a single sentence classification task, should the max length of sentence limited to half of 512, that is to say 256
hi, if i have a single sentence classification task, should the max length of sentence limited to half of 512, that is to say 256?
Why should it be limited to half of 512?<|||||>> Why should it be limited to half of 512?

cause when do train,  we have sentence embedding 0 and 1, but in a single sentence classification task ,we just embedding 0, if this get bad influence<|||||>You can just set the whole sequence to sentence 0. Create a DataProcessor class for your task and set the whole input sequence to `text_a`, example:

```
class MyProcessor(DataProcessor):
# some other methods here

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text_a = line[1]
            label = line[0]
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples
``` 
Notice `text_b=None`.<|||||>How should I do if I have not only a sentence, but a whole text? 
I don't clearly understand how to extend `BertForSequenceClassification` with my own dataset for training/evaluating.
I have a dataset consisting of text/label pairs, where text can have multiple sentences. <|||||>Just send in the whole text as one "sentence", the limit on a sequence length that can be sent at once to BERT is 512 tokens<|||||>Ok, thanks.
One more question related to classification. BERT tokenizes my sentences pretty strange:
> 04/27/2019 16:08:32 - INFO - __main__ -   tokens: [CLS] @ bra ##yy ##yy ##ant Так акт ##иви ##ровала ##сь новая карта , ст ##ара ##я и была не ##ак ##тив ##на . [SEP]

Why are more than a half of the words a separated with `#`? I mean, these words are on russian and many of them are splited to several parts with `#`, though it is one word. Should this be fixed during training?<|||||>That's the WordPiece tokenization, its a way to match subwords when an out of vocabulary word is encountered. It's explained in the bert paper with references. It's as it should be. <|||||>Ok, thank you so much.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
