使用中文回答:Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForQuestionAnswering: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias'] - This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of BertForQuestionAnswering were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
时间: 2024-04-13 17:25:37 浏览: 895
这个警告信息表明,在初始化 BertForQuestionAnswering 模型时,有一些模型权重没有被使用。这是预期的情况,如果你正在从一个在另一个任务或使用其他架构的模型的检查点中初始化 BertForQuestionAnswering(例如,从 BertForPreTraining 模型中初始化 BertForSequenceClassification 模型)。
但如果你希望从一个完全相同的模型检查点中初始化 BertForQuestionAnswering(例如,从 BertForSequenceClassification 模型中初始化 BertForSequenceClassification 模型),那么这就是意外的情况。
BertForQuestionAnswering 的一些权重没有从 bert-base-uncased 模型的检查点中初始化,并且是新初始化的权重,包括 'qa_outputs.weight' 和 'qa_outputs.bias'。
为了能够使用该模型进行预测和推理,你可能需要在一个下游任务上对该模型进行训练。
相关问题
Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight',
这个问题可能是因为你使用的预训练模型 `bert-base-chinese` 的权重和你要训练的模型 `BertForSequenceClassification` 不匹配。`BertForSequenceClassification` 比 `bert-base-chinese` 多了一些头部网络,例如分类层。这些头部网络的权重在预训练模型中是没有的,因此会出现这个警告。
你可以忽略这个警告,因为这些权重并没有被使用。如果你想要避免这个警告,你可以在加载预训练模型的时候指定 `output_hidden_states=True`,这样就会同时加载所有隐藏状态的权重,包括头部网络所需的权重。具体地,你可以这样做:
```python
from transformers import BertConfig, BertForSequenceClassification
config = BertConfig.from_pretrained('bert-base-chinese', num_labels=2)
model = BertForSequenceClassification.from_pretrained('bert-base-chinese', config=config, output_hidden_states=True)
```
这样就可以避免上述警告。
Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
这个警告通常是因为在初始化模型的时候,使用了预训练模型的checkpoint,但是预训练模型的checkpoint中包含了一些当前模型不需要的参数。这个警告是正常的,因为在预训练模型中,可能包含了当前任务不需要的一些额外参数。
如果你确定这些参数在当前任务中不需要,可以忽略这个警告。否则,你可以通过手动设置BertModel的参数来解决这个问题,只使用当前任务需要的参数。同时,你也可以选择使用BertForSequenceClassification等预训练模型,它们已经针对特定任务进行了微调,不需要手动调整参数。
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