorm.weight", "bert.encoder.layer.9.attention.output.LayerNorm.bias", "bert.encoder.layer.9.intermediate.dense.weight", "bert.encoder.layer.9.intermediate.dense.bias", "bert.encoder.layer.9.output.dense.weight", "bert.encoder.layer.9.output.dense.bias", "bert.encoder.layer.9.output.LayerNorm.weight", "bert.encoder.layer.9.output.LayerNorm.bias", "bert.encoder.layer.10.attention.self.query.weight", "bert.encoder.layer.10.attention.self.query.bias", "bert.encoder.layer.10.attention.self.key.weight", "bert.encoder.layer.10.attention.self.key.bias", "bert.encoder.layer.10.attention.self.value.weight", "bert.encoder.layer.10.attention.self.value.bias", "bert.encoder.layer.10.attention.output.dense.weight", "bert.encoder.layer.10.attention.output.dense.bias", "bert.encoder.layer.10.attention.output.LayerNorm.weight", "bert.encoder.layer.10.attention.output.LayerNorm.bias", "bert.encoder.layer.10.intermediate.dense.weight", "bert.encoder.layer.10.intermediate.dense.bias", "bert.encoder.layer.10.output.dense.weight", "bert.encoder.layer.10.output.dense.bias", "bert.encoder.layer.10.output.LayerNorm.weight", "bert.encoder.layer.10.output.LayerNorm.bias", "bert.encoder.layer.11.attention.self.query.weight", "bert.encoder.layer.11.attention.self.query.bias", "bert.encoder.layer.11.attention.self.key.weight", "bert.encoder.layer.11.attention.self.key.bias", "bert.encoder.layer.11.attention.self.value.weight", "bert.encoder.layer.11.attention.self.value.bias", "bert.encoder.layer.11.attention.output.dense.weight", "bert.encoder.layer.11.attention.output.dense.bias", "bert.encoder.layer.11.attention.output.LayerNorm.weight", "bert.encoder.layer.11.attention.output.LayerNorm.bias", "bert.encoder.layer.11.intermediate.dense.weight", "bert.encoder.layer.11.intermediate.dense.bias", "bert.encoder.layer.11.output.dense.weight", "bert.encoder.layer.11.output.dense.bias", "bert.encoder.layer.11.output.LayerNorm.weight", "bert.encoder.layer.11.output.LayerNorm.bias". 就是说，能运行但结果不对，请问这个问题该怎么解决呢？
这个看起来像是您本地的模型有问题对无法正常加载
F2023888
commented
4 months ago 本地模型是下载的对应链接中的re_robert.pth，但打印其模型参数时发现，它确实缺失很多参数，其参数如下所示： bert.embeddings.position_ids torch.Size([1, 512]) bert.embeddings.word_embeddings.weight torch.Size([21128, 768]) bert.embeddings.position_embeddings.weight torch.Size([512, 768]) bert.embeddings.token_type_embeddings.weight torch.Size([2, 768]) bert.embeddings.LayerNorm.weight torch.Size([768]) bert.embeddings.LayerNorm.bias torch.Size([768]) bert.encoder.layer.0.attention.self.query.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.query.bias torch.Size([768]) bert.encoder.layer.0.attention.self.key.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.key.bias torch.Size([768]) bert.encoder.layer.0.attention.self.value.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.value.bias torch.Size([768]) bert.encoder.layer.0.attention.output.dense.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.output.dense.bias torch.Size([768]) bert.encoder.layer.0.attention.output.LayerNorm.weight torch.Size([768]) bert.encoder.layer.0.attention.output.LayerNorm.bias torch.Size([768]) bert.encoder.layer.0.intermediate.dense.weight torch.Size([3072, 768]) bert.encoder.layer.0.intermediate.dense.bias torch.Size([3072]) bert.encoder.layer.0.output.dense.weight torch.Size([768, 3072]) bert.encoder.layer.0.output.dense.bias torch.Size([768]) bert.encoder.layer.0.output.LayerNorm.weight torch.Size([768]) bert.encoder.layer.0.output.LayerNorm.bias torch.Size([768]) bert.pooler.dense.weight torch.Size([768, 768]) bert.pooler.dense.bias torch.Size([768]) bilstm.rnn.weight_ih_l0 torch.Size([200, 768]) bilstm.rnn.weight_hh_l0 torch.Size([200, 50]) bilstm.rnn.bias_ih_l0 torch.Size([200]) bilstm.rnn.bias_hh_l0 torch.Size([200]) bilstm.rnn.weight_ih_l0_reverse torch.Size([200, 768]) bilstm.rnn.weight_hh_l0_reverse torch.Size([200, 50]) bilstm.rnn.bias_ih_l0_reverse torch.Size([200]) bilstm.rnn.bias_hh_l0_reverse torch.Size([200]) fc.weight torch.Size([51, 100]) fc.bias torch.Size([51]) 所以想求一个包含所有参数的检查点文件。
