better splitting of words

train.py

@@ -26,24 +26,30 @@ nLayers = 5
 nHead = 8
 nEmb = 512
 
-nepoch = 50
+nepoch = 50 # just a quick test. the 'epoch' here is very short
 nbatchsz = 64
-epoch_length_fixed = 10000 # make an epoch very short, so we can see the training progress
+epoch_length_fixed = 10000 # make an 'epoch' very short, so we can see the training progress
 
 ########################################################################################################
 
-print("loading data...", end="")
+print('loading data... ' + datafile)
 
 class Dataset(Dataset):
     def __init__(self, data, model_level, ctx_size):
+        print('building token list...')
         if model_level == 'word':
-            data = data.replace('\n', ' \n ').replace('  ', ' ').split(' ')
-
+            import re
+            data = re.sub(r'(\n|\.|\,|\?|\!|\:|\;|\-|\—|\||\'|\"|\`|\(|\)|[0-9]|\[|\]|\{|\}|\=|\+|\*|\\|\/|\~|\&|\$|\#|\%)', r' \g<0> ', data)
+            data = re.sub(' +',' ',data)
+            print('splitting token...')
+            data = data.lower().split(' ')
         unique = sorted(list(set(data)))
+        for u in unique:
+            print(u, end=' ')
         data_size, vocab_size = len(data), len(unique)
+        print('\n\ndata has %d %ss, %d unique.' % (data_size, model_level, vocab_size))
         self.stoi = { ch:i for i,ch in enumerate(unique) }
         self.itos = { i:ch for i,ch in enumerate(unique) }
-        print('data has %d %ss, %d unique.' % (data_size, model_level, vocab_size))
         self.ctx_size = ctx_size
         self.vocab_size = vocab_size
         self.data = data
@@ -88,7 +94,7 @@ for run in range(NUM_OF_RUNS):
     context = "It was"
 
     if model_level == 'word':
-        x = np.array([train_dataset.stoi[s] for s in context.split(' ')], dtype=np.int64)
+        x = np.array([train_dataset.stoi[s] for s in context.strip().lower().split(' ')], dtype=np.int64)
     else:
         x = np.array([train_dataset.stoi[s] for s in context], dtype=np.int64)