Xây dựng từng khối tokenizer¶

Install the Transformers, Datasets, and Evaluate libraries to run this notebook.

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!pip install datasets evaluate transformers[sentencepiece]

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from datasets import load_dataset

dataset = load_dataset("wikitext", name="wikitext-2-raw-v1", split="train")


def get_training_corpus():
    for i in range(0, len(dataset), 1000):
        yield dataset[i : i + 1000]["text"]

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with open("wikitext-2.txt", "w", encoding="utf-8") as f:
    for i in range(len(dataset)):
        f.write(dataset[i]["text"] + "\n")

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from tokenizers import (
    decoders,
    models,
    normalizers,
    pre_tokenizers,
    processors,
    trainers,
    Tokenizer,
)

tokenizer = Tokenizer(models.WordPiece(unk_token="[UNK]"))

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tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True)

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tokenizer.normalizer = normalizers.Sequence(
    [normalizers.NFD(), normalizers.Lowercase(), normalizers.StripAccents()]
)

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print(tokenizer.normalizer.normalize_str("Héllò hôw are ü?"))

Out[ ]:

hello how are u?

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tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()

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tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()

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tokenizer.pre_tokenizer.pre_tokenize_str("Let's test my pre-tokenizer.")

Out[ ]:

[('Let', (0, 3)), ("'", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),
 ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]

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pre_tokenizer = pre_tokenizers.WhitespaceSplit()
pre_tokenizer.pre_tokenize_str("Let's test my pre-tokenizer.")

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[("Let's", (0, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre-tokenizer.', (14, 28))]

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pre_tokenizer = pre_tokenizers.Sequence(
    [pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Punctuation()]
)
pre_tokenizer.pre_tokenize_str("Let's test my pre-tokenizer.")

Out[ ]:

[('Let', (0, 3)), ("'", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),
 ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]

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special_tokens = ["[UNK]", "[PAD]", "[CLS]", "[SEP]", "[MASK]"]
trainer = trainers.WordPieceTrainer(vocab_size=25000, special_tokens=special_tokens)

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tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)

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tokenizer.model = models.WordPiece(unk_token="[UNK]")
tokenizer.train(["wikitext-2.txt"], trainer=trainer)

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encoding = tokenizer.encode("Let's test this tokenizer.")
print(encoding.tokens)

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['let', "'", 's', 'test', 'this', 'tok', '##eni', '##zer', '.']

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cls_token_id = tokenizer.token_to_id("[CLS]")
sep_token_id = tokenizer.token_to_id("[SEP]")
print(cls_token_id, sep_token_id)

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(2, 3)

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tokenizer.post_processor = processors.TemplateProcessing(
    single=f"[CLS]:0 $A:0 [SEP]:0",
    pair=f"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
    special_tokens=[("[CLS]", cls_token_id), ("[SEP]", sep_token_id)],
)

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encoding = tokenizer.encode("Let's test this tokenizer.")
print(encoding.tokens)

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['[CLS]', 'let', "'", 's', 'test', 'this', 'tok', '##eni', '##zer', '.', '[SEP]']

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encoding = tokenizer.encode("Let's test this tokenizer...", "on a pair of sentences.")
print(encoding.tokens)
print(encoding.type_ids)

Out[ ]:

['[CLS]', 'let', "'", 's', 'test', 'this', 'tok', '##eni', '##zer', '...', '[SEP]', 'on', 'a', 'pair', 'of', 'sentences', '.', '[SEP]']
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]

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tokenizer.decoder = decoders.WordPiece(prefix="##")

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tokenizer.decode(encoding.ids)

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"let's test this tokenizer... on a pair of sentences."

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tokenizer.save("tokenizer.json")

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new_tokenizer = Tokenizer.from_file("tokenizer.json")

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from transformers import PreTrainedTokenizerFast

wrapped_tokenizer = PreTrainedTokenizerFast(
    tokenizer_object=tokenizer,
    # tokenizer_file="tokenizer.json", # Bạn có thể tải từ tệp tokenizer
    unk_token="[UNK]",
    pad_token="[PAD]",
    cls_token="[CLS]",
    sep_token="[SEP]",
    mask_token="[MASK]",
)

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from transformers import BertTokenizerFast

wrapped_tokenizer = BertTokenizerFast(tokenizer_object=tokenizer)

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tokenizer = Tokenizer(models.BPE())

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tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)

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tokenizer.pre_tokenizer.pre_tokenize_str("Let's test pre-tokenization!")

