AttributeError: 'str' object has no attribute 'shape' while encoding tensor using BertModel with PyTorch (Hugging Face)
AttributeError: 'str' object has no attribute 'shape' while encoding tensor using BertModel with PyTorch (Hugging Face)
AttributeError:'str' 对象没有属性 'shape',同时使用 BertModel 和 PyTorch(拥抱面)对张量进行编码。下面是代码
bert_model = BertModel.from_pretrained(r'downloads\bert-pretrained-model')
input_ids
输出为:
tensor([[ 101, 156, 13329, ..., 0, 0, 0],
[ 101, 156, 13329, ..., 0, 0, 0],
[ 101, 1302, 1251, ..., 0, 0, 0],
...,
[ 101, 25456, 1200, ..., 0, 0, 0],
[ 101, 143, 9664, ..., 0, 0, 0],
[ 101, 2586, 7340, ..., 0, 0, 0]])
后跟下面的代码
last_hidden_state, pooled_output = bert_model(
input_ids=encoding['input_ids'],
attention_mask=encoding['attention_mask']
)
后跟下面的代码
last_hidden_state.shape
输出是
AttributeError Traceback (most recent call last)
<ipython-input-70-9628339f425d> in <module>
----> 1 last_hidden_state.shape
AttributeError: 'str' object has no attribute 'shape'
完整代码 link 是'https://colab.research.google.com/drive/1FY4WtqCi2CQ9RjHj4slZwtdMhwaWv2-2?usp=sharing'
问题是 return type has changed 自 3.xx 版本的变形金刚。所以,我们明确要求一个张量元组。
因此,我们可以在调用 bert_model()
时传递一个额外的 kwarg return_dict = False
以获得对应于 [=14= 的 实际张量 ].
last_hidden_state, pooled_output = bert_model(
input_ids=encoding['input_ids'],
attention_mask=encoding['attention_mask'],
return_dict = False # this is needed to get a tensor as result
)
如果您不喜欢前面的方法,那么您可以求助于:
In [13]: bm = bert_model(
...: encoding_sample['input_ids'],
...: encoding_sample['attention_mask']
...: )
In [14]: bm.keys()
Out[14]: odict_keys(['last_hidden_state', 'pooler_output'])
# accessing last_hidden_state
In [15]: bm['last_hidden_state']
In [16]: bm['last_hidden_state'].shape
Out[16]: torch.Size([1, 17, 768])
AttributeError:'str' 对象没有属性 'shape',同时使用 BertModel 和 PyTorch(拥抱面)对张量进行编码。下面是代码
bert_model = BertModel.from_pretrained(r'downloads\bert-pretrained-model')
input_ids
输出为:
tensor([[ 101, 156, 13329, ..., 0, 0, 0],
[ 101, 156, 13329, ..., 0, 0, 0],
[ 101, 1302, 1251, ..., 0, 0, 0],
...,
[ 101, 25456, 1200, ..., 0, 0, 0],
[ 101, 143, 9664, ..., 0, 0, 0],
[ 101, 2586, 7340, ..., 0, 0, 0]])
后跟下面的代码
last_hidden_state, pooled_output = bert_model(
input_ids=encoding['input_ids'],
attention_mask=encoding['attention_mask']
)
后跟下面的代码
last_hidden_state.shape
输出是
AttributeError Traceback (most recent call last)
<ipython-input-70-9628339f425d> in <module>
----> 1 last_hidden_state.shape
AttributeError: 'str' object has no attribute 'shape'
完整代码 link 是'https://colab.research.google.com/drive/1FY4WtqCi2CQ9RjHj4slZwtdMhwaWv2-2?usp=sharing'
问题是 return type has changed 自 3.xx 版本的变形金刚。所以,我们明确要求一个张量元组。
因此,我们可以在调用 bert_model()
时传递一个额外的 kwarg return_dict = False
以获得对应于 [=14= 的 实际张量 ].
last_hidden_state, pooled_output = bert_model(
input_ids=encoding['input_ids'],
attention_mask=encoding['attention_mask'],
return_dict = False # this is needed to get a tensor as result
)
如果您不喜欢前面的方法,那么您可以求助于:
In [13]: bm = bert_model(
...: encoding_sample['input_ids'],
...: encoding_sample['attention_mask']
...: )
In [14]: bm.keys()
Out[14]: odict_keys(['last_hidden_state', 'pooler_output'])
# accessing last_hidden_state
In [15]: bm['last_hidden_state']
In [16]: bm['last_hidden_state'].shape
Out[16]: torch.Size([1, 17, 768])