The tokenization pipeline
When calling Tokenizer.encode or
Tokenizer.encode_batch, the input
text(s) go through the following pipeline:
normalizationpre-tokenizationmodelpost-processing
We’ll see in details what happens during each of those steps in detail,
as well as when you want to decode <decoding> some token ids, and how the 🤗 Tokenizers library allows you
to customize each of those steps to your needs. If you’re already
familiar with those steps and want to learn by seeing some code, jump to
our BERT from scratch example <example>.
For the examples that require a Tokenizer we will use the tokenizer we trained in the
quicktour, which you can load with:
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_file("data/tokenizer-wiki.json")Normalization
Normalization is, in a nutshell, a set of operations you apply to a raw string to make it less random or “cleaner”. Common operations include stripping whitespace, removing accented characters or lowercasing all text. If you’re familiar with Unicode normalization, it is also a very common normalization operation applied in most tokenizers.
Each normalization operation is represented in the 🤗 Tokenizers library
by a Normalizer, and you can combine
several of those by using a normalizers.Sequence. Here is a normalizer applying NFD Unicode normalization
and removing accents as an example:
from tokenizers import normalizers
from tokenizers.normalizers import NFD, StripAccents
normalizer = normalizers.Sequence([NFD(), StripAccents()])You can manually test that normalizer by applying it to any string:
normalizer.normalize_str("Héllò hôw are ü?")
# "Hello how are u?"When building a Tokenizer, you can
customize its normalizer by just changing the corresponding attribute:
tokenizer.normalizer = normalizer
Of course, if you change the way a tokenizer applies normalization, you should probably retrain it from scratch afterward.
Pre-Tokenization
Pre-tokenization is the act of splitting a text into smaller objects that give an upper bound to what your tokens will be at the end of training. A good way to think of this is that the pre-tokenizer will split your text into “words” and then, your final tokens will be parts of those words.
An easy way to pre-tokenize inputs is to split on spaces and
punctuations, which is done by the
pre_tokenizers.Whitespace
pre-tokenizer:
from tokenizers.pre_tokenizers import Whitespace
pre_tokenizer = Whitespace()
pre_tokenizer.pre_tokenize_str("Hello! How are you? I'm fine, thank you.")
# [("Hello", (0, 5)), ("!", (5, 6)), ("How", (7, 10)), ("are", (11, 14)), ("you", (15, 18)),
# ("?", (18, 19)), ("I", (20, 21)), ("'", (21, 22)), ('m', (22, 23)), ("fine", (24, 28)),
# (",", (28, 29)), ("thank", (30, 35)), ("you", (36, 39)), (".", (39, 40))]The output is a list of tuples, with each tuple containing one word and
its span in the original sentence (which is used to determine the final
offsets of our Encoding). Note that splitting on
punctuation will split contractions like "I'm" in this example.
You can combine together any PreTokenizer together. For instance, here is a pre-tokenizer that will
split on space, punctuation and digits, separating numbers in their
individual digits:
from tokenizers import pre_tokenizers
from tokenizers.pre_tokenizers import Digits
pre_tokenizer = pre_tokenizers.Sequence([Whitespace(), Digits(individual_digits=True)])
pre_tokenizer.pre_tokenize_str("Call 911!")
# [("Call", (0, 4)), ("9", (5, 6)), ("1", (6, 7)), ("1", (7, 8)), ("!", (8, 9))]As we saw in the quicktour, you can
customize the pre-tokenizer of a Tokenizer by just changing the corresponding attribute:
tokenizer.pre_tokenizer = pre_tokenizer
Of course, if you change the way the pre-tokenizer, you should probably retrain your tokenizer from scratch afterward.
Model
Once the input texts are normalized and pre-tokenized, the
Tokenizer applies the model on the
pre-tokens. This is the part of the pipeline that needs training on your
corpus (or that has been trained if you are using a pretrained
tokenizer).
The role of the model is to split your “words” into tokens, using the rules it has learned. It’s also responsible for mapping those tokens to their corresponding IDs in the vocabulary of the model.
This model is passed along when intializing the
Tokenizer so you already know how to
customize this part. Currently, the 🤗 Tokenizers library supports:
models.BPEmodels.Unigrammodels.WordLevelmodels.WordPiece
For more details about each model and its behavior, you can check here
Post-Processing
Post-processing is the last step of the tokenization pipeline, to
perform any additional transformation to the
Encoding before it’s returned, like
adding potential special tokens.
As we saw in the quick tour, we can customize the post processor of a
Tokenizer by setting the
corresponding attribute. For instance, here is how we can post-process
to make the inputs suitable for the BERT model:
from tokenizers.processors import TemplateProcessing
tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[("[CLS]", 1), ("[SEP]", 2)],
)Note that contrarily to the pre-tokenizer or the normalizer, you don’t need to retrain a tokenizer after changing its post-processor.
All together: a BERT tokenizer from scratch
Let’s put all those pieces together to build a BERT tokenizer. First,
BERT relies on WordPiece, so we instantiate a new
Tokenizer with this model:
from tokenizers import Tokenizer
from tokenizers.models import WordPiece
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))Then we know that BERT preprocesses texts by removing accents and lowercasing. We also use a unicode normalizer:
from tokenizers import normalizers
from tokenizers.normalizers import NFD, Lowercase, StripAccents
bert_tokenizer.normalizer = normalizers.Sequence([NFD(), Lowercase(), StripAccents()])The pre-tokenizer is just splitting on whitespace and punctuation:
from tokenizers.pre_tokenizers import Whitespace
bert_tokenizer.pre_tokenizer = Whitespace()And the post-processing uses the template we saw in the previous section:
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),
],
)We can use this tokenizer and train on it on wikitext like in the
quicktour:
from tokenizers.trainers import WordPieceTrainer
trainer = WordPieceTrainer(vocab_size=30522, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
files = [f"data/wikitext-103-raw/wiki.{split}.raw" for split in ["test", "train", "valid"]]
bert_tokenizer.train(files, trainer)
bert_tokenizer.save("data/bert-wiki.json")Decoding
On top of encoding the input texts, a Tokenizer also has an API for decoding, that is converting IDs
generated by your model back to a text. This is done by the methods
Tokenizer.decode (for one predicted text) and Tokenizer.decode_batch (for a batch of predictions).
The decoder will first convert the IDs back to tokens
(using the tokenizer’s vocabulary) and remove all special tokens, then
join those tokens with spaces:
output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
print(output.ids)
# [1, 27253, 16, 93, 11, 5097, 5, 7961, 5112, 6218, 0, 35, 2]
tokenizer.decode([1, 27253, 16, 93, 11, 5097, 5, 7961, 5112, 6218, 0, 35, 2])
# "Hello , y ' all ! How are you ?"If you used a model that added special characters to represent subtokens
of a given “word” (like the "##" in
WordPiece) you will need to customize the decoder to treat
them properly. If we take our previous bert_tokenizer for instance the
default decoding will give:
output = bert_tokenizer.encode("Welcome to the 🤗 Tokenizers library.")
print(output.tokens)
# ["[CLS]", "welcome", "to", "the", "[UNK]", "tok", "##eni", "##zer", "##s", "library", ".", "[SEP]"]
bert_tokenizer.decode(output.ids)
# "welcome to the tok ##eni ##zer ##s library ."But by changing it to a proper decoder, we get:
from tokenizers import decoders
bert_tokenizer.decoder = decoders.WordPiece()
bert_tokenizer.decode(output.ids)
# "welcome to the tokenizers library."