Out[ ]:

[('Let', (0, 3)), ("'s", (3, 5)), ('Ġtest', (5, 10)), ('Ġpre', (10, 14)), ('-', (14, 15)),
 ('tokenization', (15, 27)), ('!', (27, 28))]

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trainer = trainers.BpeTrainer(vocab_size=25000, special_tokens=["<|endoftext|>"])
tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)

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tokenizer.model = models.BPE()
tokenizer.train(["wikitext-2.txt"], trainer=trainer)

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encoding = tokenizer.encode("Let's test this tokenizer.")
print(encoding.tokens)

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['L', 'et', "'", 's', 'Ġtest', 'Ġthis', 'Ġto', 'ken', 'izer', '.']

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tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)

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sentence = "Let's test this tokenizer."
encoding = tokenizer.encode(sentence)
start, end = encoding.offsets[4]
sentence[start:end]

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' test'

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tokenizer.decoder = decoders.ByteLevel()

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tokenizer.decode(encoding.ids)

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"Let's test this tokenizer."

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from transformers import PreTrainedTokenizerFast

wrapped_tokenizer = PreTrainedTokenizerFast(
    tokenizer_object=tokenizer,
    bos_token="<|endoftext|>",
    eos_token="<|endoftext|>",
)

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from transformers import GPT2TokenizerFast

wrapped_tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)

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tokenizer = Tokenizer(models.Unigram())

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from tokenizers import Regex

tokenizer.normalizer = normalizers.Sequence(
    [
        normalizers.Replace("``", '"'),
        normalizers.Replace("''", '"'),
        normalizers.NFKD(),
        normalizers.StripAccents(),
        normalizers.Replace(Regex(" {2,}"), " "),
    ]
)

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tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()

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tokenizer.pre_tokenizer.pre_tokenize_str("Let's test the pre-tokenizer!")

Out[ ]:

[("▁Let's", (0, 5)), ('▁test', (5, 10)), ('▁the', (10, 14)), ('▁pre-tokenizer!', (14, 29))]

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special_tokens = ["<cls>", "<sep>", "<unk>", "<pad>", "<mask>", "<s>", "</s>"]
trainer = trainers.UnigramTrainer(
    vocab_size=25000, special_tokens=special_tokens, unk_token="<unk>"
)
tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)

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tokenizer.model = models.Unigram()
tokenizer.train(["wikitext-2.txt"], trainer=trainer)

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encoding = tokenizer.encode("Let's test this tokenizer.")
print(encoding.tokens)

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['▁Let', "'", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.']

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cls_token_id = tokenizer.token_to_id("<cls>")
sep_token_id = tokenizer.token_to_id("<sep>")
print(cls_token_id, sep_token_id)

Out[ ]:

0 1

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tokenizer.post_processor = processors.TemplateProcessing(
    single="$A:0 <sep>:0 <cls>:2",
    pair="$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2",
    special_tokens=[("<sep>", sep_token_id), ("<cls>", cls_token_id)],
)

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encoding = tokenizer.encode("Let's test this tokenizer...", "on a pair of sentences!")
print(encoding.tokens)
print(encoding.type_ids)

Out[ ]:

['▁Let', "'", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.', '.', '.', '<sep>', '▁', 'on', '▁', 'a', '▁pair', 
  '▁of', '▁sentence', 's', '!', '<sep>', '<cls>']
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]

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tokenizer.decoder = decoders.Metaspace()

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from transformers import PreTrainedTokenizerFast

wrapped_tokenizer = PreTrainedTokenizerFast(
    tokenizer_object=tokenizer,
    bos_token="<s>",
    eos_token="</s>",
    unk_token="<unk>",
    pad_token="<pad>",
    cls_token="<cls>",
    sep_token="<sep>",
    mask_token="<mask>",
    padding_side="left",
)

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from transformers import XLNetTokenizerFast

wrapped_tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)