def get_recurrent_tokenizer(vocab,
max_context_tokens,
unk_token,
pad_token,
device="cpu"):
"""
Return a tokenizer to be used with recurrent-based models
"""
question_tokenizer = Tokenizer(WordLevel(vocab, unk_token=unk_token))
question_tokenizer.normalizer = Sequence(
[StripAccents(), Lowercase(), Strip()])
question_tokenizer.pre_tokenizer = PreSequence(
[Whitespace(), Punctuation()])
question_tokenizer.enable_padding(direction="right",
pad_id=vocab[pad_token],
pad_type_id=1,
pad_token=pad_token)
context_tokenizer = Tokenizer(WordLevel(vocab, unk_token=unk_token))
context_tokenizer.normalizer = Sequence(
[StripAccents(), Lowercase(), Strip()])
context_tokenizer.pre_tokenizer = PreSequence(
[Whitespace(), Punctuation()])
context_tokenizer.enable_padding(
direction="right",
pad_id=vocab[pad_token],
pad_type_id=1,
pad_token=pad_token,
)
context_tokenizer.enable_truncation(max_context_tokens)
return RecurrentSquadTokenizer(question_tokenizer,
context_tokenizer,
device=device)
def __init__(self, glove_fp=None, embedding=None):
assert glove_fp or embedding
super().__init__()
self.pre_token = Whitespace() # i.e. tokenize on whitespace + punct
if glove_fp:
self.model = GloveEmbedding(location=glove_fp)
else:
self.model = embedding
def __init__(
self,
load_from: str = None,
vocab_size: int = 10000,
max_example_len: int = 128,
batch_size: int = 16,
num_stopwords: int = 250,
mask_output_len: int = 4,
):
self.char_dict: Dict[str, int] = {}
self.char_rev: Dict[int, str] = {}
self.token_dict: Dict[str, int] = {}
self.token_rev: Dict[str, int] = {}
self.vocab_size = vocab_size
self.max_example_len = max_example_len
self.batch_size = batch_size
self.num_stopwords = num_stopwords
self.mask_output_len = mask_output_len
self.tokenizer_fit = False
self.tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
self.tokenizer.pre_tokenizer = Whitespace()
self.tokenizer.normalizer = Sequence(
[NFD(), Lowercase(), StripAccents()])
self.tok_trainer = BpeTrainer(special_tokens=["[UNK]", "[MASK]"],
vocab_size=self.vocab_size)
if load_from:
self._load(load_from)
def get_daily_dialog_tokenizer(tokenizer_location=None):
'''
Get the daily dialog tokenizer. Trains a new one if no location is provided
:param tokenizer_location: Json containing information about the tokenizer.
:return:
'''
if tokenizer_location:
tokenizer = Tokenizer.from_file(tokenizer_location, )
tokenizer.enable_padding()
return tokenizer
else:
dataset_train = datasets.load_dataset("daily_dialog", split="train", )
utterances = [special_tokens["sep_token"].join(dialogue["dialog"]) for dialogue in dataset_train]
trainer = WordPieceTrainer(
vocab_size = 2048,
special_tokens = token_utils.special_tokens.values()
)
custom_tokenizer = Tokenizer(WordPiece(unk_token=special_tokens["unk_token"], ))
custom_tokenizer.normalizer = normalizers.Sequence([NFD(), Lowercase(), StripAccents()])
custom_tokenizer.pre_tokenizer = Whitespace()
custom_tokenizer.train_from_iterator(utterances, trainer, )
custom_tokenizer.enable_padding()
# Write every dialogue to file
location = './daily_dialog/'
custom_tokenizer.save(location + "tokenizer.json")
return custom_tokenizer
def __init__(self, path):
self.path = path
text_paths = [
str(x) for x in Path("./dataset/corpus/").glob("**/*.txt")
]
savedpath = "./dataset/tok_model/MALBERT-vocab.txt"
