Merge pull request #1384 from huggingface/encoding-qol

Quality of life enhancements in encoding + patch MLM masking

examples/run_lm_finetuning.py

@@ -61,7 +61,7 @@ class TextDataset(Dataset):
     def __init__(self, tokenizer, file_path='train', block_size=512):
         assert os.path.isfile(file_path)
         directory, filename = os.path.split(file_path)
-        cached_features_file = os.path.join(directory, 'cached_lm_{}_{}'.format(block_size, filename))
+        cached_features_file = os.path.join(directory, 'cached_lm_' + block_size + '_' + filename)
 
         if os.path.exists(cached_features_file):
             logger.info("Loading features from cached file %s", cached_features_file)
@@ -77,7 +77,7 @@ class TextDataset(Dataset):
             tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
 
             for i in range(0, len(tokenized_text)-block_size+1, block_size): # Truncate in block of block_size
-                self.examples.append(tokenizer.add_special_tokens_single_sequence(tokenized_text[i:i+block_size]))
+                self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i:i+block_size]))
             # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
             # If your dataset is small, first you should loook for a bigger one :-) and second you
             # can change this behavior by adding (model specific) padding.
@@ -139,7 +139,10 @@ def mask_tokens(inputs, tokenizer, args):
     """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
     labels = inputs.clone()
     # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
-    masked_indices = torch.bernoulli(torch.full(labels.shape, args.mlm_probability)).bool()
+    probability_matrix = torch.full(labels.shape, args.mlm_probability)
+    special_tokens_mask = [tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()]
+    probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
+    masked_indices = torch.bernoulli(probability_matrix).bool()
     labels[~masked_indices] = -1  # We only compute loss on masked tokens
 
     # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])