12.6 Different dictionarys have different rules

Content and key concepts

On the previous page, a large dictionary and a short sequence.
The word, part of the word, and byte are possible break-ups. The name of the method does not yet describe the entire contract of a particular electronizer.
The breakup is done by a particular dictionary and rules. Byte, symbol, word, and current are not required to match. The number received is the address, not the numerical meaning of the word.
Для изменения рисунка включитеJavaScript; расчёт и теория доступны далее.
The common word "tokenization" conceals different solutions
Byte-level, character-levell and Word-levele define the starting units; BPE defines the mode of learning/use merges. WordPiece uses its own dictionary and segmentation rules, and SentencePiec is a toolkit with several models, not a synonym of one algorithm. L03 confirms the work with rau sentences, but the finished SenencePies models are not downloaded here.
The comparative lab shows UTF-8 bytes, Unicode code points,
word-like regex chunks
This is not called the result of the real BERT/GPT tokenizer. An honest comparison of production tokenizers requires appropriate dictionarys, normalitation rules and versions.
For
a a
Byte/codepoint presentations have 3 elements. A break-up by non-empty words without leaving a void would have given you 2, but would no longer have allowed the exact formatting to be restored. In our Word-lake baseline gap, there is also a separate chunk.
Research.
Compare the same Unicode entries and lines with several spaces. What differences disappear at normalitation and what are retained? Do not move the measured tokens number of one algorithm to another context Windows.
Now the IDs are defined, but they don't have a geometric representation. The next step is:
Embedding trackup and vector learning
.
import re
parts=re.findall(r'\S+|\s+','a a')
assert parts==['a',' ','a'] and ''.join(parts)=='a a'
The code repeats the operations just taken care of.
Completely checked Python-example Chapter
Available with a separate file; the short piece shown does not replace its implementation.
Word-lake regex chunks are not available for WordPiece/SentencePiec.
What do you do with the addresses so that the network can compare and transform the images?
Python examples of Chapter 12
Academic base