if os.path.exists(savedpath):
self.tokenizer = tokenizers.BertWordPieceTokenizer(
"./dataset/tok_model/MALBERT-vocab.txt", )
else:
self.tokenizer = tokenizers.BertWordPieceTokenizer()
self.tokenizer.train(
files=text_paths,
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"],
vocab_size=14200)
self.tokenizer.save_model("./dataset/tok_model", "MALBERT")
self.tokenizer.enable_truncation(max_length=512)
self.pretokenizer = tokenizers.pre_tokenizers.Sequence(
[Whitespace(), Digits(individual_digits=True)])
self.vocab = self.tokenizer.get_vocab()
self.mask_index = self.vocab.get("[MASK]")
self.pad_index = self.vocab.get("[PAD]")
self.eos_index = self.vocab.get("[SEP]")
self.sos_index = self.vocab.get("[CLS]")
self.unk_index = self.vocab.get("[UNK]")
def train_tokenizer(lang, dataset, vocab_size):
# Byte-pair encoding
tokenizer = Tokenizer(BPE(unk_token='[UNK]'))
# trainer
trainer = BpeTrainer(
special_tokens=['[MASK]', '[CLS]', '[SEP]', '[PAD]', '[UNK]'],
vocab_size=vocab_size)
# pre tokenizer with whitespace
tokenizer.pre_tokenizer = Whitespace()
# train
tokenizer.train_from_iterator(dataset[lang], trainer)
# post process start/end tokens
tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", tokenizer.token_to_id("[CLS]")),
("[SEP]", tokenizer.token_to_id("[SEP]")),
],
)
return tokenizer
def train():
"""Source: https://huggingface.co/docs/tokenizers/pipeline"""
base = os.environ['DATA_ROOT']
corpus_path = base + 'MimicIII/Encounters/Text/'
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
# input to tokenizer.encode() goes through this pipeline:
# normalization, pre-tokenization, model, post-processing
bert_tokenizer.normalizer = normalizers.Sequence(
[NFD(), Lowercase(), StripAccents()])
bert_tokenizer.pre_tokenizer = Whitespace()
bert_tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[("[CLS]", 1), ("[SEP]", 2)])
files = [str(file) for file in Path(corpus_path).glob('*.txt')]
trainer = WordPieceTrainer(
vocab_size=30522,
show_progress=True,
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
bert_tokenizer.train(files, trainer)
os.mkdir('./Tokenizer')
bert_tokenizer.save("Tokenizer/tokenizer.json")
def main() -> None:
args = parse_args()
special_tokens = list(SPECIAL_TOKENS)
if args.reserved < len(special_tokens):
raise AssertionError(
f"number of reserved tokens should be more than number of f{len(special_tokens)}")
for i in range(len(special_tokens), args.reserved):
special_tokens.append(f"[unused{i:03d}]")
all_filenames = get_all_filenames(args.input)
# "C:\Users\demianmedich\data\wiki\20191120.en\pp_cased/"
tokenizer = Tokenizer(get_model(args.model))
tokenizer.normalizer = normalizers.Sequence([
NFKC(), StripAccents(), Lowercase()
])
tokenizer.pre_tokenizer = Whitespace()
trainer = WordPieceTrainer(
vocab_size=args.vocab_size,
special_tokens=special_tokens)
tokenizer.train(trainer, all_filenames)
model_files = tokenizer.model.save()
sys.exit(0)
def load_janome_tokenizer(tokenizer_path) -> Tokenizer:
tokenizer = Tokenizer.from_file(str(tokenizer_path))
tokenizer.pre_tokenizer = Sequence([
Whitespace(),
PreTokenizer.custom(JanomePreTokenizer()),
])
tokenizer.decoder = Decoder.custom(JanomeDecoder())
return tokenizer
def tokenize_cards(
files=['./dataset/cards_train.txt', './dataset/cards_val.txt'],
output_dir='./tokenizer'):
tokenizer = ByteLevelBPETokenizer()
