I've been playing with the CNN text classification lately, and it seems it trains fine, but when it comes to predictions I get this error:

Traceback (most recent call last): File "main.py", line 89, in <module> label = train.predict(args.predict, cnn, text_field, label_field) File "train.py", line 88, in predict return label_feild.vocab.itos[predicted.data[0][0]+1] TypeError: 'int' object has no attribute '__getitem__'

I changed the return label_feild.vocab.itos[predicted.data[0][0]+1] to return label_feild.vocab.itos[predicted.data[0]+1] and bypassed the error, but most predictions are not accurate now. Can you please let me know what I'm missing here.

I thing the way this code implements Static Embedding is wrong. Am I right? This code, uses x=variable(x) when wants to make the embedding static, while it should be something like: self.embed.weight.requires_grad=False

training seems to work fine , when I run "python main.py"

but when I ran predict "python main.py -predict="I feel bad" -snapshot="snapshot/2018-01-08_13-10-29/snapshot_steps9000.pt", it is giving me this error

RuntimeError: Given input size: (1, 3, 128). Calculated output size: (1, 0, 1). Output size is too small.

I haven't made any changes to code

Is there a easy way to get the original raw sentences instead of data objects during the test dataset evaluation?

def eval(data_iter, model, args):
    model.eval()
    corrects, avg_loss = 0, 0
    for batch in data_iter:
        feature, target = batch.text, batch.label
        **print (feature.original_sentence)**

I was reading the documentation for the Torchtext Field object and I noticed that preprocessing happens after tokenization. This seems to conflict with the intention of the clean_str function, as adding it to the text field's preprocessing will split contractions, etc. on individual tokens (causing tokens with spaces in them) rather than an entire sentence. To fix this, the following statement on line 74:

text_field.preprocessing = data.Pipeline(clean_str)

can be replaced with something like this:

text_field.tokenize = lambda x: clean_str(x).split()

which will apply clean_str before tokenization (str.split() is the default tokenizer used by the Field object).

Pytorch 0.4 removes tensor_type from torchtext data field and causes predict function from train.py to break.

Switched over to getting the tensor from torch based on the migration guide: https://pytorch.org/blog/pytorch-0_4_0-migration-guide/

Hi @Shawn1993

I have an issue with running the code on SST dataset. I comment line 73 and uncomment line 74 in main.py

It seems that the code will directly use the torchtext dataset package to download the SST dataset, and then run. However this will raise the error RuntimeError: Given input size: (1, 4, 128). Calculated output size: (64, 0, 1). Output size is too small. in line 44 of model.py x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [(N, Co), ...]*len(Ks).

Any hint on this possible reason? Thanks in advance.

For your information, I am running on PyTorch '0.3.1.post2' and torchtext '0.2.1'.

GitHub seems to be having some issues since the master branch for my fork of this project is heavily modified. The only actual change in this pull request should be line 74 of mydatasets.py.

Trying to run the model in python2.7 (can't upgrade my system python so I have to make do). I am getting the following error:

Loading data...
Traceback (most recent call last):
  File "./main.py", line 73, in <module>
    train_iter, dev_iter = mr(text_field, label_field, device=-1, repeat=False)
  File "./main.py", line 59, in mr
    train_data, dev_data = mydatasets.MR.splits(text_field, label_field)
  File "/home/cnn-text-classification-pytorch/mydatasets.py", line 105, in splits
    examples = cls(text_field, label_field, path=path, **kwargs).examples
  File "/home/cnn-text-classification-pytorch/mydatasets.py", line 82, in __init__
    data.Example.fromlist([line, 'negative'], fields) for line in f]
  File "/home/venv/local/lib/python2.7/site-packages/torchtext/data/example.py", line 52, in fromlist
    setattr(ex, name, field.preprocess(val))
  File "/home/venv/local/lib/python2.7/site-packages/torchtext/data/field.py", line 166, in preprocess
    x = Pipeline(lambda s: six.text_type(s, encoding='utf-8'))(x)
  File "/home/venv/local/lib/python2.7/site-packages/torchtext/data/pipeline.py", line 37, in __call__
    x = pipe.call(x, *args)
  File "/home/venv/local/lib/python2.7/site-packages/torchtext/data/pipeline.py", line 53, in call
    return self.convert_token(x, *args)
  File "/home/venv/local/lib/python2.7/site-packages/torchtext/data/field.py", line 166, in <lambda>
    x = Pipeline(lambda s: six.text_type(s, encoding='utf-8'))(x)
UnicodeDecodeError: 'utf8' codec can't decode byte 0x97 in position 110: invalid start byte

I've decoded all the data in rt-polarity.neg and rt-polarity.pos to UTF-8 ignoring errors (to remove the non-decodeable charaacters), but no luck. Any help?

This pull request is mainly do four things as follow:

1. Solve the basic import problem in python3.
2. Add -early-stop and -save-best option. I run your code in K80. It's very easy to overfitting. You use iteration not epoch, so I do the same.
3. Update model saving way. It should be avoided to save a whole model as the author smouth says. If so, you will meet many problems when fine-tune, you must keep everything environment the same to run well. So I use state_dict() and load_state_dict() instead.
4. Do a little changes to fit pep8.

Also made two other minor modifications:

1. Added space between text and label during prediction
2. Added KeyboardInterrupt functionality for quitting training

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

Hello, excuse me, there was no problem during the training, but this error occurred during the prediction. I'm actually extracting predict as a function

PATH = './snapshot/best_steps_8600.pt' args = confog_args() text_field = data.Field(lower=True) label_field = data.Field(sequential=False) args.vocabulary_size = len(text_field.vocab) args.cuda = args.device != -1 and torch.cuda.is_available()

In addition, the training data should also be loaded when predicting ？？？？？

Looking forward to your reply. Thank you

Hi there, I see that the best reported accuracy for this repo for the MR dataset is 77.5% using CNN-rand-static. When I run this, using ./main.py -device=0 -static, I get much lower numbers (~70%). Two questions:

1. What training settings are you using to get 77.5%?
2. How are you evaluating on the MR dataset to get 77.5%?

Thanks!

• 问题1：

Traceback (most recent call last):
  File "/cnn-text-classification-pytorch/main.py", line 112, in <module>
    train.train(train_iter, dev_iter, cnn, args)
  File "/cnn-text-classification-pytorch/train.py", line 25, in train
    feature.data.t_(), target.data.sub_(1)  # batch first, index align
RuntimeError: set_storage_offset is not allowed on Tensor created from .data or .detach()

Process finished with exit code 1

• 问题1解决：将【2处】feature.data.t_(), target.data.sub_(1)替换为：

 feature = feature.data.t()
 target = target.data.sub(1) 

• 问题2：

Traceback (most recent call last):
  File "/cnn-text-classification-pytorch/main.py", line 112, in <module>
    train.train(train_iter, dev_iter, cnn, args)
  File "/cnn-text-classification-pytorch/train.py", line 43, in train
    loss.data[0],
IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number

Process finished with exit code 1

• 问题2解决：将【2处】loss.data[0]替换为：loss.item()