F2023888
commented
4 months ago model_state_dict = torch.load(pth_file)
for param_tensor in model_state_dict: print(f"{param_tensor} \t {model_state_dict[param_tensor].size()}") 打印该.pth文件的代码是这样
zxlzr
commented
4 months ago 本地模型是下载的对应链接中的re_robert.pth，但打印其模型参数时发现，它确实缺失很多参数，其参数如下所示： bert.embeddings.position_ids torch.Size([1, 512]) bert.embeddings.word_embeddings.weight torch.Size([21128, 768]) bert.embeddings.position_embeddings.weight torch.Size([512, 768]) bert.embeddings.token_type_embeddings.weight torch.Size([2, 768]) bert.embeddings.LayerNorm.weight torch.Size([768]) bert.embeddings.LayerNorm.bias torch.Size([768]) bert.encoder.layer.0.attention.self.query.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.query.bias torch.Size([768]) bert.encoder.layer.0.attention.self.key.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.key.bias torch.Size([768]) bert.encoder.layer.0.attention.self.value.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.value.bias torch.Size([768]) bert.encoder.layer.0.attention.output.dense.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.output.dense.bias torch.Size([768]) bert.encoder.layer.0.attention.output.LayerNorm.weight torch.Size([768]) bert.encoder.layer.0.attention.output.LayerNorm.bias torch.Size([768]) bert.encoder.layer.0.intermediate.dense.weight torch.Size([3072, 768]) bert.encoder.layer.0.intermediate.dense.bias torch.Size([3072]) bert.encoder.layer.0.output.dense.weight torch.Size([768, 3072]) bert.encoder.layer.0.output.dense.bias torch.Size([768]) bert.encoder.layer.0.output.LayerNorm.weight torch.Size([768]) bert.encoder.layer.0.output.LayerNorm.bias torch.Size([768]) bert.pooler.dense.weight torch.Size([768, 768]) bert.pooler.dense.bias torch.Size([768]) bilstm.rnn.weight_ih_l0 torch.Size([200, 768]) bilstm.rnn.weight_hh_l0 torch.Size([200, 50]) bilstm.rnn.bias_ih_l0 torch.Size([200]) bilstm.rnn.bias_hh_l0 torch.Size([200]) bilstm.rnn.weight_ih_l0_reverse torch.Size([200, 768]) bilstm.rnn.weight_hh_l0_reverse torch.Size([200, 50]) bilstm.rnn.bias_ih_l0_reverse torch.Size([200]) bilstm.rnn.bias_hh_l0_reverse torch.Size([200]) fc.weight torch.Size([51, 100]) fc.bias torch.Size([51]) 所以想求一个包含所有参数的检查点文件。
建议您重新下载一下模型，可能是网络原因。
zxlzr
commented
4 months ago 同时依然存在错误： RuntimeError: Error(s) in loading state_dict for LM: Missing key(s) in state_dict: "bert.encoder.layer.1.attention.self.query.weight", "bert.encoder.layer.1.attention.self.query.bias", "bert.encoder.layer.1.attention.self.key.weight", "bert.encoder.layer.1.attention.self.key.bias", "bert.encoder.layer.1.attention.self.value.weight", "bert.encoder.layer.1.attention.self.value.bias", "bert.encoder.layer.1.attention.output.dense.weight", "bert.encoder.layer.1.attention.output.dense.bias", "bert.encoder.layer.1.attention.output.LayerNorm.weight", "bert.encoder.layer.1.attention.output.LayerNorm.bias", "bert.encoder.layer.1.intermediate.dense.weight", "bert.encoder.layer.1.intermediate.dense.bias", "bert.encoder.layer.1.output.dense.weight", "bert.encoder.layer.1.output.dense.bias", "bert.encoder.layer.1.output.LayerNorm.weight", "bert.encoder.layer.1.output.LayerNorm.bias", "bert.encoder.layer.2.attention.self.query.weight", "bert.encoder