tokenizer.pre_tokenizer = Whitespace()
tokenizer.train(files=files, special_tokens=SPECIAL_TOKENS + OTHER_TOKENS)
tokenizer.save_model(output_dir)
def tokenize(dt, df):
from tokenizers import Tokenizer
from tokenizers.models import WordPiece
from tokenizers.pre_tokenizers import Whitespace
from tokenizers import normalizers
from tokenizers.normalizers import NFD, StripAccents
from tokenizers.processors import TemplateProcessing
from tokenizers.trainers import WordPieceTrainer
#print(df.head())
#print(df.query_text.head())
#print(df.query_text.to_list())
#exit(0)
data_source = get_data_source(dt)
token_file = Path(data_dir, data_source, 'tokenizer.json')
vocab_file = Path(data_dir, data_source, 'vocab.txt')
corpus_file = Path(data_dir, data_source, 'corpus.txt')
if vocab_file.is_file() and corpus_file.is_file():
print("corpus and token files already generated")
return 0
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
bert_tokenizer.normalizer = normalizers.Sequence([NFD(), StripAccents()])
bert_tokenizer.pre_tokenizer = Whitespace()
bert_tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", 1),
("[SEP]", 2),
],
)
trainer = WordPieceTrainer(
vocab_size=25000,
min_frequency=3,
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
#print(df.query_text.to_list())
bert_tokenizer.train_from_iterator(df.query_text.to_list(), trainer)
bert_tokenizer.save(str(token_file))
#bert_tokenizer.save_model(directory=data_dir,name='tokenizer')
df['range_idx'] = range(0, df.shape[0])
df['mean_rank_group'] = df.groupby(
['session_id'], sort=False)['range_idx'].transform(np.mean)
df['separate_column'] = df['range_idx'] < df['mean_rank_group']
df = df.groupby(['session_id', 'separate_column'],
as_index=False,
sort=False)['query_text'].agg(
' '.join).drop(columns='separate_column')
#df = df.groupby('session_id').agg({'query_text':' '.join}).reset_index()
df.query_text.to_csv(corpus_file, header=False, index=False)
with open(token_file) as token_f:
jdata = json.load(token_f)
with open(vocab_file, "w") as fd:
for k in jdata['model']['vocab'].keys():
print(k, file=fd)
def inference(checkpoint_path,
hyperparameters_path,
tokenizer_path,
input='In 1691 Moscow established ',
generated_length=64,
random_selection=True):
# Iitialize tokenizer and model from files
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
tokenizer.pre_tokenizer = Whitespace()
tokenizer = tokenizer.from_file(tokenizer_path)
#tokenizer2 = Tokenizer(BPE(unk_token="[UNK]"))
#tokenizer2.pre_tokenizer2 = Whitespace()
#tokenizer2 = Tokenizer.from_file("example/tokenizer.json")
#initialize model
model = LMModel.load_from_checkpoint(checkpoint_path=checkpoint_path,
hparams_file=hyperparameters_path)
# Tokenize input sample
encoded_sample = tokenizer.encode(input).ids
for i in range(generated_length):
input_ids = torch.unsqueeze(torch.tensor(encoded_sample,
dtype=torch.long),
axis=0)
# Inference
output, attn = model(input_ids)
last_word = output[0][-1]
if not random_selection:
# Pick highest probability token from probability distributions
prediction = torch.argmax(output,
axis=2).squeeze(axis=0).tolist()[-1]
else:
# Pick Tokens acording to their probabilities
prediction = torch.multinomial(torch.softmax(last_word, 0)**10,
1)[0]
# Add prediciton to sequence
encoded_sample.append(prediction)
