AttributeError：“示例”object 没有属性“文本”

Question

So I have been trying to run the sample code for BERT on another dataset. Namely, this is the website I used to try to implement the BERT model, and I managed to follow the instructions and successfully run the code. However, when I attempt to run the same code on my own dataset, I get the following error:

```
model = BERT().to(device)
optimizer = optim.Adam(model.parameters(), lr=2e-5)

train(model=model, optimizer=optimizer)
``` 

```---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-57-e4474bff9c36> in <module>()
      2 optimizer = optim.Adam(model.parameters(), lr=2e-5)
      3 
----> 4 train(model=model, optimizer=optimizer)

5 frames
<ipython-input-45-4d0fa7f8acd5> in <lambda>(x)
     19 # Iterators
     20 
---> 21 train_iter = BucketIterator(train, batch_size=15, sort_key=lambda x: len(x.text),
     22                             device=device, train=True, sort=True, sort_within_batch=True)
     23 valid_iter = BucketIterator(valid, batch_size=32, sort_key=lambda x: len(x.text),

AttributeError: 'Example' object has no attribute 'text'```

         Label                                               Text
1178093      3                renal and urinary disorders common:
1170768      3                              orodispersible tablet
4339706      4  remotely manage the transmission bittorrent cl...
6513296      0                     what do you think of her, ay ?
7013664      0  how that could become a film is more than i ca... 

Additionally, I checked for empty rows as some articles and questions on StackOverflow suggested, but I there were none. These are the relevant lines of code, to which the error message points to:

```
# Model parameter
MAX_SEQ_LEN = 128
PAD_INDEX = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
UNK_INDEX = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)

# Fields

label_field = Field(sequential=True, use_vocab=False, batch_first=True, dtype=torch.float)
text_field = Field(use_vocab=False, tokenize=tokenizer.encode, lower=False, include_lengths=False, batch_first=True,
                   fix_length=MAX_SEQ_LEN, pad_token=PAD_INDEX, unk_token=UNK_INDEX)
fields = [('Label', label_field), ('Text', text_field)]

# TabularDataset


train, valid, test = TabularDataset.splits(path=source_folder, train='train_bert.csv', validation='valid_bert.csv',
                                           test='test_bert.csv', format='CSV', fields=fields, skip_header=True)

# Iterators

train_iter = BucketIterator(train, batch_size=15, sort_key=lambda x: len(x.text),
                            device=device, train=True, sort=True, sort_within_batch=True)
valid_iter = BucketIterator(valid, batch_size=32, sort_key=lambda x: len(x.text),
                            device=device, train=True, sort=True, sort_within_batch=True)
test_iter = Iterator(test, batch_size=32, device=device, train=False, shuffle=False, sort=False)
```

I've been using google colab. It might be a newbie question but I've been stuck on this for days and would really appreciate your help in this.

Answer 1

The problem was that I was trying to use a multiclass dataset on the code which is apparently meant for binary classification. Kindest thanks to everyone who tried helping!

Answer 2

I also faced this issue it was because there was an empty line in my dataset which caused this. Removing it fixed the issue