diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..331dcc5
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,46 @@
+# Collections of some helper functions
+import torch
+from torch.autograd import Variable
+
+
+# Get the mask matrix of a batched input
+def get_mask(lengths):
+    max_len = lengths[0]
+    size = len(lengths)
+    mask = lengths.new().byte().resize_(size, max_len).zero_()
+    for i in range(size):
+        mask[i][:lengths[i]].fill_(1)
+    return Variable(mask)
+
+
+def process_data(data_batch, cuda=False, eval=False):
+    data, target, length = data_batch
+
+    if cuda:
+        data = data.cuda()
+        target = target.cuda()
+        length = length.cuda()
+
+    length, idx = torch.sort(length, dim=0, descending=True)
+    max_len = length[0]
+    data = data.index_select(0, idx)
+    data = data[:, :max_len]
+    target = target.index_select(0, idx)
+    target = target[:, :max_len]
+    data = Variable(data, volatile=eval)
+    target = Variable(target)
+    return data, target, length
+
+
+def build_unigram_noise(freq):
+    """build the unigram noise from a list of frequency
+    Parameters:
+        freq: a tensor of #occurrences of the corresponding index
+    Return:
+        unigram_noise: a torch.Tensor with size ntokens,
+        elements indicate the probability distribution
+    """
+    total = freq.sum()
+    noise = freq / total
+    assert abs(noise.sum() - 1) < 0.001
+    return noise