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Building a tokenizer, block by block

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# Building a tokenizer, block by block

As we’ve seen in the previous sections, tokenization comprises several steps:

• Normalization (any cleanup of the text that is deemed necessary, such as removing spaces or accents, Unicode normalization, etc.)
• Pre-tokenization (splitting the input into words)
• Running the input through the model (using the pre-tokenized words to produce a sequence of tokens)
• Post-processing (adding the special tokens of the tokenizer, generating the attention mask and token type IDs)

As a reminder, here’s another look at the overall process:

The 🤗 Tokenizers library has been built to provide several options for each of those steps, which you can mix and match together. In this section we’ll see how we can build a tokenizer from scratch, as opposed to training a new tokenizer from an old one as we did in section 2. You’ll then be able to build any kind of tokenizer you can think of!

More precisely, the library is built around a central Tokenizer class with the building blocks regrouped in submodules:

• normalizers contains all the possible types of Normalizer you can use (complete list here).
• pre_tokenizers contains all the possible types of PreTokenizer you can use (complete list here).
• models contains the various types of Model you can use, like BPE, WordPiece, and Unigram (complete list here).
• trainers contains all the different types of Trainer you can use to train your model on a corpus (one per type of model; complete list here).
• post_processors contains the various types of PostProcessor you can use (complete list here).
• decoders contains the various types of Decoder you can use to decode the outputs of tokenization (complete list here).

You can find the whole list of building blocks here.

## Acquiring a corpus

To train our new tokenizer, we will use a small corpus of text (so the examples run fast). The steps for acquiring the corpus are similar to the ones we took at the beginning of this chapter, but this time we’ll use the WikiText-2 dataset:

from datasets import load_dataset

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

The function get_training_corpus() is a generator that will yield batches of 1,000 texts, which we will use to train the tokenizer.

🤗 Tokenizers can also be trained on text files directly. Here’s how we can generate a text file containing all the texts/inputs from WikiText-2 that we can use locally:

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

Next we’ll show you how to build your own BERT, GPT-2, and XLNet tokenizers, block by block. That will give us an example of each of the three main tokenization algorithms: WordPiece, BPE, and Unigram. Let’s start with BERT!

## Building a WordPiece tokenizer from scratch

To build a tokenizer with the 🤗 Tokenizers library, we start by instantiating a Tokenizer object with a model, then set its normalizer, pre_tokenizer, post_processor, and decoder attributes to the values we want.

For this example, we’ll create a Tokenizer with a WordPiece model:

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

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

We have to specify the unk_token so the model knows what to return when it encounters characters it hasn’t seen before. Other arguments we can set here include the vocab of our model (we’re going to train the model, so we don’t need to set this) and max_input_chars_per_word, which specifies a maximum length for each word (words longer than the value passed will be split).

The first step of tokenization is normalization, so let’s begin with that. Since BERT is widely used, there is a BertNormalizer with the classic options we can set for BERT: lowercase and strip_accents, which are self-explanatory; clean_text to remove all control characters and replace repeating spaces with a single one; and handle_chinese_chars, which places spaces around Chinese characters. To replicate the bert-base-uncased tokenizer, we can just set this normalizer:

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

Generally speaking, however, when building a new tokenizer you won’t have access to such a handy normalizer already implemented in the 🤗 Tokenizers library — so let’s see how to create the BERT normalizer by hand. The library provides a Lowercase normalizer and a StripAccents normalizer, and you can compose several normalizers using a Sequence:

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

We’re also using an NFD Unicode normalizer, as otherwise the StripAccents normalizer won’t properly recognize the accented characters and thus won’t strip them out.

