diff --git a/inference.py b/inference.py
index bcb3762..d157e3a 100644
--- a/inference.py
+++ b/inference.py
@@ -1,5 +1,7 @@
 import torch
+from utils import create_input_tensors
 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 class ViterbiDecoder():
     """
@@ -61,3 +63,64 @@ def decode(self, scores, lengths):
                             dim=1)
 
         return decoded
+    
+
+def main():
+    checkpoint = torch.load('BEST_checkpoint_lm_lstm_crf.pth.tar')
+    model = checkpoint['model']
+    optimizer = checkpoint['optimizer']
+    word_map = checkpoint['word_map']
+    lm_vocab_size = checkpoint['lm_vocab_size']
+    tag_map = checkpoint['tag_map']
+    char_map = checkpoint['char_map']
+    start_epoch = checkpoint['epoch'] + 1
+    best_f1 = checkpoint['f1']
+
+    model.eval()
+
+    sentence = "does this thing work"
+    parsed = sentence.split()
+    wmaps, cmaps_f, cmaps_b, cmarkers_f, cmarkers_b, tmaps, wmap_lengths, cmap_lengths = create_input_tensors([parsed], [[next(iter(tag_map))] * len(parsed)], word_map, char_map, tag_map)
+    
+    max_word_len = max(wmap_lengths.tolist())
+    max_char_len = max(cmap_lengths.tolist())
+
+    rev_tag_map = {v: k for k, v in tag_map.items()}
+
+    # Reduce batch's padded length to maximum in-batch sequence
+    # This saves some compute on nn.Linear layers (RNNs are unaffected, since they don't compute over the pads)
+    wmaps = wmaps[:, :max_word_len].to(device)
+    cmaps_f = cmaps_f[:, :max_char_len].to(device)
+    cmaps_b = cmaps_b[:, :max_char_len].to(device)
+    cmarkers_f = cmarkers_f[:, :max_word_len].to(device)
+    cmarkers_b = cmarkers_b[:, :max_word_len].to(device)
+    tmaps = tmaps[:, :max_word_len].to(device)
+    wmap_lengths = wmap_lengths.to(device)
+    cmap_lengths = cmap_lengths.to(device)
+
+    # Forward prop.
+    crf_scores, wmaps_sorted, tmaps_sorted, wmap_lengths_sorted, _, __ = model(cmaps_f,
+                                                                                cmaps_b,
+                                                                                cmarkers_f,
+                                                                                cmarkers_b,
+                                                                                wmaps,
+                                                                                tmaps,
+                                                                                wmap_lengths,
+                                                                                cmap_lengths)
+    
+    crf_scores = crf_scores.to('cpu')
+    wmap_lengths_sorted = wmap_lengths_sorted.to('cpu')
+
+    decoder = ViterbiDecoder(tag_map)
+    output = decoder.decode(crf_scores, wmap_lengths_sorted)
+
+    for idx, i in enumerate(output.data[0]):
+        if (idx == len(parsed)):
+            break
+        
+        print(parsed[idx] + " [" + rev_tag_map[i.item()] + "]")
+
+
+if __name__ == '__main__':
+    main()
+