.layer.2.attention.self.query.bias", "bert.encoder.layer.2.attention.self.key.weight", "bert.encoder.layer.2.attention.self.key.bias", "bert.encoder.layer.2.attention.self.value.weight", "bert.encoder.layer.2.attention.self.value.bias", "bert.encoder.layer.2.attention.output.dense.weight", "bert.encoder.layer.2.attention.output.dense.bias", "bert.encoder.layer.2.attention.output.LayerNorm.weight", "bert.encoder.layer.2.attention.output.LayerNorm.bias", "bert.encoder.layer.2.intermediate.dense.weight", "bert.encoder.layer.2.intermediate.dense.bias", "bert.encoder.layer.2.output.dense.weight", "bert.encoder.layer.2.output.dense.bias", "bert.encoder.layer.2.output.LayerNorm.weight", "bert.encoder.layer.2.output.LayerNorm.bias", "bert.encoder.layer.3.attention.self.query.weight", "bert.encoder.layer.3.attention.self.query.bias", "bert.encoder.layer.3.attention.self.key.weight", "bert.encoder.layer.3.attention.self.key.bias", "bert.encoder.layer.3.attention.self.value.weight", "bert.encoder.layer.3.attention.self.value.bias", "bert.encoder.layer.3.attention.output.dense.weight", "bert.encoder.layer.3.attention.output.dense.bias", "bert.encoder.layer.3.attention.output.LayerNorm.weight", "bert.encoder.layer.3.attention.output.LayerNorm.bias", "bert.encoder.layer.3.intermediate.dense.weight", "bert.encoder.layer.3.intermediate.dense.bias", "bert.encoder.layer.3.output.dense.weight", "bert.encoder.layer.3.output.dense.bias", "bert.encoder.layer.3.output.LayerNorm.weight", "bert.encoder.layer.3.output.LayerNorm.bias", "bert.encoder.layer.4.attention.self.query.weight", "bert.encoder.layer.4.attention.self.query.bias", "bert.encoder.layer.4.attention.self.key.weight", "bert.encoder.layer.4.attention.self.key.bias", "bert.encoder.layer.4.attention.self.value.weight", "bert.encoder.layer.4.attention.self.value.bias", "bert.encoder.layer.4.attention.output.dense.weight", "bert.encoder.layer.4.attention.output.dense.bias", "bert.encoder.layer.4.attention.output.LayerNorm.weight", "bert.encoder.layer.4.attention.output.LayerNorm.bias", "bert.encoder.layer.4.intermediate.dense.weight", "bert.encoder.layer.4.intermediate.dense.bias", "bert.encoder.layer.4.output.dense.weight", "bert.encoder.layer.4.output.dense.bias", "bert.encoder.layer.4.output.LayerNorm.weight", "bert.encoder.layer.4.output.LayerNorm.bias", "bert.encoder.layer.5.attention.self.query.weight", "bert.encoder.layer.5.attention.self.query.bias", "bert.encoder.layer.5.attention.self.key.weight", "bert.encoder.layer.5.attention.self.key.bias", "bert.encoder.layer.5.attention.self.value.weight", "bert.encoder.layer.5.attention.self.value.bias", "bert.encoder.layer.5.attention.output.dense.weight", "bert.encoder.layer.5.attention.output.dense.bias", "bert.encoder.layer.5.attention.output.LayerNorm.weight", "bert.encoder.layer.5.attention.output.LayerNorm.bias", "bert.encoder.layer.5.intermediate.dense.weight", "bert.encoder.layer.5.intermediate.dense.bias", "bert.encoder.layer.5.output.dense.weight", "bert.encoder.layer.5.output.dense.bias", "bert.encoder.layer.5.output.LayerNorm.weight", "bert.encoder.layer.5.output.LayerNorm.bias", "bert.encoder.layer.6.attention.self.query.weight", "bert.encoder.layer.6.attention.self.query.bias", "bert.encoder.layer.6.attention.self.key.weight", "bert.encoder.layer.6.attention.self.key.bias", "bert.encoder.layer.6.attention.self.value.weight", "bert.encoder.layer.6.attention.self.value.bias", "bert.encoder.layer.6.attention.output.dense.weight", "bert.encoder.layer.6.attention.output.dense.bias", "bert.encoder.layer.6.attention.output.LayerNorm.weight", "bert.encoder.layer.6.attention.output.LayerNorm.bias", "bert.encoder.layer.6.intermediate.dense.weight", "bert.encoder.layer.6.intermediate.dense.bias", "bert.encoder.layer.6.output.dense.weight", "bert.encoder.layer.6.output.dense.bias", "bert.encoder.layer.6.output.LayerNorm.weight", "bert.encoder.layer.6.output.LayerNorm.bias", "bert.encoder.layer.7.attention.self.query.weight", "bert.encoder.layer.7.attention.self.query.bias", "bert.encoder.layer.7.attention.self.key.weight", "bert.encoder.layer.7.attention.self.key.bias", "bert.encoder.layer.7.attention.self.value.weight", "bert.encoder.layer.7.attention.self.value.bias", "bert.encoder.layer.7.attention.output.dense.weight", "bert.encoder.layer.7.attention.output.dense.bias", "bert.encoder.layer.7.attention.output.LayerNorm.weight", "bert.encoder.layer.7.attention.output.LayerNorm.bias", "bert.encoder.layer.7.intermediate.dense.weight", "bert.encoder.layer.7.intermediate.dense.bias", "bert.encoder.layer.7.output.dense.weight", "bert.encoder.layer.7.output.dense.bias", "bert.encoder.layer.7.output.LayerNorm.weight", "bert.encoder.layer.7.output.LayerNorm.bias", "bert.encoder.layer.8.attention.self.query.weight", "bert.encoder.layer.8.attention.self.query.bias", "bert.encoder.layer.8.attention.self.key.weight", "bert.encoder.layer.8.attention.self.key.bias", "bert.encoder.layer.8.attention.self.value.weight", "bert.encoder.layer.8.attention.self.value.bias", "bert.encoder.layer.8.attention.output.dense.weight", "bert.encoder.layer.8.attention.output.dense.bias", "bert.encoder.layer.8.attention.output.LayerNorm.weight", "bert.encoder.layer.8.attention.output.LayerNorm.bias", "bert.encoder.layer.8.intermediate.dense.weight", "bert.encoder.layer.8.intermediate.dense.bias", "bert.encoder.layer.8.output.dense.weight", "bert.encoder.layer.8.output.dense.bias", "bert.encoder.layer.8.output.LayerNorm.weight", "bert.encoder.layer.8.output.LayerNorm.bias", "bert.encoder.layer.9.attention.self.query.weight", "bert.encoder.layer.9.attention.self.query.bias", "bert.encoder.layer.9.attention.self.key.weight", "bert.encoder.layer.9.attention.self.key.bias", "bert.encoder.layer.9.attention.self.value.weight", "bert.encoder.layer.9.attention.self.value.bias", "bert.encoder.layer.9.attention.output.dense.weight", "bert.encoder.layer.9.attention.output.dense.bias", "bert.encoder.layer.9.attention.output.LayerNorm.weight", "bert.encoder.layer.9.attention.output.LayerNorm.bias", "bert.encoder.layer.9.intermediate.dense.weight", "bert.encoder.layer.9.intermediate.dense.bias", "bert.encoder.layer.9.output.dense.weight", "bert.encoder.layer.9.output.dense.bias", "bert.encoder.layer.9.output.LayerNorm.weight", "bert.encoder.layer.9.output.LayerNorm.bias", "bert.encoder.layer.10.attention.self.query.weight", "bert.encoder.layer.10.attention.self.query.bias", "bert.encoder.layer.10.attention.self.key.weight", "bert.encoder.layer.10.attention.self.key.bias", "bert.encoder.layer.10.attention.self.value.weight", "bert.encoder.layer.10.attention.self.value.bias", "bert.encoder.layer.10.attention.output.dense.weight", "bert.encoder.layer.10.attention.output.dense.bias", "bert.encoder.layer.10.attention.output.LayerNorm.weight", "bert.encoder.layer.10.attention.output.LayerNorm.bias", "bert.encoder.layer.10.intermediate.dense.weight", "bert.encoder.layer.10.intermediate.dense.bias", "bert.encoder.layer.10.output.dense.weight", "bert.encoder.layer.10.output.dense.bias", "bert.encoder.layer.10.output.LayerNorm.weight", "bert.encoder.layer.10.output.LayerNorm.bias", "bert.encoder.layer.11.attention.self.query.weight", "bert.encoder.layer.11.attention.self.query.bias", "bert.encoder.layer.11.attention.self.key.weight", "bert.encoder.layer.11.attention.self.key.bias", "bert.encoder.layer.11.attention.self.value.weight", "bert.encoder.layer.11.attention.self.value.bias", "bert.encoder.layer.11.attention.output.dense.weight", "bert.encoder.layer.11.attention.output.dense.bias", "bert.encoder.layer.11.attention.output.LayerNorm.weight", "bert.encoder.layer.11.attention.output.LayerNorm.bias", "bert.encoder.layer.11.intermediate.dense.weight", "bert.encoder.layer.11.intermediate.dense.bias", "bert.encoder.layer.11.output.dense.weight", "bert.encoder.layer.11.output.dense.bias", "bert.encoder.layer.11.output.LayerNorm.weight", "bert.encoder.layer.11.output.LayerNorm.bias". 就是说，能运行但结果不对，请问这个问题该怎么解决呢？ 低置信度的可以过滤掉，建议您先解决模型本身完整性问题。
F2023888
commented
4 months ago 本地模型是下载的对应链接中的re_robert.pth，但打印其模型参数时发现，它确实缺失很多参数，其参数如下所示： bert.embeddings.position_ids torch.Size([1, 512]) bert.embeddings.word_embeddings.weight torch.Size([21128, 768]) bert.embeddings.position_embeddings.weight torch.Size([512, 768]) bert.embeddings.token_type_embeddings.weight torch.Size([2, 768]) bert.embeddings.LayerNorm.weight torch.Size([768]) bert.embeddings.LayerNorm.bias torch.Size([768]) bert.encoder.layer.0.attention.self.query.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.query.bias torch.Size([768]) bert.encoder.layer.0.attention.self.key.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.key.bias torch.Size([768]) bert.encoder.layer.0.attention.self.value.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.self.value.bias torch.Size([768]) bert.encoder.layer.0.attention.output.dense.weight torch.Size([768, 768]) bert.encoder.layer.0.attention.output.dense.bias torch.Size([768]) bert.encoder.layer.0.attention.output.LayerNorm.weight torch.Size([768]) bert.encoder.layer.0.attention.output.LayerNorm.bias torch.Size([768]) bert.encoder.layer.0.intermediate.dense.weight torch.Size([3072, 768]) bert.encoder.layer.0.intermediate.dense.bias torch.Size([3072]) bert.encoder.layer.0.output.dense.weight torch.Size([768, 3072]) bert.encoder.layer.0.output.dense.bias torch.Size([768]) bert.encoder.layer.0.output.LayerNorm.weight torch.Size([768]) bert.encoder.layer.0.output.LayerNorm.bias torch.Size([768]) bert.pooler.dense.weight torch.Size([768, 768]) bert.pooler.dense.bias torch.Size([768]) bilstm.rnn.weight_ih_l0 torch.Size([200, 768]) bilstm.rnn.weight_hh_l0 torch.Size([200, 50]) bilstm.rnn.bias_ih_l0 torch.Size([200]) bilstm.rnn.bias_hh_l0 torch.Size([200]) bilstm.rnn.weight_ih_l0_reverse torch.Size([200, 768]) bilstm.rnn.weight_hh_l0_reverse torch.Size([200, 50]) bilstm.rnn.bias_ih_l0_reverse torch.Size([200]) bilstm.rnn.bias_hh_l0_reverse torch.Size([200]) fc.weight torch.Size([51, 100]) fc.bias torch.Size([51]) 所以想求一个包含所有参数的检查点文件。
建议您重新下载一下模型，可能是网络原因。
好的，我刚刚重新在其所给的链接上下载了一次re_robert.pth，其参数仍旧缺失
F2023888
commented
4 months ago 我想使用的是“开源中文知识图谱抽取框架开箱即用特别版DeepKE-cnSchema”中的RE，求支招
BeasterYong
commented
4 months ago 您好，经过我们的验证该模型是没有问题的，或许您可以尝试一下百度网盘下载的方式呢？
F2023888
commented
4 months ago 谢谢，解决啦
是否使用范例[y/n]，退出请输入: exit .... D:\Anaconda_envs\envs\rz\lib\site-packages\omegaconf\basecontainer.py:225: UserWarning: cfg.pretty() is deprecated and will be removed in a future version. Use OmegaConf.to_yaml(cfg)
warnings.warn( y [2024-06-13 16:46:04,151][deepke.relation_extraction.standard.tools.preprocess][INFO] - use bert tokenizer... [2024-06-13 16:46:04,285][main][INFO] - device: cpu Some weights of the model checkpoint at D:/pycharmProject/rz/rz/DeepKE-main/example/re/standard/bert-base-chinese were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias']