import tensorflow as tf

    self.example_weights = tf.placeholder(tf.float32, [batch_size],
                                          name='example_weights')

    # get one 'word' embedding for the full tweet
    tweet_embedding = c2v.GetEmbeddings(self.x)[:,1,:]

    logits = tf.nn.xw_plus_b(tweet_embedding, hidden, bias)
    self.probs = tf.nn.softmax(logits)

    self._xent = tf.nn.softmax_cross_entropy_with_logits(logits, self.y)
    self.cost = tf.reduce_mean(self.example_weights * self._xent)


class WordLevelModel(object):
