print(v)
# Start vocabulary with all standard special tokens. (PAD=0!)
vocab = {}
for special_token in ["[PAD]", "[CLS]", "[SEP]", "[UNK]", "[MASK]", "[BOS]", "[EOS]"]:
vocab[special_token] = len(vocab)
# Add other words - if not already present.
for w in words:
if w not in vocab:
vocab[w] = len(vocab)
print(vocab)
# New tokenizer.
init_tokenizer = BertWordPieceTokenizer(vocab=vocab)
init_tokenizer.normalizer = Sequence([Replace("(", " ( "), Replace(")", " ) "), BertNormalizer()])
init_tokenizer.pre_tokenizer = Whitespace()
#init_tokenizer.pad_token_id = vocab["[PAD]"]
#print("Created tokenizer: ", init_tokenizer)
# Save the created tokenizer.
init_tokenizer.save(decoder_tokenizer_path)
print("Tokenizer saved to: ", decoder_tokenizer_path)
# Load from tokenizer file.
tokenizer = PreTrainedTokenizerFast(tokenizer_file=decoder_tokenizer_path)
tokenizer.add_special_tokens({'pad_token': '[PAD]', 'cls_token': '[CLS]', 'sep_token': '[SEP]',
'unk_token': '[UNK]', 'mask_token': '[MASK]', 'bos_token': '[BOS]', 'eos_token': '[EOS]'
})
print(f"\nLoaded tokenizer vocabulary ({len(tokenizer.get_vocab())}):\n" + "-"*50)
for k, v in tokenizer.get_vocab().items():
def pre_tokenize(self, text):
return self.moses_tokenizer.tokenize(self.mpn.normalize(text.lower() if self.do_lowercase else text))
if __name__ == '__main__':
lang = 'fr'
clean_text = False
handle_chinese_chars = True
strip_accents = False
lowercase = True
vocab_size = 30000
min_frequency = 2
spt = ["<s>", "<pad>", "</s>", "<unk>", "<mask>", "[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]"]
if lang == "fr":
train_data = "../.data/wmt19_de_fr/train.fr"
elif lang == "en":
train_data = "../.data/wmt19_en_de/train.en"
else:
raise ValueError("Undefined language {}".format(lang))
tokenizer = BertWordPieceTokenizer(clean_text=clean_text, lowercase=lowercase,
handle_chinese_chars=handle_chinese_chars, strip_accents=strip_accents)
tokenizer.pre_tokenizer = MosesPreTokenizer(lang, lowercase)
# Customize training
print("Starting to train ...")
tokenizer.train(files=train_data, vocab_size=vocab_size, show_progress=True, min_frequency=min_frequency, special_tokens=spt)
# Save files to disk
tokenizer.save_model(".", "moses-pre-tokenized-wmt-uncased-{}".format(lang))
