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Sequence special token handling for BERT and RoBERTa

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Lysandre
2019-08-20 14:15:28 -04:00
parent a690edab17
commit 2d042274ac
1 changed files with 8 additions and 2 deletions
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10
examples/run_lm_finetuning.py
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@@ -71,9 +71,15 @@ class TextDataset(Dataset):
text = f.read() text = f.read()
tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
tokenized_text = tokenizer.add_special_tokens_single_sentence(tokenized_text)
while len(tokenized_text) >= block_size: # Truncate in block of block_size while len(tokenized_text) >= block_size: # Truncate in block of block_size
self.examples.append(tokenized_text[:block_size]) if isinstance(tokenizer, (BertTokenizer, RobertaTokenizer)):
tokenized_text = tokenized_text[block_size:] self.examples.append(tokenizer.add_special_tokens_single_sentence(tokenized_text[:block_size - 2]))
tokenized_text = tokenized_text[block_size - 2:]
else:
self.examples.append(tokenized_text[:block_size])
tokenized_text = tokenized_text[block_size:]
# Note that we are loosing the last truncated example here for the sake of simplicity (no padding) # 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 # 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. # can change this behavior by adding (model specific) padding.