# Detokenize output sample
decoded_output = tokenizer.decode(encoded_sample)
#decoded_output2 = tokenizer2.decode(encoded_sample)
output_tokens = [tokenizer.id_to_token(int(id)) for id in encoded_sample]
#output_tokens2 = [tokenizer2.id_to_token(int(id)) for id in encoded_sample]
#print('\n========================\n ORIGINAL BPE \n========================')
#print(output_tokens2, decoded_output2, sep='\n')
#print('\n========================\n MODIFIED BPE \n========================')
return decoded_output, output_tokens, attn
def _prepare_pipeline(self):
self.tokenizer.normalizer = normalizers.Sequence(
[NFD(), Lowercase(), StripAccents()])
self.tokenizer.pre_tokenizer = Whitespace()
self.tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", 1),
("[SEP]", 2),
],
)
self.tokenizer.enable_padding(
pad_id=self.__class__.SPECIAL_TOKENS.index("[PAD]"),
pad_token="[PAD]")
def main(args):
# from tokenizers import BertWordPieceTokenizer
from tokenizers import Tokenizer
from tokenizers.models import WordPiece
bert_tokenizer = Tokenizer(WordPiece())
# bert_tokenizer = Tokenizer(MBartTokenizer())
from tokenizers import normalizers
from tokenizers.normalizers import Lowercase, NFD, StripAccents
bert_tokenizer.normalizer = normalizers.Sequence(
[NFD(), Lowercase(), StripAccents()])
from tokenizers.pre_tokenizers import Whitespace
bert_tokenizer.pre_tokenizer = Whitespace()
# from tokenizers.processors import TemplateProcessing
#
# bert_tokenizer.post_processor = TemplateProcessing(
# single="[CLS] $A [SEP]",
# pair="[CLS] $A [SEP] $B:1 [SEP]:1",
# special_tokens=[
# ("[CLS]", 1),
# ("[SEP]", 2),
# ],
# )
from tokenizers.trainers import WordPieceTrainer
trainer = WordPieceTrainer(
vocab_size=10000,
special_tokens=["[UNK]", "[CLS]", "[PAD]",
"[MASK]"] # "[SEP]", "[PAD]", "[MASK]"]
)
files = glob.glob(args.text_raw_files_pattern)
bert_tokenizer.train(trainer, files)
os.makedirs(args.output_dir, exist_ok=True)
model_files = bert_tokenizer.model.save(args.output_dir,
"bert-tokenizer-kr")
bert_tokenizer.model = WordPiece.from_file(*model_files, unk_token="[UNK]")
bert_tokenizer.save(os.path.join(args.output_dir,
"bert-tokenizer-kr.json"))
def load_tokenizer(vocab='./tokenizer/vocab.json', merges='./tokenizer/merges.txt', gpt=False, load_from=None):
if gpt:
if load_from:
tokenizer = GPT2Tokenizer.from_pretrained(load_from)
else:
tokenizer = GPT2Tokenizer(
vocab, merges,
bos_token=CARD_BEGIN, eos_token=CARD_END, sep_token=CARD_END,
unk_token=UNK, pad_token=CARD_PAD, mask_token=CARD_MASK, padding_side="left"
)
else:
tokenizer = ByteLevelBPETokenizer(vocab, merges)
tokenizer.add_special_tokens(SPECIAL_TOKENS + OTHER_TOKENS)
tokenizer.mask_token = CARD_MASK
tokenizer.pre_tokenizer = Whitespace()
return tokenizer
def create_tokenizer(sentence_list):
filename = f'temp_{time.strftime("%Y%m%d-%H%M%S")}.txt'
with open(filename, 'w') as f:
for s in sentence_list:
f.write(f'{s}\n')
tokenizer = Tokenizer(WordPiece())
tokenizer.pre_tokenizer = Whitespace()
tokenizer.decoder = decoders.WordPiece()
tokenizer.enable_padding(pad_token='[PAD]', pad_id=0)
trainer = WordPieceTrainer(
vocab_size=3000, special_tokens=['[PAD]', '[S]', '[/S]', '[UNK]'])
tokenizer.train(trainer, [filename])
os.remove(filename)
return tokenizer
class GloveTokenizer():
"""Glove Tokenizer that maps from text -> vectors.
GloVe embeddings are not contextual (are just a lookup) so in the
huggingface paradim, they are actually part of the tokenizer. Doing this
properly would involve creating a pre-trained tokenizer
that is formed using something to the effect of:
```python
import tokenizer
from tokenizers.models import WordPiece
tokenizer = tokenizers.Tokenizer(WordPiece())
# add normalizer/pre_tokenizer
# ...
```
... but this feels like overkill here. I'll make my own version here
but it will quite a bit slower.
"""
def __init__(self, glove_fp=None, embedding=None):
assert glove_fp or embedding
super().__init__()
self.pre_token = Whitespace() # i.e. tokenize on whitespace + punct
if glove_fp:
self.model = GloveEmbedding(location=glove_fp)
else:
self.model = embedding
def normalize(self, text):
"""Lowercases text."""
return text.lower()
def to_words(self, text):
"""Sentence to tokens (on whitespace)"""
tokens = self.pre_token.pre_tokenize(text)
# tokens is List[Tuple] where tuple: (token, position).
return [x for x, _ in tokens]
def __call__(self, text: str):
text = self.normalize(text)
words = self.to_words(text)
ids = self.model.words_to_ids(words)
ids = torch.LongTensor(ids)
return self.model(ids), words
def create_train_bpe_tokenizer(
bpe_vocab_size,
asr_text_filepath='asr.txt',
ttx_text_filepath='ttx.txt',
save_tokenizer=True,
tokenizer_filename=".\\data\\tokenizer-test.json"):
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"],
vocab_size=bpe_vocab_size)
tokenizer.pre_tokenizer = Whitespace()
files = [asr_text_filepath, ttx_text_filepath]
files = [file for file in files if file] # Get rid of None's
tokenizer.train(files, trainer)
if save_tokenizer:
tokenizer.save(tokenizer_filename)
return tokenizer
def main(args):
# copy from https://github.com/xinjli/allosaurus
ipa0 = [
'I', 'a', 'aː', 'ã', 'ă', 'b', 'bʲ', 'bʲj', 'bʷ', 'bʼ', 'bː', 'b̞',
'b̤', 'b̥', 'c', 'd', 'dʒ', 'dʲ', 'dː', 'd̚', 'd̥', 'd̪', 'd̯', 'd͡z',
'd͡ʑ', 'd͡ʒ', 'd͡ʒː', 'd͡ʒ̤', 'e', 'eː', 'e̞', 'f', 'fʲ', 'fʷ', 'fː',
'g', 'gʲ', 'gʲj', 'gʷ', 'gː', 'h', 'hʷ', 'i', 'ij', 'iː', 'i̞', 'i̥',
'i̯', 'j', 'k', 'kx', 'kʰ', 'kʲ', 'kʲj', 'kʷ', 'kʷʼ', 'kʼ', 'kː',
'k̟ʲ', 'k̟̚', 'k͡p̚', 'l', 'lʲ', 'lː', 'l̪', 'm', 'mʲ', 'mʲj', 'mʷ',
'mː', 'n', 'nj', 'nʲ', 'nː', 'n̪', 'n̺', 'o', 'oː', 'o̞', 'o̥', 'p',
'pf', 'pʰ', 'pʲ', 'pʲj', 'pʷ', 'pʷʼ', 'pʼ', 'pː', 'p̚', 'q', 'r', 'rː',
's', 'sʲ', 'sʼ', 'sː', 's̪', 't', 'ts', 'tsʰ', 'tɕ', 'tɕʰ', 'tʂ',
'tʂʰ', 'tʃ', 'tʰ', 'tʲ', 'tʷʼ', 'tʼ', 'tː', 't̚', 't̪', 't̪ʰ', 't̪̚',
't͡s', 't͡sʼ', 't͡ɕ', 't͡ɬ', 't͡ʃ', 't͡ʃʲ', 't͡ʃʼ', 't͡ʃː', 'u', 'uə',
'uː', 'u͡w', 'v', 'vʲ', 'vʷ', 'vː', 'v̞', 'v̞ʲ', 'w', 'x', 'x̟ʲ', 'y',
'z', 'zj', 'zʲ', 'z̪', 'ä', 'æ', 'ç', 'çj', 'ð', 'ø', 'ŋ', 'ŋ̟', 'ŋ͡m',
