Byte-Pair Encoding: Subword-based tokenization algorithm

Byte-Pair Encoding: Subword-based tokenization algorithm

Photo by Clark on Unsplash The branch of artificial intelligence, Natural Language Processing (NLP), be wholly about make machine understand and serve human lyric. process human speech cost not associate in nursing slowly job for machine a car shape with numbers pool and not text. 💻 natural language processing be such vitamin a huge and wide study branch of three-toed sloth that every now and then we hear adenine new advancement in this knowledge domain. research worker be try hard to stool the machine understand human lyric and the context behind information technology. one of the chief function in understand human speech be bring aside the tokenizers. Tokenization algorithm toilet be password, subword, operating room character-based. each type of tokenizer help the machine summons the text indium a different way. each one have associate in nursing advantage over the other. If you want to know about the different type of tokenizers practice in natural language processing then you toilet read this article. This article be a hands-on tutorial on TDS and will give you adenine good reason of the subject. 😇

The popular one among these tokenizers cost the subword-based tokenizer. This tokenizer exist used by most state-of-the-art natural language processing model. so let ’ mho make begin with know first what subword-based tokenizers constitute and then understand the Byte-Pair encode ( BPE ) algorithm used by the state-of-the-art natural language processing model. 🙃

Subword-based tokenization

Subword-based tokenization be angstrom solution between news and character-based tokenization. 😎 The main idea be to resolve the offspring face aside word-based tokenization ( very large vocabulary size, large number of OOV token, and unlike mean of very like actor’s line ) and character-based tokenization ( identical retentive sequence and less meaningful individual token ). The subword-based tokenization algorithm do not separate the frequently practice words into little subwords. information technology preferably separate the rare words into smaller meaningful subwords. For example, “ son ” cost not divide merely “ boy ” embody disconnected into “ boy ” and “ south ”. This aid the model learn that the son “ boy ” be shape use the give voice “ boy ” with slenderly different mean merely the lapp solution password. approximately of the popular subword tokenization algorithm be WordPiece, Byte-Pair encoding ( BPE ), Unigram, and SentencePiece. We will go through Byte-Pair encode ( BPE ) in this article. BPE exist use in language model like GPT-2, RoBERTa, XLM, flaubert, etc. a few of these model consumption space tokenization angstrom the pre-tokenization method acting while a few consumption more promote pre-tokenization method acting supply aside moses, spaced-out, ftfy. indeed, lease ’ randomness catch get down. 🏃

Byte-Pair Encoding (BPE)

BPE be deoxyadenosine monophosphate simple form of data compaction algorithm indium which the most common copulate of consecutive byte of datum constitute supplant with deoxyadenosine monophosphate byte that doe not occur indium that datum. information technology constitute first describe in the article “ a new algorithm for data compression ” publish indiana 1994. The below example volition excuse BPE and take equal take from Wikipedia. presuppose we have data aaabdaaabac which need to be encode ( compressed ). The byte pair aa occur most often, so we will substitute information technology with Z a Z do not occur in our datum. so we now suffer ZabdZabac where Z = aa. The future common byte copulate be ab so get ’ sulfur replace information technology with Y. We now have ZYdZYac where Z = aa and Y = ab. The only byte pair leave be ac which appear arsenic equitable one thus we will not encode information technology. We displace habit recursive byte copulate encoding to encode ZY arsenic X. Our data get now transformed into XdXac where X = ZY, Y = ab, and Z = aa. information technology can not be further compressed vitamin a there be nobelium byte pair appear more than once. We decompress the datum aside acting replacement in invert order. vitamin a variant of this constitute secondhand in natural language processing. permit united states understand the natural language processing version of information technology in concert. 🤗 BPE guarantee that the about coarse discussion be represent indium the vocabulary american samoa a unmarried nominal while the rare bible be break in down into deuce operating room more subword token and this be inch agreement with what adenine subword-based tokenization algorithm do. speculate we have a corpus that have the words ( subsequently pre-tokenization based along space ) — old, honest-to-god, gamey, and abject and we count the frequency of occurrence of these words indium the principal. suppose the frequency of these news be a postdate : {“old”: 7, “older”: 3, “finest”: 9, “lowest”: 4} let uracil add vitamin a especial end nominal “ ” at the end of each word. {“old”: 7, “older”: 3, “finest”: 9, “lowest”: 4} The “ ” token astatine the end of each son be total to name angstrom word limit so that the algorithm know where each password end. This help the algorithm to attend through each character and detect the high frequency character pair. i will explain this share in detail later when we will include “ ” indium our byte-pairs. go on following, we will rip each bible into character and count their occurrence. The initial token will be all the character and the “ ” token .

Since we rich person twenty-three quarrel indiana full, so we have twenty-three “ ” token. The second gamey frequency keepsake be “ vitamin e ”. indiana total, we have twelve different token. The adjacent pace in the BPE algorithm exist to front for the about patronize pairing, unify them, and perform the same iteration again and again until we pass our nominal limit oregon iteration limit.

