def sst(text_field, label_field, batch_size, **kargs):
    train_data, dev_data, test_data = datasets.SST.splits(text_field, label_field, fine_grained=True)
    text_field.build_vocab(train_data, dev_data, test_data)
    label_field.build_vocab(train_data, dev_data, test_data)
    train_iter, dev_iter, test_iter = data.BucketIterator.splits(
                                        (train_data, dev_data, test_data), 
                                        batch_sizes=(batch_size, 
                                                     len(dev_data), 
                                                     len(test_data)),
                                        **kargs)
    return train_iter, dev_iter, test_iter 


# load MR dataset 

def __init__(self, text_field, label_field, path=None, examples=None, **kwargs):
        """Create an MR dataset instance given a path and fields.

        Arguments:
            text_field: The field that will be used for text data.
            label_field: The field that will be used for label data.
            path: Path to the data file.
            examples: The examples contain all the data.
            Remaining keyword arguments: Passed to the constructor of
                data.Dataset.
        """
        def clean_str(string):
            """
            Tokenization/string cleaning for all datasets except for SST.
            Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
            """
            string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
            string = re.sub(r"\'s", " \'s", string)
            string = re.sub(r"\'ve", " \'ve", string)
            string = re.sub(r"n\'t", " n\'t", string)
            string = re.sub(r"\'re", " \'re", string)
            string = re.sub(r"\'d", " \'d", string)
            string = re.sub(r"\'ll", " \'ll", string)
            string = re.sub(r",", " , ", string)
            string = re.sub(r"!", " ! ", string)
            string = re.sub(r"\(", " \( ", string)
            string = re.sub(r"\)", " \) ", string)
            string = re.sub(r"\?", " \? ", string)
            string = re.sub(r"\s{2,}", " ", string)
            return string.strip()

        text_field.preprocessing = data.Pipeline(clean_str)
        fields = [('text', text_field), ('label', label_field)]

        if examples is None:
            path = self.dirname if path is None else path
            examples = []
            with codecs.open(os.path.join(path, 'rt-polarity.neg'), encoding='utf-8', errors='ignore') as f:
                examples += [
                    data.Example.fromlist([line, 'negative'], fields) for line in f]
            with codecs.open(os.path.join(path, 'rt-polarity.pos'), encoding='utf-8', errors='ignore') as f:
                examples += [
                    data.Example.fromlist([line, 'positive'], fields) for line in f]
        super(MR, self).__init__(examples, fields, **kwargs)