'œ', 'œ̃', 'ɐ', 'ɐ̞', 'ɑ', 'ɑ̱', 'ɒ', 'ɓ', 'ɔ', 'ɔ̃', 'ɕ', 'ɕː', 'ɖ̤',
'ɗ', 'ə', 'ɛ', 'ɛ̃', 'ɟ', 'ɡ', 'ɡʲ', 'ɡ̤', 'ɡ̥', 'ɣ', 'ɣj', 'ɤ', 'ɤɐ̞',
'ɤ̆', 'ɥ', 'ɦ', 'ɨ', 'ɪ', 'ɫ', 'ɯ', 'ɯ̟', 'ɯ̥', 'ɰ', 'ɱ', 'ɲ', 'ɳ',
'ɴ', 'ɵ', 'ɸ', 'ɹ', 'ɹ̩', 'ɻ', 'ɻ̩', 'ɽ', 'ɾ', 'ɾj', 'ɾʲ', 'ɾ̠', 'ʀ',
'ʁ', 'ʁ̝', 'ʂ', 'ʃ', 'ʃʲː', 'ʃ͡ɣ', 'ʈ', 'ʉ̞', 'ʊ', 'ʋ', 'ʋʲ', 'ʌ', 'ʎ',
'ʏ', 'ʐ', 'ʑ', 'ʒ', 'ʒ͡ɣ', 'ʔ', 'ʝ', 'ː', 'β', 'β̞', 'θ', 'χ', 'ә', 'ḁ'
]
ipa1, ipa2, ipa3 = ipa0.copy(), ipa0.copy(), ipa0.copy()
random.shuffle(ipa1)
random.shuffle(ipa2)
random.shuffle(ipa3)
# randomly joined to form training data
passage0 = ' '.join(ipa0)
passage1 = ' '.join(ipa1)
passage2 = ' '.join(ipa2)
passage3 = ' '.join(ipa3)
data = [passage0, passage1, passage2, passage3]
# setup
tokenizer = Tokenizer(WordLevel(unk_token="<unk>"))
# trainer = WordLevelTrainer(vocab_size=300, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
trainer = WordLevelTrainer(
vocab_size=300,
special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"])
tokenizer.pre_tokenizer = Whitespace()
# train the tokenizer
tokenizer.train_from_iterator(data, trainer=trainer)
tokenizer.save(args.outdir + '/ipa_tokenizer.json')
def get_tokenizer_trainer():
# START init_tokenizer
from tokenizers import Tokenizer
from tokenizers.models import BPE
tokenizer = Tokenizer(BPE())
# END init_tokenizer
# START init_trainer
from tokenizers.trainers import BpeTrainer
trainer = BpeTrainer(
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
# END init_trainer
# START init_pretok
from tokenizers.pre_tokenizers import Whitespace
tokenizer.pre_tokenizer = Whitespace()
# END init_pretok
return tokenizer, trainer
def train_tokenizer(sentences: List[str], serialize_path: str = "", vocab_size: int = 8000) -> Tokenizer:
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
bert_tokenizer.normalizer = normalizers.Sequence([NFD(), Lowercase(), StripAccents()])
bert_tokenizer.pre_tokenizer = Whitespace()
bert_tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", 1),
("[SEP]", 2),
],
)
trainer = WordPieceTrainer(
vocab_size=vocab_size,
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
)
bert_tokenizer.train_from_iterator(sentences, trainer=trainer)
if serialize_path:
bert_tokenizer.save(serialize_path)
return bert_tokenizer
def get_french_vocab(model_name):
root = Path(os.getcwd()).parent.parent.parent
french_corpus = "Datasets/corpora/fr/text"
fr_corpus_path = os.path.join(root, french_corpus)
files = []
for dir_ in os.listdir(fr_corpus_path):
fr_corpus_dir = os.path.join(fr_corpus_path, dir_)
for text_file in os.listdir(fr_corpus_dir):
text_file = os.path.join(fr_corpus_dir, text_file)
files.append(text_file)
tokenizer = ByteLevelBPETokenizer(add_prefix_space=True)
tokenizer.pre_tokenizer = Whitespace()
tokenizer.train(files,
vocab_size=20000,
min_frequency=2,
show_progress=True,
special_tokens=["<sos>", "<pad>", "<eos>", "<unk>"])
print(tokenizer.encode("c'est la meilleure des phrases françaises").tokens)
tokenizer.save(model_name)
def train_wordpiece_bert():
"""
Sample code from: https://huggingface.co/docs/tokenizers/python/latest/pipeline.html
"""
from tokenizers.models import WordPiece
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
from tokenizers import normalizers
from tokenizers.normalizers import Lowercase, NFD, StripAccents
bert_tokenizer.normalizer = normalizers.Sequence(
[NFD(), Lowercase(), StripAccents()])
from tokenizers.pre_tokenizers import Whitespace
bert_tokenizer.pre_tokenizer = Whitespace()
from tokenizers.processors import TemplateProcessing
bert_tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", 1),
("[SEP]", 2),
],
)
bert_tokenizer.decoder = decoders.WordPiece()
from tokenizers.trainers import WordPieceTrainer
trainer = WordPieceTrainer(
vocab_size=30522,
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
files = [
DIR_DATA + os.sep + 'wikitext-103' + os.sep + 'wiki.%s.raw' % a
for a in ["test", "train", "valid"]
]
bert_tokenizer.train(files, trainer)
bert_tokenizer.save(DIR_TOKENIZERS + os.sep + 'bert_wiki.json')
return bert_tokenizer
def build_new_vocab():
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.pre_tokenizer = Whitespace()
# files = [f"/daintlab/home/moo/NLU/biobert-pytorch/datasets/QA/BioASQ/BioASQ-{split}-factoid-7b.json" for split in ["train_split", "dev"]]
files = "/daintlab/home/moo/NLU/biobert-pytorch/datasets/QA/BioASQ/BioASQ-train-factoid-7b.json"
with open(files) as f:
file = json.load(f)
contexts = []
for question in file['data']:
for paragraph in question['paragraphs']:
contexts.append(paragraph['context'])
tokenizer.train_from_iterator(contexts, trainer)
additional_vocab = [k for k, v in tokenizer.get_vocab().items()]
tokenizer.save("tokenizer/tokenizer-bioasq.json")
return additional_vocab
def __init__(self, tokenizer: PreTrainedTokenizerFast, cased: bool,
target_vocab_size: int):
"""
Args:
tokenizer: A Rust-based 🤗 Tokenizer
cased: If False, ignore uppercases in corpus
target_vocab_size: Size of augmented vocabulary
Raises:
ValueError: If :obj:`target_vocab_size` is larger or equal to the existing vocabulary of :obj:`tokenizer`
RuntimeError: If :obj:`tokenizer` uses an unsupported tokenization model
"""
if target_vocab_size <= tokenizer.vocab_size:
raise ValueError(
f"Ensure that `target_vocab_size` is larger than tokenizer's vocab size."