As we’ve seen before, we can use the normalize_str() method of the normalizer to check out the effects it has on a given text:

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

To go further If you test the two versions of the previous normalizers on a string containing the unicode character u"\u0085" you will surely notice that these two normalizers are not exactly equivalent. To not over-complicate the version with normalizers.Sequence too much , we haven’t included the Regex replacements that the BertNormalizer requires when the clean_text argument is set to True - which is the default behavior. But don’t worry: it is possible to get exactly the same normalization without using the handy BertNormalizer by adding two normalizers.Replace’s to the normalizers sequence.

Next is the pre-tokenization step. Again, there is a prebuilt BertPreTokenizer that we can use:

tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()

Or we can build it from scratch:

tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()

Note that the Whitespace pre-tokenizer splits on whitespace and all characters that are not letters, digits, or the underscore character, so it technically splits on whitespace and punctuation:

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

If you only want to split on whitespace, you should use the WhitespaceSplit pre-tokenizer instead:

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

Like with normalizers, you can use a Sequence to compose several pre-tokenizers:

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

The next step in the tokenization pipeline is running the inputs through the model. We already specified our model in the initialization, but we still need to train it, which will require a WordPieceTrainer. The main thing to remember when instantiating a trainer in 🤗 Tokenizers is that you need to pass it all the special tokens you intend to use — otherwise it won’t add them to the vocabulary, since they are not in the training corpus:

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

As well as specifying the vocab_size and special_tokens, we can set the min_frequency (the number of times a token must appear to be included in the vocabulary) or change the continuing_subword_prefix (if we want to use something different from ##).

To train our model using the iterator we defined earlier, we just have to execute this command:

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

We can also use text files to train our tokenizer, which would look like this (we reinitialize the model with an empty WordPiece beforehand):

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

In both cases, we can then test the tokenizer on a text by calling the encode() method:

encoding = tokenizer.encode("Let's test this tokenizer.")
print(encoding.tokens)
['let', "'", 's', 'test', 'this', 'tok', '##eni', '##zer', '.']

The encoding obtained is an Encoding, which contains all the necessary outputs of the tokenizer in its various attributes: ids, type_ids, tokens, offsets, attention_mask, special_tokens_mask, and overflowing.

The last step in the tokenization pipeline is post-processing. We need to add the [CLS] token at the beginning and the [SEP] token at the end (or after each sentence, if we have a pair of sentences). We will use a TemplateProcessor for this, but first we need to know the IDs of the [CLS] and [SEP] tokens in the vocabulary:

cls_token_id = tokenizer.token_to_id("[CLS]")
sep_token_id = tokenizer.token_to_id("[SEP]")
print(cls_token_id, sep_token_id)
(2, 3)

To write the template for the TemplateProcessor, we have to specify how to treat a single sentence and a pair of sentences. For both, we write the special tokens we want to use; the first (or single) sentence is represented by $A, while the second sentence (if encoding a pair) is represented by $B. For each of these (special tokens and sentences), we also specify the corresponding token type ID after a colon.

The classic BERT template is thus defined as follows:

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)], ) Note that we need to pass along the IDs of the special tokens, so the tokenizer can properly convert them to their IDs. Once this is added, going back to our previous example will give: encoding = tokenizer.encode("Let's test this tokenizer.") print(encoding.tokens) ['[CLS]', 'let', "'", 's', 'test', 'this', 'tok', '##eni', '##zer', '.', '[SEP]'] And on a pair of sentences, we get the proper result: encoding = tokenizer.encode("Let's test this tokenizer...", "on a pair of sentences.") print(encoding.tokens) print(encoding.type_ids) ['[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] We’ve almost finished building this tokenizer from scratch — the last step is to include a decoder: tokenizer.decoder = decoders.WordPiece(prefix="##") Let’s test it on our previous encoding: tokenizer.decode(encoding.ids) "let's test this tokenizer... on a pair of sentences." Great! We can save our tokenizer in a single JSON file like this: tokenizer.save("tokenizer.json") We can then reload that file in a Tokenizer object with the from_file() method: new_tokenizer = Tokenizer.from_file("tokenizer.json") To use this tokenizer in 🤗 Transformers, we have to wrap it in a PreTrainedTokenizerFast. We can either use the generic class or, if our tokenizer corresponds to an existing model, use that class (here, BertTokenizerFast). If you apply this lesson to build a brand new tokenizer, you will have to use the first option. To wrap the tokenizer in a PreTrainedTokenizerFast, we can either pass the tokenizer we built as a tokenizer_object or pass the tokenizer file we saved as tokenizer_file. The key thing to remember is that we have to manually set all the special tokens, since that class can’t infer from the tokenizer object which token is the mask token, the [CLS] token, etc.: from transformers import PreTrainedTokenizerFast wrapped_tokenizer = PreTrainedTokenizerFast( tokenizer_object=tokenizer, # tokenizer_file="tokenizer.json", # You can load from the tokenizer file, alternatively unk_token="[UNK]", pad_token="[PAD]", cls_token="[CLS]", sep_token="[SEP]", mask_token="[MASK]", ) If you are using a specific tokenizer class (like BertTokenizerFast), you will only need to specify the special tokens that are different from the default ones (here, none): from transformers import BertTokenizerFast wrapped_tokenizer = BertTokenizerFast(tokenizer_object=tokenizer) You can then use this tokenizer like any other 🤗 Transformers tokenizer. You can save it with the save_pretrained() method, or upload it to the Hub with the push_to_hub() method. Now that we’ve seen how to build a WordPiece tokenizer, let’s do the same for a BPE tokenizer. We’ll go a bit faster since you know all the steps, and only highlight the differences. ## Building a BPE tokenizer from scratch Let’s now build a GPT-2 tokenizer. Like for the BERT tokenizer, we start by initializing a Tokenizer with a BPE model: tokenizer = Tokenizer(models.BPE()) Also like for BERT, we could initialize this model with a vocabulary if we had one (we would need to pass the vocab and merges in this case), but since we will train from scratch, we don’t need to do that. We also don’t need to specify an unk_token because GPT-2 uses byte-level BPE, which doesn’t require it. GPT-2 does not use a normalizer, so we skip that step and go directly to the pre-tokenization: tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False) The option we added to ByteLevel here is to not add a space at the beginning of a sentence (which is the default otherwise). We can have a look at the pre-tokenization of an example text like before: tokenizer.pre_tokenizer.pre_tokenize_str("Let's test pre-tokenization!") [('Let', (0, 3)), ("'s", (3, 5)), ('Ġtest', (5, 10)), ('Ġpre', (10, 14)), ('-', (14, 15)), ('tokenization', (15, 27)), ('!', (27, 28))] Next is the model, which needs training. For GPT-2, the only special token is the end-of-text token: trainer = trainers.BpeTrainer(vocab_size=25000, special_tokens=["<|endoftext|>"]) tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer) Like with the WordPieceTrainer, as well as the vocab_size and special_tokens, we can specify the min_frequency if we want to, or if we have an end-of-word suffix (like </w>), we can set it with end_of_word_suffix. This tokenizer can also be trained on text files: tokenizer.model = models.BPE() tokenizer.train(["wikitext-2.txt"], trainer=trainer) Let’s have a look at the tokenization of a sample text: encoding = tokenizer.encode("Let's test this tokenizer.") print(encoding.tokens) ['L', 'et', "'", 's', 'Ġtest', 'Ġthis', 'Ġto', 'ken', 'izer', '.'] We apply the byte-level