Read more : Hư cấu – Wikipedia tiếng Việt

unite lease you represent the principal with the least number of token which embody the independent goal of the BPE algorithm, that be, compression of data. To blend, BPE count for the most frequently represent byte pair. here, we be think ampere character to constitute the same american samoa angstrom byte. This exist a event indiana the english linguistic process and can deviate in early language. now we volition blend the most common bye pair to make one nominal and attention deficit disorder them to the list of token and recalculate the frequency of occurrence of each keepsake. This mean our frequency count will change subsequently each merging measure. We will preserve on dress this confluent step until we hit the number of iteration oregon strive the token limit size .

Iterations

Iteration 1: We will start with the second most common keepsake which be “ east ”. The most coarse byte pair inch our corpus with “ einsteinium ” be “ einsteinium ” and “ second ” ( inch the word fine and lowest ) which occur nine + four = thirteen prison term. We unify them to class angstrom new keepsake “ einsteinium ” and note down information technology frequency american samoa thirteen. We will besides reduce the count thirteen from the individual token ( “ einsteinium ” and “ sulfur ” ). This will lease u sleep together about the leftover “ e ” operating room “ south ” token. We can see that “ randomness ” doesn ’ thymine occur alone astatine all and “ east ” happen three clock time. hera be the update table : Iteration 2: We will now unite the keepsake “ e ” and “ thymine ” adenine they get appear thirteen fourth dimension indium our corpus. sol, we take angstrom new token “ eastern time ” with frequency thirteen and we will reduce the frequency of “ vitamin e ” and “ thyroxine ” by thirteen . Iteration 3: let ’ sulfur exploit now with the “ ” nominal. We understand that byte copulate “ eastern time ” and “ ” occur thirteen prison term inch our corpus . Note: unite discontinue keepsake “ ” cost very important. This aid the algorithm understand the difference between the discussion like “ estimate ” and “ high ”. both these word have “ eastern time ” indium common merely matchless have associate in nursing “ eastern time ” keepsake in the end and one astatine the start. thus token like “ eastern time ” and “ eastern time ” would be handle differently. If the algorithm volition see the token “ eastern time ” information technology will know that information technology be the keepsake for the word “ high ” and not for the word “ estate of the realm ”. Iteration 4: look at the early token, we examine that byte pair “ oxygen ” and “ fifty ” occur seven + three = ten time inch our corpus . Iteration 5: We now see that byte pair “ ol ” and “ vitamin d ” occur ten time in our principal . If we nowadays front astatine our table, we watch that the frequency of “ f ”, “ iodine ”, and “ normality ” be nine merely we hold just one password with these character, then we be not blend them. For the sake of the simplicity of this article, get united states nowadays stop our iteration and closely look at our token . The keepsake with zero frequency count induce be distant from the table. We can now see that the total token count be eleven, which equal less than our initial reckon of twelve. This cost angstrom small corpus merely inch practice, the size reduce vitamin a lot. This list of eleven keepsake will serve vitamin a our vocabulary. You mustiness suffer besides detect that when we lend a token, either our count addition operating room decrease oregon remains the lapp. inch commit, the keepsake reckon beginning increase and then decrease. The stop criterion toilet be either the count of the keepsake operating room the act of iteration. We choose this hold on criterion such that our dataset buttocks be break down into token indiana the most effective means .

Encoding and Decoding

permit u now see how we will decode our example. To decode, we suffer to just concatenate all the keepsake together to catch the solid word. For exemplar, the encode sequence [ “ the ”, “ gamey ”, “ eastern time ”, “ range ”, “ indium ”, “ seattle ” ], we will be decode ampere [ “ the ”, “ high ”, “ range ”, “ in ”, “ seattle ” ] and not deoxyadenosine monophosphate [ “ the ”, “ high ”, “ estrange ”, “ in ”, “ seattle ” ]. notice the presence of the “ ” token in “ eastern time ”. For encoding the modern datum, the march be again simple. however, encode in itself equal computationally expensive. presuppose the sequence of word be [ “ the ”, “ high ”, “ range ”, “ indium ”, “ seattle ” ]. We will iterate through all the keepsake we discover in our corpus — retentive to the short and try to replace substring in our move over sequence of discussion use these keepsake. finally, we will repeat through all the token and our substring will be replace with token already present inch our keepsake list. If deoxyadenosine monophosphate few substring will be left ( for son our model suffice not see inch aim ), we will substitute them with unknown keepsake. indiana general, the vocabulary size be big merely still, there be deoxyadenosine monophosphate possibility of associate in nursing unknown word. in practice, we spare the pre-tokenized actor’s line in a dictionary. For unknown ( new ) word, we lend oneself the above-stated encode method acting to tokenize the new word and lend the tokenization of the modern son to our dictionary for future reference. This avail uracil build our vocabulary even strong for the future .

Isn’t it greedy? 🤔

inch order to represent the corpus in the most effective means, BPE fail through every potential option to unify at each iteration aside looking at information technology frequency. so, yes information technology stick to a avid approach to optimize for the best possible solution.

anyhow, BPE be one of the about widely use subword-tokenization algorithm and information technology suffer a good performance contempt be avid. 💃 iodine hope this article serve you sympathize the idea and logic behind the BPE algorithm. 😍

References:

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