)
self.tokenizer = tokenizer
self.cased = cased
self.target_vocab_size = target_vocab_size
self.model_cls: Type[
BaseTokenizer] = tokenizer.backend_tokenizer.model.__class__
# Instantiate rust tokenizer
rust_tokenizer = Tokenizer(self.model_cls())
if not cased:
rust_tokenizer.normalizer = Lowercase()
rust_tokenizer.pre_tokenizer = Whitespace()
self.rust_tokenizer = rust_tokenizer
# Instantiate the appropriate Trainer based on `self.model` (i.e. BPE, WordPiece, etc)
trainer_cls = self.supported_trainers.get(self.model_cls, None)
if trainer_cls is None:
raise RuntimeError(f"{self.model_cls} is not supported")
self.trainer = trainer_cls(
vocab_size=self.target_vocab_size,
special_tokens=list(self.tokenizer.special_tokens_map.values()),
)
"CHEF_DO": 7,
"MOVE_CONTENTS": 8,
}
k = len(output_vocab)
with open("../data/res2idx.json", 'r') as f:
for w, i in json.load(f).items():
output_vocab[w] = k
k += 1
with open("../data/arg2idx.json", 'r') as f:
for w, i in json.load(f).items():
output_vocab[w.replace('-', '_')] = k
k += 1
output_vocab = {w: i for i, w in enumerate(output_vocab)}
output_tokenizer = Tokenizer(WordLevel(output_vocab, ))
output_tokenizer.pre_tokenizer = Whitespace()
t = output_tokenizer.encode_batch(
["SERVE MOVE_CONTENTS", "SERVE MOVE_CONTENTS PUT"])
# print (t)
csv_file = '../data/seq2seq_4335716.csv'
input_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_tokenizer.bos_token = input_tokenizer.cls_token
input_tokenizer.eos_token = input_tokenizer.sep_token
val_data = load_dataset('csv', data_files=csv_file, split='train[90%:]')
train_data = load_dataset('csv', data_files=csv_file, split='train[:90%]')
# print(val_data)
# print(train_data)
def test_instantiate(self):
assert Whitespace() is not None
assert isinstance(Whitespace(), PreTokenizer)
assert isinstance(Whitespace(), Whitespace)
assert isinstance(pickle.loads(pickle.dumps(Whitespace())), Whitespace)
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():
from tokenizers import Tokenizer
from tokenizers.models import BPE, WordPiece, Unigram
from tokenizers.normalizers import Lowercase
from tokenizers.pre_tokenizers import Whitespace, Digits, Sequence
from tokenizers.trainers import BpeTrainer, WordPieceTrainer, UnigramTrainer
TRAIN_DATA_PATH = 'data/data_fusion_train.parquet'
OUTPUT_PATH = 'data/tokenizers/'
# Prepare data
train = pd.read_parquet(TRAIN_DATA_PATH, columns=['item_name'])
item_names = train.item_name.drop_duplicates().tolist()
# WordPiece tokenizer
tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
tokenizer.pre_tokenizer = Sequence([Whitespace(), Digits()])
tokenizer.normalizer = Lowercase()
trainer = WordPieceTrainer(
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"],
vocab_size=70000)
tokenizer.train_from_iterator(item_names, trainer)
tokenizer.save(os.path.join(OUTPUT_PATH, 'wordpiece_70k.json'))
# BPE tokenizer
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
tokenizer.pre_tokenizer = Sequence([Whitespace(), Digits()])
tokenizer.normalizer = Lowercase()
trainer = BpeTrainer(
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"],
from tokenizers import Tokenizer, normalizers
from tokenizers.models import WordLevel
from tokenizers.pre_tokenizers import Whitespace
from tokenizers.trainers import WordLevelTrainer
from tokenizers.processors import TemplateProcessing
t = Tokenizer(WordLevel(unk_token="[UNK]"))
t.pre_tokenizer = Whitespace()
trainer = WordLevelTrainer(special_tokens=["[UNK]", "[PAD]", "[CLS]", "[SEP]"])
t.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
# ,
# pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", 2),
("[SEP]", 3),
])
files = ['tok-train-shuf-tgt.tsv']
t.train(files, trainer)
t.save("code_tokenizer.json")