post-processing for the GPT-2 tokenizer as follows: tokenizer.post_processor = processors.ByteLevel(trim_offsets=False) The trim_offsets = False option indicates to the post-processor that we should leave the offsets of tokens that begin with ‘Ġ’ as they are: this way the start of the offsets will point to the space before the word, not the first character of the word (since the space is technically part of the token). Let’s have a look at the result with the text we just encoded, where 'Ġtest' is the token at index 4: sentence = "Let's test this tokenizer." encoding = tokenizer.encode(sentence) start, end = encoding.offsets[4] sentence[start:end] ' test' Finally, we add a byte-level decoder: tokenizer.decoder = decoders.ByteLevel() and we can double-check it works properly: tokenizer.decode(encoding.ids) "Let's test this tokenizer." Great! Now that we’re done, we can save the tokenizer like before, and wrap it in a PreTrainedTokenizerFast or GPT2TokenizerFast if we want to use it in 🤗 Transformers: from transformers import PreTrainedTokenizerFast wrapped_tokenizer = PreTrainedTokenizerFast( tokenizer_object=tokenizer, bos_token="<|endoftext|>", eos_token="<|endoftext|>", ) or: from transformers import GPT2TokenizerFast wrapped_tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer) As the last example, we’ll show you how to build a Unigram tokenizer from scratch. ## Building a Unigram tokenizer from scratch Let’s now build an XLNet tokenizer. Like for the previous tokenizers, we start by initializing a Tokenizer with a Unigram model: tokenizer = Tokenizer(models.Unigram()) Again, we could initialize this model with a vocabulary if we had one. For the normalization, XLNet uses a few replacements (which come from SentencePiece): from tokenizers import Regex tokenizer.normalizer = normalizers.Sequence( [ normalizers.Replace("", '"'), normalizers.Replace("''", '"'), normalizers.NFKD(), normalizers.StripAccents(), normalizers.Replace(Regex(" {2,}"), " "), ] ) This replaces “ and ” with ” and any sequence of two or more spaces with a single space, as well as removing the accents in the texts to tokenize. The pre-tokenizer to use for any SentencePiece tokenizer is Metaspace: tokenizer.pre_tokenizer = pre_tokenizers.Metaspace() We can have a look at the pre-tokenization of an example text like before: tokenizer.pre_tokenizer.pre_tokenize_str("Let's test the pre-tokenizer!") [("▁Let's", (0, 5)), ('▁test', (5, 10)), ('▁the', (10, 14)), ('▁pre-tokenizer!', (14, 29))] Next is the model, which needs training. XLNet has quite a few special tokens: 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) A very important argument not to forget for the UnigramTrainer is the unk_token. We can also pass along other arguments specific to the Unigram algorithm, such as the shrinking_factor for each step where we remove tokens (defaults to 0.75) or the max_piece_length to specify the maximum length of a given token (defaults to 16). This tokenizer can also be trained on text files: tokenizer.model = models.Unigram() tokenizer.train(["wikitext-2.txt"], trainer=trainer) Let’s have a look at the tokenization of a sample text: encoding = tokenizer.encode("Let's test this tokenizer.") print(encoding.tokens) ['▁Let', "'", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.'] A peculiarity of XLNet is that it puts the <cls> token at the end of the sentence, with a type ID of 2 (to distinguish it from the other tokens). It’s padding on the left, as a result. We can deal with all the special tokens and token type IDs with a template, like for BERT, but first we have to get the IDs of the <cls> and <sep> tokens: cls_token_id = tokenizer.token_to_id("<cls>") sep_token_id = tokenizer.token_to_id("<sep>") print(cls_token_id, sep_token_id) 0 1 The template looks like this: 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)],
)

And we can test it works by encoding a pair of sentences:

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

Finally, we add a Metaspace decoder:

tokenizer.decoder = decoders.Metaspace()

and we’re done with this tokenizer! We can save the tokenizer like before, and wrap it in a PreTrainedTokenizerFast or XLNetTokenizerFast if we want to use it in 🤗 Transformers. One thing to note when using PreTrainedTokenizerFast is that on top of the special tokens, we need to tell the 🤗 Transformers library to pad on the left:

from transformers import PreTrainedTokenizerFast

wrapped_tokenizer = PreTrainedTokenizerFast(
tokenizer_object=tokenizer,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
)
from transformers import XLNetTokenizerFast
wrapped_tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)